As I Lay Dying by William Faulkner – Download – Public Domain

As I Lay Dying by William Faulkner – Download – Public Domain

Characters

  • Addie Bundren – Addie is married to Anse and the mother of Cash, Darl, Jewel, Dewey Dell, and Vardaman.
  • Anse Bundren – Anse is Addie’s husband, later her widower. He is the father of all the children but Jewel.
  • Cash Bundren – Cash is a skilled and helpful carpenter and the eldest son of the family. In his late twenties, he builds Addie’s coffin. Throughout the novel, he builds an attachment to his tools and proves to be heroic, but to a fault.
  • Darl Bundren – The second eldest of Addie’s children, Darl is about two years younger than Cash. Darl is the most articulate character in the book; he narrates 19 of the 59 chapters. Much of the plot is fueled and narrated by Darl as, throughout the book, he descends into insanity.
  • Jewel Bundren – Jewel is the third of the Bundren children, most likely around nineteen years of age. A half-brother to the other children and the favorite of Addie, he is the illegitimate son of Addie and Reverend Whitfield. No one, other than Addie, seems to know this.
  • Dewey Dell Bundren – Dewey Dell is the only daughter of Anse and Addie Bundren; at seventeen years old, she is the second youngest of the Bundren children. She was impregnated by Lafe and, as the family journeys to Jefferson, she unsuccessfully seeks an abortion.
  • Vardaman Bundren – Vardaman is the youngest Bundren child, somewhere between seven and ten years old.
  • Vernon Tull – Vernon is a good friend of the Bundrens, who appears in the book as a good farmer, less religious than his wife.
  • Cora Tull – Cora is the wife of Vernon Tull. She is very religious and judgmental.
  • Eula Tull – Cora and Vernon’s daughter.
  • Kate Tull – Cora and Vernon’s other daughter.
  • Peabody – Peabody is the Bundrens’ doctor; he narrates two chapters of the book. Anse sends for him shortly before Addie’s death, too late for Peabody to do anything more than watch Addie die. Toward the end of the book, when he is working on Cash’s leg, Peabody candidly assesses Anse and the entire Bundren family from the perspective of the community at large. Dr. Peabody is also a recurring character in the Yoknapatawpha County universe.
  • Lafe – Lafe is a farmer who has impregnated Dewey Dell and given her $10 to get an abortion.
  • Reverend Whitfield – Whitfield is the local minister with whom Addie had an affair, resulting in the birth of Jewel.
  • Samson – Samson is a local farmer who lets the Bundren family stay with him the first night on their journey to Jefferson. Samson’s wife, Rachel, is disgusted with the way the family is treating Addie by dragging her coffin through the countryside.
  • Moseley – Moseley is a pharmacist in Mottson who refuses Dewey Dell medicine to abort her and Lafe’s unborn child.
  • Other narrators: MacGowan and Armstid

WILLIAM FAULKNER’S WORKS
THE MARBLE FAUN (1924) SOLDIER’S PAY (1926)
MOSQUITOES (1927)
SARTORIS (1929) [FLAGS IN THE DUST (1973)]
THE SOUND AND THE FURY (1929) AS I LAY DYING (1930) SANCTUARY (1931)
THESE 13 (1931) LIGHT IN AUGUST (1932) A GREEN BOUGH (1933)
DOCTOR MARTINO AND OTHER STORIES (1934) PYLON (1935)
ABSALOM, ABSALOM! (1936)
THE UNVANQUISHED (1938)
THE WILD PALMS IF I FORGET THEE JERUSALEM THE HAMLET (1940)
GO DOWN, MOSES AND OTHER STORIES (1942) INTRUDER IN THE DUST (1948)
KNIGHT’S GAMBIT (1949)
COLLECTED STORIES OF WILLIAM FAULKNER (1950) NOTES ON A HORSETHIEF (1951)
REQUIEM FOR A NUN (1954) A FABLE (1954)
BIG WOODS (1955)
THE TOWN (1957)
THE MANSION (1959)
THE REIVERS (1962)
UNCOLLECTED STORIES OF WILLIAM FAULKNER (1979, POSTHUMOUS)

Copyright 1930 by William Faulkner Copyright renewed 1957 by William Faulkner
Notes copyright © 1985 by Literary Classics of the United States, Inc.

All rights reserved under International and Pan-American Copyright Conventions. Published in the United States by Vintage Books, a division of Random House, Inc., New York, and simultaneously in Canada by Random House of Canada Limited, Toronto. Originally published by Jonathan Cape & Harrison Smith, Inc., in 1930.


Library of Congress Cataloging-in-Publication Data Faulkner, William, 1897–1962.
As I lay dying : the corrected text / William Faulkner
—1st Vintage international ed.
p. cm.—(Vintage international) eISBN: 978-0-307-79216-7
I. Title. [PS3511.A86A85 1990]
813′.52—dc20 90–50261v3.1_r1

DARL

Jewel and I come up from the field, following the path in single file. Although I am fifteen feet
ahead of him, anyone watching us from the cottonhouse can see Jewel’s frayed and broken straw hat a
full head above my own.
The path runs straight as a plumb-line, worn smooth by feet and baked brick-hard by July, between
the green rows of laidby cotton, to the cottonhouse in the center of the field, where it turns and
circles the cottonhouse at four soft right angles and goes on across the field again, worn so by
feet in fading precision.
The cottonhouse is of rough logs, from between which the chinking has long fallen. Square, with a
broken roof set at a single pitch, it leans in empty and shimmering dilapidation in the sunlight, a
single broad window in two opposite walls giving onto the approaches of the path. When we reach it
I turn and follow the path which circles the house. Jewel, fifteen feet behind me, looking straight
ahead, steps in a single stride through the window. Still staring straight ahead, his pale eyes
like wood set into his wooden face, he crosses the floor in four strides with the rigid gravity of
a cigar store Indian dressed in patched overalls and endued with life from the hips down, and steps
in a single stride through the opposite window and into the path again just as I come around the
corner. In single file and five feet apart and Jewel now in front, we go on up the path toward the
foot of the bluff.
Tull’s wagon stands beside the spring, hitched to the rail, the reins
wrapped about the seat stanchion. In the wagon bed are two chairs. Jewel stops at the spring and
takes the gourd from the willow branch and drinks. I pass him and mount the path, beginning to hear
Cash’s saw.
When I reach the top he has quit sawing. Standing in a litter of chips, he is fitting two of the
boards together. Between the shadow spaces they are yellow as gold, like soft gold, bearing on
their flanks in smooth undulations the marks of the adze blade: a good carpenter,

Cash is. He holds the two planks on the trestle, fitted along the edges in a quarter of the
finished box. He kneels and squints along the edge of them, then he lowers them and takes up the
adze. A good carpenter. Addie Bundren could not want a better one, a better box to lie in. It will
give her confidence and comfort. I go on to the house, followed by the
Chuck. Chuck. Chuck.
of the adze.

CORA

So I saved out the eggs and baked yesterday. The cakes turned out right well. We depend a lot on
our chickens. They are good layers, what few we have left after the possums and such. Snakes too,
in the summer. A snake will break up a hen-house quicker than anything. So after they were going to
cost so much more than Mr Tull thought, and after I promised that the difference in the number of
eggs would make it up, I had to be more careful than ever because it was on my final say-so we took
them. We could have stocked cheaper chickens, but I gave my promise as Miss Lawington said when she
advised me to get a good breed, because Mr Tull himself admits that a good breed of cows or hogs
pays in the long run. So when we lost so many of them we couldn’t afford to use the eggs ourselves,
because I could not have had Mr Tull chide me when it was on my say-so we took them. So when Miss
Lawington told me about the cakes I thought that I could bake them and earn enough at one time to
increase the net value of the flock the equivalent of two head. And that by saving the eggs out one
at a time, even the eggs wouldn’t be costing anything. And that week they laid so well that I not
only saved out enough eggs above what we had engaged to sell, to bake the cakes with, I had saved
enough so that the flour and the sugar and the stove wood would not be costing anything. So I baked
yesterday, more careful than ever I baked in my life, and the cakes turned out right well. But when
we got to town this morning Miss Lawington told me the lady had changed her mind and was not going
to have the party after all.
“She ought to taken those cakes anyway,” Kate says.
“Well,” I say, “I reckon she never had no use for them now.”
“She ought to taken them,” Kate says. “But those rich town ladies can change their minds. Poor
folks cant.”
Riches is nothing in the face of the Lord, for He can see into the heart. “Maybe I can sell them at
the bazaar Saturday,” I say. They turned out real well.

“You cant get two dollars a piece for them,” Kate says.
“Well, it isn’t like they cost me anything,” I say. I saved them out and swapped a dozen of them
for the sugar and flour. It isn’t like the cakes cost me anything, as Mr Tull himself realises that
the eggs I saved were over and beyond what we had engaged to sell, so it was like we had found the
eggs or they had been given to us.
“She ought to taken those cakes when she same as gave you her word,” Kate says. The Lord can see
into the heart. If it is His will that some folks has different ideas of honesty from other folks,
it is not my place to question His decree.
“I reckon she never had any use for them,” I say. They turned out real well, too.
The quilt is drawn up to her chin, hot as it is, with only her two hands and her face outside. She
is propped on the pillow, with her head raised so she can see out the window, and we can hear him
every time he takes up the adze or the saw. If we were deaf we could almost watch her face and hear
him, see him. Her face is wasted away so that the bones draw just under the skin in white lines.
Her eyes are like two candles when you watch them gutter down into the sockets of iron
candle-sticks. But the eternal and the everlasting salvation and grace is not upon her.
“They turned out real nice,” I say. “But not like the cakes Addie used to bake.” You can see that
girl’s washing and ironing in the pillow-slip, if ironed it ever was. Maybe it will reveal her
blindness to her, laying there at the mercy and the ministration of four men and a tom-boy girl.
“There’s not a woman in this section could ever bake with Addie Bundren,” I say. “First thing we
know she’ll be up and baking again, and then we wont have any sale for ours at all.” Under the
quilt she makes no more of a hump than a rail would, and the only way you can tell she is breathing
is by the sound of the mattress shucks. Even the hair at her cheek does not move, even with that
girl standing right over her, fanning her with the fan. While we watch she swaps the fan to the
other hand without stopping it.
“Is she sleeping?” Kate whispers.
“She’s just watching Cash yonder,” the girl says. We can hear the saw in the board. It sounds like
snoring. Eula turns on the trunk and looks out the window. Her necklace looks real nice with her
red hat. You wouldn’t think it only cost twenty-five cents.
“She ought to taken those cakes,” Kate says.
I could have used the money real well. But it’s not like they cost me

anything except the baking. I can tell him that anybody is likely to make a miscue, but it’s not
all of them that can get out of it without loss, I can tell him. It’s not everybody can eat their
mistakes, I can tell him.
Someone comes through the hall. It is Darl. He does not look in as he passes the door. Eula watches
him as he goes on and passes from sight again toward the back. Her hand rises and touches her beads
lightly, and then her hair. When she finds me watching her, her eyes go blank.

DARL

Pa and Vernon are sitting on the back porch. Pa is tilting snuff from the lid of his snuff-box into
his lower lip, holding the lip outdrawn between thumb and finger. They look around as I cross the
porch and dip the gourd into the water bucket and drink.
“Where’s Jewel?” pa says. When I was a boy I first learned how much better water tastes when it has
set a while in a cedar bucket. Warmish-cool, with a faint taste like the hot July wind in cedar
trees smells. It has to set at least six hours, and be drunk from a gourd. Water should never be
drunk from metal.
And at night it is better still. I used to lie on the pallet in the hall, waiting until I could
hear them all asleep, so I could get up and go back to the bucket. It would be black, the shelf
black, the still surface of the water a round orifice in nothingness, where before I stirred it
awake with the dipper I could see maybe a star or two in the bucket, and maybe in the dipper a star
or two before I drank. After that I was bigger, older. Then I would wait until they all went to
sleep so I could lie with my shirt-tail up, hearing them asleep, feeling myself without touching
myself, feeling the cool silence blowing upon my parts and wondering if Cash was yonder in the
darkness doing it too, had been doing it perhaps for the last two years before I could have wanted
to or could have.
Pa’s feet are badly splayed, his toes cramped and bent and warped, with no toenail at all on his
little toes, from working so hard in the wet in homemade shoes when he was a boy. Beside his chair
his brogans sit. They look as though they had been hacked with a blunt axe out of pig-iron. Vernon
has been to town. I have never seen him go to town in overalls. His wife, they say. She taught
school too, once. I fling the dipper dregs to the ground and wipe my mouth on my sleeve. It is
going to rain before morning. Maybe before dark. “Down
to the barn,” I say. “Harnessing the team.”
Down there fooling with that horse. He will go on through the barn,

into the pasture. The horse will not be in sight: he is up there among the pine seedlings, in the
cool. Jewel whistles, once and shrill. The horse snorts, then Jewel sees him, glinting for a gaudy
instant among the blue shadows. Jewel whistles again; the horse comes dropping down the slope,
stiff-legged, his ears cocking and flicking, his mismatched eyes rolling, and fetches up twenty
feet away, broadside on, watching Jewel over his shoulder in an attitude kittenish and alert.
“Come here, sir,” Jewel says. He moves. Moving that quick his coat, bunching, tongues swirling like
so many flames. With tossing mane and tail and rolling eye the horse makes another short curvetting
rush and stops again, feet bunched, watching Jewel. Jewel walks steadily toward him, his hands at
his sides. Save for Jewel’s legs they are like two figures carved for a tableau savage in the sun.
When Jewel can almost touch him, the horse stands on his hind legs and slashes down at Jewel. Then
Jewel is enclosed by a glittering maze of hooves as by an illusion of wings; among them, beneath
the upreared chest, he moves with the flashing limberness of a snake. For an instant before the
jerk comes onto his arms he sees his whole body earth-free, horizontal, whipping snake-limber,
until he finds the horse’s nostrils and touches earth again. Then they are rigid,
motionless, terrific, the horse back-thrust on stiffened, quivering legs, with lowered head; Jewel
with dug heels, shutting off the horse’s wind with one hand, with the other patting the horse’s
neck in short strokes myriad and caressing, cursing the horse with obscene ferocity.
They stand in rigid terrific hiatus, the horse trembling and groaning. Then Jewel is on the horse’s
back. He flows upward in a stooping swirl like the lash of a whip, his body in midair shaped to the
horse. For another moment the horse stands spraddled, with lowered head, before it bursts into
motion. They descend the hill in a series of spine-jolting jumps, Jewel high, leech-like on the
withers, to the fence where the horse bunches to a scuttering halt again.
“Well,” Jewel says, “you can quit now, if you got a-plenty.”
Inside the barn Jewel slides running to the ground before the horse stops. The horse enters the
stall, Jewel following. Without looking back the horse kicks at him, slamming a single hoof into
the wall with a pistol-like report. Jewel kicks him in the stomach; the horse arches his neck back,
crop-toothed; Jewel strikes him across the face with his fist and slides on to the trough and
mounts upon it. Clinging to the hay-rack he lowers his head and peers out across the stall tops and

through the doorway. The path is empty; from here he cannot even hear Cash sawing. He reaches up
and drags down hay in hurried armsful and crams it into the rack.
“Eat,” he says. “Get the goddamn stuff out of sight while you got a chance, you pussel-gutted
bastard. You sweet son of a bitch,” he says.

JEWEL

It’s because he stays out there, right under the window, hammering and sawing on that goddamn box.
Where she’s got to see him. Where every breath she draws is full of his knocking and sawing where
she can see him saying See. See what a good one I am making for you. I told him to go somewhere
else. I said Good God do you want to see her in it. It’s like when he was a little boy and she says
if she had some fertilizer she would try to raise some flowers and he taken the bread pan and
brought it back from the barn full of dung.
And now them others sitting there, like buzzards. Waiting, fanning themselves. Because I said If
you wouldn’t keep on sawing and nailing at it until a man cant sleep even and her hands laying on
the quilt like two of them roots dug up and tried to wash and you couldn’t get them clean. I can
see the fan and Dewey Dell’s arm. I said if you’d just let her alone. Sawing and knocking, and
keeping the air always moving so fast on her face that when you’re tired you cant breathe it, and
that goddamn adze going One lick less. One lick less. One lick less until everybody that passes in
the road will have to stop and see it and say what a fine carpenter he is. If it had just been me
when Cash fell off of that church and if it had just been me when pa laid sick with that load of
wood fell on him, it would not be happening with every bastard in the county coming in to stare at
her because if there is a God what the hell is He for. It would just be me and her on a high hill
and me rolling the rocks down the hill at their faces, picking them up and throwing them down the
hill faces and teeth and all by God until she was quiet and not that goddamn adze going One lick
less. One lick less and we could be quiet.

DARL

We watch him come around the corner and mount the steps. He does not look at us. “You ready?” he
says.
“If you’re hitched up,” I say. I say “Wait.” He stops, looking at pa. Vernon spits, without moving.
He spits with decorous and deliberate precision into the pocked dust below the porch. Pa rubs his
hands slowly on his knees. He is gazing out beyond the crest of the bluff, out across the land.
Jewel watches him a moment, then he goes on to the pail and drinks again.
“I mislike undecision as much as ere a man,” pa says.
“It means three dollars,” I say. The shirt across pa’s hump is faded lighter than the rest of it.
There is no sweat stain on his shirt. I have never seen a sweat stain on his shirt. He was sick
once from working in the sun when he was twenty-two years old, and he tells people that if he ever
sweats, he will die. I suppose he believes it.
“But if she dont last until you get back,” he says. “She will be disappointed.”
Vernon spits into the dust. But it will rain before morning.
“She’s counted on it,” pa says. “She’ll want to start right away. I know her. I promised her I’d
keep the team here and ready, and she’s counting on it.”
“We’ll need that three dollars then, sure,” I say. He gazes out over the land, rubbing his hands on
his knees. Since he lost his teeth his mouth collapses in slow repetition when he dips. The stubble
gives his lower face that appearance that old dogs have. “You’d better make up your mind soon, so
we can get there and get a load on before dark,” I say.
“Ma aint that sick,” Jewel says. “Shut up, Darl.”
“That’s right,” Vernon says. “She seems more like herself today than she has in a week. Time you
and Jewel get back, she’ll be setting up.”
“You ought to know,” Jewel says. “You been here often enough looking at her. You or your folks.”
Vernon looks at him. Jewel’s eyes

look like pale wood in his high-blooded face. He is a head taller than any of the rest of us,
always was. I told them that’s why ma always whipped him and petted him more. Because he was
peakling around the house more. That’s why she named him Jewel I told them.
“Shut up, Jewel,” pa says, but as though he is not listening much.
He gazes out across the land, rubbing his knees.
“You could borrow the loan of Vernon’s team and we could catch up with you,” I say. “If she didn’t
wait for us.”
“Ah, shut your goddamn mouth,” Jewel says.
“She’ll want to go in ourn,” pa says. He rubs his knees. “Dont ere a man mislike it more.”
“It’s laying there, watching Cash whittle on that damn.……” Jewel says. He says it harshly,
savagely, but he does not say the word. Like a little boy in the dark to flail his courage and
suddenly aghast into silence by his own noise.
“She wanted that like she wants to go in our own wagon,” pa says. “She’ll rest easier for knowing
it’s a good one, and private. She was ever a private woman. You know it well.”
“Then let it be private,” Jewel says. “But how the hell can you expect it to be——” he looks at the
back of pa’s head, his eyes like pale wooden eyes.
“Sho,” Vernon says, “she’ll hold on till it’s finished. She’ll hold on till everything’s ready,
till her own good time. And with the roads like they are now, it wont take you no time to get her
to town.”
“It’s fixing up to rain,” pa says. “I am a luckless man. I have ever been.” He rubs his hands on
his knees. “It’s that durn doctor, liable to come at any time. I couldn’t get word to him till so
late. If he was to come tomorrow and tell her the time was nigh, she wouldn’t wait. I know her.
Wagon or no wagon, she wouldn’t wait. Then she’d be upset, and I wouldn’t upset her for the living
world. With that family burying-ground in Jefferson and them of her blood waiting for her there,
she’ll be impatient. I promised my word me and the boys would get her there quick as mules could
walk it, so she could rest quiet.” He rubs his hands on his knees. “No man ever misliked it more.”
“If everybody wasn’t burning hell to get her there,” Jewel says in that harsh, savage voice. “With
Cash all day long right under the window, hammering and sawing at that——”
“It was her wish,” pa says. “You got no affection nor gentleness for her. You never had. We would
be beholden to no man,” he says, “me and her. We have never yet been, and she will rest quieter for

knowing it and that it was her own blood sawed out the boards and drove the nails. She was ever one
to clean up after herself.”
“It means three dollars,” I say. “Do you want us to go, or not?” Pa rubs his knees. “We’ll be back
by tomorrow sundown.”
“Well.……” pa says. He looks out over the land, awry-haired, mouthing the snuff slowly against his
gums.
“Come on,” Jewel says. He goes down the steps. Vernon spits neatly into the dust.
“By sundown, now,” pa says. “I would not keep her waiting.”
Jewel glances back, then he goes on around the house. I enter the hall, hearing the voices before I
reach the door. Tilting a little down the hill, as our house does, a breeze draws through the hall
all the time, upslanting. A feather dropped near the front door will rise and brush along the
ceiling, slanting backward, until it reaches the down- turning current at the back door: so with
voices. As you enter the hall, they sound as though they were speaking out of the air about your
head.

CORA

It was the sweetest thing I ever saw. It was like he knew he would never see her again, that Anse
Bundren was driving him from his mother’s death bed, never to see her in this world again. I always
said Darl was different from those others. I always said he was the only one of them that had his
mother’s nature, had any natural affection. Not that Jewel, the one she labored so to bear and
coddled and petted so and him flinging into tantrums or sulking spells, inventing devilment to
devil her until I would have frailed him time and time. Not him to come and tell her goodbye. Not
him to miss a chance to make that extra three dollars at the price of his mother’s goodbye kiss. A
Bundren through and through, loving nobody, caring for nothing except how to get something with the
least amount of work. Mr Tull says Darl asked them to wait. He said Darl almost begged them on his
knees not to force him to leave her in her condition. But nothing would do but Anse and Jewel must
make that three dollars. Nobody that knows Anse could have expected different, but to think of that
boy, that Jewel, selling all those years of self-denial and down-right partiality—they couldn’t
fool me: Mr Tull says Mrs Bundren liked Jewel the least of all, but I knew better. I knew she was
partial to him, to the same quality in him that let her put up with Anse Bundren when Mr Tull said
she ought to poisoned him—for three dollars, denying his dying mother the goodbye kiss.
Why, for the last three weeks I have been coming over every time I
could, coming sometimes when I shouldn’t have, neglecting my own family and duties so that somebody
would be with her in her last moments and she would not have to face the Great Unknown without one
familiar face to give her courage. Not that I deserve credit for it: I will expect the same for
myself. But thank God it will be the faces of my loved kin, my blood and flesh, for in my husband
and children I have been more blessed than most, trials though they have been at times.

She lived, a lonely woman, lonely with her pride, trying to make folks believe different, hiding
the fact that they just suffered her, because she was not cold in the coffin before they were
carting her forty miles away to bury her, flouting the will of God to do it. Refusing to let her
lie in the same earth with those Bundrens.
“But she wanted to go,” Mr Tull said. “It was her own wish to lie among her own people.”
“Then why didn’t she go alive?” I said. “Not one of them would have stopped her, with even that
little one almost old enough now to be selfish and stone-hearted like the rest of them.”
“It was her own wish,” Mr Tull said. “I heard Anse say it was.”
“And you would believe Anse, of course,” I said. “A man like you would. Dont tell me.”
“I’d believe him about something he couldn’t expect to make anything off of me by not telling,” Mr
Tull said.
“Dont tell me,” I said. “A woman’s place is with her husband and children, alive or dead. Would you
expect me to want to go back to Alabama and leave you and the girls when my time comes, that I left
of my own will to cast my lot with yours for better and worse, until death and after?”
“Well, folks are different,” he said.
I should hope so. I have tried to live right in the sight of God and man, for the honor and comfort
of my Christian husband and the love and respect of my Christian children. So that when I lay me
down in the consciousness of my duty and reward I will be surrounded by loving faces, carrying the
farewell kiss of each of my loved ones into my reward. Not like Addie Bundren dying alone, hiding
her pride and her broken heart. Glad to go. Lying there with her head propped up so she could watch
Cash building the coffin, having to watch him so he would not skimp on it, like as not, with those
men not worrying about anything except if there was time to earn another three dollars before the
rain come and the river got too high to get across it. Like as not, if they hadn’t decided to make
that last load, they would have loaded her into the wagon on a quilt and crossed the river first
and then stopped and give her time to die what Christian death they would let her.
Except Darl. It was the sweetest thing I ever saw. Sometimes I lose
faith in human nature for a time; I am assailed by doubt. But always the Lord restores my faith and
reveals to me His bounteous love for His creatures. Not Jewel, the one she had always cherished,
not him.

He was after that three extra dollars. It was Darl, the one that folks say is queer, lazy,
pottering about the place no better than Anse, with Cash a good carpenter and always more building
than he can get around to, and Jewel always doing something that made him some money or got him
talked about, and that near-naked girl always standing over Addie with a fan so that every time a
body tried to talk to her and cheer her up, would answer for her right quick, like she was trying
to keep anybody from coming near her at all.
It was Darl. He come to the door and stood there, looking at his dying mother. He just looked at
her, and I felt the bounteous love of the Lord again and His mercy. I saw that with Jewel she had
just been pretending, but that it was between her and Darl that the understanding and the
true love was. He just looked at her, not even coming in where she could see him and get upset,
knowing that Anse was driving him away and he would never see her again. He said nothing, just
looking at her.
“What you want, Darl?” Dewey Dell said, not stopping the fan, speaking up quick, keeping even him
from her. He didn’t answer. He just stood and looked at his dying mother, his heart too full for
words.

DEWEY DELL

The first time me and Lafe picked on down the row. Pa dassent sweat because he will catch his death
from the sickness so everybody that comes to help us. And Jewel dont care about anything he is not
kin to us in caring, not care-kin. And Cash like sawing the long hot sad yellow days up into planks
and nailing them to something. And pa thinks because neighbors will always treat one another that
way because he has always been too busy letting neighbors do for him to find out. And I did not
think that Darl would, that sits at the supper table with his eyes gone further than the food and
the lamp, full of the land dug out of his skull and the holes filled with distance beyond the land.
We picked on down the row, the woods getting closer and closer and the secret shade, picking on
into the secret shade with my sack and Lafe’s sack. Because I said will I or wont I when the sack
was half full because I said if the sack is full when we get to the woods it wont be me. I said if
it dont mean for me to do it the sack will not be full and I will turn up the next row but if the
sack is full, I cannot help it. It will be that I had to do it all the time and I cannot help it.
And we picked on toward the secret shade and our eyes would drown together touching on his hands
and my hands and I didn’t say anything. I said “What are you doing?” and he said “I am picking into
your sack.” And so it was full when we came to the end of the row and I could not help it.
And so it was because I could not help it. It was then, and then I saw Darl and he knew. He said he
knew without the words like he told me that ma is going to die without words, and I knew he knew
because if he had said he knew with the words I would not have believed that he had been there and
saw us. But he said he did know and I said “Are you going to tell pa are you going to kill him?”
without the words I said it and he said “Why?” without the words. And that’s why I can talk to him
with knowing with hating because he

knows.
He stands in the door, looking at her. “What you want, Darl?” I say.
“She is going to die,” he says. And old turkey-buzzard Tull coming to watch her die but I can fool
them.
“When is she going to die?” I say. “Before we get back,” he says.
“Then why are you taking Jewel?” I say. “I want him to help me load,” he says.

TULL

Anse keeps on rubbing his knees. His overalls are faded; on one knee a serge patch cut out of a
pair of Sunday pants, wore iron-slick. “No man mislikes it more than me,” he says.
“A fellow’s got to guess ahead now and then,” I say. “But, come long and short, it wont be no harm
done neither way.”
“She’ll want to get started right off,” he says. “It’s far enough to Jefferson at best.”
“But the roads is good now,” I say. It’s fixing to rain tonight, too. His folks buries at New Hope,
too, not three miles away. But it’s just like him to marry a woman born a day’s hard ride away and
have her die on him.
He looks out over the land, rubbing his knees. “No man so mislikes it,” he says.
“They’ll get back in plenty of time,” I say. “I wouldn’t worry none.” “It means three dollars,” he
says.
“Might be it wont be no need for them to rush back, no ways,” I say. “I hope it.”
“She’s a-going,” he says. “Her mind is set on it.”
It’s a hard life on women, for a fact. Some women. I mind my mammy lived to be seventy and more.
Worked every day, rain or shine; never a sick day since her last chap was born until one day she
kind of looked around her and then she went and taken that lace- trimmed night gown she had had
forty-five years and never wore out of the chest and put it on and laid down on the bed and pulled
the covers up and shut her eyes. “You all will have to look out for pa the best you can,” she said.
“I’m tired.”
Anse rubs his hands on his knees. “The Lord giveth,” he says. We can hear Cash a-hammering and
sawing beyond the corner.
It’s true. Never a truer breath was ever breathed. “The Lord giveth,” I say.
That boy comes up the hill. He is carrying a fish nigh long as he is.

He slings it to the ground and grunts “Hah” and spits over his shoulder like a man. Durn nigh long
as he is.
“What’s that?” I say. “A hog? Where’d you get it?”
“Down to the bridge,” he says. He turns it over, the under side caked over with dust where it is
wet, the eye coated over, humped under the dirt.
“Are you aiming to leave it laying there?” Anse says.
“I aim to show it to ma,” Vardaman says. He looks toward the door. We can hear the talking, coming
out on the draft. Cash too, knocking and hammering at the boards. “There’s company in there,” he
says.
“Just my folks,” I say. “They’d enjoy to see it too.”
He says nothing, watching the door. Then he looks down at the fish laying in the dust. He turns it
over with his foot and prods at the eye- bump with his toe, gouging at it. Anse is looking out over
the land. Vardaman looks at Anse’s face, then at the door. He turns, going toward the corner of the
house, when Anse calls him without looking around.
“You clean that fish,” Anse says.
Vardaman stops. “Why cant Dewey Dell clean it?” he says. “You clean that fish,” Anse says.
“Aw, pa,” Vardaman says.
“You clean it,” Anse says. He dont look around. Vardaman comes back and picks up the fish. It
slides out of his hands, smearing wet dirt onto him, and flops down, dirtying itself again,
gapmouthed, goggle- eyed, hiding into the dust like it was ashamed of being dead, like it was in a
hurry to get back hid again. Vardaman cusses it. He cusses it like a grown man, standing a-straddle
of it. Anse dont look around. Vardaman picks it up again. He goes on around the house, toting it in
both arms like a armful of wood, it overlapping him on both ends, head and tail. Durn nigh big as
he is.
Anse’s wrists dangle out of his sleeves: I never see him with a shirt on that looked like it was
his in all my life. They all looked like Jewel might have give him his old ones. Not Jewel, though.
He’s long- armed, even if he is spindling. Except for the lack of sweat. You could tell they aint
been nobody else’s but Anse’s that way without no mistake. His eyes look like pieces of burnt-out
cinder fixed in his face, looking out over the land.
When the shadow touches the steps he says “It’s five oclock.”
Just as I get up Cora comes to the door and says it’s time to get on. Anse reaches for his shoes.
“Now, Mr Bundren,” Cora says, “dont you

get up now.” He puts his shoes on, stomping into them, like he does everything, like he is hoping
all the time he really cant do it and can quit trying to. When we go up the hall we can hear them
clumping on the floor like they was iron shoes. He comes toward the door where she is, blinking his
eyes, kind of looking ahead of hisself before he sees, like he is hoping to find her setting up, in
a chair maybe or maybe sweeping, and looks into the door in that surprised way like he looks in and
finds her still in bed every time and Dewey Dell still a- fanning her with the fan. He stands
there, like he dont aim to move again nor nothing else.
“Well, I reckon we better get on,” Cora says. “I got to feed the chickens.” It’s fixing to rain,
too. Clouds like that dont lie, and the cotton making every day the Lord sends. That’ll be
something else for him. Cash is still trimming at the boards. “If there’s ere a thing we can do,”
Cora says.
“Anse’ll let us know,” I say.
Anse dont look at us. He looks around, blinking, in that surprised way, like he had wore hisself
down being surprised and was even surprised at that. If Cash just works that careful on my barn.
“I told Anse it likely wont be no need,” I say. “I so hope it.” “Her mind is set on it,” he says.
“I reckon she’s bound to go.” “It comes to all of us,” Cora says. “Let the Lord comfort you.”
“About that corn,” I say. I tell him again I will help him out if he gets into a tight, with her
sick and all. Like most folks around here, I done holp him so much already I cant quit now.
“I aimed to get to it today,” he says. “Seems like I cant get my mind on nothing.”
“Maybe she’ll hold out till you are laid-by,” I say. “If God wills it,” he says.
“Let Him comfort you,” Cora says.
If Cash just works that careful on my barn. He looks up when we pass. “Dont reckon I’ll get to you
this week,” he says.
“ ’Taint no rush,” I say. “Whenever you get around to it.”
We get into the wagon. Cora sets the cake box on her lap. It’s fixing to rain, sho.
“I dont know what he’ll do,” Cora says. “I just dont know.”
“Poor Anse,” I say. “She kept him at work for thirty-odd years. I reckon she is tired.”
“And I reckon she’ll be behind him for thirty years more,” Kate says. “Or if it aint her, he’ll get
another one before cotton-picking.”

“I reckon Cash and Darl can get married now,” Eula says. “That poor boy,” Cora says. “The poor
little tyke.”
“What about Jewel?” Kate says. “He can, too,” Eula says.
“Hmph,” Kate says. “I reckon he will. I reckon so. I reckon there’s more gals than one around here
that dont want to see Jewel tied down. Well, they needn’t to worry.”
“Why, Kate!” Cora says. The wagon begins to rattle. “The poor little tyke,” Cora says.
It’s fixing to rain this night. Yes, sir. A rattling wagon is mighty dry weather, for a Birdsell.
But that’ll be cured. It will for a fact.
“She ought to taken them cakes after she said she would,” Kate says.

ANSE

Durn that road. And it fixing to rain, too. I can stand here and same as see it with second-sight,
a-shutting down behind them like a wall, shutting down betwixt them and my given promise. I do the
best I can, much as I can get my mind on anything, but durn them boys.
A-laying there, right up to my door, where every bad luck that comes and goes is bound to find it.
I told Addie it want any luck living on a road when it come by here, and she said, for the world
like a woman, “Get up and move, then.” But I told her it want no luck in it, because the Lord put
roads for travelling: why He laid them down flat on the earth. When He aims for something to be
always a-moving, He makes it long ways, like a road or a horse or a wagon, but when He aims for
something to stay put, He makes it up-and-down ways, like a tree or a man. And so He never aimed
for folks to live on a road, because which gets there first, I says, the road or the house? Did you
ever know Him to set a road down by a house? I says. No you never, I says, because it’s always men
cant rest till they gets the house set where everybody that passes in a wagon can spit in the
doorway, keeping the folks restless and wanting to get up and go somewheres else when He aimed for
them to stay put like a tree or a stand of corn. Because if He’d a aimed for man to be always
a-moving and going somewheres else, wouldn’t He a put him longways on his belly, like a snake? It
stands to reason He would.
Putting it where every bad luck prowling can find it and come
straight to my door, charging me taxes on top of it. Making me pay for Cash having to get them
carpenter notions when if it hadn’t been no road come there, he wouldn’t a got them; falling off of
churches and lifting no hand in six months and me and Addie slaving and a- slaving, when there’s
plenty of sawing on this place he could do if he’s got to saw.
And Darl too. Talking me out of him, durn them. It aint that I am afraid of work; I always is fed
me and mine and kept a roof above us:

it’s that they would short-hand me just because he tends to his own business, just because he’s got
his eyes full of the land all the time. I says to them, he was alright at first, with his eyes full
of the land, because the land laid up-and-down ways then; it wasn’t till that ere road come and
switched the land around longways and his eyes still full of the land, that they begun to threaten
me out of him, trying to short-hand me with the law.
Making me pay for it. She was well and hale as ere a woman ever were, except for that road. Just
laying down, resting herself in her own bed, asking naught of none. “Are you sick, Addie?” I said.
“I am not sick,” she said.
“You lay you down and rest you,” I said. “I knowed you are not sick. You’re just tired. You lay you
down and rest.”
“I am not sick,” she said. “I will get up.”
“Lay still and rest,” I said. “You are just tired. You can get up tomorrow.” And she was laying
there, well and hale as ere a woman ever were, except for that road.
“I never sent for you,” I said. “I take you to witness I never sent for you.”
“I know you didn’t,” Peabody said. “I bound that. Where is she?” “She’s a-laying down,” I said.
“She’s just a little tired, but she’ll
——”
“Get outen here, Anse,” he said. “Go set on the porch a while.”
And now I got to pay for it, me without a tooth in my head, hoping to get ahead enough so I could
get my mouth fixed where I could eat God’s own victuals as a man should, and her hale and well as
ere a woman in the land until that day. Got to pay for being put to the need of that three dollars.
Got to pay for the way for them boys to have to go away to earn it. And now I can see same as
second sight the rain shutting down betwixt us, a-coming up that road like a durn man, like it want
ere a other house to rain on in all the living land.
I have heard men cuss their luck, and right, for they were sinful men. But I do not say it’s a
curse on me, because I have done no wrong to be cussed by. I am not religious, I reckon. But peace
is in my heart: I know it is. I have done things but neither better nor worse than them that
pretend otherlike, and I know that Old Marster will care for me as for ere a sparrow that falls.
But it seems hard that a man in his need could be so flouted by a road.
Vardaman comes around the house, bloody as a hog to his knees, and that ere fish chopped up with
the axe like as not, or maybe

throwed away for him to lie about the dogs et it. Well, I reckon I aint no call to expect no more
of him than of his man-growed brothers. He comes along, watching the house, quiet, and sits on the
steps. “Whew,” he says, “I’m pure tired.”
“Go wash them hands,” I say. But couldn’t no woman strove harder than Addie to make them right, man
and boy: I’ll say that for her.
“It was full of blood and guts as a hog,” he says. But I just cant seem to get no heart into
anything, with this here weather sapping me, too. “Pa,” he says, “is ma sick some more?”
“Go wash them hands,” I say. But I just cant seem to get no heart into it.

DARL

He has been to town this week: the back of his neck is trimmed close, with a white line between
hair and sunburn like a joint of white bone. He has not once looked back.
“Jewel,” I say. Back running, tunnelled between the two sets of bobbing mule ears, the road
vanishes beneath the wagon as though it were a ribbon and the front axle were a spool. “Do you know
she is going to die, Jewel?”
It takes two people to make you, and one people to die. That’s how the world is going to end.
I said to Dewey Dell: “You want her to die so you can get to town: is that it?” She wouldn’t say
what we both knew. “The reason you will not say it is, when you say it, even to yourself, you will
know it is true: is that it? But you know it is true now. I can almost tell you the day when you
knew it is true. Why wont you say it, even to yourself?” She will not say it. She just keeps on
saying Are you going to tell pa? Are you going to kill him? “You cannot believe it is true because
you cannot believe that Dewey Dell, Dewey Dell Bundren, could have such bad luck: is that it?”
The sun, an hour above the horizon, is poised like a bloody egg upon a crest of thunderheads; the
light has turned copper: in the eye portentous, in the nose sulphurous, smelling of lightning. When
Peabody comes, they will have to use the rope. He has pussel-gutted himself eating cold greens.
With the rope they will haul him up the path, balloon-like up the sulphurous air.
“Jewel,” I say, “do you know that Addie Bundren is going to die?
Addie Bundren is going to die?”

PEABODY

When Anse finally sent for me of his own accord, I said “He has wore her out at last.” And I said a
damn good thing, and at first I would not go because there might be something I could do and I
would have to haul her back, by God. I thought maybe they have the same sort of fool ethics in
heaven they have in the Medical College and that it was maybe Vernon Tull sending for me again,
getting me there in the nick of time, as Vernon always does things, getting the most for Anse’s
money like he does for his own. But when it got far enough into the day for me to read weather sign
I knew it couldn’t have been anybody but Anse that sent. I knew that nobody but a luckless man
could ever need a doctor in the face of a cyclone. And I knew that if it had finally occurred to
Anse himself that he needed one, it was already too late.
When I reach the spring and get down and hitch the team, the sun has gone down behind a bank of
black cloud like a topheavy mountain range, like a load of cinders dumped over there, and there is
no wind. I could hear Cash sawing for a mile before I got there. Anse is standing at the top of the
bluff above the path.
“Where’s the horse?” I say.
“Jewel’s taken and gone,” he says. “Cant nobody else ketch hit.
You’ll have to walk up, I reckon.”
“Me, walk up, weighing two hundred and twenty-five pounds?” I say. “Walk up that durn wall?” He
stands there beside a tree. Too bad the Lord made the mistake of giving trees roots and giving the
Anse Bundrens He makes feet and legs. If He’d just swapped them, there wouldn’t ever be a worry
about this country being deforested someday. Or any other country. “What do you aim for me to do?”
I say. “Stay here and get blowed clean out of the county when that cloud breaks?” Even with the
horse it would take me fifteen minutes to ride up across the pasture to the top of the ridge and
reach the house. The path looks like a crooked limb blown against the bluff. Anse has not been in
town in twelve years. And how his mother ever

got up there to bear him, he being his mother’s son. “Vardaman’s gittin the rope,” he says.
After a while Vardaman appears with the plowline. He gives the end of it to Anse and comes down the
path, uncoiling it.
“You hold it tight,” I say. “I done already wrote this visit onto my books, so I’m going to charge
you just the same, whether I get there or not.”
“I got hit,” Anse says. “You kin come on up.”
I’ll be damned if I can see why I dont quit. A man seventy years old, weighing two hundred and odd
pounds, being hauled up and down a damn mountain on a rope. I reckon it’s because I must reach the
fifty thousand dollar mark of dead accounts on my books before I can quit. “What the hell does your
wife mean,” I say, “taking sick on top of a durn mountain?”
“I’m right sorry,” he says. He let the rope go, just dropped it, and he has turned toward the
house. There is a little daylight up here still, of the color of sulphur matches. The boards look
like strips of sulphur. Cash does not look back. Vernon Tull says he brings each board up to the
window for her to see it and say it is all right. The boy overtakes us. Anse looks back at him.
“Wher’s the rope?” he says.
“It’s where you left it,” I say. “But never you mind that rope. I got to get back down that bluff.
I dont aim for that storm to catch me up here. I’d blow too durn far once I got started.”
The girl is standing by the bed, fanning her. When we enter she turns her head and looks at us. She
has been dead these ten days. I suppose it’s having been a part of Anse for so long that she cannot
even make that change, if change it be. I can remember how when I was young I believed death to be
a phenomenon of the body; now I know it to be merely a function of the mind—and that of the minds
of the ones who suffer the bereavement. The nihilists say it is the end; the fundamentalists, the
beginning; when in reality it is no more than a single tenant or family moving out of a tenement or
a town.
She looks at us. Only her eyes seem to move. It’s like they touch us, not with sight or sense, but
like the stream from a hose touches you, the stream at the instant of impact as dissociated from
the nozzle as though it had never been there. She does not look at Anse at all. She looks at me,
then at the boy. Beneath the quilt she is no more than a bundle of rotten sticks.
“Well, Miss Addie,” I say. The girl does not stop the fan. “How are you, sister?” I say. Her head
lies gaunt on the pillow, looking at the

boy. “You picked out a fine time to get me out here and bring up a storm.” Then I send Anse and the
boy out. She watches the boy as he leaves the room. She has not moved save her eyes.
He and Anse are on the porch when I come out, the boy sitting on the steps, Anse standing by a
post, not even leaning against it, his arms dangling, the hair pushed and matted up on his head
like a dipped rooster. He turns his head, blinking at me.
“Why didn’t you send for me sooner?” I say.
“Hit was jest one thing and then another,” he says. “That ere corn me and the boys was aimin to git
up with, and Dewey Dell a-takin good keer of her, and folks comin in, a-offerin to help and sich,
till I jest thought.……”
“Damn the money,” I say. “Did you ever hear of me worrying a fellow before he was ready to pay?”
“Hit aint begrudgin the money,” he says. “I jest kept a-thinkin.
…… She’s goin, is she?” The durn little tyke is sitting on the top step, looking smaller than ever
in the sulphur-colored light. That’s the one trouble with this country: everything, weather, all,
hangs on too long. Like our rivers, our land: opaque, slow, violent; shaping and creating the life
of man in its implacable and brooding image. “I knowed hit,” Anse says. “All the while I made sho.
Her mind is sot on hit.”
“And a damn good thing, too,” I say. “With a trifling——” He sits on the top step, small, motionless
in faded overalls. When I came out he looked up at me, then at Anse. But now he has stopped looking
at us. He just sits there.
“Have you told her yit?” Anse says. “What for?” I say. “What the devil for?”
“She’ll know hit. I knowed that when she see you she would know hit, same as writing. You wouldn’t
need to tell her. Her mind——”
Behind us the girl says, “Paw.” I look at her, at her face. “You better go quick,” I say.
When we enter the room she is watching the door. She looks at me. Her eyes look like lamps blaring
up just before the oil is gone. “She wants you to go out,” the girl says.
“Now, Addie,” Anse says, “when he come all the way from Jefferson to git you well?” She watches me:
I can feel her eyes. It’s like she was shoving at me with them. I have seen it before in women.
Seen them drive from the room them coming with sympathy and pity, with actual help, and clinging to
some trifling animal to whom they never were more than pack-horses. That’s what they mean by the
love that

passeth understanding: that pride, that furious desire to hide that abject nakedness which we bring
here with us, carry with us into operating rooms, carry stubbornly and furiously with us into the
earth again. I leave the room. Beyond the porch Cash’s saw snores steadily into the board. A minute
later she calls his name, her voice harsh and strong.
“Cash,” she says; “you, Cash!”

DARL

Pa stands beside the bed. From behind his leg Vardaman peers, with his round head and his eyes
round and his mouth beginning to open. She looks at pa; all her failing life appears to drain into
her eyes, urgent, irremediable. “It’s Jewel she wants,” Dewey Dell says.
“Why, Addie,” pa says, “him and Darl went to make one more load. They thought there was time. That
you would wait for them, and that three dollars and all.……” He stoops laying his hand on hers. For
a while yet she looks at him, without reproach, without anything at all, as if her eyes alone are
listening to the irrevocable cessation of his voice. Then she raises herself, who has not moved in
ten days. Dewey Dell leans down, trying to press her back.
“Ma,” she says; “ma.”
She is looking out the window, at Cash stooping steadily at the board in the failing light,
laboring on toward darkness and into it as though the stroking of the saw illumined its own motion,
board and saw engendered.
“You, Cash,” she shouts, her voice harsh, strong, and unimpaired. “You, Cash!”
He looks up at the gaunt face framed by the window in the twilight. It is a composite picture of
all time since he was a child. He drops the saw and lifts the board for her to see, watching the
window in which the face has not moved. He drags a second plank into position and slants the two of
them into their final juxtaposition, gesturing toward the ones yet on the ground, shaping with his
empty hand in pantomime the finished box. For a while still she looks down at him from the
composite picture, neither with censure nor approbation. Then the face disappears.
She lies back and turns her head without so much as glancing at pa. She looks at Vardaman; her
eyes, the life in them, rushing suddenly upon them; the two flames glare up for a steady instant.
Then they go out as though someone had leaned down and blown upon them.

“Ma,” Dewey Dell says; “ma!” Leaning above the bed, her hands lifted a little, the fan still moving
like it has for ten days, she begins to keen. Her voice is strong, young, tremulous and clear, rapt
with its own timbre and volume, the fan still moving steadily up and down, whispering the useless
air. Then she flings herself across Addie Bundren’s knees, clutching her, shaking her with the
furious strength of the young before sprawling suddenly across the handful of rotten bones that
Addie Bundren left, jarring the whole bed into a chattering sibilance of mattress shucks, her arms
out-flung and the fan in one hand still beating with expiring breath into the quilt.
From behind pa’s leg Vardaman peers, his mouth full open and all color draining from his face into
his mouth, as though he has by some means fleshed his own teeth in himself, sucking. He begins to
move slowly backward from the bed, his eyes round, his pale face fading into the dusk like a piece
of paper pasted on a failing wall, and so out of the door.
Pa leans above the bed in the twilight, his humped silhouette partaking of that owl-like quality of
awry-feathered, disgruntled outrage within which lurks a wisdom too profound or too inert for even
thought.
“Durn them boys,” he says.
Jewel, I say. Overhead the day drives level and gray, hiding the sun by a flight of gray spears. In
the rain the mules smoke a little, splashed yellow with mud, the off one clinging in sliding lunges
to the side of the road above the ditch. The tilted lumber gleams dull yellow, water-soaked and
heavy as lead, tilted at a steep angle into the ditch above the broken wheel; about the shattered
spokes and about Jewel’s ankles a runnel of yellow neither water nor earth swirls, curving with the
yellow road neither of earth nor water, down the hill dissolving into a streaming mass of dark
green neither of earth nor sky. Jewel, I say
Cash comes to the door, carrying the saw. Pa stands beside the bed, humped, his arms dangling. He
turns his head, his shabby profile, his chin collapsing slowly as he works the snuff against his
gums.
“She’s gone,” Cash says.
“She taken and left us,” pa says. Cash does not look at him. “How nigh are you done?” pa says. Cash
does not answer. He enters, carrying the saw. “I reckon you better get at it,” pa says. “You’ll
have to do the best you can, with them boys gone off that-a-way.” Cash looks down at her face. He
is not listening to pa at all. He does not approach the bed. He stops in the middle of the floor,
the saw against

his leg, his sweating arms powdered lightly with sawdust, his face composed. “If you get in a
tight, maybe some of them’ll get here tomorrow and help you,” pa says. “Vernon could.” Cash is not
listening. He is looking down at her peaceful, rigid face fading into the dusk as though darkness
were a precursor of the ultimate earth, until at last the face seems to float detached upon it,
lightly as the reflection of a dead leaf. “There is Christians enough to help you,” pa says. Cash
is not listening. After a while he turns without looking at pa and leaves the room. Then the saw
begins to snore again. “They will help us in our sorrow,” pa says.
The sound of the saw is steady, competent, unhurried, stirring the dying light so that at each
stroke her face seems to wake a little into an expression of listening and of waiting, as though
she were counting the strokes. Pa looks down at the face, at the black sprawl of Dewey Dell’s hair,
the out-flung arms, the clutched fan now motionless on the fading quilt. “I reckon you better get
supper on,” he says.
Dewey Dell does not move.
“Git up, now, and put supper on,” pa says. “We got to keep our strength up. I reckon Doctor
Peabody’s right hungry, coming all this way. And Cash’ll need to eat quick and get back to work so
he can finish it in time.”
Dewey Dell rises, heaving to her feet. She looks down at the face. It is like a casting of fading
bronze upon the pillow, the hands alone still with any semblance of life: a curled, gnarled
inertness; a spent yet alert quality from which weariness, exhaustion, travail has not yet
departed, as though they doubted even yet the actuality of rest, guarding with horned and penurious
alertness the cessation which they know cannot last.
Dewey Dell stoops and slides the quilt from beneath them and draws it up over them to the chin,
smoothing it down, drawing it smooth. Then without looking at pa she goes around the bed and leaves
the room.
She will go out where Peabody is, where she can stand in the twilight and look at his back with
such an expression that, feeling her eyes and turning, he will say: I would not let it grieve me,
now. She was old, and sick too. Suffering more than we knew. She couldn’t have got well. Vardaman’s
getting big now, and with you to take good care of them all. I would try not to let it grieve me. I
expect you’d better go and get some supper ready. It dont have to be much. But they’ll need to eat,
and she looking at him, saying You could do so much for me if you just would. If

you just knew. I am I and you are you and I know it and you dont know it and you could do so much
for me if you just would and if you just would then I could tell you and then nobody would have to
know it except you and me and Darl
Pa stands over the bed, dangle-armed, humped, motionless. He raises his hand to his head, scouring
his hair, listening to the saw. He comes nearer and rubs his hand, palm and back, on his thigh and
lays it on her face and then on the hump of quilt where her hands are. He touches the quilt as he
saw Dewey Dell do, trying to smoothe it up to the chin, but disarranging it instead. He tries to
smoothe it again, clumsily, his hand awkward as a claw, smoothing at the wrinkles which he made and
which continue to emerge beneath his hand with perverse ubiquity, so that at last he desists, his
hand falling to his side and stroking itself again, palm and back, on his thigh. The sound of the
saw snores steadily into the room. Pa breathes with a quiet, rasping sound, mouthing the snuff
against his gums. “God’s will be done,” he says. “Now I can get them teeth.”
Jewel’s hat droops limp about his neck, channelling water onto the
soaked towsack tied about his shoulders as, ankle-deep in the running ditch, he pries with a
slipping two-by-four, with a piece of rotting log for fulcrum, at the axle. Jewel, I say, she is
dead, Jewel. Addie Bundren is dead

VARDAMAN

Then I begin to run. I run toward the back and come to the edge of the porch and stop. Then I begin
to cry. I can feel where the fish was in the dust. It is cut up into pieces of not-fish now,
not-blood on my hands and overalls. Then it wasn’t so. It hadn’t happened then. And now she is
getting so far ahead I cannot catch her.
The trees look like chickens when they ruffle out into the cool dust on the hot days. If I jump off
the porch I will be where the fish was, and it all cut up into not-fish now. I can hear the bed and
her face and them and I can feel the floor shake when he walks on it that came and did it. That
came and did it when she was all right but he came and did it.
“The fat son of a bitch.”
I jump from the porch, running. The top of the barn comes swooping up out of the twilight. If I
jump I can go through it like the pink lady in the circus, into the warm smelling, without having
to wait. My hands grab at the bushes; beneath my feet the rocks and dirt go rubbling down.
Then I can breathe again, in the warm smelling. I enter the stall, trying to touch him, and then I
can cry then I vomit the crying. As soon as he gets through kicking I can and then I can cry, the
crying can.
“He kilt her. He kilt her.”
The life in him runs under the skin, under my hand, running through the splotches, smelling up into
my nose where the sickness is beginning to cry, vomiting the crying, and then I can breathe,
vomiting it. It makes a lot of noise. I can smell the life running up from under my hands, up my
arms, and then I can leave the stall.
I cannot find it. In the dark, along the dust, the walls I cannot find it. The crying makes a lot
of noise. I wish it wouldn’t make so much noise. Then I find it in the wagon shed, in the dust, and
I run across the lot and into the road, the stick jouncing on my shoulder.

They watch me as I run up, beginning to jerk back, their eyes rolling, snorting, jerking back on
the hitch-rein. I strike. I can hear the stick striking; I can see it hitting their heads, the
breast-yoke, missing altogether sometimes as they rear and plunge, but I am glad.
“You kilt my maw!”
The stick breaks, they rearing and snorting, their feet popping loud on the ground; loud because it
is going to rain and the air is empty for the rain. But it is still long enough. I run this way and
that as they rear and jerk at the hitch-rein, striking.
“You kilt her!”
I strike at them, striking, they wheeling in a long lunge, the buggy wheeling onto two wheels and
motionless like it is nailed to the ground and the horses motionless like they are nailed by the
hind feet to the center of a whirling plate.
I run in the dust. I cannot see, running in the sucking dust where the buggy vanishes tilted on two
wheels. I strike, the stick hitting into the ground, bouncing, striking into the dust and then into
the air again and the dust sucking on down the road faster than if a car was in it. And then I can
cry, looking at the stick. It is broken down to my hand, not longer than stove wood that was a long
stick. I throw it away and I can cry. It does not make so much noise now.
The cow is standing in the barn door, chewing. When she sees me come into the lot she lows, her
mouth full of flopping green, her tongue flopping.
“I aint a-goin to milk you. I aint a-goin to do nothing for them.”
I hear her turn when I pass. When I turn she is just behind me with her sweet, hot, hard breath.
“Didn’t I tell you I wouldn’t?”
She nudges me, snuffing. She moans deep inside, her mouth closed.
I jerk my hand, cursing her like Jewel does. “Git, now.”
I stoop my hand to the ground and run at her. She jumps back and whirls away and stops, watching
me. She moans. She goes on to the path and stands there, looking up the path.
It is dark in the barn, warm, smelling, silent. I can cry quietly, watching the top of the hill.
Cash comes to the hill, limping where he fell off of the church. He looks down at the spring, then
up the road and back toward the barn. He comes down the path stiffly and looks at the broken
hitch-rein and at the dust in the road and then up the road, where the dust is gone.

“I hope they’ve got clean past Tull’s by now. I so hope hit.” Cash turns and limps up the path.
“Durn him. I showed him. Durn him.”
I am not crying now. I am not anything. Dewey Dell comes to the hill and calls me. Vardaman. I am
not anything. I am quiet. You, Vardaman. I can cry quiet now, feeling and hearing my tears.
“Then hit want. Hit hadn’t happened then. Hit was a-layin right there on the ground. And now she’s
gittin ready to cook hit.”
It is dark. I can hear wood, silence: I know them. But not living sounds, not even him. It is as
though the dark were resolving him out of his integrity, into an unrelated scattering of
components—snuffings and stampings; smells of cooling flesh and ammoniac hair; an illusion of a
coordinated whole of splotched hide and strong bones within which, detached and secret and
familiar, an is different from my is. I see him dissolve—legs, a rolling eye, a gaudy splotching
like cold flames—and float upon the dark in fading solution; all one yet neither; all either yet
none. I can see hearing coil toward him, caressing, shaping his hard shape—fetlock, hip, shoulder
and head; smell and sound. I am not afraid.
“Cooked and et. Cooked and et.”

DEWEY DELL

He could do so much for me if he just would. He could do everything for me. It’s like everything in
the world for me is inside a tub full of guts, so that you wonder how there can be any room in it
for anything else very important. He is a big tub of guts and I am a little tub of guts and if
there is not any room for anything else important in a big tub of guts, how can it be room in a
little tub of guts. But I know it is there because God gave women a sign when something has
happened bad.
It’s because I am alone. If I could just feel it, it would be different, because I would not be
alone. But if I were not alone, everybody would know it. And he could do so much for me, and then I
would not be alone. Then I could be all right alone.
I would let him come in between me and Lafe, like Darl came in between me and Lafe, and so Lafe is
alone too. He is Lafe and I am Dewey Dell, and when mother died I had to go beyond and outside of
me and Lafe and Darl to grieve because he could do so much for me and he dont know it. He dont even
know it.
From the back porch I cannot see the barn. Then the sound of Cash’s sawing comes in from that way.
It is like a dog outside the house, going back and forth around the house to whatever door you come
to, waiting to come in. He said I worry more than you do and I said You dont know what worry is so
I cant worry. I try to but I cant think long enough to worry.
I light the kitchen lamp. The fish, cut into jagged pieces, bleeds quietly in the pan. I put it
into the cupboard quick, listening into the hall, hearing. It took her ten days to die; maybe she
dont know it is yet. Maybe she wont go until Cash. Or maybe until Jewel. I take the dish of greens
from the cupboard and the bread pan from the cold stove, and I stop, watching the door.
“Where’s Vardaman?” Cash says. In the lamp his saw-dusted arms look like sand.

“I dont know. I aint seen him.”
“Peabody’s team run away. See if you can find Vardaman. The horse will let him catch him.”
“Well. Tell them to come to supper.”
I cannot see the barn. I said, I dont know how to worry. I dont know how to cry. I tried, but I
cant. After a while the sound of the saw comes around, coming dark along the ground in the
dust-dark. Then I can see him, going up and down above the plank.
“You come in to supper,” I say. “Tell him.” He could do everything for me. And he dont know it. He
is his guts and I am my guts. And I am Lafe’s guts. That’s it. I dont see why he didn’t stay in
town. We are country people, not as good as town people. I dont see why he didn’t. Then I can see
the top of the barn. The cow stands at the foot of the path, lowing. When I turn back, Cash is
gone.
I carry the buttermilk in. Pa and Cash and he are at the table. “Where’s that big fish Bud caught,
sister?” he says.
I set the milk on the table. “I never had no time to cook it.”
“Plain turnip greens is mighty spindling eating for a man my size,” he says. Cash is eating. About
his head the print of his hat is sweated into his hair. His shirt is blotched with sweat. He has
not washed his hands and arms.
“You ought to took time,” pa says. “Where’s Vardaman?” I go toward the door. “I cant find him.”
“Here, sister,” he says; “never mind about the fish. It’ll save, I reckon. Come on and sit down.”
“I aint minding it,” I say. “I’m going to milk before it sets in to rain.”
Pa helps himself and pushes the dish on. But he does not begin to eat. His hands are halfclosed on
either side of his plate, his head bowed a little, his awry hair standing into the lamplight. He
looks like right after the maul hits the steer and it no longer alive and dont yet know that it is
dead.
But Cash is eating, and he is too. “You better eat something,” he says. He is looking at pa. “Like
Cash and me. You’ll need it.”
“Ay,” pa says. He rouses up, like a steer that’s been kneeling in a pond and you run at it. “She
would not begrudge me it.”
When I am out of sight of the house, I go fast. The cow lows at the foot of the bluff. She nuzzles
at me, snuffing, blowing her breath in a sweet, hot blast, through my dress, against my hot
nakedness, moaning. “You got to wait a little while. Then I’ll tend to you.” She

follows me into the barn where I set the bucket down. She breathes into the bucket, moaning. “I
told you. You just got to wait, now. I got more to do than I can tend to.” The barn is dark. When I
pass, he kicks the wall a single blow. I go on. The broken plank is like a pale plank standing on
end. Then I can see the slope, feel the air moving on my face again, slow, pale with lesser dark
and with empty seeing, the pine clumps blotched up the tilted slope, secret and waiting.
The cow in silhouette against the door nuzzles at the silhouette of the bucket, moaning.
Then I pass the stall. I have almost passed it. I listen to it saying for a long time before it can
say the word and the listening part is afraid that there may not be time to say it. I feel my body,
my bones and flesh beginning to part and open upon the alone, and the process of coming unalone is
terrible. Lafe. Lafe. “Lafe” Lafe. Lafe. I lean a little forward, one foot advanced with dead
walking. I feel the darkness rushing past my breast, past the cow; I begin to rush upon the
darkness but the cow stops me and the darkness rushes on upon the sweet blast of her moaning
breath, filled with wood and with silence.
“Vardaman. You, Vardaman.”
He comes out of the stall. “You durn little sneak! You durn little sneak!”
He does not resist; the last of rushing darkness flees whistling away. “What? I aint done nothing.”
“You durn little sneak!” My hands shake him, hard. Maybe I couldn’t stop them. I didn’t know they
could shake so hard. They shake both of us, shaking.
“I never done it,” he says. “I never touched them.”
My hands stop shaking him, but I still hold him. “What are you doing here? Why didn’t you answer
when I called you?”
“I aint doing nothing.”
“You go on to the house and get your supper.”
He draws back. I hold him. “You quit now. You leave me be.” “What were you doing down here? You
didn’t come down here to
sneak after me?”
“I never. I never. You quit, now. I didn’t even know you was down here. You leave me be.”
I hold him, leaning down to see his face, feel it with my eyes. He is about to cry. “Go on, now. I
done put supper on and I’ll be there soon as I milk. You better go on before he eats everything up.
I hope that team runs clean back to Jefferson.”

“He kilt her,” he says. He begins to cry. “Hush.”
“She never hurt him and he come and kilt her.” “Hush.” He struggles. I hold him. “Hush.”
“He kilt her.” The cow comes up behind us, moaning. I shake him again.
“You stop it, now. Right this minute. You’re fixing to make yourself sick and then you cant go to
town. You go on to the house and eat your supper.”
“I dont want no supper. I dont want to go to town.”
“We’ll leave you here, then. Lessen you behave, we will leave you. Go on, now, before that old
green-eating tub of guts eats everything up from you.” He goes on, disappearing slowly into the
hill. The crest, the trees, the roof of the house stand against the sky. The cow nuzzles at me,
moaning. “You’ll just have to wait. What you got in you aint nothing to what I got in me, even if
you are a woman too.” She follows me, moaning. Then the dead, hot, pale air breathes on my face
again. He could fix it all right, if he just would. And he dont even know it. He could do
everything for me if he just knowed it. The cow breathes upon my hips and back, her breath warm,
sweet, stertorous, moaning. The sky lies flat down the slope, upon the secret clumps. Beyond the
hill sheet-lightning stains upward and fades. The dead air shapes the dead earth in the dead
darkness, further away than seeing shapes the dead earth. It lies dead and warm upon me, touching
me naked through my clothes. I said You dont know what worry is. I dont know what it is. I dont
know whether I am worrying or not. Whether I can or not. I dont know whether I can cry or not. I
dont know whether I have tried to or not. I feel like a wet seed wild in the hot blind earth.

VARDAMAN

When they get it finished they are going to put her in it and then for a long time I couldn’t say
it. I saw the dark stand up and go whirling away and I said “Are you going to nail her up in it,
Cash? Cash? Cash?” I got shut up in the crib the new door it was too heavy for me it went shut I
couldn’t breathe because the rat was breathing up all the air. I said “Are you going to nail it
shut, Cash? Nail it? Nail it?”
Pa walks around. His shadow walks around, over Cash going up and down above the saw, at the
bleeding plank.
Dewey Dell said we will get some bananas. The train is behind the glass, red on the track. When it
runs the track shines on and off. Pa said flour and sugar and coffee costs so much. Because I am a
country boy because boys in town. Bicycles. Why do flour and sugar and coffee cost so much when he
is a country boy. “Wouldn’t you ruther have some bananas instead?” Bananas are gone, eaten. Gone.
When it runs on the track shines again. “Why aint I a town boy, pa?” I said. God made me. I did not
said to God to made me in the country. If He can make the train, why cant He make them all in the
town because flour and sugar and coffee. “Wouldn’t you ruther have bananas?”
He walks around. His shadow walks around.
It was not her. I was there, looking. I saw. I thought it was her, but it was not. It was not my
mother. She went away when the other one laid down in her bed and drew the quilt up. She went away.
“Did she go as far as town?” “She went further than town.” “Did all those rabbits and possums go
further than town?” God made the rabbits and possums. He made the train. Why must He make a
different place for them to go if she is just like the rabbit.
Pa walks around. His shadow does. The saw sounds like it is asleep.
And so if Cash nails the box up, she is not a rabbit. And so if she is not a rabbit I couldn’t
breathe in the crib and Cash is going to nail it up. And so if she lets him it is not her. I know.
I was there. I saw when it did not be her. I saw. They think it is and Cash is going to nail it up.

It was not her because it was laying right yonder in the dirt. And now it’s all chopped up. I
chopped it up. It’s laying in the kitchen in the bleeding pan, waiting to be cooked and et. Then it
wasn’t and she was, and now it is and she wasn’t. And tomorrow it will be cooked and et and she
will be him and pa and Cash and Dewey Dell and there wont be anything in the box and so she can
breathe. It was laying right yonder on the ground. I can get Vernon. He was there and he seen it,
and with both of us it will be and then it will not be.

TULL

It was nigh to midnight and it had set in to rain when he woke us. It had been a misdoubtful night,
with the storm making; a night when a fellow looks for most anything to happen before he can get
the stock fed and himself to the house and supper et and in bed with the rain starting, and when
Peabody’s team come up, lathered, with the broke harness dragging and the neck-yoke betwixt the off
critter’s legs, Cora says “It’s Addie Bundren. She’s gone at last.”
“Peabody mought have been to ere a one of a dozen houses hereabouts,” I says. “Besides, how do you
know it’s Peabody’s team?”
“Well, aint it?” she says. “You hitch up, now.”
“What for?” I says. “If she is gone, we cant do nothing till morning.
And it fixing to storm, too.”
“It’s my duty,” she says. “You put the team in.”
But I wouldn’t do it. “It stands to reason they’d send for us if they needed us. You dont even know
she’s gone yet.”
“Why, dont you know that’s Peabody’s team? Do you claim it aint? Well, then.” But I wouldn’t go.
When folks wants a fellow, it’s best to wait till they sends for him, I’ve found. “It’s my
Christian duty,” Cora says. “Will you stand between me and my Christian duty?”
“You can stay there all day tomorrow, if you want,” I says.
So when Cora waked me it had set in to rain. Even while I was going to the door with the lamp and
it shining on the glass so he could see I am coming, it kept on knocking. Not loud, but steady,
like he might have gone to sleep thumping, but I never noticed how low down on the door the
knocking was till I opened it and never seen nothing. I held the lamp up, with the rain sparkling
across it and Cora back in the hall saying “Who is it, Vernon?” but I couldn’t see nobody a-tall at
first until I looked down and around the door, lowering the lamp.
He looked like a drownded puppy, in them overalls, without no hat, splashed up to his knees where
he had walked them four miles in the

mud. “Well, I’ll be durned,” I says. “Who is it, Vernon?” Cora says.
He looked at me, his eyes round and black in the middle like when you throw a light in a owl’s
face. “You mind that ere fish,” he says.
“Come in the house,” I says. “What is it? Is your maw——” “Vernon,” Cora says.
He stood kind of around behind the door, in the dark. The rain was blowing onto the lamp, hissing
on it so I am scared every minute it’ll break. “You was there,” he says. “You seen it.”
Then Cora come to the door. “You come right in outen the rain,” she says, pulling him in and him
watching me. He looked just like a drownded puppy. “I told you,” Cora says. “I told you it was a-
happening. You go and hitch.”
“But he aint said——” I says.
He looked at me, dripping onto the floor. “He’s a-ruining the rug,” Cora says. “You go get the team
while I take him to the kitchen.”
But he hung back, dripping, watching me with them eyes. “You was there. You seen it laying there.
Cash is fixing to nail her up, and it was a-laying right there on the ground. You seen it. You seen
the mark in the dirt. The rain never come up till after I was a-coming here. So we can get back in
time.”
I be durn if it didn’t give me the creeps, even when I didn’t know yet. But Cora did. “You get that
team quick as you can,” she says. “He’s outen his head with grief and worry.”
I be durn if it didn’t give me the creeps. Now and then a fellow gets to thinking. About all the
sorrow and afflictions in this world; how it’s liable to strike anywhere, like lightning. I reckon
it does take a powerful trust in the Lord to guard a fellow, though sometimes I think that Cora’s a
mite over-cautious, like she was trying to crowd the other folks away and get in closer than
anybody else. But then, when something like this happens, I reckon she is right and you got to keep
after it and I reckon I am blessed in having a wife that ever strives for sanctity and well-doing
like she says I am.
Now and then a fellow gets to thinking about it. Not often, though. Which is a good thing. For the
Lord aimed for him to do and not to spend too much time thinking, because his brain it’s like a
piece of machinery: it wont stand a whole lot of racking. It’s best when it all runs along the
same, doing the day’s work and not no one part used no more than needful. I have said and I say
again, that’s ever living thing the matter with Darl: he just thinks by himself too much. Cora’s

right when she says all he needs is a wife to straighten him out. And when I think about that, I
think that if nothing but being married will help a man, he’s durn nigh hopeless. But I reckon
Cora’s right when she says the reason the Lord had to create women is because man dont know his own
good when he sees it.
When I come back to the house with the team, they was in the kitchen. She was dressed on top of her
nightgownd, with a shawl over her head and her umbrella and her bible wrapped up in the oilcloth,
and him sitting on a up-turned bucket on the stove-zinc where she had put him, dripping onto the
floor. “I cant get nothing outen him except about a fish,” she says. “It’s a judgment on them. I
see the hand of the Lord upon this boy for Anse Bundren’s judgment and warning.”
“The rain never come up till after I left,” he says. “I had done left. I was on the way. And so it
was there in the dust. You seen it. Cash is fixing to nail her, but you seen it.”
When we got there it was raining hard, and him sitting on the seat between us, wrapped up in Cora’s
shawl. He hadn’t said nothing else, just sitting there with Cora holding the umbrella over him. Now
and then Cora would stop singing long enough to say “It’s a judgment on Anse Bundren. May it show
him the path of sin he is a-trodding.” Then she would sing again, and him sitting there between us,
leaning forward a little like the mules couldn’t go fast enough to suit him.
“It was laying right yonder,” he says, “but the rain come up after I taken and left. So I can go
and open the windows, because Cash aint nailed her yet.”
It was long a-past midnight when we drove the last nail, and almost dust-dawn when I got back home
and taken the team out and got back in bed, with Cora’s nightcap laying on the other pillow. And be
durned if even then it wasn’t like I could still hear Cora singing and feel that boy leaning
forward between us like he was ahead of the mules, and still see Cash going up and down with that
saw, and Anse standing there like a scarecrow, like he was a steer standing knee- deep in a pond
and somebody come by and set the pond up on edge and he aint missed it yet.
It was nigh toward daybreak when we drove the last nail and toted it into the house, where she was
laying on the bed with the window open and the rain blowing on her again. Twice he did it, and him
so dead for sleep that Cora says his face looked like one of these here Christmas masts that had
done been buried a while and then dug up, until at last they put her into it and nailed it down so
he couldn’t open

the window on her no more. And the next morning they found him in his shirt tail, laying asleep on
the floor like a felled steer, and the top of the box bored clean full of holes and Cash’s new
auger broke off in the last one. When they taken the lid off they found that two of them had bored
on into her face.
If it’s a judgment, it aint right. Because the Lord’s got more to do than that. He’s bound to have.
Because the only burden Anse Bundren’s ever had is himself. And when folks talks him low, I think
to myself he aint that less of a man or he couldn’t a bore himself this long.
It aint right. I be durn if it is. Because He said Suffer little children to come unto Me dont make
it right, neither. Cora said, “I have bore you what the Lord God sent me. I faced it without fear
nor terror because my faith was strong in the Lord, a-bolstering and sustaining me. If you have no
son, it’s because the Lord has decreed otherwise in His wisdom. And my life is and has ever been a
open book to ere a man or woman among His creatures because I trust in my God and my reward.”
I reckon she’s right. I reckon if there’s ere a man or woman anywhere that He could turn it all
over to and go away with His mind at rest, it would be Cora. And I reckon she would make a few
changes, no matter how He was running it. And I reckon they would be for man’s good. Leastways, we
would have to like them. Leastways, we might as well go on and make like we did.

DARL

The lantern sits on a stump. Rusted, grease-fouled, its cracked chimney smeared on one side with a
soaring smudge of soot, it sheds a feeble and sultry glare upon the trestles and the boards and the
adjacent earth. Upon the dark ground the chips look like random smears of soft pale paint on a
black canvas. The boards look like long smooth tatters torn from the flat darkness and turned
backside out.
Cash labors about the trestles, moving back and forth, lifting and placing the planks with long
clattering reverberations in the dead air as though he were lifting and dropping them at the bottom
of an invisible well, the sounds ceasing without departing, as if any movement might dislodge them
from the immediate air in reverberant repetition. He saws again, his elbow flashing slowly, a thin
thread of fire running along the edge of the saw, lost and recovered at the top and bottom of each
stroke in unbroken elongation, so that the saw appears to be six feet long, into and out of pa’s
shabby and aimless silhouette. “Give me that plank,” Cash says. “No; the other one.” He puts the
saw down and comes and picks up the plank he wants, sweeping pa away with the long swinging gleam
of the balanced board.
The air smells like sulphur. Upon the impalpable plane of it their
shadows form as upon a wall, as though like sound they had not gone very far away in falling but
had merely congealed for a moment, immediate and musing. Cash works on, half turned into the feeble
light, one thigh and one pole-thin arm braced, his face sloped into the light with a rapt, dynamic
immobility above his tireless elbow. Below the sky sheet-lightning slumbers lightly; against it the
trees, motionless, are ruffled out to the last twig, swollen, increased as though quick with young.
It begins to rain. The first harsh, sparse, swift drops rush through the leaves and across the
ground in a long sigh, as though of relief from intolerable suspense. They are big as buckshot,
warm as though

fired from a gun; they sweep across the lantern in a vicious hissing. Pa lifts his face,
slack-mouthed, the wet black rim of snuff plastered close along the base of his gums; from behind
his slack-faced astonishment he muses as though from beyond time, upon the ultimate outrage. Cash
looks once at the sky, then at the lantern. The saw has not faltered, the running gleam of its
pistoning edge unbroken. “Get something to cover the lantern,” he says.
Pa goes to the house. The rain rushes suddenly down, without thunder, without warning of any sort;
he is swept onto the porch upon the edge of it and in an instant Cash is wet to the skin. Yet the
motion of the saw has not faltered, as though it and the arm functioned in a tranquil conviction
that rain was an illusion of the mind. Then he puts down the saw and goes and crouches above the
lantern, shielding it with his body, his back shaped lean and scrawny by his wet shirt as though he
had been abruptly turned wrong-side out, shirt and all.
Pa returns. He is wearing Jewel’s raincoat and carrying Dewey Dell’s. Squatting over the lantern,
Cash reaches back and picks up four sticks and drives them into the earth and takes Dewey Dell’s
raincoat from pa and spreads it over the sticks, forming a roof above the lantern. Pa watches him.
“I dont know what you’ll do,” he says. “Darl taken his coat with him.”
“Get wet,” Cash says. He takes up the saw again; again it moves up and down, in and out of that
unhurried imperviousness as a piston moves in the oil; soaked, scrawny, tireless, with the lean
light body of a boy or an old man. Pa watches him, blinking, his face streaming; again he looks up
at the sky with that expression of dumb and brooding outrage and yet of vindication, as though he
had expected no less; now and then he stirs, moves, gaunt and streaming, picking up a board or a
tool and then laying it down. Vernon Tull is there now, and Cash is wearing Mrs Tull’s raincoat and
he and Vernon are hunting the saw. After a while they find it in pa’s hand.
“Why dont you go on to the house, out of the rain?” Cash says. Pa looks at him, his face streaming
slowly. It is as though upon a face carved by a savage caricaturist a monstrous burlesque
of all bereavement flowed. “You go on in,” Cash says. “Me and Vernon can finish it.”
Pa looks at them. The sleeves of Jewel’s coat are too short for him. Upon his face the rain
streams, slow as cold glycerin. “I dont begrudge her the wetting,” he says. He moves again and
falls to shifting the planks, picking them up, laying them down again carefully, as though

they are glass. He goes to the lantern and pulls at the propped raincoat until he knocks it down
and Cash comes and fixes it back.
“You get on to the house,” Cash says. He leads pa to the house and returns with the raincoat and
folds it and places it beneath the shelter where the lantern sits. Vernon has not stopped. He looks
up, still sawing.
“You ought to done that at first,” he says. “You knowed it was fixing to rain.”
“It’s his fever,” Cash says. He looks at the board. “Ay,” Vernon says. “He’d a come, anyway.”
Cash squints at the board. On the long flank of it the rain crashes steadily, myriad, fluctuant.
“I’m going to bevel it,” he says.
“It’ll take more time,” Vernon says. Cash sets the plank on edge; a moment longer Vernon watches
him, then he hands him the plane.
Vernon holds the board steady while Cash bevels the edge of it with the tedious and minute care of
a jeweler. Mrs Tull comes to the edge of the porch and calls Vernon. “How near are you done?” she
says.
Vernon does not look up. “Not long. Some, yet.”
She watches Cash stooping at the plank, the turgid savage gleam of the lantern slicking on the
raincoat as he moves. “You go down and get some planks off the barn and finish it and come in out
of the rain,” she says. “You’ll both catch your death.” Vernon does not move. “Vernon,” she says.
“We wont be long,” he says. “We’ll be done after a spell.” Mrs Tull watches them a while. Then she
reenters the house.
“If we get in a tight, we could take some of them planks,” Vernon says. “I’ll help you put them
back.”
Cash ceases the plane and squints along the plank, wiping it with his palm. “Give me the next one,”
he says.
Some time toward dawn the rain ceases. But it is not yet day when Cash drives the last nail and
stands stiffly up and looks down at the finished coffin, the others watching him. In the lantern
light his face is calm, musing; slowly he strokes his hands on his raincoated thighs in a gesture
deliberate, final and composed. Then the four of them— Cash and pa and Vernon and Peabody—raise the
coffin to their shoulders and turn toward the house. It is light, yet they move slowly; empty, yet
they carry it carefully; lifeless, yet they move with hushed precautionary words to one another,
speaking of it as though, complete, it now slumbered lightly alive, waiting to come awake. On the
dark floor their feet clump awkwardly, as though for a long time

they have not walked on floors.
They set it down by the bed. Peabody says quietly: “Let’s eat a snack. It’s almost daylight.
Where’s Cash?”
He has returned to the trestles, stooped again in the lantern’s feeble glare as he gathers up his
tools and wipes them on a cloth carefully and puts them into the box with its leather sling to go
over the shoulder. Then he takes up box, lantern and raincoat and returns to the house, mounting
the steps into faint silhouette against the paling east.
In a strange room you must empty yourself for sleep. And before you are emptied for sleep, what are
you. And when you are emptied for sleep, you are not. And when you are filled with sleep, you never
were. I dont know what I am. I dont know if I am or not. Jewel knows he is, because he does not
know that he does not know whether he is or not. He cannot empty himself for sleep because he is
not what he is and he is what he is not. Beyond the unlamped wall I can hear the rain shaping the
wagon that is ours, the load that is no longer theirs that felled and sawed it nor yet theirs that
bought it and which is not ours either, lie on our wagon though it does, since only the wind and
the rain shape it only to Jewel and me, that are not asleep. And since sleep is is-not and rain and
wind are was, it is not. Yet the wagon is, because when the wagon is was, Addie Bundren will not
be. And Jewel is, so Addie Bundren must be. And then I must be, or I could not empty myself for
sleep in a strange room. And so if I am not emptied yet, I am is.
How often have I lain beneath rain on a strange roof, thinking of
home.

CASH

I made it on the bevel.

  1. There is more surface for the nails to grip.
  2. There is twice the gripping-surface to each seam.
  3. The water will have to seep into it on a slant. Water moves easiest up and down or straight
    across.
  4. In a house people are upright two thirds of the time. So the seams and joints are made
    up-and-down. Because the stress is up-and- down.
  5. In a bed where people lie down all the time, the joints and seams are made sideways, because the
    stress is sideways.
  6. Except.
  7. A body is not square like a crosstie.
  8. Animal magnetism.
  9. The animal magnetism of a dead body makes the stress come slanting, so the seams and joints of a
    coffin are made on the bevel.
  10. You can see by an old grave that the earth sinks down on the bevel.
  11. While in a natural hole it sinks by the center, the stress being up- and-down.
  12. So I made it on the bevel.
  13. It makes a neater job.

VARDAMAN

My mother is a fish.

TULL

It was ten oclock when I got back, with Peabody’s team hitched on to the back of the wagon. They
had already dragged the buckboard back from where Quick found it upside down straddle of the ditch
about a mile from the spring. It was pulled out of the road at the spring, and about a dozen wagons
was already there. It was Quick found it. He said the river was up and still rising. He said it had
already covered the highest water-mark on the bridge-piling he had ever seen. “That bridge wont
stand a whole lot of water,” I said. “Has somebody told Anse about it?”
“I told him,” Quick said. “He says he reckons them boys has heard and unloaded and are on the way
back by now. He says they can load up and get across.”
“He better go on and bury her at New Hope,” Armstid said. “That bridge is old. I wouldn’t monkey
with it.”
“His mind is set on taking her to Jefferson,” Quick said. “Then he better get at it soon as he
can,” Armstid said.
Anse meets us at the door. He has shaved, but not good. There is a long cut on his jaw, and he is
wearing his Sunday pants and a white shirt with the neckband buttoned. It is drawn smooth over his
hump, making it look bigger than ever, like a white shirt will, and his face is different too. He
looks folks in the eye now, dignified, his face tragic and composed, shaking us by the hand as we
walk up onto the porch and scrape our shoes, a little stiff in our Sunday clothes, our Sunday
clothes rustling, not looking full at him as he meets us.
“The Lord giveth,” we say. “The Lord giveth.”
That boy is not there. Peabody told about how he come into the kitchen, hollering, swarming and
clawing at Cora when he found her cooking that fish, and how Dewey Dell taken him down to the barn.
“My team all right?” Peabody says.
“All right,” I tell him. “I give them a bait this morning. Your buggy

seems all right too. It aint hurt.”
“And no fault of somebody’s,” he says. “I’d give a nickel to know where that boy was when that team
broke away.”
“If it’s broke anywhere, I’ll fix it,” I say.
The women folks go on into the house. We can hear them, talking and fanning. The fans go whish.
whish. whish and them talking, the talking sounding kind of like bees murmuring in a water bucket.
The men stop on the porch, talking some, not looking at one another.
“Howdy, Vernon,” they say. “Howdy, Tull.” “Looks like more rain.”
“It does for a fact.”
“Yes, sir. It will rain some more.” “It come up quick.”
“And going away slow. It dont fail.”
I go around to the back. Cash is filling up the holes he bored in the top of it. He is trimming out
plugs for them, one at a time, the wood wet and hard to work. He could cut up a tin can and hide
the holes and nobody wouldn’t know the difference. Wouldn’t mind, anyway. I have seen him spend a
hour trimming out a wedge like it was glass he was working, when he could have reached around and
picked up a dozen sticks and drove them into the joint and made it do.
When we finished I go back to the front. The men have gone a little piece from the house, sitting
on the ends of the boards and on the saw-horses where we made it last night, some sitting and some
squatting. Whitfield aint come yet.
They look up at me, their eyes asking. “It’s about,” I say. “He’s ready to nail.”
While they are getting up Anse comes to the door and looks at us and we return to the porch. We
scrape our shoes again, careful, waiting for one another to go in first, milling a little at the
door. Anse stands inside the door, dignified, composed. He waves us in and leads the way into the
room.
They had laid her in it reversed. Cash made it clock-shape, like this
with every joint and seam bevelled and scrubbed with the plane, tight as a drum and neat as a
sewing basket, and they had laid her in it head to foot so it wouldn’t crush her dress. It was her
wedding dress and it had a flare-out bottom, and they had laid her head to foot in it so the dress
could spread out, and they had made her a veil out of a mosquito bar so the auger holes in her face

wouldn’t show.
When we are going out, Whitfield comes. He is wet and muddy to the waist, coming in. “The Lord
comfort this house,” he says. “I was late because the bridge has gone. I went down to the old ford
and swum my horse over, the Lord protecting me. His grace be upon this house.”
We go back to the trestles and plank-ends and sit or squat. “I knowed it would go,” Armstid says.
“It’s been there a long time, that ere bridge,” Quick says.
“The Lord has kept it there, you mean,” Uncle Billy says. “I dont know ere a man that’s touched
hammer to it in twenty-five years.”
“How long has it been there, Uncle Billy?” Quick says.
“It was built in.……let me see.…… It was in the year 1888,” Uncle Billy says. “I mind it because the
first man to cross it was Peabody coming to my house when Jody was born.”
“If I’d a crossed it every time your wife littered since, it’d a been wore out long before this,
Billy,” Peabody says.
We laugh, suddenly loud, then suddenly quiet again. We look a little aside at one another.
“Lots of folks has crossed it that wont cross no more bridges,” Houston says.
“It’s a fact,” Littlejohn says. “It’s so.”
“One more aint, no ways,” Armstid says. “It’d taken them two-three days to got her to town in the
wagon. They’d be gone a week, getting her to Jefferson and back.”
“What’s Anse so itching to take her to Jefferson for, anyway?” Houston says.
“He promised her,” I say. “She wanted it. She come from there. Her mind was set on it.”
“And Anse is set on it, too,” Quick says.
“Ay,” Uncle Billy says. “It’s like a man that’s let everything slide all his life to get set on
something that will make the most trouble for everybody he knows.”
“Well, it’ll take the Lord to get her over that river now,” Peabody says. “Anse cant do it.”
“And I reckon He will,” Quick says. “He’s took care of Anse a long time, now.”
“It’s a fact,” Littlejohn says.
“Too long to quit now,” Armstid says.
“I reckon He’s like everybody else around here,” Uncle Billy says.

“He’s done it so long now He cant quit.”
Cash comes out. He has put on a clean shirt; his hair, wet, is combed smooth down on his brow,
smooth and black as if he had painted it onto his head. He squats stiffly among us, we watching
him.
“You feeling this weather, aint you?” Armstid says. Cash says nothing.
“A broke bone always feels it,” Littlejohn says. “A fellow with a broke bone can tell it a-coming.”
“Lucky Cash got off with just a broke leg,” Armstid says. “He might have hurt himself bed-rid. How
far’d you fall, Cash?”
“Twenty-eight foot, four and a half inches, about,” Cash says. I move over beside him.
“A fellow can sho slip quick on wet planks,” Quick says. “It’s too bad,” I say. “But you couldn’t a
holp it.”
“It’s them durn women,” he says. “I made it to balance with her. I made it to her measure and
weight.”
If it takes wet boards for folks to fall, it’s fixing to be lots of falling before this spell is
done.
“You couldn’t have holp it,” I say.
I dont mind the folks falling. It’s the cotton and corn I mind. Neither does Peabody mind the folks
falling. How bout it, Doc?
It’s a fact. Washed clean outen the ground it will be. Seems like something is always happening to
it.
Course it does. That’s why it’s worth anything. If nothing didn’t happen and everybody made a big
crop, do you reckon it would be worth the raising?
Well, I be durn if I like to see my work washed outen the ground, work I sweat over.
It’s a fact. A fellow wouldn’t mind seeing it washed up if he could just turn on the rain himself.
Who is that man can do that? Where is the color of his eyes?
Ay. The Lord made it to grow. It’s Hisn to wash up if He sees it fitten so. “You couldn’t have holp
it,” I say.
“It’s them durn women,” he says.
In the house the women begin to sing. We hear the first line commence, beginning to swell as they
take hold, and we rise and move toward the door, taking off our hats and throwing our chews away.
We do not go in. We stop at the steps, clumped, holding our hats between our lax hands in front or
behind, standing with one foot advanced and our heads lowered, looking aside, down at our hats in

our hands and at the earth or now and then at the sky and at one another’s grave, composed face.
The song ends; the voices quaver away with a rich and dying fall. Whitfield begins. His voice is
bigger than him. It’s like they are not the same. It’s like he is one, and his voice is one,
swimming on two horses side by side across the ford and coming into the house, the mud-splashed one
and the one that never even got wet, triumphant and sad. Somebody in the house begins to cry. It
sounds like her eyes and her voice were turned back inside her, listening; we move, shifting to the
other leg, meeting one another’s eye and making like they hadn’t touched.
Whitfield stops at last. The women sing again. In the thick air it’s like their voices come out of
the air, flowing together and on in the sad, comforting tunes. When they cease it’s like they
hadn’t gone away. It’s like they had just disappeared into the air and when we moved we would loose
them again out of the air around us, sad and comforting. Then they finish and we put on our hats,
our movements stiff, like we hadn’t never wore hats before.
On the way home Cora is still singing. “I am bounding toward my God and my reward,” she sings,
sitting on the wagon, the shawl around her shoulders and the umbrella open over her, though it is
not raining.
“She has hern,” I say. “Wherever she went, she has her reward in being free of Anse Bundren.” She
laid there three days in that box, waiting for Darl and Jewel to come clean back home and get a new
wheel and go back to where the wagon was in the ditch. Take my team, Anse, I said.
We’ll wait for ourn, he said. She’ll want it so. She was ever a particular woman.
On the third day they got back and they loaded her into the wagon and started and it already too
late. You’ll have to go all the way round by Samson’s bridge. It’ll take you a day to get there.
Then you’ll be forty miles from Jefferson. Take my team, Anse.
We’ll wait for ourn. She’ll want it so.
It was about a mile from the house we saw him, sitting on the edge of the slough. It hadn’t had a
fish in it never that I knowed. He looked around at us, his eyes round and calm, his face dirty,
the pole across his knees. Cora was still singing.
“This aint no good day to fish,” I said. “You come on home with us and me and you’ll go down to the
river first thing in the morning and

catch some fish.”
“It’s one in here,” he said. “Dewey Dell seen it.” “You come on with us. The river’s the best
place.” “It’s in here,” he said. “Dewey Dell seen it.”
“I’m bounding toward my God and my reward,” Cora sung.

DARL

It’s not your horse that’s dead, Jewel,” I say. He sits erect on the seat, leaning a little
forward, wooden-backed. The brim of his hat has soaked free of the crown in two places, drooping
across his wooden face so that, head lowered, he looks through it like through the visor of a
helmet, looking long across the valley to where the barn leans against the bluff, shaping the
invisible horse. “See them?” I say. High above the house, against the quick thick sky, they hang in
narrowing circles. From here they are no more than specks, implacable, patient, portentous. “But
it’s not your horse that’s dead.”
“Goddamn you,” he says. “Goddamn you.”
I cannot love my mother because I have no mother. Jewel’s mother is a horse.
Motionless, the tall buzzards hang in soaring circles, the clouds giving them an illusion of
retrograde.
Motionless, wooden-backed, wooden-faced, he shapes the horse in a rigid stoop like a hawk,
hook-winged. They are waiting for us, ready for the moving of it, waiting for him. He enters the
stall and waits until it kicks at him so that he can slip past and mount onto the trough and pause,
peering out across the intervening stall-tops toward the empty path, before he reaches into the
loft.
“Goddamn him. Goddamn him.”

CASH

It wont balance. If you want it to tote and ride on a balance, we will have——”
“Pick up. Goddamn you, pick up.”
“I’m telling you it wont tote and it wont ride on a balance unless
——”
“Pick up! Pick up, goddamn your thick-nosed soul to hell, pick up!”
It wont balance. If they want it to tote and ride on a balance, they will have

DARL

He stoops among us above it, two of the eight hands. In his face the blood goes in waves. In
between them his flesh is greenish looking, about that smooth, thick, pale green of cow’s cud; his
face suffocated, furious, his lip lifted upon his teeth. “Pick up!” he says. “Pick up, goddamn your
thick-nosed soul!”
He heaves, lifting one whole side so suddenly that we all spring into the lift to catch and balance
it before he hurls it completely over. For an instant it resists, as though volitional, as though
within it her pole- thin body clings furiously, even though dead, to a sort of modesty, as she
would have tried to conceal a soiled garment that she could not prevent her body soiling. Then it
breaks free, rising suddenly as though the emaciation of her body had added buoyancy to the planks
or as though, seeing that the garment was about to be torn from her, she rushes suddenly after it
in a passionate reversal that flouts its own desire and need. Jewel’s face goes completely green
and I can hear teeth in his breath.
We carry it down the hall, our feet harsh and clumsy on the floor, moving with shuffling steps, and
through the door.
“Steady it a minute, now,” pa says, letting go. He turns back to shut and lock the door, but Jewel
will not wait.
“Come on,” he says in that suffocating voice. “Come on.”
We lower it carefully down the steps. We move, balancing it as though it were something infinitely
precious, our faces averted, breathing through our teeth to keep our nostrils closed. We go down
the path, toward the slope.
“We better wait,” Cash says. “I tell you it aint balanced now. We’ll need another hand on that
hill.”
“Then turn loose,” Jewel says. He will not stop. Cash begins to fall behind, hobbling to keep up,
breathing harshly; then he is distanced and Jewel carries the entire front end alone, so that,
tilting as the path begins to slant, it begins to rush away from me and slip down the air

like a sled upon invisible snow, smoothly evacuating atmosphere in which the sense of it is still
shaped.
“Wait, Jewel,” I say. But he will not wait. He is almost running now and Cash is left behind. It
seems to me that the end which I now carry alone has no weight, as though it coasts like a rushing
straw upon the furious tide of Jewel’s despair. I am not even touching it when, turning, he lets it
overshoot him, swinging, and stops it and sloughs it into the wagon bed in the same motion and
looks back at me, his face suffused with fury and despair.
“Goddamn you. Goddamn you.”

VARDAMAN

We are going to town. Dewey Dell says it wont be sold because it belongs to Santa Claus and he
taken it back with him until next Christmas. Then it will be behind the glass again, shining with
waiting.
Pa and Cash are coming down the hill, but Jewel is going to the barn. “Jewel,” pa says. Jewel does
not stop. “Where you going?” pa says. But Jewel does not stop. “You leave that horse here,” pa
says. Jewel stops and looks at pa. Jewel’s eyes look like marbles. “You leave that horse here,” pa
says. “We’ll all go in the wagon with ma, like she wanted.”
But my mother is a fish. Vernon seen it. He was there. “Jewel’s mother is a horse,” Darl said.
“Then mine can be a fish, cant it, Darl?” I said. Jewel is my brother.
“Then mine will have to be a horse, too,” I said.
“Why?” Darl said. “If pa is your pa, why does your ma have to be a horse just because Jewel’s is?”
“Why does it?” I said. “Why does it, Darl?” Darl is my brother.
“Then what is your ma, Darl?” I said.
“I haven’t got ere one,” Darl said. “Because if I had one, it is was.
And if it is was, it cant be is. Can it?” “No,” I said.
“Then I am not,” Darl said. “Am I?” “No,” I said.
I am. Darl is my brother. “But you are, Darl,” I said.
“I know it,” Darl said. “That’s why I am not is. Are is too many for one woman to foal.”
Cash is carrying his tool box. Pa looks at him. “I’ll stop at Tull’s on the way back,” Cash says.
“Get on that barn roof.”

“It aint respectful,” pa says. “It’s a deliberate flouting of her and of me.”
“Do you want him to come all the way back here and carry them up to Tull’s afoot?” Darl says. Pa
looks at Darl, his mouth chewing. Pa shaves every day now because my mother is a fish.
“It aint right,” pa says.
Dewey Dell has the package in her hand. She has the basket with our dinner too.
“What’s that?” pa says.
“Mrs Tull’s cakes,” Dewey Dell says, getting into the wagon. “I’m taking them to town for her.”
“It aint right,” pa says. “It’s a flouting of the dead.”
It’ll be there. It’ll be there come Christmas, she says, shining on the track. She says he wont
sell it to no town boys.

DARL

He goes on toward the barn, entering the lot, wooden-backed.
Dewey Dell carries the basket on one arm, in the other hand something wrapped square in a
newspaper. Her face is calm and sullen, her eyes brooding and alert; within them I can see
Peabody’s back like two round peas in two thimbles: perhaps in Peabody’s back two of those worms
which work surreptitious and steady through you and out the other side and you waking suddenly from
sleep or from waking, with on your face an expression sudden, intent, and concerned. She
sets the basket into the wagon and climbs in, her leg coming long from beneath her tightening
dress: that lever which moves the world; one of that caliper which measures the length and breadth
of life. She sits on the seat beside Vardaman and sets the parcel on her lap.
Then he enters the barn. He has not looked back.
“It aint right,” pa says. “It’s little enough for him to do for her.”
“Go on,” Cash says. “Leave him stay if he wants. He’ll be all right here. Maybe he’ll go up to
Tull’s and stay.”
“He’ll catch us,” I say. “He’ll cut across and meet us at Tull’s lane.” “He would have rid that
horse, too,” pa says, “if I hadn’t a stopped
him. A durn spotted critter wilder than a cattymount. A deliberate flouting of her and of me.”
The wagon moves; the mules’ ears begin to bob. Behind us, above the house, motionless in tall and
soaring circles, they diminish and disappear.

ANSE

I told him not to bring that horse out of respect for his dead ma, because it wouldn’t look right,
him prancing along on a durn circus animal and her wanting us all to be in the wagon with her that
sprung from her flesh and blood, but we hadn’t no more than passed Tull’s lane when Darl begun to
laugh. Setting back there on the plank seat with Cash, with his dead ma laying in her coffin at his
feet, laughing. How many times I told him it’s doing such things as that that makes folks talk
about him, I dont know. I says I got some regard for what folks says about my flesh and blood even
if you haven’t, even if I have raised such a durn passel of boys, and when you fixes it so folks
can say such about you, it’s a reflection on your ma, I says, not me: I am a man and I can stand
it; it’s on your womenfolks, your ma and sister that you should care for, and I turned and looked
back at him and him setting there, laughing.
“I dont expect you to have no respect for me,” I says. “But with
your own ma not cold in her coffin yet.”
“Yonder,” Cash says, jerking his head toward the lane. The horse is still a right smart piece away,
coming up at a good pace, but I dont have to be told who it is. I just looked back at Darl, setting
there laughing.
“I done my best,” I says. “I tried to do as she would wish it. The Lord will pardon me and excuse
the conduct of them He sent me.” And Darl setting on the plank seat right above her where she was
laying, laughing.

DARL

He comes up the lane fast, yet we are three hundred yards beyond the mouth of it when he turns into
the road, the mud flying beneath the flicking drive of the hooves. Then he slows a little, light
and erect in the saddle, the horse mincing through the mud.
Tull is in his lot. He looks at us, lifts his hand. We go on, the wagon creaking, the mud
whispering on the wheels. Vernon still stands there. He watches Jewel as he passes, the horse
moving with a light, high- kneed driving gait, three hundred yards back. We go on, with a motion so
soporific, so dreamlike as to be uninferant of progress, as though time and not space were
decreasing between us and it.
It turns off at right angles, the wheel-marks of last Sunday healed away now: a smooth, red
scoriation curving away into the pines; a white signboard with faded lettering: New Hope Church. 3
mi. It wheels up like a motionless hand lifted above the profound desolation of the ocean; beyond
it the red road lies like a spoke of which Addie Bundren is the rim. It wheels past, empty,
unscarred, the white signboard turns away its fading and tranquil assertion. Cash looks up the road
quietly, his head turning as we pass it like an owl’s head, his face composed. Pa looks straight
ahead, humped. Dewey Dell looks at the road too, then she looks back at me, her eyes watchful and
repudiant, not like that question which was in those of Cash, for a smoldering while. The signboard
passes; the unscarred road wheels on. Then Dewey Dell turns her head. The wagon creaks on.
Cash spits over the wheel. “In a couple of days now it’ll be
smelling,” he says.
“You might tell Jewel that,” I say.
He is motionless now, sitting the horse at the junction, upright, watching us, no less still than
the signboard that lifts its fading capitulation opposite him.
“It aint balanced right for no long ride,” Cash says. “Tell him that, too,” I say. The wagon creaks
on.

A mile further along he passes us, the horse, archnecked, reined back to a swift singlefoot. He
sits lightly, poised, upright, wooden- faced in the saddle, the broken hat raked at a swaggering
angle. He passes us swiftly, without looking at us, the horse driving, its hooves hissing in the
mud. A gout of mud, backflung, plops onto the box. Cash leans forward and takes a tool from his box
and removes it carefully. When the road crosses Whiteleaf, the willows leaning near enough, he
breaks off a branch and scours at the stain with the wet leaves.

ANSE

It’s a hard country on man; it’s hard. Eight miles of the sweat of his body washed up outen the
Lord’s earth, where the Lord Himself told him to put it. Nowhere in this sinful world can a honest,
hardworking man profit. It takes them that runs the stores in the towns, doing no sweating, living
off of them that sweats. It aint the hardworking man, the farmer. Sometimes I wonder why we keep at
it. It’s because there is a reward for us above, where they cant take their autos and such. Every
man will be equal there and it will be taken from them that have and give to them that have not by
the Lord.
But it’s a long wait, seems like. It’s bad that a fellow must earn the reward of his right-doing by
flouting hisself and his dead. We drove all the rest of the day and got to Samson’s at dust-dark
and then that bridge was gone, too. They hadn’t never see the river so high, and it not done
raining yet. There was old men that hadn’t never see nor hear of it being so in the memory of man.
I am the chosen of the Lord, for who He loveth, so doeth He chastiseth. But I be durn if He dont
take some curious ways to show it, seems like.
But now I can get them teeth. That will be a comfort. It will.

SAMSON

It was just before sundown. We were sitting on the porch when the wagon came up the road with the
five of them in it and the other one on the horse behind. One of them raised his hand, but they was
going on past the store without stopping.
“Who’s that?” MacCallum says: I cant think of his name: Rafe’s twin; that one it was.
“It’s Bundren, from down beyond New Hope,” Quick says. “There’s one of them Snopes horses Jewel’s
riding.”
“I didn’t know there was ere a one of them horses left,” MacCallum says. “I thought you folks down
there finally contrived to give them all away.”
“Try and get that one,” Quick says. The wagon went on. “I bet old man Lon never gave it to him,” I
says.
“No,” Quick says. “He bought it from pappy.” The wagon went on. “They must not a heard about the
bridge,” he says.
“What’re they doing up here, anyway?” MacCallum says.
“Taking a holiday since he got his wife buried, I reckon,” Quick says. “Heading for town, I reckon,
with Tull’s bridge gone too. I wonder if they aint heard about the bridge.”
“They’ll have to fly, then,” I says. “I dont reckon there’s ere a bridge between here and Mouth of
Ishatawa.”
They had something in the wagon. But Quick had been to the funeral three days ago and we naturally
never thought anything about it except that they were heading away from home mighty late and that
they hadn’t heard about the bridge. “You better holler at them,” MacCallum says. Durn it, the name
is right on the tip of my tongue. So Quick hollered and they stopped and he went to the wagon and
told them.
He come back with them. “They’re going to Jefferson,” he says. “The bridge at Tull’s is gone, too.”
Like we didn’t know it, and his face looked funny, around the nostrils, but they just sat there,
Bundren and

the girl and the chap on the seat, and Cash and the second one, the one folks talks about, on a
plank across the tail-gate, and the other one on that spotted horse. But I reckon they was used to
it by then, because when I said to Cash that they’d have to pass by New Hope again and what they’d
better do, he just says,
“I reckon we can get there.”
I aint much for meddling. Let every man run his own business to suit himself, I say. But after I
talked to Rachel about them not having a regular man to fix her and it being July and all, I went
back down to the barn and tried to talk to Bundren about it.
“I give her my promise,” he says. “Her mind was set on it.”
I notice how it takes a lazy man, a man that hates moving, to get set on moving once he does get
started off, the same as he was set on staying still, like it aint the moving he hates so much as
the starting and the stopping. And like he would be kind of proud of whatever come up to make the
moving or the setting still look hard. He set there on the wagon, hunched up, blinking, listening
to us tell about how quick the bridge went and how high the water was, and I be durn if he didn’t
act like he was proud of it, like he had made the river rise himself.
“You say it’s higher than you ever see it before?” he says. “God’s will be done,” he says. “I
reckon it wont go down much by morning, neither,” he says.
“You better stay here tonight,” I says, “and get a early start for New Hope tomorrow morning.” I
was just sorry for them bone-gaunted mules. I told Rachel, I says, “Well, would you have had me
turn them away at dark, eight miles from home? What else could I do,” I says. “It wont be but one
night, and they’ll keep it in the barn, and they’ll sholy get started by daylight.” And so I says,
“You stay here tonight and early tomorrow you can go back to New Hope. I got tools enough, and the
boys can go on right after supper and have it dug and ready if they want” and then I found that
girl watching me. If her eyes had a been pistols, I wouldn’t be talking now. I be dog if they
didn’t blaze at me. And so when I went down to the barn I come on them, her talking so she never
noticed when I come up.
“You promised her,” she says. “She wouldn’t go until you promised. She thought she could depend on
you. If you dont do it, it will be a curse on you.”
“Cant no man say I dont aim to keep my word,” Bundren says. “My heart is open to ere a man.”

“I dont care what your heart is,” she says. She was whispering, kind of, talking fast. “You
promised her. You’ve got to. You——” then she seen me and quit, standing there. If they’d been
pistols, I wouldn’t be talking now. So when I talked to him about it, he says,
“I give her my promise. Her mind is set on it.”
“But seems to me she’d rather have her ma buried close by, so she could——”
“It’s Addie I give the promise to,” he says. “Her mind is set on it.”
So I told them to drive it into the barn, because it was threatening rain again, and that supper
was about ready. Only they didn’t want to come in.
“I thank you,” Bundren says. “We wouldn’t discommode you. We got a little something in the basket.
We can make out.”
“Well,” I says, “since you are so particular about your womenfolks, I am too. And when folks stops
with us at meal time and wont come to the table, my wife takes it as a insult.”
So the girl went on to the kitchen to help Rachel. And then Jewel come to me.
“Sho,” I says. “Help yourself outen the loft. Feed him when you bait the mules.”
“I rather pay you for him,” he says.
“What for?” I says. “I wouldn’t begrudge no man a bait for his horse.”
“I rather pay you,” he says; I thought he said extra. “Extra for what?” I says. “Wont he eat hay
and corn?”
“Extra feed,” he says. “I feed him a little extra and I dont want him beholden to no man.”
“You cant buy no feed from me, boy,” I says. “And if he can eat that loft clean, I’ll help you load
the barn onto the wagon in the morning.” “He aint never been beholden to no man,” he says. “I
rather pay
you for it.”
And if I had my rathers, you wouldn’t be here a-tall, I wanted to say. But I just says, “Then it’s
high time he commenced. You cant buy no feed from me.”
When Rachel put supper on, her and the girl went and fixed some beds. But wouldn’t any of them come
in. “She’s been dead long enough to get over that sort of foolishness,” I says. Because I got just
as much respect for the dead as ere a man, but you’ve got to respect the dead themselves, and a
woman that’s been dead in a box four days, the best way to respect her is to get her into the
ground as quick

as you can. But they wouldn’t do it.
“It wouldn’t be right,” Bundren says. “Course, if the boys wants to go to bed, I reckon I can set
up with her. I dont begrudge her it.”
So when I went back down there they were squatting on the ground around the wagon, all of them.
“Let that chap come to the house and get some sleep, anyway,” I says. “And you better come too,” I
says to the girl. I wasn’t aiming to interfere with them. And I sholy hadn’t done nothing to her
that I knowed.
“He’s done already asleep,” Bundren says. They had done put him to bed in the trough in a empty
stall.
“Well, you come on, then,” I says to her. But still she never said nothing. They just squatted
there. You couldn’t hardly see them. “How about you boys?” I says. “You got a full day tomorrow.”
After a while Cash says,
“I thank you. We can make out.”
“We wouldn’t be beholden,” Bundren says. “I thank you kindly.”
So I left them squatting there. I reckon after four days they was used to it. But Rachel wasn’t.
“It’s a outrage,” she says. “A outrage.”
“What could he a done?” I says. “He give her his promised word.” “Who’s talking about him?” she
says. “Who cares about him?” she
says, crying. “I just wish that you and him and all the men in the world that torture us alive and
flout us dead, dragging us up and down the country——”
“Now, now,” I says. “You’re upset.”
“Dont you touch me!” she says. “Dont you touch me!”
A man cant tell nothing about them. I lived with the same one fifteen years and I be durn if I can.
And I imagined a lot of things coming up between us, but I be durn if I ever thought it would be a
body four days dead and that a woman. But they make life hard on them, not taking it as it comes
up, like a man does.
So I laid there, hearing it commence to rain, thinking about them down there, squatting around the
wagon and the rain on the roof, and thinking about Rachel crying there until after a while it was
like I could still hear her crying even after she was asleep, and smelling it even when I knowed I
couldn’t. I couldn’t decide even then whether I could or not, or if it wasn’t just knowing it was
what it was.
So next morning I never went down there. I heard them hitching up and then when I knowed they must
be about ready to take out, I went out the front and went down the road toward the bridge until I
heard

the wagon come out of the lot and go back toward New Hope. And then when I come back to the house,
Rachel jumped on me because I wasn’t there to make them come in to breakfast. You cant tell about
them. Just about when you decide they mean one thing, I be durn if you not only haven’t got to
change your mind, like as not you got to take a rawhiding for thinking they meant it.
But it was still like I could smell it. And so I decided then that it wasn’t smelling it, but it
was just knowing it was there, like you will get fooled now and then. But when I went to the barn I
knew different. When I walked into the hallway I saw something. It kind of hunkered up when I come
in and I thought at first it was one of them got left, then I saw what it was. It was a buzzard. It
looked around and saw me and went on down the hall, spraddle-legged, with its wings kind of
hunkered out, watching me first over one shoulder and then over the other, like a old baldheaded
man. When it got outdoors it begun to fly. It had to fly a long time before it ever got up into the
air, with it thick and heavy and full of rain like it was.
If they was bent on going to Jefferson, I reckon they could have gone around up by Mount Vernon,
like MacCallum did. He’ll get home about day after tomorrow, horseback. Then they’d be just
eighteen miles from town. But maybe this bridge being gone too has learned him the Lord’s sense and
judgment.
That MacCallum. He’s been trading with me off and on for twelve years. I have known him from a boy
up; know his name as well as I do my own. But be durn if I can say it.

DEWEY DELL

The signboard comes in sight. It is looking out at the road now, because it can wait. New Hope. 3
mi. it will say. New Hope. 3 mi. New Hope. 3 mi. And then the road will begin, curving away into
the trees, empty with waiting, saying New Hope three miles.
I heard that my mother is dead. I wish I had time to let her die. I wish I had time to wish I had.
It is because in the wild and outraged earth too soon too soon too soon. It’s not that I wouldn’t
and will not it’s that it is too soon too soon too soon.
Now it begins to say it. New Hope three miles. New Hope three miles. That’s what they mean by the
womb of time: the agony and the despair of spreading bones, the hard girdle in which lie the
outraged entrails of events Cash’s head turns slowly as we approach, his pale empty sad composed
and questioning face following the red and empty curve; beside the back wheel Jewel sits the horse,
gazing straight ahead.
The land runs out of Darl’s eyes; they swim to pin points. They begin at my feet and rise along my
body to my face, and then my dress is gone: I sit naked on the seat above the unhurrying mules,
above the travail. Suppose I tell him to turn. He will do what I say. Dont you know he will do what
I say? Once I waked with a black void rushing under me. I could not see. I saw Vardaman rise and go
to the window and strike the knife into the fish, the blood gushing, hissing like steam but I could
not see. He’ll do as I say. He always does. I can persuade him to anything. You know I can. Suppose
I say Turn here. That was when I died that time. Suppose I do. We’ll go to New Hope. We wont have
to go to town. I rose and took the knife from the streaming fish still hissing and I killed Darl.
When I used to sleep with Vardaman I had a nightmare once I thought I was awake but I couldn’t see
and couldn’t feel I couldn’t feel the bed under me and I couldn’t think what I was I couldn’t think
of my name I couldn’t even think I am a girl I couldn’t even think I nor even think I want to wake

up nor remember what was opposite to awake so I could do that I knew that something was passing but
I couldn’t even think of time then all of a sudden I knew that something was it was wind blowing
over me it was like the wind came and blew me back from where it was I was not blowing the room and
Vardaman asleep and all of them back under me again and going on like a piece of cool silk dragging
across my naked legs
It blows cool out of the pines, a sad steady sound. New Hope. Was 3 mi. Was 3 mi. I believe in God
I believe in God.
“Why didn’t we go to New Hope, pa?” Vardaman says. “Mr Samson said we was, but we done passed the
road.”
Darl says, “Look, Jewel.” But he is not looking at me. He is looking at the sky. The buzzard is as
still as if he were nailed to it.
We turn into Tull’s lane. We pass the barn and go on, the wheels whispering in the mud, passing the
green rows of cotton in the wild earth, and Vernon little across the field behind the plow. He
lifts his hand as we pass and stands there looking after us for a long while.
“Look, Jewel,” Darl says. Jewel sits on his horse like they were both made out of wood, looking
straight ahead.
I believe in God, God. God, I believe in God.

TULL

After they passed I taken the mule out and looped up the trace chains and followed. They was
setting in the wagon at the end of the levee. Anse was setting there, looking at the bridge where
it was swagged down into the river with just the two ends in sight. He was looking at it like he
had believed all the time that folks had been lying to him about it being gone, but like he was
hoping all the time it really was. Kind of pleased astonishment he looked, setting on the wagon in
his Sunday pants, mumbling his mouth. Looking like a uncurried horse dressed up: I dont know.
The boy was watching the bridge where it was mid-sunk and logs and such drifted up over it and it
swagging and shivering like the whole thing would go any minute, big-eyed he was watching it, like
he was to a circus. And the gal too. When I come up she looked around at me, her eyes kind of
blaring up and going hard like I had made to touch her. Then she looked at Anse again and then back
at the water again.
It was nigh up to the levee on both sides, the earth hid except for the tongue of it we was on
going out to the bridge and then down into the water, and except for knowing how the road and the
bridge used to look, a fellow couldn’t tell where was the river and where the land. It was just a
tangle of yellow and the levee not less wider than a knife- back kind of, with us setting in the
wagon and on the horse and the mule.
Darl was looking at me, and then Cash turned and looked at me with that look in his eyes like when
he was figuring on whether the planks would fit her that night, like he was measuring them inside
of him and not asking you to say what you thought and not even letting on he was listening if you
did say it, but listening all right. Jewel hadn’t moved. He sat there on the horse, leaning a
little forward, with that same look on his face when him and Darl passed the house yesterday,
coming back to get her.

“If it was just up, we could drive across,” Anse says. “We could drive right on across it.”
Sometimes a log would get shoved over the jam and float on, rolling and turning, and we could watch
it go on to where the ford used to be. It would slow up and whirl crossways and hang out of water
for a minute, and you could tell by that that the ford used to be there.
“But that dont show nothing,” I say. “It could be a bar of quicksand built up there.” We watch the
log. Then the gal is looking at me again.
“Mr Whitfield crossed it,” she says.
“He was a horse-back,” I say. “And three days ago. It’s riz five foot since.”
“If the bridge was just up,” Anse says.
The log bobs up and goes on again. There is a lot of trash and foam, and you can hear the water.
“But it’s down,” Anse says.
Cash says, “A careful fellow could walk across yonder on the planks and logs.”
“But you couldn’t tote nothing,” I say. “Likely time you set foot on that mess, it’ll all go, too.
What you think, Darl?”
He is looking at me. He dont say nothing; just looks at me with them queer eyes of hisn that makes
folks talk. I always say it aint never been what he done so much or said or anything so much as how
he looks at you. It’s like he had got into the inside of you, someway. Like somehow you was looking
at yourself and your doings outen his eyes. Then I can feel that gal watching me like I had made to
touch her. She says something to Anse. “.…… Mr Whitfield.…” she says.
“I give her my promised word in the presence of the Lord,” Anse says. “I reckon it aint no need to
worry.”
But still he does not start the mules. We set there above the water. Another log bobs up over the
jam and goes on; we watch it check up and swing slow for a minute where the ford used to be. Then
it goes on.
“It might start falling tonight,” I say. “You could lay over one more day.”
Then Jewel turns sideways on the horse. He has not moved until then, and he turns and looks at me.
His face is kind of green, then it would go red and then green again. “Get to hell on back to your
damn plowing,” he says. “Who the hell asked you to follow us here?”
“I never meant no harm,” I say.

“Shut up, Jewel,” Cash says. Jewel looks back at the water, his face gritted, going red and green
and then red. “Well,” Cash says after a while, “what you want to do?”
Anse dont say nothing. He sets humped up, mumbling his mouth. “If it was just up, we could drive
across it,” he says.
“Come on,” Jewel says, moving the horse.
“Wait,” Cash says. He looks at the bridge. We look at him, except Anse and the gal. They are
looking at the water. “Dewey Dell and Vardaman and pa better walk across on the bridge,” Cash says.
“Vernon can help them,” Jewel says. “And we can hitch his mule ahead of ourn.”
“You aint going to take my mule into that water,” I say.
Jewel looks at me. His eyes look like pieces of a broken plate. “I’ll pay for your damn mule. I’ll
buy it from you right now.”
“My mule aint going into that water,” I say.
“Jewel’s going to use his horse,” Darl says. “Why wont you risk your mule, Vernon?”
“Shut up, Darl,” Cash says. “You and Jewel both.” “My mule aint going into that water,” I say.

DARL

He sits the horse, glaring at Vernon, his lean face suffused up to and beyond the pale rigidity of
his eyes. The summer when he was fifteen, he took a spell of sleeping. One morning when I went to
feed the mules the cows were still in the tie-up and then I heard pa go back to the house and call
him. When we came on back to the house for breakfast he passed us, carrying the milk buckets,
stumbling along like he was drunk, and he was milking when we put the mules in and went on to the
field without him. We had been there an hour and still he never showed up. When Dewey Dell came
with our lunch, pa sent her back to find Jewel. They found him in the tie-up, sitting on the stool,
asleep.
After that, every morning pa would go in and wake him. He would go to sleep at the supper table and
soon as supper was finished he would go to bed, and when I came in to bed he would be lying there
like a dead man. Yet still pa would have to wake him in the morning. He would get up, but he
wouldn’t hardly have half sense: he would stand for pa’s jawing and complaining without a word and
take the milk buckets and go to the barn, and once I found him asleep at the cow, the bucket in
place and half full and his hands up to the wrists in the milk and his head against the cow’s
flank.
After that Dewey Dell had to do the milking. He still got up when pa waked him, going about what we
told him to do in that dazed way. It was like he was trying hard to do them; that he was as puzzled
as anyone else.
“Are you sick?” ma said. “Dont you feel all right?” “Yes,” Jewel said. “I feel all right.”
“He’s just lazy, trying me,” pa said, and Jewel standing there, asleep on his feet like as not.
“Aint you?” he said, waking Jewel up again to answer.
“No,” Jewel said.
“You take off and stay in the house today,” ma said.

“With that whole bottom piece to be busted out?” pa said. “If you aint sick, what’s the matter with
you?”
“Nothing,” Jewel said. “I’m all right.”
“All right?” pa said. “You’re asleep on your feet this minute.” “No,” Jewel said. “I’m all right.”
“I want him to stay at home today,” ma said.
“I’ll need him,” pa said. “It’s tight enough, with all of us to do it.” “You’ll just have to do the
best you can with Cash and Darl,” ma
said. “I want him to stay in today.”
But he wouldn’t do it. “I’m all right,” he said, going on. But he wasn’t all right. Anybody could
see it. He was losing flesh, and I have seen him go to sleep chopping; watched the hoe going slower
and slower up and down, with less and less of an arc, until it stopped and he leaning on it
motionless in the hot shimmer of the sun.
Ma wanted to get the doctor, but pa didn’t want to spend the money without it was needful, and
Jewel did seem all right except for his thinness and his way of dropping off to sleep at any
moment. He ate hearty enough, except for his way of going to sleep in his plate, with a piece of
bread half way to his mouth and his jaws still chewing. But he swore he was all right.
It was ma that got Dewey Dell to do his milking, paid her somehow, and the other jobs around the
house that Jewel had been doing before supper she found some way for Dewey Dell and Vardaman to do
them. And doing them herself when pa wasn’t there. She would fix him special things to eat and hide
them for him. And that may have been when I first found it out, that Addie Bundren should be hiding
anything she did, who had tried to teach us that deceit was such that, in a world where it was,
nothing else could be very bad or very important, not even poverty. And at times when I went in to
go to bed she would be sitting in the dark by Jewel where he was asleep. And I knew that she was
hating herself for that deceit and hating Jewel because she had to love him so that she had to act
the deceit.
One night she was taken sick and when I went to the barn to put the team in and drive to Tull’s, I
couldn’t find the lantern. I remembered noticing it on the nail the night before, but it wasn’t
there now at midnight. So I hitched in the dark and went on and came back with Mrs Tull just after
daylight. And there the lantern was, hanging on the nail where I remembered it and couldn’t find it
before. And then one morning while Dewey Dell was milking just before sunup, Jewel came into the
barn from the back, through the hole in

the back wall, with the lantern in his hand.
I told Cash, and Cash and I looked at one another. “Rutting,” Cash said.
“Yes,” I said. “But why the lantern? And every night, too. No wonder he’s losing flesh. Are you
going to say anything to him?”
“Wont do any good,” Cash said.
“What he’s doing now wont do any good, either.”
“I know. But he’ll have to learn that himself. Give him time to realise that it’ll save, that
there’ll be just as much more tomorrow, and he’ll be all right. I wouldn’t tell anybody, I reckon.”
“No,” I said. “I told Dewey Dell not to. Not ma, anyway.” “No. Not ma.”
After that I thought it was right comical: he acting so bewildered and willing and dead for sleep
and gaunt as a bean-pole, and thinking he was so smart with it. And I wondered who the girl was. I
thought of all I knew that it might be, but I couldn’t say for sure.
“ ’Taint any girl,” Cash said. “It’s a married woman somewhere. Aint any young girl got that much
daring and staying power. That’s what I dont like about it.”
“Why?” I said. “She’ll be safer for him than a girl would. More judgment.”
He looked at me, his eyes fumbling, the words fumbling at what he was trying to say. “It aint
always the safe things in this world that a fellow.……”
“You mean, the safe things are not always the best things?”
“Ay; best,” he said, fumbling again. “It aint the best things, the things that are good for him.……
A young boy. A fellow kind of hates to see.……wallowing in somebody else’s mire.……” That’s what he
was trying to say. When something is new and hard and bright, there ought to be something a little
better for it than just being safe, since the safe things are just the things that folks have been
doing so long they have worn the edges off and there’s nothing to the doing of them that leaves a
man to say, That was not done before and it cannot be done again.
So we didn’t tell, not even when after a while he’d appear suddenly in the field beside us and go
to work, without having had time to get home and make out he had been in bed all night. He would
tell ma that he hadn’t been hungry at breakfast or that he had eaten a piece of bread while he was
hitching up the team. But Cash and I knew that he hadn’t been home at all on those nights and he
had come up out of

the woods when we got to the field. But we didn’t tell. Summer was almost over then; we knew that
when the nights began to get cool, she would be done if he wasn’t.
But when fall came and the nights began to get longer, the only difference was that he would always
be in bed for pa to wake him, getting him up at last in that first state of semi-idiocy like when
it first started, worse than when he had stayed out all night.
“She’s sure a stayer,” I told Cash. “I used to admire her, but I downright respect her now.”
“It aint a woman,” he said.
“You know,” I said. But he was watching me. “What is it, then?” “That’s what I aim to find out,” he
said.
“You can trail him through the woods all night if you want to,” I said. “I’m not.”
“I aint trailing him,” he said. “What do you call it, then?”
“I aint trailing him,” he said. “I dont mean it that way.”
And so a few nights later I heard Jewel get up and climb out the window, and then I heard Cash get
up and follow him. The next morning when I went to the barn, Cash was already there, the mules fed,
and he was helping Dewey Dell milk. And when I saw him I knew that he knew what it was. Now and
then I would catch him watching Jewel with a queer look, like having found out where Jewel went and
what he was doing had given him something to really think about at last. But it was not a worried
look; it was the kind of look I would see on him when I would find him doing some of Jewel’s work
around the house, work that pa still thought Jewel was doing and that ma thought Dewey Dell was
doing. So I said nothing to him, believing that when he got done digesting it in his mind, he would
tell me. But he never did.
One morning—it was November then, five months since it started—
Jewel was not in bed and he didn’t join us in the field. That was the first time ma learned
anything about what had been going on. She sent Vardaman down to find where Jewel was, and after a
while she came down too. It was as though, so long as the deceit ran along quiet and monotonous,
all of us let ourselves be deceived, abetting it unawares or maybe through cowardice, since all
people are cowards and naturally prefer any kind of treachery because it has a bland outside. But
now it was like we had all—and by a kind of telepathic agreement of admitted fear—flung the whole
thing back like covers

on the bed and we all sitting bolt upright in our nakedness, staring at one another and saying “Now
is the truth. He hasn’t come home. Something has happened to him. We let something happen to him.”
Then we saw him. He came up along the ditch and then turned straight across the field, riding the
horse. Its mane and tail were going, as though in motion they were carrying out the splotchy
pattern of its coat: he looked like he was riding on a big pinwheel, barebacked, with a rope
bridle, and no hat on his head. It was a descendant of those Texas ponies Flem Snopes brought here
twenty- five years ago and auctioned off for two dollars a head and nobody but old Lon Quick ever
caught his and still owned some of the blood because he could never give it away.
He galloped up and stopped, his heels in the horse’s ribs and it dancing and swirling like the
shape of its mane and tail and the splotches of its coat had nothing whatever to do with the
flesh-and- bone horse inside them, and he sat there, looking at us.
“Where did you get that horse?” pa said. “Bought it,” Jewel said. “From Mr Quick.”
“Bought it?” pa said. “With what? Did you buy that thing on my word?”
“It was my money,” Jewel said. “I earned it. You wont need to worry about it.”
“Jewel,” ma said; “Jewel.”
“It’s all right,” Cash said. “He earned the money. He cleaned up that forty acres of new ground
Quick laid out last spring. He did it single handed, working at night by lantern. I saw him. So I
dont reckon that horse cost anybody anything except Jewel. I dont reckon we need worry.”
“Jewel,” ma said. “Jewel——” Then she said: “You come right to the house and go to bed.”
“Not yet,” Jewel said. “I aint got time. I got to get me a saddle and bridle. Mr Quick says he——”
“Jewel,” ma said, looking at him. “I’ll give——I’ll give——give
——” Then she began to cry. She cried hard, not hiding her face, standing there in her faded
wrapper, looking at him and him on the horse, looking down at her, his face growing cold and a
little sick looking, until he looked away quick and Cash came and touched her.
“You go on to the house,” Cash said. “This here ground is too wet for you. You go on, now.” She put
her hands to her face then and after a while she went on, stumbling a little on the plow-marks. But
pretty

soon she straightened up and went on. She didn’t look back. When she reached the ditch she stopped
and called Vardaman. He was looking at the horse, kind of dancing up and down by it.
“Let me ride, Jewel,” he said. “Let me ride, Jewel.”
Jewel looked at him, then he looked away again, holding the horse reined back. Pa watched him,
mumbling his lip.
“So you bought a horse,” he said. “You went behind my back and bought a horse. You never consulted
me; you know how tight it is for us to make by, yet you bought a horse for me to feed. Taken the
work from your flesh and blood and bought a horse with it.”
Jewel looked at pa, his eyes paler than ever. “He wont never eat a mouthful of yours,” he said.
“Not a mouthful. I’ll kill him first. Dont you never think it. Dont you never.”
“Let me ride, Jewel,” Vardaman said. “Let me ride, Jewel.” He sounded like a cricket in the grass,
a little one. “Let me ride, Jewel.”
That night I found ma sitting beside the bed where he was sleeping, in the dark. She cried hard,
maybe because she had to cry so quiet; maybe because she felt the same way about tears she did
about deceit, hating herself for doing it, hating him because she had to. And then I knew that I
knew. I knew that as plain on that day as I knew about Dewey Dell on that day.

TULL

So they finally got Anse to say what he wanted to do, and him and the gal and the boy got out of
the wagon. But even when we were on the bridge Anse kept on looking back, like he thought maybe,
once he was outen the wagon, the whole thing would kind of blow up and he would find himself back
yonder in the field again and her laying up there in the house, waiting to die and it to do all
over again.
“You ought to let them taken your mule,” he says, and the bridge shaking and swaying under us,
going down into the moiling water like it went clean through to the other side of the earth, and
the other end coming up outen the water like it wasn’t the same bridge a-tall and that them that
would walk up outen the water on that side must come from the bottom of the earth. But it was still
whole; you could tell that by the way when this end swagged, it didn’t look like the other end
swagged at all: just like the other trees and the bank yonder were swinging back and forth slow
like on a big clock. And them logs scraping and bumping at the sunk part and tilting end-up and
shooting clean outen the water and tumbling on toward the ford and the waiting, slick, whirling,
and foamy.
“What good would that a done?” I says. “If your team cant find the ford and haul it across, what
good would three mules or even ten mules do?”
“I aint asking it of you,” he says. “I can always do for me and mine. I aint asking you to risk
your mule. It aint your dead; I am not blaming you.”
“They ought to went back and laid over until tomorrow,” I says. The water was cold. It was thick,
like slush ice. Only it kind of lived. One part of you knowed it was just water, the same thing
that had been running under this same bridge for a long time, yet when them logs would come spewing
up outen it, you were not surprised, like they was a part of water, of the waiting and the threat.
It was like when we was across, up out of the water again and the

hard earth under us, that I was surprised. It was like we hadn’t expected the bridge to end on the
other bank, on something tame like the hard earth again that we had tromped on before this time and
knowed well. Like it couldn’t be me here, because I’d have had better sense than to done what I
just done. And when I looked back and saw the other bank and saw my mule standing there where I
used to be and knew that I’d have to get back there someway, I knew it couldn’t be, because I just
couldn’t think of anything that could make me cross that bridge ever even once. Yet here I was, and
the fellow that could make himself cross it twice, couldn’t be me, not even if Cora told him to.
It was that boy. I said “Here; you better take a holt of my hand” and he waited and held to me. I
be durn if it wasn’t like he come back and got me; like he was saying They wont nothing hurt you.
Like he was saying about a fine place he knowed where Christmas come twice with Thanksgiving and
lasts on through the winter and the spring and the summer, and if I just stayed with him I’d be all
right too.
When I looked back at my mule it was like he was one of these here spy-glasses and I could look at
him standing there and see all the broad land and my house sweated outen it like it was the more
the sweat, the broader the land; the more the sweat, the tighter the house because it would take a
tight house for Cora, to hold Cora like a jar of milk in the spring: you’ve got to have a tight jar
or you’ll need a powerful spring, so if you have a big spring, why then you have the incentive to
have tight, wellmade jars, because it is your milk, sour or not, because you would rather have milk
that will sour than to have milk that wont, because you are a man.
And him holding to my hand, his hand that hot and confident, so that I was like to say:
Look-a-here. Cant you see that mule yonder? He never had no business over here, so he never come,
not being nothing but a mule. Because a fellow can see ever now and then that children have more
sense than him. But he dont like to admit it to them until they have beards. After they have a
beard, they are too busy because they dont know if they’ll ever quite make it back to where they
were in sense before they was haired, so you dont mind admitting then to folks that are worrying
about the same thing that aint worth the worry that you are yourself.
Then we was over and we stood there, looking at Cash turning the wagon around. We watched them
drive back down the road to where the trail turned off into the bottom. After a while the wagon was
out

of sight.
“We better get on down to the ford and git ready to help,” I said.
“I give her my word,” Anse says. “It is sacred on me. I know you begrudge it, but she will bless
you in heaven.”
“Well, they got to finish circumventing the land before they can dare the water,” I said. “Come
on.”
“It’s the turning back,” he said. “It aint no luck in turning back.”
He was standing there, humped, mournful, looking at the empty road beyond the swagging and swaying
bridge. And that gal, too, with the lunch basket on one arm and that package under the other. Just
going to town. Bent on it. They would risk the fire and the earth and the water and all just to eat
a sack of bananas. “You ought to laid over a day,” I said. “It would a fell some by morning. It
mought not a rained tonight. And it cant get no higher.”
“I give my promise,” he says. “She is counting on it.”

DARL

Before us the thick dark current runs. It talks up to us in a murmur become ceaseless and myriad,
the yellow surface dimpled monstrously into fading swirls travelling along the surface for an
instant, silent, impermanent and profoundly significant, as though just beneath the surface
something huge and alive waked for a moment of lazy alertness out of and into light slumber again.
It clucks and murmurs among the spokes and about the mules’ knees, yellow, skummed with flotsam and
with thick soiled gouts of foam as though it had sweat, lathering, like a driven horse. Through the
undergrowth it goes with a plaintive sound, a musing sound; in it the unwinded cane and saplings
lean as before a little gale, swaying without reflections as though suspended on invisible wires
from the branches overhead. Above the ceaseless surface they stand—trees, cane, vines—rootless,
severed from the earth, spectral above a scene of immense yet circumscribed desolation filled with
the voice of the waste and mournful water.
Cash and I sit in the wagon; Jewel sits the horse at the off rear wheel. The horse is trembling,
its eye rolling wild and baby-blue in its long pink face, its breathing stertorous like groaning.
He sits erect, poised, looking quietly and steadily and quickly this way and that, his face calm, a
little pale, alert. Cash’s face is also gravely composed; he and I look at one another with long
probing looks, looks that plunge unimpeded through one another’s eyes and into the ultimate secret
place where for an instant Cash and Darl crouch flagrant and unabashed in all the old terror and
the old foreboding, alert and secret and without shame. When we speak our voices are quiet,
detached.
“I reckon we’re still in the road, all right.”
“Tull taken and cut them two big whiteoaks. I heard tell how at high water in the old days they
used to line up the ford by them trees.”
“I reckon he did that two years ago when he was logging down

here. I reckon he never thought that anybody would ever use this ford again.”
“I reckon not. Yes, it must have been then. He cut a sight of timber outen here then. Payed off
that mortgage with it, I hear tell.”
“Yes. Yes, I reckon so. I reckon Vernon could have done that.” “That’s a fact. Most folks that logs
in this here country, they need a
durn good farm to support the sawmill. Or maybe a store. But I reckon Vernon could.”
“I reckon so. He’s a sight.”
“Ay. Vernon is. Yes, it must still be here. He never would have got that timber out of here if he
hadn’t cleaned out that old road. I reckon we are still on it.” He looks about quietly, at the
position of the trees, leaning this way and that, looking back along the floorless road shaped
vaguely high in air by the position of the lopped and felled trees, as if the road too had been
soaked free of earth and floated upward, to leave in its spectral tracing a monument to a still
more profound desolation than this above which we now sit, talking quietly of old security and old
trivial things. Jewel looks at him, then at me, then his face turns in in that quiet, constant,
questing about the scene, the horse trembling quietly and steadily between his knees.
“He could go on ahead slow and sort of feel it out,” I say.
“Yes,” Cash says, not looking at me. His face is in profile as he looks forward where Jewel has
moved on ahead.
“He cant miss the river,” I say. “He couldn’t miss seeing it fifty yards ahead.”
Cash does not look at me, his face in profile. “If I’d just suspicioned it, I could a come down
last week and taken a sight on it.”
“The bridge was up then,” I say. He does not look at me. “Whitfield crossed it a-horseback.”
Jewel looks at us again, his expression sober and alert and subdued.
His voice is quiet. “What you want me to do?”
“I ought to come down last week and taken a sight on it,” Cash says.
“We couldn’t have known,” I say. “There wasn’t any way for us to know.”
“I’ll ride on ahead,” Jewel says. “You can follow where I am.” He lifts the horse. It shrinks,
bowed; he leans to it, speaking to it, lifting it forward almost bodily, it setting its feet down
with gingerly splashings, trembling, breathing harshly. He speaks to it, murmurs to it. “Go on,” he
says. “I aint going to let nothing hurt you. Go on,

now.”
“Jewel,” Cash says. Jewel does not look back. He lifts the horse on. “He can swim,” I say. “If
he’ll just give the horse time, anyhow.
……” When he was born, he had a bad time of it. Ma would sit in the lamp-light, holding him on a
pillow on her lap. We would wake and find her so. There would be no sound from them.
“That pillow was longer than him,” Cash says. He is leaning a little forward. “I ought to come down
last week and sighted. I ought to done it.”
“That’s right,” I say. “Neither his feet nor his head would reach the end of it. You couldn’t have
known,” I say.
“I ought to done it,” he says. He lifts the reins. The mules move, into the traces; the wheels
murmur alive in the water. He looks back and down at Addie. “It aint on a balance,” he says.
At last the trees open; against the open river Jewel sits the horse, half turned, it belly deep
now. Across the river we can see Vernon and pa and Vardaman and Dewey Dell. Vernon is waving at us,
waving us further down stream.
“We are too high up,” Cash says. Vernon is shouting too, but we cannot make out what he says for
the noise of the water. It runs steady and deep now, unbroken, without sense of motion until a log
comes along, turning slowly. “Watch it,” Cash says. We watch it and see it falter and hang for a
moment, the current building up behind it in a thick wave, submerging it for an instant before it
shoots up and tumbles on.
“There it is,” I say.
“Ay,” Cash says. “It’s there.” We look at Vernon again. He is now flapping his arms up and down. We
move on down stream, slowly and carefully, watching Vernon. He drops his hands. “This is the
place,” Cash says.
“Well, goddamn it, let’s get across, then,” Jewel says. He moves the horse on.
“You wait,” Cash says. Jewel stops again.
“Well, by God——” he says. Cash looks at the water, then he looks back at Addie. “It aint on a
balance,” he says.
“Then go on back to the goddamn bridge and walk across,” Jewel says. “You and Darl both. Let me on
that wagon.”
Cash does not pay him any attention. “It aint on a balance,” he says. “Yes, sir. We got to watch
it.”
“Watch it, hell,” Jewel says. “You get out of that wagon and let me

have it. By God, if you’re afraid to drive it over.……” His eyes are pale as two bleached chips in
his face. Cash is looking at him.
“We’ll get it over,” he says. “I tell you what you do. You ride on back and walk across the bridge
and come down the other bank and meet us with the rope. Vernon’ll take your horse home with him and
keep it till we get back.”
“You go to hell,” Jewel says.
“You take the rope and come down the bank and be ready with it,” Cash says. “Three cant do no more
than two can—one to drive and one to steady it.”
“Goddamn you,” Jewel says.
“Let Jewel take the end of the rope and cross upstream of us and brace it,” I say. “Will you do
that, Jewel?”
Jewel watches me, hard. He looks quick at Cash, then back at me, his eyes alert and hard. “I dont
give a damn. Just so we do something. Setting here, not lifting a goddamn hand.…”
“Let’s do that, Cash,” I say.
“I reckon we’ll have to,” Cash says.
The river itself is not a hundred yards across, and pa and Vernon and Vardaman and Dewey Dell are
the only things in sight not of that single monotony of desolation leaning with that terrific
quality a little from right to left, as though we had reached the place where the motion of the
wasted world accelerates just before the final precipice. Yet they appear dwarfed. It is as though
the space between us were time: an irrevocable quality. It is as though time, no longer running
straight before us in a diminishing line, now runs parallel between us like a looping string, the
distance being the doubling accretion of the thread and not the interval between. The mules stand,
their fore quarters already sloped a little, their rumps high. They too are breathing now with a
deep groaning sound; looking back once, their gaze sweeps across us with in their eyes a wild, sad,
profound and despairing quality as though they had already seen in the thick water the shape of the
disaster which they could not speak and we could not see.
Cash turns back into the wagon. He lays his hands flat on Addie,
rocking her a little. His face is calm, down-sloped, calculant, concerned. He lifts his box of
tools and wedges it forward under the seat; together we shove Addie forward, wedging her between
the tools and the wagon bed. Then he looks at me.
“No,” I say. “I reckon I’ll stay. Might take both of us.”

From the tool box he takes his coiled rope and carries the end twice around the seat stanchion and
passes the end to me without tying it. The other end he pays out to Jewel, who takes a turn about
his saddle horn.
He must force the horse down into the current. It moves, highkneed, archnecked, boring
and chafing. Jewel sits lightly forward, his knees lifted a little; again his swift alert calm gaze
sweeps upon us and on. He lowers the horse into the stream, speaking to it in a soothing murmur.
The horse slips, goes under to the saddle, surges to its feet again, the current building up
against Jewel’s thighs.
“Watch yourself,” Cash says.
“I’m on it now,” Jewel says. “You can come ahead now.”
Cash takes the reins and lowers the team carefully and skillfully into the stream.
I felt the current take us and I knew we were on the ford by that reason, since it was only by
means of that slipping contact that we could tell that we were in motion at all. What had once been
a flat surface was now a succession of troughs and hillocks lifting and falling about us, shoving
at us, teasing at us with light lazy touches in the vain instants of solidity underfoot. Cash
looked back at me, and then I knew that we were gone. But I did not realise the reason for the rope
until I saw the log. It surged up out of the water and stood for an instant upright upon that
surging and heaving desolation like Christ. Get out and let the current take you down to the bend,
Cash said, You can make it all right. No, I said, I’d get just as wet that way as this
The log appears suddenly between two hills, as if it had rocketed suddenly from the bottom of the
river. Upon the end of it a long gout of foam hangs like the beard of an old man or a goat. When
Cash speaks to me I know that he has been watching it all the time, watching it and watching Jewel
ten feet ahead of us. “Let the rope go,” he says. With his other hand he reaches down and reeves
the two turns from the stanchion. “Ride on, Jewel,” he says; “see if you can pull us ahead of the
log.”
Jewel shouts at the horse; again he appears to lift it bodily between his knees. He is just above
the top of the ford and the horse has a purchase of some sort for it surges forward, shining wetly
half out of water, crashing on in a succession of lunges. It moves unbelievably fast; by that token
Jewel realises at last that the rope is free, for I can see him sawing back on the reins, his head
turned, as the log rears in a long sluggish lunge between us, bearing down upon the team. They

see it too; for a moment they also shine black out of water. Then the downstream one vanishes,
dragging the other with him; the wagon sheers crosswise, poised on the crest of the ford as the log
strikes it, tilting it up and on. Cash is half turned, the reins running taut from his hand and
disappearing into the water, the other hand reached back upon Addie, holding her jammed over
against the high side of the wagon. “Jump clear,” he says quietly. “Stay away from the team and
dont try to fight it. It’ll swing you into the bend all right.”
“You come too,” I say. Vernon and Vardaman are running along the bank, pa and Dewey Dell stand
watching us, Dewey Dell with the basket and the package in her arms. Jewel is trying to fight the
horse back. The head of one mule appears, its eyes wide; it looks back at us for an instant, making
a sound almost human. The head vanishes again.
“Back, Jewel,” Cash shouts. “Back, Jewel.” For another instant I see him leaning to the tilting
wagon, his arm braced back against Addie and his tools; I see the bearded head of the rearing log
strike up again, and beyond it Jewel holding the horse upreared, its head wrenched around,
hammering its head with his fist. I jump from the wagon on the downstream side. Between two hills I
see the mules once more. They roll up out of the water in succession, turning completely over,
their legs stiffly extended as when they had lost contact with the earth.

VARDAMAN

Cash tried but she fell off and Darl jumped going under he went under and Cash hollering to catch
her and I hollering running and hollering and Dewey Dell hollering at me Vardaman you vardaman you
vardaman and Vernon passed me because he was seeing her come up and she jumped into the water again
and Darl hadn’t caught her yet
He came up to see and I hollering catch her Darl catch her and he didn’t come back because she was
too heavy he had to go on catching at her and I hollering catch her darl catch her darl because in
the water she could go faster than a man and Darl had to grabble for her so I knew he could catch
her because he is the best grabbler even with the mules in the way again they dived up rolling
their feet stiff rolling down again and their backs up now and Darl had to again because in the
water she could go faster than a man or a woman and I passed Vernon and he wouldn’t get in the
water and help Darl he wouldn’t grabble for her with Darl he knew but he wouldn’t help
The mules dived up again diving their legs stiff their stiff legs rolling slow and then Darl again
and I hollering catch her darl catch her head her into the bank darl and Vernon wouldn’t help and
then Darl dodged past the mules where he could he had her under the water coming in to the bank
coming in slow because in the water she fought to stay under the water but Darl is strong and he
was coming in slow and so I knew he had her because he came slow and I ran down into the water to
help and I couldn’t stop hollering because Darl was strong and steady holding her under the water
even if she did fight he would not let her go he was seeing me and he would hold her and it was all
right now it was all right now it was all right
Then he comes up out of the water. He comes a long way up slow before his hands do but he’s got to
have her got to so I can bear it. Then his hands come up and all of him above the water. I cant
stop. I have not got time to try. I will try to when I can but his hands came empty out of the
water emptying the water emptying away

“Where is ma, Darl?” I said. “You never got her. You knew she is a fish but you let her get away.
You never got her. Darl. Darl. Darl.” I began to run along the bank, watching the mules dive up
slow again and then down again.

TULL

When I told Cora how Darl jumped out of the wagon and left Cash sitting there trying to save it and
the wagon turning over, and Jewel that was almost to the bank fighting that horse back where it had
more sense than to go, she says “And you’re one of the folks that says Darl is the queer one, the
one that aint bright, and him the only one of them that had sense enough to get off that wagon. I
notice Anse was too smart to been on it a-tall.”
“He couldn’t a done no good, if he’d been there,” I said. “They was going about it right and they
would have made it if it hadn’t a been for that log.”
“Log, fiddlesticks,” Cora said. “It was the hand of God.”
“Then how can you say it was foolish?” I said. “Nobody cant guard against the hand of God. It would
be sacrilege to try to.”
“Then why dare it?” Cora says. “Tell me that.”
“Anse didn’t,” I said. “That’s just what you faulted him for.”
“His place was there,” Cora said. “If he had been a man, he would a been there instead of making
his sons do what he dursn’t.”
“I dont know what you want, then,” I said. “One breath you say they was daring the hand of God to
try it, and the next breath you jump on Anse because he wasn’t with them.” Then she begun to sing
again, working at the washtub, with that singing look in her face like she had done give up folks
and all their foolishness and had done went on ahead of them, marching up the sky, singing.
The wagon hung for a long time while the current built up under it, shoving it off the ford, and
Cash leaning more and more, trying to keep the coffin braced so it wouldn’t slip down and finish
tilting the wagon over. Soon as the wagon got tilted good, to where the current could finish it,
the log went on. It headed around the wagon and went on good as a swimming man could have done. It
was like it had been sent there to do a job and done it and went on.
When the mules finally kicked loose, it looked for a minute like

maybe Cash would get the wagon back. It looked like him and the wagon wasn’t moving at all, and
just Jewel fighting that horse back to the wagon. Then that boy passed me, running and hollering at
Darl and the gal trying to catch him, and then I see the mules come rolling slow up out of the
water, their legs spraddled stiff like they had balked upside down, and roll on into the water
again.
Then the wagon tilted over and then it and Jewel and the horse was all mixed up together. Cash went
outen sight, still holding the coffin braced, and then I couldn’t tell anything for the horse
lunging and splashing. I thought that Cash had give up then and was swimming for it and I was
yelling at Jewel to come on back and then all of a sudden him and the horse went under too and I
thought they was all going. I knew that the horse had got dragged off the ford too, and with that
wild drowning horse and that wagon and that loose box, it was going to be pretty bad, and there I
was, standing knee deep in the water, yelling at Anse behind me: “See what you done now? See what
you done now?”
The horse come up again. It was headed for the bank now, throwing its head up, and then I saw one
of them holding to the saddle on the downstream side, so I started running along the bank, trying
to catch sight of Cash because he couldn’t swim, yelling at Jewel where Cash was like a durn fool,
bad as that boy that was on down the bank still hollering at Darl.
So I went down into the water so I could still keep some kind of a grip in the mud, when I saw
Jewel. He was middle deep, so I knew he was on the ford, anyway, leaning hard upstream, and then I
see the rope, and then I see the water building up where he was holding the wagon snubbed just
below the ford.
So it was Cash holding to the horse when it come splashing and scrambling up the bank, moaning and
groaning like a natural man. When I come to it it was just kicking Cash loose from his holt on the
saddle. His face turned up a second when he was sliding back into the water. It was gray, with his
eyes closed and a long swipe of mud across his face. Then he let go and turned over in the water.
He looked just like a old bundle of clothes kind of washing up and down against the bank. He looked
like he was laying there in the water on his face, rocking up and down a little, looking at
something on the bottom.
We could watch the rope cutting down into the water, and we could feel the weight of the wagon kind
of blump and lunge lazy like, like it

just as soon as not, and that rope cutting down into the water hard as a iron bar. We could hear
the water hissing on it like it was red hot. Like it was a straight iron bar stuck into the bottom
and us holding the end of it, and the wagon lazing up and down, kind of pushing and prodding at us
like it had come around and got behind us, lazy like, like it just as soon as not when it made up
its mind. There was a shoat come by, blowed up like a balloon: one of them spotted shoats of Lon
Quick’s. It bumped against the rope like it was a iron bar and bumped off and went on, and us
watching that rope slanting down into the water. We watched it.

DARL

Cash lies on his back on the earth, his head raised on a rolled garment. His eyes are closed, his
face is gray, his hair plastered in a smooth smear across his forehead as though done with a paint
brush. His face appears sunken a little, sagging from the bony ridges of eye sockets, nose, gums,
as though the wetting had slacked the firmness which had held the skin full; his teeth, set in pale
gums, are parted a little as if he had been laughing quietly. He lies pole-thin in his wet clothes,
a little pool of vomit at his head and a thread of it running from the corner of his mouth and down
his cheek where he couldn’t turn his head quick or far enough, until Dewey Dell stoops and wipes it
away with the hem of her dress.
Jewel approaches. He has the plane. “Vernon just found the square,” he says. He looks down at Cash,
dripping too. “Aint he talked none yet?”
“He had his saw and hammer and chalk-line and rule,” I say. “I know that.”
Jewel lays the square down. Pa watches him. “They cant be far away,” pa says. “It all went
together. Was there ere a such misfortunate man.”
Jewel does not look at pa. “You better call Vardaman back here,” he says. He looks at Cash. Then he
turns and goes away. “Get him to talk soon as he can,” he says, “so he can tell us what else there
was.”
We return to the river. The wagon is hauled clear, the wheels chocked (carefully: we all helped; it
is as though upon the shabby, familiar, inert shape of the wagon there lingered somehow, latent yet
still immediate, that violence which had slain the mules that drew it not an hour since) above the
edge of the flood. In the wagon bed it lies profoundly, the long pale planks hushed a little with
wetting yet still yellow, like gold seen through water, save for two long muddy smears. We pass it
and go on to the bank.
One end of the rope is made fast to a tree. At the edge of the

stream, knee-deep, Vardaman stands, bent forward a little, watching Vernon with rapt absorption. He
has stopped yelling and he is wet to the armpits. Vernon is at the other end of the rope,
shoulder-deep in the river, looking back at Vardaman. “Further back than that,” he says. “You git
back by the tree and hold the rope for me, so it cant slip.”
Vardaman backs along the rope, to the tree, moving blindly, watching Vernon. When we come up he
looks at us once, his eyes round and a little dazed. Then he looks at Vernon again in that posture
of rapt alertness.
“I got the hammer too,” Vernon says. “Looks like we ought to done already got that chalk-line. It
ought to floated.”
“Floated clean away,” Jewel says. “We wont get it. We ought to find the saw, though.”
“I reckon so,” Vernon says. He looks at the water. “That chalk-line, too. What else did he have?”
“He aint talked yet,” Jewel says, entering the water. He looks back at me. “You go back and get him
roused up to talk,” he says.
“Pa’s there,” I say. I follow Jewel into the water, along the rope. It feels alive in my hand,
bellied faintly in a prolonged and resonant arc. Vernon is watching me.
“You better go,” he says. “You better be there.”
“Let’s see what else we can get before it washes on down,” I say.
We hold to the rope, the current curling and dimpling about our shoulders. But beneath that false
blandness the true force of it leans against us lazily. I had not thought that water in July could
be so cold. It is like hands molding and prodding at the very bones. Vernon is still looking back
toward the bank.
“Reckon it’ll hold us all?” he says. We too look back, following the rigid bar of the rope as it
rises from the water to the tree and Vardaman crouched a little beside it, watching us. “Wish my
mule wouldn’t strike out for home,” Vernon says.
“Come on,” Jewel says. “Let’s get outen here.”
We submerge in turn, holding to the rope, being clutched by one another while the cold wall of the
water sucks the slanting mud backward and upstream from beneath our feet and we are suspended so,
groping along the cold bottom. Even the mud there is not still. It has a chill, scouring quality,
as though the earth under us were in motion too. We touch and fumble at one another’s extended
arms, letting ourselves go cautiously against the rope; or, erect in turn,

watch the water suck and boil where one of the other two gropes beneath the surface. Pa has come
down to the shore, watching us.
Vernon comes up, streaming, his face sloped down into his pursed blowing mouth. His mouth is
bluish, like a circle of weathered rubber. He has the rule.
“He’ll be glad of that,” I say. “It’s right new. He bought it just last month out of the
catalogue.”
“If we just knowed for sho what else,” Vernon says, looking over his shoulder and then turning to
face where Jewel had disappeared. “Didn’t he go down fore me?” Vernon says.
“I dont know,” I say. “I think so. Yes. Yes, he did.”
We watch the thick curling surface, streaming away from us in slow whorls.
“Give him a pull on the rope,” Vernon says. “He’s on your end of it,” I say.
“Aint nobody on my end of it,” he says.
“Pull it in,” I say. But he has already done that, holding the end above the water; and then we see
Jewel. He is ten yards away; he comes up, blowing, and looks at us, tossing his long hair back with
a jerk of his head, then he looks toward the bank; we can see him filling his lungs.
“Jewel,” Vernon says, not loud, but his voice going full and clear along the water, peremptory yet
tactful. “It’ll be back here. Better come back.”
Jewel dives again. We stand there, leaning back against the current, watching the water where he
disappeared, holding the dead rope between us like two men holding the nozzle of a fire hose,
waiting for the water. Suddenly Dewey Dell is behind us in the water. “You make him come back,” she
says. “Jewel!” she says. He comes up again, tossing his hair back from his eyes. He is swimming
now, toward the bank, the current sweeping him downstream quartering. “You, Jewel!” Dewey
Dell says. We stand holding the rope and see him gain the bank and climb out. As he rises from the
water, he stoops and picks up something. He comes back along the bank. He has found the chalk-line.
He comes opposite us and stands there, looking about as if he were seeking something. Pa goes on
down the bank. He is going back to look at the mules again where their round bodies float and rub
quietly together in the slack water within the bend.
“What did you do with the hammer, Vernon?” Jewel says.
“I give it to him,” Vernon says, jerking his head at Vardaman.

Vardaman is looking after pa. Then he looks at Jewel. “With the square.” Vernon is watching Jewel.
He moves toward the bank, passing Dewey Dell and me.
“You get on out of here,” I say. She says nothing, looking at Jewel and Vernon.
“Where’s the hammer?” Jewel says. Vardaman scuttles up the bank and fetches it.
“It’s heavier than the saw,” Vernon says. Jewel is tying the end of the chalk-line about the hammer
shaft.
“Hammer’s got the most wood in it,” Jewel says. He and Vernon face one another, watching Jewel’s
hands.
“And flatter, too,” Vernon says. “It’d float three to one, almost. Try the plane.”
Jewel looks at Vernon. Vernon is tall, too; long and lean, eye to eye they stand in their close wet
clothes. Lon Quick could look even at a cloudy sky and tell the time to ten minutes. Big Lon I
mean, not little Lon.
“Why dont you get out of the water?” I say. “It wont float like a saw,” Jewel says.
“It’ll float nigher to a saw than a hammer will,” Vernon says. “Bet you,” Jewel says.
“I wont bet,” Vernon says.
They stand there, watching Jewel’s still hands. “Hell,” Jewel says. “Get the plane, then.”
So they get the plane and tie it to the chalk-line and enter the water again. Pa comes back along
the bank. He stops for a while and looks at us, hunched, mournful, like a failing steer or an old
tall bird.
Vernon and Jewel return, leaning against the current. “Get out of the way,” Jewel says to Dewey
Dell. “Get out of the water.”
She crowds against me a little so they can pass, Jewel holding the plane high as though it were
perishable, the blue string trailing back over his shoulder. They pass us and stop; they fall to
arguing quietly about just where the wagon went over.
“Darl ought to know,” Vernon says. They look at me. “I dont know,” I says. “I wasn’t there that
long.”
“Hell,” Jewel says. They move on, gingerly, leaning against the current, reading the ford with
their feet.
“Have you got a holt of the rope?” Vernon says. Jewel does not answer. He glances back at the
shore, calculant, then at the water. He flings the plane outward, letting the string run through
his fingers, his

fingers turning blue where it runs over them. When the line stops, he hands it back to Vernon.
“Better let me go this time,” Vernon says. Again Jewel does not answer; we watch him duck beneath
the surface.
“Jewel,” Dewey Dell whimpers.
“It aint so deep there,” Vernon says. He does not look back. He is watching the water where Jewel
went under.
When Jewel comes up he has the saw.
When we pass the wagon pa is standing beside it, scrubbing at the two mud smears with a handful of
leaves. Against the jungle Jewel’s horse looks like a patchwork quilt hung on a line.
Cash has not moved. We stand above him, holding the plane, the saw, the hammer, the square, the
rule, the chalk-line, while Dewey Dell squats and lifts Cash’s head. “Cash,” she says; “Cash.”
He opens his eyes, staring profoundly up at our inverted faces. “If ever was such a misfortunate
man,” pa says.
“Look, Cash,” we say, holding the tools up so he can see; “what else did you have?”
He tries to speak, rolling his head, shutting his eyes. “Cash,” we say; “Cash.”
It is to vomit he is turning his head. Dewey Dell wipes his mouth on the wet hem of her dress; then
he can speak.
“It’s his saw-set,” Jewel says. “The new one he bought when he bought the rule.” He moves, turning
away. Vernon looks up after him, still squatting. Then he rises and follows Jewel down to the
water.
“If ever was such a misfortunate man,” pa says. He looms tall above us as we squat; he looks like a
figure carved clumsily from tough wood by a drunken caricaturist. “It’s a trial,” he says. “But I
dont begrudge her it. No man can say I begrudge her it.” Dewey Dell has laid Cash’s head back on
the folded coat, twisting his head a little to avoid the vomit. Beside him his tools lie. “A fellow
might call it lucky it was the same leg he broke when he fell offen that church,” pa says. “But I
dont begrudge her it.”
Jewel and Vernon are in the river again. From here they do not appear to violate the surface at
all; it is as though it had severed them both at a single blow, the two torsos moving with
infinitesimal and ludicrous care upon the surface. It looks peaceful, like machinery does after you
have watched it and listened to it for a long time. As though the clotting which is you had
dissolved into the myriad original motion, and seeing and hearing in themselves blind and deaf;
fury in

itself quiet with stagnation. Squatting, Dewey Dell’s wet dress shapes for the dead eyes of three
blind men those mammalian ludicrosities which are the horizons and the valleys of the earth.

CASH

It wasn’t on a balance. I told them that if they wanted it to tote and ride on a balance, they
would have to

CORA

One day we were talking. She had never been pure religious, not even after that summer at the camp
meeting when Brother Whitfield wrestled with her spirit, singled her out and strove with the vanity
in her mortal heart, and I said to her many a time, “God gave you children to comfort your hard
human lot and for a token of His own suffering and love, for in love you conceived and bore them.”
I said that because she took God’s love and her duty to Him too much as a matter of course, and
such conduct is not pleasing to Him. I said, “He gave us the gift to raise our voices in His
undying praise” because I said there is more rejoicing in heaven over one sinner than over a
hundred that never sinned. And she said “My daily life is an acknowledgment and expiation of my
sin” and I said “Who are you, to say what is sin and what is not sin? It is the Lord’s part to
judge; ours to praise His mercy and His holy name in the hearing of our fellow mortals” because He
alone can see into the heart, and just because a woman’s life is right in the sight of man, she
cant know if there is no sin in her heart without she opens her heart to the Lord and receives His
grace. I said, “Just because you have been a faithful wife is no sign that there is no sin in your
heart, and just because your life is hard is no sign that the Lord’s grace is absolving you.” And
she said, “I know my own sin. I know that I deserve my punishment. I do not begrudge it.” And I
said, “It is out of your vanity that you would judge sin and salvation in the Lord’s place. It is
our mortal lot to suffer and to raise our voices in praise of Him who judges the sin and offers the
salvation through our trials and tribulations time out of mind amen. Not even after Brother
Whitfield, a godly man if ever one breathed God’s breath, prayed for you and strove as never a man
could except him,” I said.
Because it is not us that can judge our sins or know what is sin in
the Lord’s eyes. She has had a hard life, but so does every woman. But you’d think from the way she
talked that she knew more about sin

and salvation than the Lord God Himself, than them who have strove and labored with the sin in this
human world. When the only sin she ever committed was being partial to Jewel that never loved her
and was its own punishment, in preference to Darl that was touched by God Himself and considered
queer by us mortals and that did love her. I said, “There is your sin. And your punishment too.
Jewel is your punishment. But where is your salvation? And life is short enough,” I said, “to win
eternal grace in. And God is a jealous God. It is His to judge and to mete; not yours.”
“I know,” she said. “I——” Then she stopped, and I said, “Know what?”
“Nothing,” she said. “He is my cross and he will be my salvation. He will save me from the water
and from the fire. Even though I have laid down my life, he will save me.”
“How do you know, without you open your heart to Him and lift your voice in His praise?” I said.
Then I realised that she did not mean God. I realised that out of the vanity of her heart she had
spoken sacrilege. And I went down on my knees right there. I begged her to kneel and open her heart
and cast from it the devil of vanity and cast herself upon the mercy of the Lord. But she wouldn’t.
She just sat there, lost in her vanity and her pride, that had closed her heart to God and set that
selfish mortal boy in His place. Kneeling there I prayed for her. I prayed for that poor blind
woman as I had never prayed for me and mine.

ADDIE

In the afternoon when school was out and the last one had left with his little dirty snuffling
nose, instead of going home I would go down the hill to the spring where I could be quiet and hate
them. It would be quiet there then, with the water bubbling up and away and the sun slanting quiet
in the trees and the quiet smelling of damp and rotting leaves and new earth; especially in the
early spring, for it was worst then.
I could just remember how my father used to say that the reason for living was to get ready to stay
dead a long time. And when I would have to look at them day after day, each with his and her secret
and selfish thought, and blood strange to each other blood and strange to mine, and think that this
seemed to be the only way I could get ready to stay dead, I would hate my father for having ever
planted me. I would look forward to the times when they faulted, so I could whip them. When the
switch fell I could feel it upon my flesh; when it welted and ridged it was my blood that ran, and
I would think with each blow of the switch: Now you are aware of me! Now I am something in your
secret and selfish life, who have marked your blood with my own for ever and ever.
And so I took Anse. I saw him pass the school house three or four times before I learned that he
was driving four miles out of his way to do it. I noticed then how he was beginning to hump—a tall
man and young—so that he looked already like a tall bird hunched in the cold weather, on the wagon
seat. He would pass the school house, the wagon creaking slow, his head turning slow to watch the
door of the school house as the wagon passed, until he went on around the curve and out of sight.
One day I went to the door and stood there when he passed. When he saw me he looked quickly away
and did not look back again.
In the early spring it was worst. Sometimes I thought that I could not bear it, lying in bed at
night, with the wild geese going north and

their honking coming faint and high and wild out of the wild darkness, and during the day it would
seem as though I couldn’t wait for the last one to go so I could go down to the spring. And so when
I looked up that day and saw Anse standing there in his Sunday clothes, turning his hat round and
round in his hands, I said:
“If you’ve got any womenfolks, why in the world dont they make you get your hair cut?”
“I aint got none,” he said. Then he said suddenly, driving his eyes at me like two hounds in a
strange yard: “That’s what I come to see you about.”
“And make you hold your shoulders up,” I said. “You haven’t got any? But you’ve got a house. They
tell me you’ve got a house and a good farm. And you live there alone, doing for yourself, do you?”
He just looked at me, turning the hat in his hands. “A new house,” I said. “Are you going to get
married?”
And he said again, holding his eyes to mine: “That’s what I come to see you about.”
Later he told me, “I aint got no people. So that wont be no worry to you. I dont reckon you can say
the same.”
“No. I have people. In Jefferson.”
His face fell a little. “Well, I got a little property. I’m forehanded; I got a good honest name. I
know how town folks are, but maybe when they talk to me.……”
“They might listen,” I said. “But they’ll be hard to talk to.” He was watching my face. “They’re in
the cemetery.”
“But your living kin,” he said. “They’ll be different.”
“Will they?” I said. “I dont know. I never had any other kind.”
So I took Anse. And when I knew that I had Cash, I knew that living was terrible and that this was
the answer to it. That was when I learned that words are no good; that words dont ever fit even
what they are trying to say at. When he was born I knew that motherhood was invented by someone who
had to have a word for it because the ones that had the children didn’t care whether there was a
word for it or not. I knew that fear was invented by someone that had never had the fear; pride,
who never had the pride. I knew that it had been, not that they had dirty noses, but that we had
had to use one another by words like spiders dangling by their mouths from a beam, swinging and
twisting and never touching, and that only through the blows of the switch could my blood and their
blood flow as one stream. I knew that it had been, not that my aloneness had to be violated over
and

over each day, but that it had never been violated until Cash came. Not even by Anse in the nights.
He had a word, too. Love, he called it. But I had been used to words for a long time. I knew that
that word was like the others: just a shape to fill a lack; that when the right time came, you
wouldn’t need a word for that anymore than for pride or fear. Cash did not need to say it to me nor
I to him, and I would say, Let Anse use it, if he wants to. So that it was Anse or love; love or
Anse: it didn’t matter.
I would think that even while I lay with him in the dark and Cash asleep in the cradle within the
swing of my hand. I would think that if he were to wake and cry, I would suckle him, too. Anse or
love: it didn’t matter. My aloneness had been violated and then made whole again by the violation:
time, Anse, love, what you will, outside the circle.
Then I found that I had Darl. At first I would not believe it. Then I believed that I would kill
Anse. It was as though he had tricked me, hidden within a word like within a paper screen and
struck me in the back through it. But then I realised that I had been tricked by words older than
Anse or love, and that the same word had tricked Anse too, and that my revenge would be that he
would never know I was taking revenge. And when Darl was born I asked Anse to promise to take me
back to Jefferson when I died, because I knew that father had been right, even when he couldn’t
have known he was right anymore than I could have known I was wrong.
“Nonsense,” Anse said; “you and me aint nigh done chapping yet, with just two.”
He did not know that he was dead, then. Sometimes I would lie by him in the dark, hearing the land
that was now of my blood and flesh, and I would think: Anse. Why Anse. Why are you Anse. I would
think about his name until after a while I could see the word as a shape, a vessel, and I would
watch him liquify and flow into it like cold molasses flowing out of the darkness into the vessel,
until the jar stood full and motionless: a significant shape profoundly without life like an empty
door frame; and then I would find that I had forgotten the name of the jar. I would think: The
shape of my body where I used to be a virgin is in the shape of a and I couldn’t think Anse,
couldn’t remember Anse. It was not that I could think of myself as no longer unvirgin, because I
was three now. And when I would think Cash and Darl that way until their names would die and
solidify into a shape and then fade away, I would say, All right. It doesn’t matter. It

doesn’t matter what they call them.
And so when Cora Tull would tell me I was not a true mother, I would think how words go straight up
in a thin line, quick and harmless, and how terribly doing goes along the earth, clinging to it, so
that after a while the two lines are too far apart for the same person to straddle from one to the
other; and that sin and love and fear are just sounds that people who never sinned nor loved nor
feared have for what they never had and cannot have until they forget the words. Like Cora, who
could never even cook.
She would tell me what I owed to my children and to Anse and to God. I gave Anse the children. I
did not ask for them. I did not even ask him for what he could have given me: not-Anse. That was my
duty to him, to not ask that, and that duty I fulfilled. I would be I; I would let him be the shape
and echo of his word. That was more than he asked, because he could not have asked for that and
been Anse, using himself so with a word.
And then he died. He did not know he was dead. I would lie by him in the dark, hearing the dark
land talking of God’s love and His beauty and His sin; hearing the dark voicelessness in which the
words are the deeds, and the other words that are not deeds, that are just the gaps in people’s
lacks, coming down like the cries of the geese out of the wild darkness in the old terrible nights,
fumbling at the deeds like orphans to whom are pointed out in a crowd two faces and told, That is
your father, your mother.
I believed that I had found it. I believed that the reason was the duty to the alive, to the
terrible blood, the red bitter flood boiling through the land. I would think of sin as I would
think of the clothes we both wore in the world’s face, of the circumspection necessary because he
was he and I was I; the sin the more utter and terrible since he was the instrument ordained by God
who created the sin, to sanctify that sin He had created. While I waited for him in the woods,
waiting for him before he saw me, I would think of him as dressed in sin. I would think of him as
thinking of me as dressed also in sin, he the more beautiful since the garment which he had
exchanged for sin was sanctified. I would think of the sin as garments which we would remove in
order to shape and coerce the terrible blood to the forlorn echo of the dead word high in the air.
Then I would lay with Anse again—I did not lie to him: I just refused, just as I refused my breast
to Cash and Darl after their time was up—hearing the dark land talking the voiceless speech.

I hid nothing. I tried to deceive no one. I would not have cared. I merely took the precautions
that he thought necessary for his sake, not for my safety, but just as I wore clothes in the
world’s face. And I would think then when Cora talked to me, of how the high dead words in time
seemed to lose even the significance of their dead sound.
Then it was over. Over in the sense that he was gone and I knew that, see him again though I would,
I would never again see him coming swift and secret to me in the woods dressed in sin like a
gallant garment already blowing aside with the speed of his secret coming.
But for me it was not over. I mean, over in the sense of beginning and ending, because to me there
was no beginning nor ending to anything then. I even held Anse refraining still, not that I was
holding him recessional, but as though nothing else had ever been. My children were of me alone, of
the wild blood boiling along the earth, of me and of all that lived; of none and of all. Then I
found that I had Jewel. When I waked to remember to discover it, he was two months gone.
My father said that the reason for living is getting ready to stay dead. I knew at last what he
meant and that he could not have known what he meant himself, because a man cannot know anything
about cleaning up the house afterward. And so I have cleaned my house. With Jewel—I lay by the
lamp, holding up my own head, watching him cap and suture it before he breathed—the wild blood
boiled away and the sound of it ceased. Then there was only the milk, warm and calm, and I lying
calm in the slow silence, getting ready to clean my house.
I gave Anse Dewey Dell to negative Jewel. Then I gave him Vardaman to replace the child I had
robbed him of. And now he has three children that are his and not mine. And then I could get ready
to die.
One day I was talking to Cora. She prayed for me because she believed I was blind to sin, wanting
me to kneel and pray too, because people to whom sin is just a matter of words, to them salvation
is just words too.

WHITFIELD

When they told me she was dying, all that night I wrestled with Satan, and I emerged victorious. I
woke to the enormity of my sin; I saw the true light at last, and I fell on my knees and confessed
to God and asked His guidance and received it. “Rise,” He said; “repair to that home in which you
have put a living lie, among those people with whom you have outraged My Word; confess your sin
aloud. It is for them, for that deceived husband, to forgive you: not I.” So I went. I heard that
Tull’s bridge was gone; I said “Thanks, O Lord, O Mighty Ruler of all;” for by those dangers and
difficulties which I should have to surmount I saw that He had not abandoned me; that my reception
again into His holy peace and love would be the sweeter for it. “Just let me not perish before I
have begged the forgiveness of the man whom I betrayed,” I prayed; “let me not be too late; let not
the tale of mine and her transgression come from her lips instead of mine. She had sworn then that
she would never tell it, but eternity is a fearsome thing to face: have I not wrestled thigh to
thigh with Satan myself? let me not have also the sin of her broken vow upon my soul. Let not the
waters of Thy Mighty Wrath encompass me until I have cleansed my soul in the presence of them whom
I injured.”
It was His hand that bore me safely above the flood, that fended
from me the dangers of the waters. My horse was frightened, and my own heart failed me as the logs
and the uprooted trees bore down upon my littleness. But not my soul: time after time I saw them
averted at destruction’s final instant, and I lifted my voice above the noise of the flood: “Praise
to Thee, O Mighty Lord and King. By this token shall I cleanse my soul and gain again into the fold
of Thy undying love.”
I knew then that forgiveness was mine. The flood, the danger, behind, and as I rode on across the
firm earth again and the scene of my Gethsemane drew closer and closer, I framed the words which I
should use. I would enter the house; I would stop her before she had

spoken; I would say to her husband: “Anse, I have sinned. Do with me as you will.”
It was already as though it were done. My soul felt freer, quieter than it had in years; already I
seemed to dwell in abiding peace again as I rode on. To either side I saw His hand; in my heart I
could hear His voice: “Courage. I am with thee.”
Then I reached Tull’s house. His youngest girl came out and called to me as I was passing. She told
me that she was already dead.
I have sinned, O Lord. Thou knowest the extent of my remorse and the will of my spirit. But He is
merciful; He will accept the will for the deed, Who knew that when I framed the words of my
confession it was to Anse I spoke them, even though he was not there. It was He in His infinite
wisdom that restrained the tale from her dying lips as she lay surrounded by those who loved and
trusted her; mine the travail by water which I sustained by the strength of His hand. Praise to
Thee in Thy bounteous and omnipotent love; O praise.
I entered the house of bereavement, the lowly dwelling where another erring mortal lay while her
soul faced the awful and irrevocable judgment, peace to her ashes.
“God’s grace upon this house,” I said.

DARL

On the horse he rode up to Armstid’s and came back on the horse, leading Armstid’s team. We hitched
up and laid Cash on top of Addie. When we laid him down he vomited again, but he got his head over
the wagon bed in time.
“He taken a lick in the stomach, too,” Vernon said.
“The horse may have kicked him in the stomach too,” I said. “Did he kick you in the stomach, Cash?”
He tried to say something. Dewey Dell wiped his mouth again. “What’s he say?” Vernon said.
“What is it, Cash?” Dewey Dell said. She leaned down. “His tools,” she said. Vernon got them and
put them into the wagon. Dewey Dell lifted Cash’s head so he could see. We drove on, Dewey Dell and
I sitting beside Cash to steady him and he riding on ahead on the horse. Vernon stood watching us
for a while. Then he turned and went back toward the bridge. He walked gingerly, beginning to flap
the wet sleeves of his shirt as though he had just got wet.
He was sitting the horse before the gate. Armstid was waiting at the gate. We stopped and he got
down and we lifted Cash down and carried him into the house, where Mrs Armstid had the bed ready.
We left her and Dewey Dell undressing him.
We followed pa out to the wagon. He went back and got into the wagon and drove on, we following on
foot, into the lot. The wetting had helped, because Armstid said, “You’re welcome to the house. You
can put it there.” He followed, leading the horse, and stood beside the wagon, the reins in his
hand.
“I thank you,” pa said. “We’ll use in the shed yonder. I know it’s a imposition on you.”
“You’re welcome to the house,” Armstid said. He had that wooden look on his face again; that bold,
surly, high-colored rigid look like his face and eyes were two colors of wood, the wrong one pale
and the wrong one dark. His shirt was beginning to dry, but it still clung close upon him when

he moved.
“She would appreciate it,” pa said.
We took the team out and rolled the wagon back under the shed.
One side of the shed was open.
“It wont rain under,” Armstid said. “But if you’d rather.……”
Back of the barn was some rusted sheets of tin roofing. We took two of them and propped them
against the open side.
“You’re welcome to the house,” Armstid said.
“I thank you,” pa said. “I’d take it right kind if you’d give them a little snack.”
“Sho,” Armstid said. “Lula’ll have supper ready soon as she gets Cash comfortable.” He had gone
back to the horse and he was taking the saddle off, his damp shirt lapping flat to him when he
moved.
Pa wouldn’t come in the house.
“Come in and eat,” Armstid said. “It’s nigh ready.” “I wouldn’t crave nothing,” pa said. “I thank
you.”
“You come in and dry and eat,” Armstid said. “It’ll be all right here.”
“It’s for her,” pa said. “It’s for her sake I am taking the food. I got no team, no nothing. But
she will be grateful to ere a one of you.”
“Sho,” Armstid said. “You folks come in and dry.”
But after Armstid gave pa a drink, he felt better, and when we went in to see about Cash he hadn’t
come in with us. When I looked back he was leading the horse into the barn he was already talking
about getting another team, and by supper time he had good as bought it. He is down there in the
barn, sliding fluidly past the gaudy lunging swirl, into the stall with it. He climbs onto the
manger and drags the hay down and leaves the stall and seeks and finds the curry-comb. Then he
returns and slips quickly past the single crashing thump and up against the horse, where it cannot
overreach. He applies the curry-comb, holding himself within the horse’s striking radius with the
agility of an acrobat, cursing the horse in a whisper of obscene caress. Its head flashes back,
tooth-cropped; its eyes roll in the dusk like marbles on a gaudy velvet cloth as he strikes it upon
the face with the back of the curry-comb.

ARMSTID

But time I give him another sup of whisky and supper was about ready, he had done already bought a
team from somebody, on a credit. Picking and choosing he were by then, saying how he didn’t like
this span and wouldn’t put his money in nothing so-and-so owned, not even a hen coop.
“You might try Snopes,” I said. “He’s got three-four span. Maybe one of them would suit you.”
Then he begun to mumble his mouth, looking at me like it was me that owned the only span of mules
in the county and wouldn’t sell them to him, when I knew that like as not it would be my team that
would ever get them out of the lot at all. Only I dont know what they would do with them, if they
had a team. Littlejohn had told me that the levee through Haley bottom had done gone for two miles
and that the only way to get to Jefferson would be to go around by Mottson. But that was Anse’s
business.
“He’s a close man to trade with,” he says, mumbling his mouth. But when I give him another sup
after supper, he cheered up some. He was aiming to go back to the barn and set up with her. Maybe
he thought that if he just stayed down there ready to take out, Santa Claus would maybe bring him a
span of mules. “But I reckon I can talk him around,” he says. “A man’ll always help a fellow in a
tight, if he’s got ere a drop of Christian blood in him.”
“Of course you’re welcome to the use of mine,” I said, me knowing how much he believed that was the
reason.
“I thank you,” he said. “She’ll want to go in ourn,” and him knowing how much I believed that was
the reason.
After supper Jewel rode over to the Bend to get Peabody. I heard he was to be there today at
Varner’s. Jewel come back about midnight. Peabody had gone down below Inverness somewhere, but
Uncle Billy come back with him, with his satchel of horse-physic. Like he says, a man aint so
different from a horse or a mule, come long come short,

except a mule or a horse has got a little more sense. “What you been into now, boy?” he says,
looking at Cash. “Get me a mattress and a chair and a glass of whisky,” he says.
He made Cash drink the whisky, then he run Anse out of the room. “Lucky it was the same leg he
broke last summer,” Anse says, mournful, mumbling and blinking. “That’s something.”
We folded the mattress across Cash’s legs and set the chair on the mattress and me and Jewel set on
the chair and the gal held the lamp and Uncle Billy taken a chew of tobacco and went to work. Cash
fought pretty hard for a while, until he fainted. Then he laid still, with big balls of sweat
standing on his face like they had started to roll down and then stopped to wait for him.
When he waked up, Uncle Billy had done packed up and left. He kept on trying to say something until
the gal leaned down and wiped his mouth. “It’s his tools,” she said.
“I brought them in,” Darl said. “I got them.”
He tried to talk again; she leaned down. “He wants to see them,” she said. So Darl brought them in
where he could see them. They shoved them under the side of the bed, where he could reach his hand
and touch them when he felt better. Next morning Anse taken that horse and rode over to the Bend to
see Snopes. Him and Jewel stood in the lot talking a while, then Anse got on the horse and rode
off. I reckon that was the first time Jewel ever let anybody ride that horse, and until Anse come
back he hung around in that swole-up way, watching the road like he was half a mind to take out
after Anse and get the horse back.
Along toward nine oclock it begun to get hot. That was when I see the first buzzard. Because of the
wetting, I reckon. Anyway it wasn’t until well into the day that I see them. Lucky the breeze was
setting away from the house, so it wasn’t until well into the morning. But soon as I see them it
was like I could smell it in the field a mile away from just watching them, and them circling and
circling for everybody in the county to see what was in my barn.
I was still a good half a mile from the house when I heard that boy yelling. I thought maybe he
might have fell into the well or something, so I whipped up and come into the lot on the lope.
There must have been a dozen of them setting along the ridge-pole of the barn, and that boy was
chasing another one around the lot like it was a turkey and it just lifting enough to dodge him and
go flopping back to the roof of the shed again where he had found it setting on

the coffin. It had got hot then, right, and the breeze had dropped or changed or something, so I
went and found Jewel, but Lula come out.
“You got to do something,” she said. “It’s a outrage.” “That’s what I aim to do,” I said.
“It’s a outrage,” she said. “He should be lawed for treating her so.” “He’s getting her into the
ground the best he can,” I said. So I found
Jewel and asked him if he didn’t want to take one of the mules and go over to the Bend and see
about Anse. He didn’t say nothing. He just looked at me with his jaws going bone-white and them
bone-white eyes of hisn, then he went and begun to call Darl.
“What you fixing to do?” I said.
He didn’t answer. Darl come out. “Come on,” Jewel said. “What you aim to do?” Darl said.
“Going to move the wagon,” Jewel said over his shoulder.
“Dont be a fool,” I said. “I never meant nothing. You couldn’t help it.” And Darl hung back too,
but nothing wouldn’t suit Jewel.
“Shut your goddamn mouth,” he says.
“It’s got to be somewhere,” Darl said. “We’ll take out soon as pa gets back.”
“You wont help me?” Jewel says, them white eyes of hisn kind of blaring and his face shaking like
he had a aguer.
“No,” Darl said. “I wont. Wait till pa gets back.”
So I stood in the door and watched him push and haul at that wagon. It was on a downhill, and once
I thought he was fixing to beat out the back end of the shed. Then the dinner bell rung. I called
him, but he didn’t look around. “Come on to dinner,” I said. “Tell that boy.” But he didn’t answer,
so I went on to dinner. The gal went down to get that boy, but she come back without him. About
half through dinner we heard him yelling again, running that buzzard out.
“It’s a outrage,” Lula said; “a outrage.”
“He’s doing the best he can,” I said. “A fellow dont trade with Snopes in thirty minutes. They’ll
set in the shade all afternoon to dicker.”
“Do?” she says. “Do? He’s done too much, already.”
And I reckon he had. Trouble is, his quitting was just about to start our doing. He couldn’t buy no
team from nobody, let alone Snopes, withouten he had something to mortgage he didn’t know would
mortgage yet. And so when I went back to the field I looked at my mules and same as told them
goodbye for a spell. And when I come back that evening and the sun shining all day on that shed, I
wasn’t so

sho I would regret it.
He come riding up just as I went out to the porch, where they all was. He looked kind of funny:
kind of more hangdog than common, and kind of proud too. Like he had done something he thought was
cute but wasn’t so sho now how other folks would take it.
“I got a team,” he said.
“You bought a team from Snopes?” I said.
“I reckon Snopes aint the only man in this country that can drive a trade,” he said.
“Sho,” I said. He was looking at Jewel, with that funny look, but Jewel had done got down from the
porch and was going toward the horse. To see what Anse had done to it, I reckon.
“Jewel,” Anse says. Jewel looked back. “Come here,” Anse says.
Jewel come back a little and stopped again. “What you want?” he said.
“So you got a team from Snopes,” I said. “He’ll send them over tonight, I reckon? You’ll want a
early start tomorrow, long as you’ll have to go by Mottson.”
Then he quit looking like he had been for a while. He got that badgered look like he used to have,
mumbling his mouth.
“I do the best I can,” he said. “Fore God, if there were ere a man in the living world suffered the
trials and floutings I have suffered.”
“A fellow that just beat Snopes in a trade ought to feel pretty good,” I said. “What did you give
him, Anse?”
He didn’t look at me. “I give a chattel mortgage on my cultivator and seeder,” he said.
“But they aint worth forty dollars. How far do you aim to get with a forty dollar team?”
They were all watching him now, quiet and steady. Jewel was stopped, halfway back, waiting to go on
to the horse. “I give other things,” Anse said. He begun to mumble his mouth again, standing there
like he was waiting for somebody to hit him and him with his mind already made up not to do nothing
about it.
“What other things?” Darl said.
“Hell,” I said. “You take my team. You can bring them back. I’ll get along someway.”
“So that’s what you were doing in Cash’s clothes last night,” Darl said. He said it just like he
was reading it outen the paper. Like he never give a durn himself one way or the other. Jewel had
come back now, standing there, looking at Anse with them marble eyes of hisn.

“Cash aimed to buy that talking machine from Suratt with that money,” Darl said.
Anse stood there, mumbling his mouth. Jewel watched him. He aint never blinked yet.
“But that’s just eight dollars more,” Darl said, in that voice like he was just listening and never
give a durn himself. “That still wont buy a team.”
Anse looked at Jewel, quick, kind of sliding his eyes that way, then he looked down again. “God
knows, if there were ere a man,” he says. Still they didn’t say nothing. They just watched him,
waiting, and him sliding his eyes toward their feet and up their legs but no higher. “And the
horse,” he says.
“What horse?” Jewel said. Anse just stood there. I be durn, if a man cant keep the upper hand of
his sons, he ought to run them away from home, no matter how big they are. And if he cant do that,
I be durn if he oughtn’t to leave himself. I be durn if I wouldn’t. “You mean, you tried to swap my
horse?” Jewel says.
Anse stands there, dangle-armed. “For fifteen years I aint had a tooth in my head,” he says. “God
knows it. He knows in fifteen years I aint et the victuals He aimed for man to eat to keep his
strength up, and me saving a nickel here and a nickel there so my family wouldn’t suffer it, to buy
them teeth so I could eat God’s appointed food. I give that money. I thought that if I could do
without eating, my sons could do without riding. God knows I did.”
Jewel stands with his hands on his hips, looking at Anse. Then he looks away. He looked out across
the field, his face still as a rock, like it was somebody else talking about somebody else’s horse
and him not even listening. Then he spit, slow, and said “Hell” and he turned and went on to the
gate and unhitched the horse and got on it. It was moving when he come into the saddle and by the
time he was on it they was tearing down the road like the Law might have been behind them. They
went out of sight that way, the two of them looking like some kind of a spotted cyclone.
“Well,” I says. “You take my team,” I said. But he wouldn’t do it. And they wouldn’t even stay, and
that boy chasing them buzzards all day in the hot sun until he was nigh as crazy as the rest of
them. “Leave Cash here, anyway,” I said. But they wouldn’t do that. They made a pallet for him with
quilts on top of the coffin and laid him on it and set his tools by him, and we put my team in and
hauled the wagon about a mile down the road.

“If we’ll bother you here,” Anse says, “just say so.”
“Sho,” I said. “It’ll be fine here. Safe, too. Now let’s go back and eat supper.”
“I thank you,” Anse said. “We got a little something in the basket.
We can make out.”
“Where’d you get it?” I said. “We brought it from home.”
“But it’ll be stale now,” I said. “Come and get some hot victuals.” But they wouldn’t come. “I
reckon we can make out,” Anse said. So
I went home and et and taken a basket back to them and tried again to make them come back to the
house.
“I thank you,” he said. “I reckon we can make out.” So I left them there, squatting around a little
fire, waiting; God knows what for.
I come on home. I kept thinking about them there, and about that fellow tearing away on that horse.
And that would be the last they would see of him. And I be durn if I could blame him. Not for
wanting to not give up his horse, but for getting shut of such a durn fool as Anse.
Or that’s what I thought then. Because be durn if there aint something about a durn fellow like
Anse that seems to make a man have to help him, even when he knows he’ll be wanting to kick himself
next minute. Because about a hour after breakfast next morning Eustace Grimm that works Snopes’
place come up with a span of mules, hunting Anse.
“I thought him and Anse never traded,” I said.
“Sho,” Eustace said. “All they liked was the horse. Like I said to Mr Snopes, he was letting this
team go for fifty dollars, because if his uncle Flem had a just kept them Texas horses when he
owned them, Anse wouldn’t a never——”
“The horse?” I said. “Anse’s boy taken that horse and cleared out last night, probably half way to
Texas by now, and Anse——”
“I didn’t know who brung it,” Eustace said. “I never see them. I just found the horse in the barn
this morning when I went to feed, and I told Mr Snopes and he said to bring the team on over here.”
Well, that’ll be the last they’ll ever see of him now, sho enough. Come Christmas time they’ll
maybe get a postal card from him in Texas, I reckon. And if it hadn’t a been Jewel, I reckon it’d a
been me; I owe him that much, myself. I be durn if Anse dont conjure a man, some way. I be durn if
he aint a sight.

VARDAMAN

Now there are seven of them, in little tall black circles. “Look, Darl,” I say; “see?”
He looks up. We watch them in little tall black circles of not- moving.
“Yesterday there were just four,” I say. There were more than four on the barn.
“Do you know what I would do if he tries to light on the wagon again?” I say.
“What would you do?” Darl says.
“I wouldn’t let him light on her,” I say. “I wouldn’t let him light on Cash, either.”
Cash is sick. He is sick on the box. But my mother is a fish.
“We got to get some medicine in Mottson,” pa says. “I reckon we’ll just have to.”
“How do you feel, Cash?” Darl says. “It dont bother none,” Cash says.
“Do you want it propped a little higher?” Darl says.
Cash has a broken leg. He has had two broken legs. He lies on the box with a quilt rolled under his
head and a piece of wood under his knee.
“I reckon we ought to left him at Armstid’s,” pa says.
I haven’t got a broken leg and pa hasn’t and Darl hasn’t and “It’s just the bumps,” Cash says. “It
kind of grinds together a little on a bump. It dont bother none.” Jewel has gone away. He and his
horse went away one supper time
“It’s because she wouldn’t have us beholden,” pa says. “Fore God, I do the best that ere a man” Is
it because Jewel’s mother is a horse Darl? I said.
“Maybe I can draw the ropes a little tighter,” Darl says. That’s why Jewel and I were both in the
shed and she was in the wagon because the horse lives in the barn and I had to keep on running the
buzzard away

from
“If you just would,” Cash says. And Dewey Dell hasn’t got a broken leg and I haven’t. Cash is my
brother.
We stop. When Darl loosens the rope Cash begins to sweat again.
His teeth look out. “Hurt?” Darl says.
“I reckon you better put it back,” Cash says.
Darl puts the rope back, pulling hard. Cash’s teeth look out. “Hurt?” Darl says.
“It dont bother none,” Cash says.
“Do you want pa to drive slower?” Darl says.
“No,” Cash says. “Aint no time to hang back. It dont bother none.” “We’ll have to get some medicine
at Mottson,” pa says. “I reckon
we’ll have to.”
“Tell him to go on,” Cash says. We go on. Dewey Dell leans back and wipes Cash’s face. Cash is my
brother. But Jewel’s mother is a horse. My mother is a fish. Darl says that when we come to the
water again I might see her and Dewey Dell said, She’s in the box; how could she have got out? She
got out through the holes I bored, into the water I said, and when we come to the water again I am
going to see her. My mother is not in the box. My mother does not smell like that. My mother is a
fish
“Those cakes will be in fine shape by the time we get to Jefferson,” Darl says.
Dewey Dell does not look around.
“You better try to sell them in Mottson,” Darl says. “When will we get to Mottson, Darl?” I say.
“Tomorrow,” Darl says. “If this team dont rack to pieces. Snopes must have fed them on sawdust.”
“Why did he feed them on sawdust, Darl?” I say. “Look,” Darl says. “See?”
Now there are nine of them, tall in little tall black circles.
When we come to the foot of the hill pa stops and Darl and Dewey Dell and I get out. Cash cant walk
because he has a broken leg. “Come up, mules,” pa says. The mules walk hard; the wagon creaks. Darl
and Dewey Dell and I walk behind the wagon, up the hill. When we come to the top of the hill pa
stops and we get back into the wagon.
Now there are ten of them, tall in little tall black circles on the sky.

MOSELEY

I happened to look up, and saw her outside the window, looking in. Not close to the glass, and not
looking at anything in particular; just standing there with her head turned this way and her eyes
full on me and kind of blank too, like she was waiting for a sign. When I looked up again she was
moving toward the door.
She kind of bumbled at the screen door a minute, like they do, and came in. She had on a
stiff-brimmed straw hat setting on the top of her head and she was carrying a package wrapped in
newspaper: I thought that she had a quarter or a dollar at the most, and that after she stood
around a while she would maybe buy a cheap comb or a bottle of nigger toilet water, so I never
disturbed her for a minute or so except to notice that she was pretty in a kind of sullen, awkward
way, and that she looked a sight better in her gingham dress and her own complexion than she would
after she bought whatever she would finally decide on. Or tell that she wanted. I knew that she had
already decided before she came in. But you have to let them take their time. So I went on with
what I was doing, figuring to let Albert wait on her when he caught up at the fountain, when he
came back to me.
“That woman,” he said. “You better see what she wants.”
“What does she want?” I said.
“I dont know. I cant get anything out of her. You better wait on her.”
So I went around the counter. I saw that she was barefooted, standing with her feet flat and easy
on the floor, like she was used to it. She was looking at me, hard, holding the package; I saw she
had about as black a pair of eyes as ever I saw, and she was a stranger. I never remembered seeing
her in Mottson before. “What can I do for you?” I said.
Still she didn’t say anything. She stared at me without winking. Then she looked back at the folks
at the fountain. Then she looked past me, toward the back of the store.

“Do you want to look at some toilet things?” I said. “Or is it medicine you want?”
“That’s it,” she said. She looked quick back at the fountain again. So I thought maybe her ma or
somebody had sent her in for some of this female dope and she was ashamed to ask for it. I knew she
couldn’t have a complexion like hers and use it herself, let alone not being much more than old
enough to barely know what it was for. It’s a shame, the way they poison themselves with it. But a
man’s got to stock it or go out of business in this country.
“Oh,” I said. “What do you use? We have——” She looked at me again, almost like she had said hush,
and looked toward the back of the store again.
“I’d liefer go back there,” she said.
“All right,” I said. You have to humor them. You save time by it. I followed her to the back. She
put her hand on the gate. “There’s nothing back there but the prescription case,” I said. “What do
you want?” She stopped and looked at me. It was like she had taken some kind of a lid off her face,
her eyes. It was her eyes: kind of dumb and hopeful and sullenly willing to be disappointed all at
the same time. But she was in trouble of some sort; I could see that. “What’s your trouble?” I
said. “Tell me what it is you want. I’m pretty busy.” I wasn’t meaning to hurry her, but a man just
hasn’t got the time they have out there.
“It’s the female trouble,” she said.
“Oh,” I said. “Is that all?” I thought maybe she was younger than she looked, and her first one had
scared her, or maybe one had been a little abnormal as it will in young women. “Where’s your ma?” I
said. “Haven’t you got one?”
“She’s out yonder in the wagon,” she said.
“Why not talk to her about it before you take any medicine,” I said. “Any woman would have told you
about it.” She looked at me, and I looked at her again and said, “How old are you?”
“Seventeen,” she said.
“Oh,” I said. “I thought maybe you were.……” She was watching me. But then, in the eyes all of them
look like they had no age and knew everything in the world, anyhow. “Are you too regular, or not
regular enough?”
She quit looking at me but she didn’t move. “Yes,” she said. “I reckon so. Yes.”
“Well, which?” I said. “Dont you know?” It’s a crime and a shame;

but after all, they’ll buy it from somebody. She stood there, not looking at me. “You want
something to stop it?” I said. “Is that it?”
“No,” she said. “That’s it. It’s already stopped.”
“Well, what——” Her face was lowered a little, still, like they do in all their dealings with a man
so he dont ever know just where the lightning will strike next. “You are not married, are you?” I
said.
“No.”
“Oh,” I said. “And how long has it been since it stopped? about five months maybe?”
“It aint been but two,” she said.
“Well, I haven’t got anything in my store you want to buy,” I said, “unless it’s a nipple. And I’d
advise you to buy that and go back home and tell your pa, if you have one, and let him make
somebody buy you a wedding license. Was that all you wanted?”
But she just stood there, not looking at me. “I got the money to pay you,” she said.
“Is it your own, or did he act enough of a man to give you the money?”
“He give it to me. Ten dollars. He said that would be enough.”
“A thousand dollars wouldn’t be enough in my store and ten cents wouldn’t be enough,” I said. “You
take my advice and go home and tell your pa or your brothers if you have any or the first man you
come to in the road.”
But she didn’t move. “Lafe said I could get it at the drugstore. He said to tell you me and him
wouldn’t never tell nobody you sold it to us.”
“And I just wish your precious Lafe had come for it himself; that’s what I wish. I dont know: I’d
have had a little respect for him then. And you can go back and tell him I said so—if he aint
halfway to Texas by now, which I dont doubt. Me, a respectable druggist, that’s kept store and
raised a family and been a church-member for fifty-six years in this town. I’m a good mind to tell
your folks myself, if I can just find who they are.”
She looked at me now, her eyes and face kind of blank again like when I first saw her through the
window. “I didn’t know,” she said. “He told me I could get something at the drug store. He said
they might not want to sell it to me, but if I had ten dollars and told them I wouldn’t never tell
nobody.…”
“He never said this drug-store,” I said. “If he did or mentioned my name, I defy him to prove it. I
defy him to repeat it or I’ll prosecute

him to the full extent of the law, and you can tell him so.” “But maybe another drug store would,”
she said.
“Then I dont want to know it. Me, that’s——” Then I looked at her. But it’s a hard life they have;
sometimes a man.……if there can ever be any excuse for sin, which it cant be. And then, life wasn’t
made to be easy on folks: they wouldn’t ever have any reason to be good and die. “Look here,” I
said. “You get that notion out of your head. The Lord gave you what you have, even if He did use
the devil to do it; you let Him take it away from you if it’s His will to do so. You go on back to
Lafe and you and him take that ten dollars and get married with it.”
“Lafe said I could get something at the drugstore,” she said. “Then go and get it,” I said. “You
wont get it here.”
She went out, carrying the package, her feet making a little hissing on the floor. She bumbled
again at the door and went out. I could see her through the glass going on down the street.
It was Albert told me about the rest of it. He said the wagon was stopped in front of Grummet’s
hardware store, with the ladies all scattering up and down the street with handkerchiefs to their
noses, and a crowd of hard-nosed men and boys standing around the wagon, listening to the marshal
arguing with the man. He was a kind of tall, gaunted man sitting on the wagon, saying it was a
public street and he reckoned he had as much right there as anybody, and the marshal telling him he
would have to move on; folks couldn’t stand it. It had been dead eight days, Albert said. They came
from some place out in Yoknapatawpha county, trying to get to Jefferson with it. It must have been
like a piece of rotten cheese coming into an ant-hill, in that ramshackle wagon that Albert said
folks were scared would fall all to pieces before they could get it out of town, with that
home-made box and another fellow with a broken leg lying on a quilt on top of it, and the father
and a little boy sitting on the seat and the marshal trying to make them get out of town.
“It’s a public street,” the man says. “I reckon we can stop to buy
something same as airy other man. We got the money to pay for hit, and hit aint airy law that says
a man cant spend his money where he wants.”
They had stopped to buy some cement. The other son was in Grummet’s, trying to make Grummet break a
sack and let him have ten cents’ worth, and finally Grummet broke the sack to get him out. They
wanted the cement to fix the fellow’s broken leg, someway.

“Why, you’ll kill him,” the marshal said. “You’ll cause him to lose his leg. You take him on to a
doctor, and you get this thing buried soon as you can. Dont you know you’re liable to jail for
endangering the public health?”
“We’re doing the best we can,” the father said. Then he told a long tale about how they had to wait
for the wagon to come back and how the bridge was washed away and how they went eight miles to
another bridge and it was gone too so they came back and swum the ford and the mules got drowned
and how they got another team and found that the road was washed out and they had to come clean
around by Mottson, and then the one with the cement came back and told him to shut up.
“We’ll be gone in a minute,” he told the marshal. “We never aimed to bother nobody,” the father
said.
“You take that fellow to a doctor,” the marshal told the one with the cement.
“I reckon he’s all right,” he said.
“It aint that we’re hard-hearted,” the marshal said. “But I reckon you can tell yourself how it
is.”
“Sho,” the other said. “We’ll take out soon as Dewey Dell comes back. She went to deliver a
package.”
So they stood there with the folks backed off with handkerchiefs to their faces, until in a minute
the girl came up with that newspaper package.
“Come on,” the one with the cement said, “we’ve lost too much time.” So they got in the wagon and
went on. And when I went to supper it still seemed like I could smell it. And the next day I met
the marshal and I began to sniff and said,
“Smell anything?”
“I reckon they’re in Jefferson by now,” he said. “Or in jail. Well, thank the Lord it’s not our
jail.” “That’s a fact,” he said.

DARL

“Here’s a place,” pa says. He pulls the team up and sits looking at the house. “We could get some
water over yonder.”
“All right,” I say. “You’ll have to borrow a bucket from them, Dewey Dell.”
“God knows,” pa says. “I wouldn’t be beholden, God knows.”
“If you see a good-sized can, you might bring it,” I say. Dewey Dell gets down from the wagon,
carrying the package. “You had more touble than you expected, selling those cakes in Mottson,” I
say. How do our lives ravel out into the no-wind, no-sound, the weary gestures wearily
recapitulant: echoes of old compulsions with no-hand on no- strings: in sunset we fall into furious
attitudes, dead gestures of dolls. Cash broke his leg and now the sawdust is running out. He is
bleeding to death is Cash.
“I wouldn’t be beholden,” pa says. “God knows.”
“Then make some water yourself,” I say. “We can use Cash’s hat.”
When Dewey Dell comes back the man comes with her. Then he stops and she comes on and he stands
there and after a while he goes back to the house and stands on the porch, watching us.
“We better not try to lift him down,” pa says. “We can fix it here.” “Do you want to be lifted
down, Cash?” I say.
“Wont we get to Jefferson tomorrow?” he says. He is watching us, his eyes interrogatory, intent,
and sad. “I can last it out.”
“It’ll be easier on you,” pa says. “It’ll keep it from rubbing together.”
“I can last it,” Cash says. “We’ll lose time stopping.” “We done bought the cement, now,” pa says.
“I could last it,” Cash says. “It aint but one more day. It dont bother to speak of.” He looks at
us, his eyes wide in his thin gray face, questioning. “It sets up so,” he says.
“We done bought it now,” pa says.
I mix the cement in the can, stirring the slow water into the pale

green thick coils. I bring the can to the wagon where Cash can see. He lies on his back, his thin
profile in silhouette, ascetic and profound against the sky. “Does that look about right?” I say.
“You dont want too much water, or it wont work right,” he says. “Is this too much?”
“Maybe if you could get a little sand,” he says. “It aint but one more day,” he says. “It dont
bother me none.”
Vardaman goes back down the road to where we crossed the branch and returns with sand. He pours it
slowly into the thick coiling in the can. I go to the wagon again.
“Does that look all right?”
“Yes,” Cash says. “I could have lasted. It dont bother me none.” We loosen the splints and pour the
cement over his leg slow.
“Watch out for it,” Cash says. “Dont get none on it if you can help.” “Yes,” I say. Dewey Dell
tears a piece of paper from the package
and wipes the cement from the top of it as it drips from Cash’s leg. “How does that feel?”
“It feels fine,” he says. “It’s cold. It feels fine.”
“If it’ll just help you,” pa says. “I asks your forgiveness. I never foreseen it no more than you.”
“It feels fine,” Cash says.
If you could just ravel out into time. That would be nice. It would be nice if you could just ravel
out into time.
We replace the splints, the cords, drawing them tight, the cement in thick pale green slow surges
among the cords, Cash watching us quietly with that profound questioning look.
“That’ll steady it,” I say.
“Ay,” Cash says. “I’m obliged.”
Then we all turn on the wagon and watch him. He is coming up the road behind us, wooden-backed,
wooden-faced, moving only from his hips down. He comes up without a word, with his pale rigid eyes
in his high sullen face, and gets into the wagon.
“Here’s a hill,” pa says. “I reckon you’ll have to get out and walk.”

VARDAMAN

Darl and Jewel and Dewey Dell and I are walking up the hill, behind the wagon. Jewel came back. He
came up the road and got into the wagon. He was walking. Jewel hasn’t got a horse anymore. Jewel is
my brother. Cash is my brother. Cash has a broken leg. We fixed Cash’s leg so it doesn’t hurt. Cash
is my brother. Jewel is my brother too, but he hasn’t got a broken leg.
Now there are five of them, tall in little tall black circles.
“Where do they stay at night, Darl?” I say. “When we stop at night in the barn, where do they
stay?”
The hill goes off into the sky. Then the sun comes up from behind the hill and the mules and the
wagon and pa walk on the sun. You cannot watch them, walking slow on the sun. In Jefferson it is
red on the track behind the glass. The track goes shining round and round. Dewey Dell says so.
Tonight I am going to see where they stay while we are in the barn.

DARL

“Jewel,” I say, “whose son are you?”
The breeze was setting up from the barn, so we put her under the apple tree, where the moonlight
can dapple the apple tree upon the long slumbering flanks within which now and then she talks in
little trickling bursts of secret and murmurous bubbling. I took Vardaman to listen. When we came
up the cat leaped down from it and flicked away with silver claw and silver eye into the shadow.
“Your mother was a horse, but who was your father, Jewel?” “You goddamn lying son of a bitch.”
“Dont call me that,” I say.
“You goddamn lying son of a bitch.”
“Dont you call me that, Jewel.” In the tall moonlight his eyes look like spots of white paper
pasted on a high small football.
After supper Cash began to sweat a little. “It’s getting a little hot,” he said. “It was the sun
shining on it all day, I reckon.”
“You want some water poured on it?” we say. “Maybe that will ease it some.”
“I’d be obliged,” Cash said. “It was the sun shining on it, I reckon. I ought to thought and kept
it covered.”
“We ought to thought,” we said. “You couldn’t have suspicioned.” “I never noticed it getting hot,”
Cash said. “I ought to minded it.”
So we poured the water over it. His leg and foot below the cement looked like they had been boiled.
“Does that feel better?” we said.
“I’m obliged,” Cash said. “It feels fine.”
Dewey Dell wipes his face with the hem of her dress. “See if you can get some sleep,” we say.
“Sho,” Cash says. “I’m right obliged. It feels fine now.”
Jewel, I say, Who was your father, Jewel? Goddamn you. Goddamn you.

VARDAMAN

She was under the apple tree and Darl and I go across the moon and the cat jumps down and runs and
we can hear her inside the wood.
“Hear?” Darl says. “Put your ear close.”
I put my ear close and I can hear her. Only I cant tell what she is saying.
“What is she saying, Darl?” I say. “Who is she talking to?”
“She’s talking to God,” Darl says. “She is calling on Him to help her.”
“What does she want Him to do?” I say.
“She wants Him to hide her away from the sight of man,” Darl says. “Why does she want to hide her
away from the sight of man, Darl?” “So she can lay down her life,” Darl says.
“Why does she want to lay down her life, Darl?”
“Listen,” Darl says. We hear her. We hear her turn over on her side. “Listen,” Darl says.
“She’s turned over,” I say. “She’s looking at me through the wood.” “Yes,” Darl says.
“How can she see through the wood, Darl?”
“Come,” Darl says. “We must let her be quiet. Come.”
“She cant see out there, because the holes are in the top,” I say. “How can she see, Darl?”
“Let’s go see about Cash,” Darl says.
And I saw something Dewey Dell told me not to tell nobody
Cash is sick in his leg. We fixed his leg this afternoon, but he is sick in it again, lying on the
bed. We pour water on his leg and then he feels fine.
“I feel fine,” Cash says. “I’m obliged to you.” “Try to get some sleep,” we say.
“I feel fine,” Cash says. “I’m obliged to you.”
And I saw something Dewey Dell told me not to tell nobody. It is not about pa and it is not about
Cash and it is not about Jewel and it is not

about Dewey Dell and it is not about me
Dewey Dell and I are going to sleep on the pallet. It is on the back porch, where we can see the
barn, and the moon shines on half of the pallet and we will lie half in the white and half in the
black, with the moonlight on our legs. And then I am going to see where they stay at night while we
are in the barn. We are not in the barn tonight but I can see the barn and so I am going to find
where they stay at night.
We lie on the pallet, with our legs in the moon.
“Look,” I say, “my legs look black. Your legs look black, too.” “Go to sleep,” Dewey Dell says.
Jefferson is a far piece. “Dewey Dell.”
“What.”
“If it’s not Christmas now, how will it be there?”
It goes round and round on the shining track. Then the track goes shining round and round.
“Will what be there?”
“That train. In the window.”
“You go to sleep. You can see tomorrow if it’s there.” Maybe Santa Claus wont know they are town
boys. “Dewey Dell.”
“You go to sleep. He aint going to let none of them town boys have it.”
It was behind the window, red on the track, the track shining round and round. It made my heart
hurt. And then it was pa and Jewel and Darl and Mr. Gillespie’s boy. Mr Gillespie’s boy’s legs come
down under his nightshirt. When he goes into the moon, his legs fuzz. They go on around the house
toward the apple tree.
“What are they going to do, Dewey Dell?”
They went around the house toward the apple tree. “I can smell her,” I say. “Can you smell her,
too?”
“Hush,” Dewey Dell says. “The wind’s changed. Go to sleep.”
And so I am going to know where they stay at night soon. They come around the house, going across
the yard in the moon, carrying her on their shoulders. They carry her down to the barn, the moon
shining flat and quiet on her. Then they come back and go into the house again. While they were in
the moon, Mr Gillespie’s boy’s leg fuzzed. And then I waited and I said Dewey Dell? and then I
waited and then I went to find where they stay at night and I saw something that Dewey Dell told me
not to tell nobody.

DARL

Against the dark doorway he seems to materialise out of darkness, lean as a race horse in his
underclothes in the beginning of the glare. He leaps to the ground with on his face an expression
of furious unbelief. He has seen me without even turning his head or his eyes in which the glare
swims like two small torches. “Come on,” he says, leaping down the slope toward the barn.
For an instant longer he runs silver in the moonlight, then he springs out like a flat figure cut
leanly from tin against an abrupt and soundless explosion as the whole loft of the barn takes fire
at once, as though it had been stuffed with powder. The front, the conical façade with the square
orifice of doorway broken only by the square squat shape of the coffin on the sawhorses like a
cubistic bug, comes into relief. Behind me pa and Gillespie and Mack and Dewey Dell and Vardaman
emerge from the house.
He pauses at the coffin, stooping, looking at me, his face furious. Overhead the flames sound like
thunder; across us rushes a cool draft: there is no heat in it at all yet, and a handful of chaff
lifts suddenly and sucks swiftly along the stalls where a horse is screaming. “Quick,” I say; “the
horses.”
He glares a moment longer at me, then at the roof overhead, then he leaps toward the stall where
the horse screams. It plunges and kicks, the sound of the crashing blows sucking up into the sound
of the flames. They sound like an interminable train crossing an endless trestle. Gillespie and
Mack pass me, in knee-length nightshirts, shouting, their voices thin and high and meaningless and
at the same time profoundly wild and sad: “.……cow.……stall.……” Gillespie’s nightshirt rushes ahead
of him on the draft, ballooning about his hairy thighs.
The stall door has swung shut. Jewel thrusts it back with his buttocks and he appears, his back
arched, the muscles ridged through his garment as he drags the horse out by its head. In the glare
its eyes

roll with soft, fleet, wild opaline fire; its muscles bunch and run as it flings its head about,
lifting Jewel clear of the ground. He drags it on, slowly, terrifically; again he gives me across
his shoulder a single glare furious and brief. Even when they are clear of the barn the horse
continues to fight and lash backward toward the doorway until Gillespie passes me, stark-naked, his
nightshirt wrapped about the mule’s head, and beats the maddened horse on out of the door.
Jewel returns, running; again he looks down at the coffin. But he comes on. “Where’s cow?” he
cries, passing me. I follow him. In the stall Mack is struggling with the other mule. When its head
turns into the glare I can see the wild rolling of its eye too, but it makes no sound. It just
stands there, watching Mack over its shoulder, swinging its hind quarters toward him whenever he
approaches. He looks back at us, his eyes and mouth three round holes in his face on which the
freckles look like english peas on a plate. His voice is thin, high, faraway.
“I cant do nothing.……” It is as though the sound had been swept from his lips and up and away,
speaking back to us from an immense distance of exhaustion. Jewel slides past us; the mule whirls
and lashes out, but he has already gained its head. I lean to Mack’s ear:
“Nightshirt. Around his head.”
Mack stares at me. Then he rips the nightshirt off and flings it over the mule’s head, and it
becomes docile at once. Jewel is yelling at him: “Cow? Cow?”
“Back,” Mack cries. “Last stall.”
The cow watches us as we enter. She is backed into the corner, head lowered, still chewing though
rapidly. But she makes no move. Jewel has paused, looking up, and suddenly we watch the entire
floor to the loft dissolve. It just turns to fire; a faint litter of sparks rains down. He glances
about. Back under the trough is a three legged milking stool. He catches it up and swings it into
the planking of the rear wall. He splinters a plank, then another, a third; we tear the fragments
away. While we are stooping to the opening something charges into us from behind. It is the cow;
with a single whistling breath she rushes between us and through the gap and into the outer glare,
her tail erect and rigid as a broom nailed upright to the end of her spine.
Jewel turns back into the barn. “Here,” I say; “Jewel!” I grasp at him; he strikes my hand down.
“You fool,” I say, “dont you see you cant make it back yonder?” The hallway looks like a
searchlight turned into rain. “Come on,” I say, “around this way.”

When we are through the gap he begins to run. “Jewel,” I say, running. He darts around the corner.
When I reach it he has almost reached the next one, running against the glare like that figure cut
from tin. Pa and Gillespie and Mack are some distance away, watching the barn, pink against the
darkness where for the time the moonlight has been vanquished. “Catch him!” I cry; “stop him!”
When I reach the front, he is struggling with Gillespie; the one lean in underclothes, the other
stark naked. They are like two figures in a Greek frieze, isolated out of all reality by the red
glare. Before I can reach them he has struck Gillespie to the ground and turned and run back into
the barn.
The sound of it has become quite peaceful now, like the sound of the river did. We watch through
the dissolving proscenium of the doorway as Jewel runs crouching to the far end of the coffin and
stoops to it. For an instant he looks up and out at us through the rain of burning hay like a
portière of flaming beads, and I can see his mouth shape as he calls my name.
“Jewel!” Dewey Dell cries; “Jewel!” It seems to me that I now hear the accumulation of her voice
through the last five minutes, and I hear her scuffling and struggling as pa and Mack hold her,
screaming “Jewel! Jewel!” But he is no longer looking at us. We see his shoulders strain as he
upends the coffin and slides it single-handed from the saw-horses. It looms unbelievably tall,
hiding him: I would not have believed that Addie Bundren would have needed that much room to lie
comfortable in; for another instant it stands upright while the sparks rain on it in scattering
bursts as though they engendered other sparks from the contact. Then it topples forward, gaining
momentum, revealing Jewel and the sparks raining on him too in engendering gusts, so that he
appears to be enclosed in a thin nimbus of fire. Without stopping it overends and rears again,
pauses, then crashes slowly forward and through the curtain. This time Jewel is riding upon it,
clinging to it, until it crashes down and flings him forward and clear and Mack leaps forward into
a thin smell of scorching meat and slaps at the widening crimson-edged holes that bloom like
flowers in his undershirt.

VARDAMAN

When I went to find where they stay at night, I saw something They said, “Where is Darl? Where did
Darl go?”
They carried her back under the apple tree.
The barn was still red, but it wasn’t a barn now. It was sunk down, and the red went swirling up.
The barn went swirling up in little red pieces, against the sky and the stars so that the stars
moved backward.
And then Cash was still awake. He turned his head from side to side, with sweat on his face.
“Do you want some more water on it, Cash?” Dewey Dell said. Cash’s leg and foot turned black. We
held the lamp and looked at
Cash’s foot and leg where it was black.
“Your foot looks like a nigger’s foot, Cash,” I said. “I reckon we’ll have to bust it off,” pa
said.
“What in the tarnation you put it on there for,” Mr Gillespie said.
“I thought it would steady it some,” pa said. “I just aimed to help him.”
They got the flat iron and the hammer. Dewey Dell held the lamp.
They had to hit it hard. And then Cash went to sleep.
“He’s asleep now,” I said. “It cant hurt him while he’s asleep.” It just cracked. It wouldn’t come
off.
“It’ll take the hide, too,” Mr Gillespie said. “Why in the tarnation you put it on there. Didn’t
none of you think to grease his leg first?”
“I just aimed to help him,” pa said. “It was Darl put it on.” “Where is Darl?” they said.
“Didn’t none of you have more sense than that?” Mr Gillespie said. “I’d a thought he would,
anyway.”
Jewel was lying on his face. His back was red. Dewey Dell put the medicine on it. The medicine was
made out of butter and soot, to draw out the fire. Then his back was black.
“Does it hurt, Jewel?” I said. “Your back looks like a nigger’s, Jewel,” I said. Cash’s foot and
leg looked like a nigger’s. Then they

broke it off. Cash’s leg bled.
“You go on back and lay down,” Dewey Dell said. “You ought to be asleep.”
“Where is Darl?” they said.
He is out there under the apple tree with her, lying on her. He is there so the cat wont come back.
I said, “Are you going to keep the cat away, Darl?”
The moonlight dappled on him too. On her it was still, but on Darl it dappled up and down.
“You needn’t to cry,” I said. “Jewel got her out. You needn’t to cry, Darl.”
The barn is still red. It used to be redder than this. Then it went swirling, making the stars run
backward without falling. It hurt my heart like the train did.
When I went to find where they stay at night, I saw something that Dewey Dell says I mustn’t tell
nobody

DARL

We have been passing the signs for sometime now: the drug stores, the clothing stores, the patent
medicine and the garages and cafés, and the mile-boards diminishing, becoming more starkly
reaccruent: 3 mi. 2 mi. From the crest of a hill, as we get into the wagon again, we can see the
smoke low and flat, seemingly unmoving in the unwinded afternoon.
“Is that it, Darl?” Vardaman says. “Is that Jefferson?” He too has lost flesh; like ours, his face
has an expression strained, dreamy, and gaunt.
“Yes,” I say. He lifts his head and looks at the sky. High against it they hang in narrowing
circles, like the smoke, with an outward semblance of form and purpose, but with no inference of
motion, progress or retrograde. We mount the wagon again where Cash lies on the box, the jagged
shards of cement cracked about his leg. The shabby mules droop rattling and clanking down the hill.
“We’ll have to take him to the doctor,” pa says. “I reckon it aint no way around it.” The back of
Jewel’s shirt, where it touches him, stains slow and black with grease. Life was created in the
valleys. It blew up onto the hills on the old terrors, the old lusts, the old despairs. That’s why
you must walk up the hills so you can ride down.
Dewey Dell sits on the seat, the newspaper package on her lap. When we reach the foot of the hill
where the road flattens between close walls of trees, she begins to look about quietly from one
side of the road to the other. At last she says,
“I got to stop.”
Pa looks at her, his shabby profile that of anticipant and disgruntled annoyance. He does not check
the team. “What for?”
“I got to go to the bushes,” Dewey Dell says.
Pa does not check the team. “Cant you wait till we get to town? It aint over a mile now.”
“Stop,” Dewey Dell says. “I got to go to the bushes.”

Pa stops in the middle of the road and we watch Dewey Dell descend, carrying the package. She does
not look back.
“Why not leave your cakes here?” I say. “We’ll watch them.” She descends steadily, not looking at
us.
“How would she know where to go if she waited till we get to town?” Vardaman says. “Where would you
go to do it in town, Dewey Dell?”
She lifts the package down and turns and disappears among the trees and undergrowth.
“Dont be no longer than you can help,” pa says. “We aint got no time to waste.” She does not
answer. After a while we cannot hear her even. “We ought to done like Armstid and Gillespie said
and sent word to town and had it dug and ready,” he says.
“Why didn’t you?” I say. “You could have telephoned.”
“What for?” Jewel says. “Who the hell cant dig a hole in the ground?”
A car comes over the hill. It begins to sound the horn, slowing. It runs along the roadside in low
gear, the outside wheels in the ditch, and passes us and goes on. Vardaman watches it until it is
out of sight.
“How far is it now, Darl?” he says. “Not far,” I say.
“We ought to done it,” pa says. “I just never wanted to be beholden to none except her flesh and
blood.”
“Who the hell cant dig a damn hole in the ground?” Jewel says.
“It aint respectful, talking that way about her grave,” pa says. “You all dont know what it is. You
never pure loved her, none of you.” Jewel does not answer. He sits a little stiffly erect, his body
arched away from his shirt. His high-colored jaw juts.
Dewey Dell returns. We watch her emerge from the bushes, carrying the package, and climb into the
wagon. She now wears her Sunday dress, her beads, her shoes and stockings.
“I thought I told you to leave them clothes to home,” pa says. She does not answer, does not look
at us. She sets the package in the wagon and gets in. The wagon moves on.
“How many more hills now, Darl?” Vardaman says.
“Just one,” I say. “The next one goes right up into town.”
This hill is red sand, bordered on either hand by negro cabins; against the sky ahead the massed
telephone lines run, and the clock on the courthouse lifts among the trees. In the sand the wheels

whisper, as though the very earth would hush our entry. We descend as the hill commences to rise.
We follow the wagon, the whispering wheels, passing the cabins where faces come suddenly to the
doors, white-eyed. We hear sudden voices, ejaculant. Jewel has been looking from side to side; now
his head turns forward and I can see his ears taking on a still deeper tone of furious red. Three
negroes walk beside the road ahead of us; ten feet ahead of them a white man walks. When we pass
the negroes their heads turn suddenly with that expression of shock and instinctive
outrage. “Great God,” one says; “what they got in that wagon?”
Jewel whirls. “Son of a bitches,” he says. As he does so he is abreast of the white man, who has
paused. It is as though Jewel had gone blind for the moment, for it is the white man toward whom he
whirls. “Darl!” Cash says from the wagon. I grasp at Jewel. The white man has fallen back a pace,
his face still slack-jawed; then his jaw tightens,
claps to. Jewel leans above him, his jaw muscles gone white. “What did you say?” he says.
“Here,” I say. “He dont mean anything, mister. Jewel,” I say. When I touch him he swings at the
man. I grasp his arm; we struggle. Jewel has never looked at me. He is trying to free his arm. When
I see the man again he has an open knife in his hand.
“Hold up, mister,” I say; “I’ve got him. Jewel,” I say.
“Thinks because he’s a goddamn town fellow,” Jewel says, panting, wrenching at me. “Son of a
bitch,” he says.
The man moves. He begins to edge around me, watching Jewel, the knife low against his flank. “Cant
no man call me that,” he says. Pa has got down, and Dewey Dell is holding Jewel, pushing at him. I
release him and face the man.
“Wait,” I say. “He dont mean nothing. He’s sick; got burned in a fire last night, and he aint
himself.”
“Fire or no fire,” the man says, “cant no man call me that.” “He thought you said something to
him,” I say.
“I never said nothing to him. I never see him before.” “Fore God,” pa says; “fore God.”
“I know,” I say. “He never meant anything. He’ll take it back.” “Let him take it back then.”
“Put up your knife, and he will.”
The man looks at me. He looks at Jewel. Jewel is quiet now. “Put up your knife.” I say.

The man shuts the knife.
“Fore God,” pa says. “Fore God.”
“Tell him you didn’t mean anything, Jewel,” I say.
“I thought he said something,” Jewel says. “Just because he’s——” “Hush,” I say. “Tell him you
didn’t mean it.”
“I didn’t mean it,” Jewel says.
“He better not,” the man says. “Calling me a——” “Do you think he’s afraid to call you that?” I say.
The man looks at me. “I never said that,” he said. “Dont think it, neither,” Jewel says.
“Shut up,” I say. “Come on. Drive on, pa.”
The wagon moves. The man stands watching us. Jewel does not look back. “Jewel would a whipped him,”
Vardaman says.
We approach the crest, where the street runs, where cars go back and forth; the mules haul the
wagon up and onto the crest and the street. Pa stops them. The street runs on ahead, where the
square opens and the monument stands before the courthouse. We mount again while the heads turn
with that expression which we know; save Jewel. He does not get on, even though the wagon has
started again. “Get in, Jewel,” I say. “Come on. Let’s get away from here.” But he does not get in.
Instead he sets his foot on the turning hub of the rear wheel, one hand grasping the stanchion, and
with the hub turning smoothly under his sole he lifts the other foot and squats there, staring
straight ahead, motionless, lean, wooden-backed, as though carved squatting out of the lean wood.

CASH

It wasn’t nothing else to do. It was either send him to Jackson, or have Gillespie sue us, because
he knowed some way that Darl set fire to it. I dont know how he knowed, but he did. Vardaman seen
him do it, but he swore he never told nobody but Dewey Dell and that she told him not to tell
nobody. But Gillespie knowed it. But he would a suspicioned it sooner or later. He could have done
it that night just watching the way Darl acted.
And so pa said, “I reckon there aint nothing else to do,” and Jewel said,
“You want to fix him now?” “Fix him?” pa said.
“Catch him and tie him up,” Jewel said. “Goddamn it, do you want to wait until he sets fire to the
goddamn team and wagon?”
But there wasn’t no use in that. “There aint no use in that,” I said. “We can wait till she is
underground.” A fellow that’s going to spend the rest of his life locked up, he ought to be let to
have what pleasure he can have before he goes.
“I reckon he ought to be there,” pa says. “God knows, it’s a trial on me. Seems like it aint no end
to bad luck when once it starts.”
Sometimes I aint so sho who’s got ere a right to say when a man is crazy and when he aint.
Sometimes I think it aint none of us pure crazy and aint none of us pure sane until the balance of
us talks him that-a-way. It’s like it aint so much what a fellow does, but it’s the way the
majority of folks is looking at him when he does it.
Because Jewel is too hard on him. Of course it was Jewel’s horse was traded to get her that nigh to
town, and in a sense it was the value of the horse Darl tried to burn up. But I thought more than
once before we crossed the river and after, how it would be God’s blessing if He did take her outen
our hands and get shut of her in some clean way, and it seemed to me that when Jewel worked so to
get her outen the river, he was going against God in a way, and then when Darl

seen that it looked like one of us would have to do something, I can almost believe he done right
in a way. But I dont reckon nothing excuses setting fire to a man’s barn and endangering his stock
and destroying his property. That’s how I reckon a man is crazy. That’s how he cant see eye to eye
with other folks. And I reckon they aint nothing else to do with him but what the most folks say is
right.
But it’s a shame, in a way. Folks seem to get away from the olden right teaching that says to drive
the nails down and trim the edges well always like it was for your own use and comfort you were
making it. It’s like some folks has the smooth, pretty boards to build a courthouse with and others
dont have no more than rough lumber fitten to build a chicken coop. But it’s better to build a
tight chicken coop than a shoddy courthouse, and when they both build shoddy or build well, neither
because it’s one or tother is going to make a man feel the better nor the worse.
So we went up the street, toward the square, and he said, “We better take Cash to the doctor first.
We can leave him there and come back for him.” That’s it. It’s because me and him was born close
together, and it nigh ten years before Jewel and Dewey Dell and Vardaman begun to come along. I
feel kin to them, all right, but I dont know. And me being the oldest, and thinking already the
very thing that he done: I dont know.
Pa was looking at me, then at him, mumbling his mouth. “Go on,” I said. “We’ll get it done first.”
“She would want us all there,” pa says.
“Let’s take Cash to the doctor first,” Darl said. “She’ll wait. She’s already waited nine days.”
“You all dont know,” pa says. “The somebody you was young with and you growed old in her and she
growed old in you, seeing the old coming on and it was the one somebody you could hear say it dont
matter and know it was the truth outen the hard world and all a man’s grief and trials. You all
dont know.”
“We got the digging to do, too,” I said.
“Armstid and Gillespie both told you to send word ahead,” Darl said. “Dont you want to go to
Peabody’s now, Cash?”
“Go on,” I said. “It feels right easy now. It’s best to get things done in the right place.”
“If it was just dug,” pa says. “We forgot our spade, too.”
“Yes,” Darl said. “I’ll go to the hardware store. We’ll have to buy one.”

“It’ll cost money,” pa says.
“Do you begrudge her it?” Darl says.
“Go on and get a spade,” Jewel said. “Here. Give me the money.”
But pa didn’t stop. “I reckon we can get a spade,” he said. “I reckon there are Christians here.”
So Darl set still and we went on, with Jewel squatting on the tail-gate, watching the back of
Darl’s head. He looked like one of these bull dogs, one of these dogs that dont bark none,
squatting against the rope, watching the thing he was waiting to jump at.
He set that way all the time we was in front of Mrs Bundren’s house, hearing the music, watching
the back of Darl’s head with them hard white eyes of hisn.
The music was playing in the house. It was one of them graphophones. It was natural as a
music-band.
“Do you want to go to Peabody’s?” Darl said. “They can wait here and tell pa, and I’ll drive you to
Peabody’s and come back for them.”
“No,” I said. It was better to get her underground, now we was this close, just waiting until pa
borrowed the shovel. He drove along the street until we could hear the music.
“Maybe they got one here,” he said. He pulled up at Mrs Bundren’s. It was like he knowed. Sometimes
I think that if a working man could see work as far ahead as a lazy man can see laziness. So he
stopped there like he knowed, before that little new house, where the music was. We waited there,
hearing it. I believe I could have dickered Suratt down to five dollars on that one of his. It’s a
comfortable thing, music is. “Maybe they got one here,” pa says.
“You want Jewel to go,” Darl says, “or do you reckon I better?”
“I reckon I better,” pa says. He got down and went up the path and around the house to the back.
The music stopped, then it started again.
“He’ll get it, too,” Darl said.
“Ay,” I said. It was just like he knowed, like he could see through the walls and into the next ten
minutes.
Only it was more than ten minutes. The music stopped and never commenced again for a good spell,
where her and pa was talking at the back. We waited in the wagon.
“You let me take you back to Peabody’s,” Darl said. “No,” I said. “We’ll get her underground.”
“If he ever gets back,” Jewel said. He begun to cuss. He started to get down from the wagon. “I’m
going,” he said.

Then we saw pa coming back. He had two spades, coming around the house. He laid them in the wagon
and got in and we went on. The music never started again. Pa was looking back at the house. He kind
of lifted his hand a little and I saw the shade pulled back a little at the window and her face in
it.
But the curiousest thing was Dewey Dell. It surprised me. I see all the while how folks could say
he was queer, but that was the very reason couldn’t nobody hold it personal. It was like he was
outside of it too, same as you, and getting mad at it would be kind of like getting mad at a
mud-puddle that splashed you when you stepped in it. And then I always kind of had a idea that him
and Dewey Dell kind of knowed things betwixt them. If I’d a said it was ere a one of us she liked
better than ere a other, I’d a said it was Darl. But when we got it filled and covered and drove
out the gate and turned into the lane where them fellows was waiting, when they come out and come
on him and he jerked back, it was Dewey Dell that was on him before even Jewel could get at him.
And then I believed I knowed how Gillespie knowed about how his barn taken fire.
She hadn’t said a word, hadn’t even looked at him, but when them
fellows told him what they wanted and that they had come to get him and he throwed back, she jumped
on him like a wild cat so that one of the fellows had to quit and hold her and her scratching and
clawing at him like a wild cat, while the other one and pa and Jewel throwed Darl down and held him
lying on his back, looking up at me.
“I thought you would have told me,” he said. “I never thought you wouldn’t have.”
“Darl,” I said. But he fought again, him and Jewel and the fellow, and the other one holding Dewey
Dell and Vardaman yelling and Jewel saying,
“Kill him. Kill the son of a bitch.”
It was bad so. It was bad. A fellow cant get away from a shoddy job. He cant do it. I tried to tell
him, but he just said, “I thought you’d a told me. It’s not that I,” he said, then he begun to
laugh. The other fellow pulled Jewel off of him and he sat there on the ground, laughing.
I tried to tell him. If I could have just moved, even set up. But I tried to tell him and he quit
laughing, looking up at me.
“Do you want me to go?” he said.
“It’ll be better for you,” I said. “Down there it’ll be quiet, with none of the bothering and such.
It’ll be better for you, Darl,” I said.

“Better,” he said. He begun to laugh again. “Better,” he said. He couldn’t hardly say it for
laughing. He sat on the ground and us watching him, laughing and laughing. It was bad. It was bad
so. I be durn if I could see anything to laugh at. Because there just aint nothing justifies the
deliberate destruction of what a man has built with his own sweat and stored the fruit of his sweat
into.
But I aint so sho that ere a man has the right to say what is crazy and what aint. It’s like there
was a fellow in every man that’s done a- past the sanity or the insanity, that watches the sane and
the insane doings of that man with the same horror and the same astonishment.

PEABODY

I said, “I reckon a man in a tight might let Bill Varner patch him up like a damn mule, but I be
damned if the man that’d let Anse Bundren treat him with raw cement aint got more spare legs than I
have.”
“They just aimed to ease hit some,” he said.
“Aimed, hell,” I said. “What in hell did Armstid mean by even letting them put you on that wagon
again?”
“Hit was gittin right noticeable,” he said. “We never had time to wait.” I just looked at him. “Hit
never bothered me none,” he said.
“Dont you lie there and try to tell me you rode six days on a wagon without springs, with a broken
leg and it never bothered you.”
“It never bothered me much,” he said.
“You mean, it never bothered Anse much,” I said. “No more than it bothered him to throw that poor
devil down in the public street and handcuff him like a damn murderer. Dont tell me. And dont tell
me it aint going to bother you to lose sixty-odd square inches of skin to get that concrete off.
And dont tell me it aint going to bother you to have to limp around on one short leg for the
balance of your life—if you walk at all again. Concrete,” I said. “God Almighty, why didn’t Anse
carry you to the nearest sawmill and stick your leg in the saw? That would have cured it. Then you
all could have stuck his head into the saw and cured a whole family.…… Where is Anse, anyway?
What’s he up to now?”
“He’s taking back them spades he borrowed,” he said.
“That’s right,” I said. “Of course he’d have to borrow a spade to bury his wife with. Unless he
could borrow a hole in the ground. Too bad you all didn’t put him in it too.…… Does that hurt?”
“Not to speak of,” he said, and the sweat big as marbles running down his face and his face about
the color of blotting paper.
“Course not,” I said. “About next summer you can hobble around fine on this leg. Then it wont
bother you, not to speak of.….… If you had anything you could call luck, you might say it was lucky
this is

the same leg you broke before,” I said. “Hit’s what paw says,” he said.

MacGOWAN

It happened I am back of the prescription case, pouring up some chocolate sauce, when Jody comes
back and says, “Say, Skeet, there’s a woman up front that wants to see the doctor and when I said
What doctor you want to see, she said she wants to see the doctor that works here and when I said
There aint any doctor works here, she just stood there, looking back this way.”
“What kind of a woman is it?” I says. “Tell her to go upstairs to Alford’s office.”
“Country woman,” he says.
“Send her to the courthouse,” I says. “Tell her all the doctors have gone to Memphis to a Barbers’
Convention.”
“All right,” he says, going away. “She looks pretty good for a country girl,” he says.
“Wait,” I says. He waited and I went and peeped through the crack. But I couldn’t tell nothing
except she had a good leg against the light. “Is she young, you say?” I says.
“She looks like a pretty hot mamma, for a country girl,” he says. “Take this,” I says, giving him
the chocolate. I took off my apron
and went up there. She looked pretty good. One of them black eyed ones that look like she’d as soon
put a knife in you as not if you two- timed her. She looked pretty good. There wasn’t nobody else
in the store; it was dinner time.
“What can I do for you?” I says. “Are you the doctor?” she says.
“Sure,” I says. She quit looking at me and was kind of looking around.
“Can we go back yonder?” she says.
It was just a quarter past twelve, but I went and told Jody to kind of watch out and whistle if the
old man come in sight, because he never got back before one.
“You better lay off of that,” Jody says. “He’ll fire your stern out of

here so quick you cant wink.”
“He dont never get back before one,” I says. “You can see him go into the postoffice. You keep your
eye peeled, now, and give me a whistle.”
“What you going to do?” he says.
“You keep your eye out. I’ll tell you later.”
“Aint you going to give me no seconds on it?” he says.
“What the hell do you think this is?” I says; “a stud-farm? You watch out for him. I’m going into
conference.”
So I go on to the back. I stopped at the glass and smoothed my hair, then I went behind the
prescription case, where she was waiting. She is looking at the medicine cabinet, then she looks at
me.
“Now, madam,” I says; “what is your trouble?”
“It’s the female trouble,” she says, watching me. “I got the money,” she says.
“Ah,” I says. “Have you got female troubles or do you want female troubles? If so, you come to the
right doctor.” Them country people. Half the time they dont know what they want, and the balance of
the time they cant tell it to you. The clock said twenty past twelve.
“No,” she says.
“No which?” I says.
“I aint had it,” she says. “That’s it.” She looked at me. “I got the money,” she says.
So I knew what she was talking about.
“Oh,” I says. “You got something in your belly you wish you didn’t have.” She looks at me. “You
wish you had a little more or a little less, huh?”
“I got the money,” she says. “He said I could git something at the drugstore for hit.”
“Who said so?” I says.
“He did,” she says, looking at me.
“You dont want to call no names,” I says. “The one that put the acorn in your belly? He the one
that told you?” She dont say nothing. “You aint married, are you?” I says. I never saw no ring. But
like as not, they aint heard yet out there that they use rings.
“I got the money,” she says. She showed it to me, tied up in her handkerchief: a ten spot.
“I’ll swear you have,” I says. “He give it to you?” “Yes,” she says.
“Which one?” I says. She looks at me. “Which one of them give it to

you?”
“It aint but one,” she says. She looks at me.
“Go on,” I says. She dont say nothing. The trouble about the cellar is, it aint but one way out and
that’s back up the inside stairs. The clock says twenty-five to one. “A pretty girl like you,” I
says.
She looks at me. She begins to tie the money back up in the handkerchief. “Excuse me a minute,” I
says. I go around the prescription case. “Did you hear about that fellow sprained his ear?” I says.
“After that he couldn’t even hear a belch.”
“You better get her out from back there before the old man comes,” Jody says.
“If you’ll stay up there in front where he pays you to stay, he wont catch nobody but me,” I says.
He goes on, slow, toward the front. “What you doing to her, Skeet?” he says.
“I cant tell you,” I says. “It wouldn’t be ethical. You go on up there and watch.”
“Say, Skeet,” he says.
“Ah, go on,” I says. “I aint doing nothing but filling a prescription.” “He may not do nothing
about that woman back there, but if he
finds you monkeying with that prescription case, he’ll kick your stern clean down them cellar
stairs.”
“My stern has been kicked by bigger bastards than him,” I says. “Go back and watch out for him,
now.”
So I come back. The clock said fifteen to one. She is tying the money in the handkerchief. “You
aint the doctor,” she says.
“Sure I am,” I says. She watches me. “Is it because I look too young, or am I too handsome?” I
says. “We used to have a bunch of old water-jointed doctors here,” I says; “Jefferson used to be a
kind of Old Doctors’ Home for them. But business started falling off and folks stayed so well until
one day they found out that the women wouldn’t never get sick at all. So they run all the old
doctors out and got us young good-looking ones that the women would like and then the women begun
to get sick again and so business picked up. They’re doing that all over the country. Hadn’t you
heard about it? Maybe it’s because you aint never needed a doctor.”
“I need one now,” she says.
“And you come to the right one,” I says. “I already told you that.” “Have you got something for
it?” she says. “I got the money.” “Well,” I says, “of course a doctor has to learn all sorts of
things

while he’s learning to roll calomel; he cant help himself. But I dont know about your trouble.”
“He told me I could get something. He told me I could get it at the drugstore.”
“Did he tell you the name of it?” I says. “You better go back and ask him.”
She quit looking at me, kind of turning the handkerchief in her hands. “I got to do something,” she
says.
“How bad do you want to do something?” I says. She looks at me. “Of course, a doctor learns all
sorts of things folks dont think he knows. But he aint supposed to tell all he knows. It’s against
the law.”
Up front Jody says, “Skeet.”
“Excuse me a minute,” I says. I went up front. “Do you see him?” I says.
“Aint you done yet?” he says. “Maybe you better come up here and watch and let me do that
consulting.”
“Maybe you’ll lay a egg,” I says. I come back. She is looking at me. “Of course you realise that I
could be put in the penitentiary for doing what you want,” I says. “I would lose my license and
then I’d have to go to work. You realise that?”
“I aint got but ten dollars,” she says. “I could bring the rest next month, maybe.”
“Pooh,” I says, “ten dollars? You see, I cant put no price on my knowledge and skill. Certainly not
for no little paltry sawbuck.”
She looks at me. She dont even blink. “What you want, then?”
The clock said four to one. So I decided I better get her out. “You guess three times and then I’ll
show you,” I says.
She dont even blink her eyes. “I got to do something,” she says. She looks behind her and around,
then she looks toward the front. “Gimme the medicine first,” she says.
“You mean, you’re ready to right now?” I says. “Here?” “Gimme the medicine first,” she says.
So I took a graduated glass and kind of turned my back to her and picked out a bottle that looked
all right, because a man that would keep poison setting around in a unlabelled bottle ought to be
in jail, anyway. It smelled like turpentine. I poured some into the glass and give it to her. She
smelled it, looking at me across the glass.
“Hit smells like turpentine,” she says.
“Sure,” I says. “That’s just the beginning of the treatment. You come back at ten o’clock tonight
and I’ll give you the rest of it and perform

the operation.” “Operation?” she says.
“It wont hurt you. You’ve had the same operation before. Ever hear about the hair of the dog?”
She looks at me. “Will it work?” she says. “Sure it’ll work. If you come back and get it.”
So she drunk whatever it was without batting a eye, and went out. I went up front.
“Didn’t you get it?” Jody says. “Get what?” I says.
“Ah, come on,” he says. “I aint going to try to beat your time.”
“Oh, her,” I says. “She just wanted a little medicine. She’s got a bad case of dysentery and she’s
a little ashamed about mentioning it with a stranger there.”
It was my night, anyway, so I helped the old bastard check up and I got his hat on him and got him
out of the store by eight-thirty. I went as far as the corner with him and watched him until he
passed under two street lamps and went on out of sight. Then I came back to the store and waited
until nine-thirty and turned out the front lights and locked the door and left just one light
burning at the back, and I went back and put some talcum powder into six capsules and kind of
cleared up the cellar and then I was all ready.
She come in just at ten, before the clock had done striking. I let her in and she come in, walking
fast. I looked out the door, but there wasn’t nobody but a boy in overalls sitting on the curb.
“You want something?” I says. He never said nothing, just looking at me. I locked the door and
turned off the light and went on back. She was waiting. She didn’t look at me now.
“Where is it?” she said.
I gave her the box of capsules. She held the box in her hand, looking at the capsules.
“Are you sure it’ll work?” she says.
“Sure,” I says. “When you take the rest of the treatment.” “Where do I take it?” she says.
“Down in the cellar,” I says.

VARDAMAN

Now it is wider and lighter, but the stores are dark because they have all gone home. The stores
are dark, but the lights pass on the windows when we pass. The lights are in the trees around the
courthouse. They roost in the trees, but the courthouse is dark. The clock on it looks four ways,
because it is not dark. The moon is not dark too. Not very dark. Darl he went to Jackson is my
brother Darl is my brother Only it was over that way, shining on the track.
“Let’s go that way, Dewey Dell,” I say.
“What for?” Dewey Dell says. The track went shining around the window, it red on the track. But she
said he would not sell it to the town boys. “But it will be there Christmas,” Dewey Dell says.
“You’ll have to wait till then, when he brings it back.”
Darl went to Jackson. Lots of people didn’t go to Jackson. Darl is my brother. My brother is going
to Jackson
While we walk the lights go around, roosting in the trees. On all sides it is the same. They go
around the courthouse and then you cannot see them. But you can see them in the black windows
beyond. They have all gone home to bed except me and Dewey Dell.
Going on the train to Jackson. My brother
There is a light in the store, far back. In the window are two big glasses of soda water, red and
green. Two men could not drink them. Two mules could not. Two cows could not. Darl
A man comes to the door. He looks at Dewey Dell. “You wait out here,” Dewey Dell says.
“Why cant I come in?” I say. “I want to come in, too.” “You wait out here,” she says.
“All right,” I say. Dewey Dell goes in.
Darl is my brother. Darl went crazy
The walk is harder than sitting on the ground. He is in the open door. He looks at me. “You want
something?” he says. His head is

slick. Jewel’s head is slick sometimes. Cash’s head is not slick. Darl he went to Jackson my
brother Darl In the street he ate a banana. Wouldn’t you rather have bananas? Dewey Dell said. You
wait till Christmas. It’ll be there then. Then you can see it. So we are going to have some
bananas. We are going to have a bag full, me and Dewey Dell. He locks the door. Dewey Dell is
inside. Then the light winks out.
He went to Jackson. He went crazy and went to Jackson both. Lots of people didn’t go crazy. Pa and
Cash and Jewel and Dewey Dell and me didn’t go crazy. We never did go crazy. We didn’t go to
Jackson either. Darl
I hear the cow a long time, clopping on the street. Then she comes into the square. She goes
across the square, her head down clopping . She lows. There was nothing in the square before
she lowed, but it wasn’t empty. Now it is empty after she lowed. She goes on, clopping . She
lows. My brother is Darl. He went to Jackson on the train. He didn’t go on the train to go crazy.
He went crazy in our wagon. Darl She has been in there a long time. And the cow is gone too. A long
time. She has been in there longer than the cow was. But not as long as empty. Darl is my brother.
My brother Darl
Dewey Dell comes out. She looks at me. “Let’s go around that way now,” I say.
She looks at me. “It aint going to work,” she says. “That son of a bitch.”
“What aint going to work, Dewey Dell?”
“I just know it wont,” she says. She is not looking at anything. “I just know it.”
“Let’s go that way,” I say.
“We got to go back to the hotel. It’s late. We got to slip back in.” “Cant we go by and see,
anyway?”
“Hadn’t you rather have bananas? Hadn’t you rather?”
“All right.” My brother he went crazy and he went to Jackson too.
Jackson is further away than crazy
“It wont work,” Dewey Dell says. “I just know it wont.”
“What wont work?” I say. He had to get on the train to go to Jackson. I have not been on the train,
but Darl has been on the train. Darl. Darl is my brother. Darl. Darl

DARL

Darl has gone to Jackson. They put him on the train, laughing, down the long car laughing, the
heads turning like the heads of owls when he passed. “What are you laughing at?” I said.
“Yes yes yes yes yes.”
Two men put him on the train. They wore mismatched coats, bulging behind over their right hip
pockets. Their necks were shaved to a hairline, as though the recent and simultaneous barbers had
had a chalk-line like Cash’s. “Is it the pistols you’re laughing at?” I said. “Why do you laugh?” I
said. “Is it because you hate the sound of laughing?”
They pulled two seats together so Darl could sit by the window to laugh. One of them sat beside
him, the other sat on the seat facing him, riding backward. One of them had to ride backward
because the state’s money has a face to each backside and a backside to each face, and they are
riding on the state’s money which is incest. A nickel has a woman on one side and a buffalo on the
other; two faces and no back. I dont know what that is. Darl had a little spy-glass he got in
France at the war. In it it had a woman and a pig with two backs and no face. I know what that is.
“Is that why you are laughing, Darl?”
“Yes yes yes yes yes yes.”
The wagon stands on the square, hitched, the mules motionless, the reins wrapped about the
seat-spring, the back of the wagon toward the courthouse. It looks no different from a hundred
other wagons there; Jewel standing beside it and looking up the street like any other man in town
that day, yet there is something different, distinctive. There is about it that unmistakable air of
definite and imminent departure that trains have, perhaps due to the fact that Dewey Dell and
Vardaman on the seat and Cash on a pallet in the wagon bed are eating bananas from a paper bag. “Is
that why you are laughing, Darl?”
Darl is our brother, our brother Darl. Our brother Darl in a cage in

Jackson where, his grimed hands lying light in the quiet interstices, looking out he foams.
“Yes yes yes yes yes yes yes yes.”

DEWEY DELL

When he saw the money I said, “It’s not my money, it doesn’t belong to me.”
“Whose is it, then?”
“It’s Cora Tull’s money. It’s Mrs Tull’s. I sold the cakes for it.” “Ten dollars for two cakes?”
“Dont you touch it. It’s not mine.”
“You never had them cakes. It’s a lie. It was them Sunday clothes you had in that package.”
“Dont you touch it! If you take it you are a thief.”
“My own daughter accuses me of being a thief. My own daughter.” “Pa. Pa.”
“I have fed you and sheltered you. I give you love and care, yet my own daughter, the daughter of
my dead wife, calls me a thief over her mother’s grave.”
“It’s not mine, I tell you. If it was, God knows you could have it.” “Where did you get ten
dollars?”
“Pa. Pa.”
“You wont tell me. Did you come by it so shameful you dare not?” “It’s not mine, I tell you. Cant
you understand it’s not mine?”
“It’s not like I wouldn’t pay it back. But she calls her own father a thief.”
“I cant, I tell you. I tell you it’s not my money. God knows you could have it.”
“I wouldn’t take it. My own born daughter that has et my food for seventeen years, begrudges me the
loan of ten dollars.”
“It’s not mine, I cant.” “Whose is it, then?”
“It was give to me. To buy something with.” “To buy what with?”
“Pa. Pa.”
“It’s just a loan. God knows, I hate for my blooden children to

reproach me. But I give them what was mine without stint. Cheerful I give them, without stint. And
now they deny me. Addie. It was lucky for you you died, Addie.”
“Pa. Pa.”
“God knows it is.”
He took the money and went out.

CASH

So when we stopped there to borrow the shovels we heard the graphophone playing in the house, and
so when we got done with the shovels pa says, “I reckon I better take them back.”
So we went back to the house. “We better take Cash on to Peabody’s,” Jewel said.
“It wont take but a minute,” pa said. He got down from the wagon.
The music was not playing now.
“Let Vardaman do it,” Jewel said. “He can do it in half the time you can. Or here, you let me——”
“I reckon I better do it,” pa says. “Long as it was me that borrowed them.”
So we set in the wagon, but the music wasn’t playing now. I reckon it’s a good thing we aint got
ere a one of them. I reckon I wouldn’t never get no work done a-tall for listening to it. I dont
know if a little music aint about the nicest thing a fellow can have. Seems like when he comes in
tired of a night, it aint nothing could rest him like having a little music played and him resting.
I have seen them that shuts up like a hand-grip, with a handle and all, so a fellow can carry it
with him wherever he wants.
“What you reckon he’s doing?” Jewel says. “I could a toted them shovels back and forth ten times by
now.”
“Let him take his time,” I said. “He aint as spry as you, remember.” “Why didn’t he let me take
them back, then? We got to get your leg
fixed up so we can start home tomorrow.”
“We got plenty of time,” I said. “I wonder what them machines costs on the installment.”
“Installment of what?” Jewel said. “What you got to buy it with?” “A fellow cant tell,” I said. “I
could a bought that one from Suratt
for five dollars, I believe.”
And so pa come back and we went to Peabody’s. While we was there pa said he was going to the
barbershop and get a shave. And so

that night he said he had some business to tend to, kind of looking away from us while he said it,
with his hair combed wet and slick and smelling sweet with perfume, but I said leave him be; I
wouldn’t mind hearing a little more of that music myself.
And so next morning he was gone again, then he come back and told us to get hitched up and ready to
take out and he would meet us and when they was gone he said,
“I dont reckon you got no more money.”
“Peabody just give me enough to pay the hotel with,” I said. “We dont need nothing else, do we?”
“No,” pa said; “no. We dont need nothing.” He stood there, not looking at me.
“If it is something we got to have, I reckon maybe Peabody,” I said. “No,” he said; “it aint
nothing else. You all wait for me at the
corner.”
So Jewel got the team and come for me and they fixed me a pallet in the wagon and we drove across
the square to the corner where pa said, and we was waiting there in the wagon, with Dewey Dell and
Vardaman eating bananas, when we see them coming up the street. Pa was coming along with that kind
of daresome and hangdog look all at once like when he has been up to something he knows ma aint
going to like, carrying a grip in his hand, and Jewel says,
“Who’s that?”
Then we see it wasn’t the grip that made him look different; it was his face, and Jewel says, “He
got them teeth.”
It was a fact. It made him look a foot taller, kind of holding his head up, hangdog and proud too,
and then we see her behind him, carrying the other grip—a kind of duck-shaped woman all dressed up,
with them kind of hardlooking pop eyes like she was daring ere a man to say nothing. And there we
set watching them, with Dewey Dell’s and Vardaman’s mouth half open and half-et bananas in their
hands and her coming around from behind pa, looking at us like she dared ere a man. And then I see
that the grip she was carrying was one of them little graphophones. It was for a fact, all shut up
as pretty as a picture, and everytime a new record would come from the mail order and us setting in
the house in the winter, listening to it, I would think what a shame Darl couldn’t be to enjoy it
too. But it is better so for him. This world is not his world; this life his life.
“It’s Cash and Jewel and Vardaman and Dewey Dell,” pa says, kind
of hangdog and proud too, with his teeth and all, even if he wouldn’t

look at us. “Meet Mrs Bundren,” he says.

EDITORS’ NOTE

This volume reproduces the text of As I Lay Dying that has been established by Noel Polk. The
copy-text for this novel is William Faulkner’s own ribbon typescript setting copy, which has been
emended to account for his revisions in proof, his indisputable typing errors, and certain other
mistakes and inconsistencies that clearly demand correction. Faulkner typed and proofread this
document himself, and it also bears alterations of varying degrees of seriousness by his editors.
According to Faulkner’s sarcastic testimony in his notorious introduction to the Modern Library
Sanctuary in 1932, he wrote As I Lay Dying “in six weeks, without changing a word.” The manuscript
and typescript reveal that he did not, of course, write it “without changing a word,” although the
dates on the manuscript indicate that he did indeed complete the holograph version in about eight
weeks, between October 25 and December 29, 1929. “I set out deliberately to write a tour-de-force,”
he claimed later. “Before I ever put pen to paper and set down the first words, I knew what the
last word would be.… Before I began I said, I am going to write a book by which, at a pinch, I can
stand or fall if I never touch ink again.” He wrote As I Lay Dying at the University of Mississippi
power plant, where he was employed as a fireman and night watchman, mostly in the early morning,
after everybody had gone to bed and power needs had diminished. He finished the typing, according
to the date on the carbon typescript, on January 12, 1930, and sent it to Harrison Smith, who
published it with very few editorial changes on October 6, 1930.
Extant documents relevant to the editing of As I Lay Dying are the
holograph manuscript and the carbon typescript, at the Alderman Library of the University of
Virginia, and the ribbon typesetting copy, at the Humanities Research Center of the University of
Texas. No proof is known to survive; this is unfortunate, since there are a number of differences
between the typescript and the published book

that must have occurred in proof.
American English continues to fluctuate; for example, a word may be spelled in more than one way,
even in the same work. Commas are sometimes used expressively to suggest the movements of voice,
and capitals are sometimes meant to give significances to a word beyond those it might have in its
uncapitalized form. Since standardization would remove such effects, this volume preserves the
spelling, punctuation, capitalization, and wording of the text established by Noel Polk, which
strives to be as faithful to Faulkner’s usage as surviving evidence permits.

The following notes were prepared by Joseph Blotner and are reprinted with permission from Novels
1930—1935, one volume of the edition of Faulkner’s collected works published by The Library of
America, 1985. For further information, consult Calvin S. Brown, A Glossary of Faulkner’s South
(New Haven: Yale University Press, 1976); Jessie McGuire Coffee, Faulkner’s Un-Christlike
Christians: Biblical Allusions in the Novels (Ann Arbor: UMI Research Press, 1983); André
Bleikasten, Faulkner’s As I Lay Dying (Bloomington: Indiana University Press, rev. ed., 1973); and
William Faulkner’s “As I Lay Dying,” ed. by Dianne L. Cox (New York: Garland Publishing, 1984).

AS I LAY DYING] When asked the source of his title, Faulkner would sometimes quote from memory the
1 speech of Agamemnon to Odysseus in the Odyssey, Book XI: “As I lay dying the woman with the
dog’s eyes would not close my eyes for me as I descended into Hades.”
laidby cotton] A cultivated crop that will require no
2
further attention until it is picked at harvest time.
3 pussel-gutted] Faulkner defined this to mean “bloated.”
4 frailed] Variant of flailed. To whip or beat.
5 laid-by] See note 2.
6 I … falls.] See Matt. 10:29.
Christmas masts] According to Faulkner, comic masks

7 worn by children at Christmas and Halloween.

8 sweat … Lord.] Cf. Gen. 3:19 and Matt. 13:12.
9 I … chastiseth.] Anse’s garbled recollection of Heb. 12:6. busted out] Plowed or harrowed in
preparation for
10
planting.
It … away.] Book Four of The Hamlet (1940) tells the story of the incursion of these “spotted
horses” into
11
Yoknapatawpha County in the first decade of the
twentieth century.
there … sinned] See Jesus’ parable of the lost sheep in
12
Luke 15:7.
Inverness] A town about ninety miles southwest of
13
Oxford.
14 aguer] An ague, a malarial fever.
Yoknapatawpha county] The first appearance of the name of what Faulkner would call “my apocryphal
county.” Mississippi’s Lafayette County, where Faulkner
15 spent most of his life, is bounded on the south by the Yocona River. Some early maps
transliterated the river’s Chickasaw name as Yockney-Patafa. According to
Faulkner, it meant “water runs slow through flat land.”

ABOUT THIS GUIDE
The questions, discussion topics, and author biography that follow are designed to enhance your
group’s reading of three of William Faulkner’s greatest novels: The Sound and the Fury, As I Lay
Dying, and Absalom, Absalom! We hope that they will provide you with new ways of thinking and
talking about three works that stand as major landmarks in the history of modern American
literature, works that exemplify Faulkner’s bold stylistic and formal innovations, his creation of
unforgettably powerful voices and characters, and his brilliant insight into the psychological,
economic, and social realities of life in the South in the transition from the Civil War to the
modern era. In their intellectual and aesthetic richness, these novels raise nearly endless
possibilities for discussion. The questions below will necessarily be limited and are meant to open
several, but certainly not all, areas of inquiry for your reading group.
READER’S GUIDE

  1. Which are the most intelligent and sympathetic voices in the novel? With whom do you most and
    least identify? Is Faulkner controlling your closeness to some characters and not others? How is
    this done, given the seemingly equal mode of presentation for all voices?
  2. Even the reader of such an unusual book may be surprised to come upon Addie Bundren’s narrative
    on this page, if only because Addie has been dead since this page. Why is Addie’s narrative placed
    where it is, and what is the effect of hearing Addie’s voice at this point in the book? Is this one
    of the ways in which Faulkner shows Addie’s continued “life” in the minds and hearts of her family?
    How do the issues raised by Addie here relate to the book as a whole?
  3. Faulkner allows certain characters—especially Darl and Vardaman
    —to express themselves in language and imagery that would be impossible, given their lack of
    education and experience in the world. Why does he break with the realistic representation of
    character in this way?
  4. What makes Darl different from the other characters? Why is he able to describe Addie’s death
    [see here] when he is not present? How

is he able to intuit the fact of Dewey Dell’s pregnancy? What does this uncanny visionary power
mean, particularly in the context of what happens to Darl at the end of the novel? Darl has fought
in World War I; why do you think Faulkner has chosen to include this information about him? What
are the sources and meaning of his madness?

  1. Anse Bundren is surely one of the most feckless characters in literature, yet he alone thrives
    in the midst of disaster. How does he manage to command the obedience and cooperation of his
    children? Why are other people so generous with him? He gets his new teeth at the end of the novel
    and he also gets a new wife. What is the secret of Anse’s charm? How did he manage to make Addie
    marry him, when she is clearly more intelligent than he is?
  2. Some critics have spoken of Cash as the novel’s most gentle character, while others have felt
    that he is too rigid, too narrow- minded, to be sympathetic. What does Cash’s list of the thirteen
    reasons for beveling the edges of the coffin tell us about him? What does it tell us about his
    feeling for his mother? Does Cash’s carefully reasoned response to Darl’s imprisonment seem fair to
    you, or is it a betrayal of his brother?
  3. Jewel is the result of Addie’s affair with the evangelical preacher Whitfield (an aspect of
    the plot that bears comparison with Hawthorne’s The Scarlet Letter). When we read Whitfield’s
    section, we realize that Addie has again allied herself with a man who is not her equal. How would
    you characterize the preacher? What is the meaning of this passionate alliance, now repudiated by
    Whitfield? Does Jewel know who his father is?
  4. What is your response to the section spoken by Vardaman, which states simply, “My mother is a
    fish”? What sort of psychological state or process does this declaration indicate? What are some of
    the ways in which Vardaman insists on keeping his mother alive, even as he struggles to understand
    that she is dead? In what other ways does the novel show characters wrestling with ideas of
    identity and embodiment?
  5. This is a novel full of acts of love, not the least of which is the prolonged search in the
    river for Cash’s tools. Consider some of the other ways that love is expressed among the members of
    the family. What compels loyalty in this family? What are the ways in which that loyalty is
    betrayed? Which characters are most self-interested?
  6. The saga of the Bundren family is participated in, and reflected upon, by many other
    characters. What does the involvement of Doctor

Peabody, of Armstid, and of Cora and Vernon Tull say about the importance of community in country
life? Are the characters in the town meant to provide a contrast with country people?

  1. Does Faulkner deliberately make humor and the grotesque interdependent in this novel? What is
    the effect of such horrific details as Vardaman’s accidental drilling of holes in his dead mother’s
    face? Of Darl and Vardaman listening to the decaying body of Addie “speaking”? Of Vardaman’s
    anxiety about the growing number of buzzards trying to get at the coffin? Of Cash’s bloody broken
    leg, set in concrete and suppurating in the heat? Of Jewel’s burnt flesh? Of the “cure” that Dewey
    Dell is tricked into?
  2. In one of the novel’s central passages, Addie meditates upon the distance between words and
    actions: “I would think how words go straight up in a thin line, quick and harmless, and how
    terribly doing goes along the earth, clinging to it, so that after a while the two lines are too
    far apart for the same person to straddle from one to the other; and that sin and love and fear are
    just sounds that people who never sinned nor loved nor feared have for what they never had and
    cannot have until they forget the words” [see here]. What light does this passage shed upon the
    meaning of the novel? Aren’t words necessary in order to give form to the story of the Bundrens? Or
    is Faulkner saying that words—his own chosen medium—are inadequate?
  3. What does the novel reveal about the ways in which human beings deal with death, grieving, and
    letting go of our loved ones?

WILLIAM FAULKNER (1897–1962)

illiam Cuthbert Faulkner was born in 1897 in New Albany, Mississippi, the first of four sons of
Murry and Maud Butler Falkner (he later added the “u” to the family name himself). In 1904 the
family moved to the university town of Oxford, Mississippi, where Faulkner was to spend most of his
life. He was named for his greatgrandfather “The Old Colonel,” a Civil War veteran who built a
railroad, wrote a bestselling romantic novel called The White Rose of Memphis, became a Mississippi
state legislator, and was eventually killed in what may or may not have been a duel with a
disgruntled business partner. Faulkner identified with this robust and energetic
ancestor and often said that he inherited the “ink stain” from him.

Never fond of school, Faulkner left at the end of football season his senior year of high school,
and began working at his grandfather’s bank. In 1918, after his plans to marry his sweetheart
Estelle Oldham were squashed by their families, he tried to enlist as a pilot in the U.S. Army but
was rejected because he did not meet the height and weight requirements. He went to Canada, where
he pretended to be an Englishman and joined the RAF training program there. Although he did not
complete his training until after the war ended and never saw combat, he returned to his hometown
in uniform, boasting of war wounds. He briefly attended the University of Mississippi, where he
began to publish his poetry.
After spending a short time living in New York, he again returned to Oxford, where he worked at the
university post office. His first book, a collection of poetry, The Marble Faun, was published at
Faulkner’s own expense in 1924. The writer Sherwood Anderson, whom he met in New Orleans in 1925,
encouraged him to try writing fiction, and his first novel, Soldier’s Pay, was published in 1926.
It was followed by Mosquitoes. His next novel, which he titled Flags in the Dust, was rejected by
his publisher and twelve others to whom he

submitted it. It was eventually published in drastically edited form as Sartoris (the original
version was not issued until after his death). Meanwhile, he was writing The Sound and the Fury,
which, after being rejected by one publisher, came out in 1929 and received many ecstatic reviews,
although it sold poorly. Yet again, a new novel, Sanctuary, was initially rejected by his
publisher, this time as “too shocking.” While working on the night shift at a power plant, Faulkner
wrote what he was determined would be his masterpiece, As I Lay Dying. He finished it in about
seven weeks, and it was published in 1930, again to generally good reviews and mediocre sales.

In 1929 Faulkner had finally married his childhood sweetheart, Estelle, after her divorce from her
first husband. They had a premature daughter, Alabama, who died ten days after birth in 1931; a
second daughter, Jill, was born in 1933.

With the eventual publication of his most sensational and violent (as well as, up till then, most
successful) novel, Sanctuary (1931), Faulkner was invited to write scripts for MGM and Warner
Brothers, where he was responsible for much of the dialogue in the film versions of Hemingway’s To
Have and Have Not and Chandler’s The Big Sleep, and many other films. He continued to write novels
and published many stories in the popular magazines. Light in August (1932) was his first attempt
to address the racial issues of the South, an effort continued in Absalom, Absalom! (1936), and Go
Down, Moses (1942). By 1946, most of Faulkner’s novels were out of print in the United States
(although they remained well-regarded in Europe), and he was seen as a minor, regional writer. But
then the influential editor and critic Malcolm Cowley, who had earlier championed Hemingway and
Fitzgerald and others of their generation, put together the Portable Faulkner, and once again
Faulkner’s genius was recognized, this time for good. He received the 1949 Nobel Prize for
Literature as well as many other awards and accolades, including the National Book Award and the
Gold Medal from the American Academy of Arts and Letters and France’s Legion of Honor.

In addition to several collections of short fiction, his other novels include Pylon (1935), The
Unvanquished (1938), The Wild Palms

(1939), The Hamlet (1940), Intruder in the Dust (1948), A Fable (1954),
The Town (1957), The Mansion (1959), and The Reivers (1962).

William Faulkner died of a heart attack on July 6, 1962, in Oxford, Mississippi, where he is
buried.

“He is the greatest artist the South has produced.… Indeed, through his many novels and short
stories, Faulkner fights out the moral problem which was repressed after the nineteenth century
[yet] for all his concern with the South, Faulkner was actually seeking out the nature of man. Thus
we must turn to him for that continuity of moral purpose which made for the greatness of our
classics.”
—RALPH ELLISON

“Faulkner, more than most men, was aware of human strength as well of human weakness. He knew
that the understanding and the resolution of fear are a large part of the writer’s reason for
being.”
—JOHN STEINBECK

“For range of effect, philosophical weight, originality of style, variety of characterization,
humor, and tragic intensity, [Faulkner’s works] are without equal in our time and country.”
—ROBERT PENN WARREN

“No man ever put more of his heart and soul into the written word than did William Faulkner. If you
want to know all you can about that heart and soul, the fiction where he put it is still right
there.”
—EUDORA WELTY

APPROACHING WILLIAM FAULKNER
As with any great literature, there are probably as many ways to read William Faulkner’s writing as
there are readers. There are hundreds of books devoted to interpretations of his novels, numerous
biographies, and every year high school teachers and college professors guide their students
through one or more of the novels. But after all is said and done, there are the books themselves,
and the pleasure of reading them can be deep and lasting. The language Faulkner uses ranges from
the poetically beautiful, nearly biblical to the coarse sounds of rough dialect. His characters
linger in the mind, whether for their heroism or villainy, their stoicism or self-indulgence, their
honesty or deceitfulness or self-deception, their wisdom or stupidity, their gentleness or cruelty.
In short, like Shakespeare, William Faulkner understood what it means to be human.

Much of Faulkner’s fiction is set in the fictional Mississippi county Yoknapatawpha (Yok’na pa taw
pha) and most of his characters are southerners who to one degree or another, are struggling with
life in a country that has experienced defeat, resisting change, and dealing with a lingering
nostalgia for a time that many of them never knew. Faulkner’s South is, of course, a segregated
South, and most of his characters are white southerners, many of whom have not and will not accept
the reality of racial equality. Faulkner himself became involved in the early Civil Rights
struggle, but being a southerner who rarely left the small Mississippi college town where he grew
up, he understood the difficulty of the racial divide, and in his writing we can find some of the
most subtle explanations of the difficult relationship between blacks and white, as well as some of
the most horrifying descriptions of the effects of racial hatred.

But if Faulkner were only concerned with the lives of southerners in the long period after the
Civil War and into the first half of the twentieth century, his writing would not have the appeal
it does (and he might not have received the Nobel Prize for Literature). Faulkner deals with
universal themes, and his characters, speaking in their own, sometimes barely articulate, sometimes
profoundly insightful voices, express the fears, joys, and confusion of struggling with life:

the voices of the Bundren family and their neighbors and acquaintances alternating in As I
Lay Dying lend the narrative much more power than a simple telling of the plot would. Allowing the
“idiot” Benjy to narrate the first section of The Sound and the Fury, in which time is confused and
details accumulate slowly, makes the reader consider how events are interpreted and what the mind
makes of memories. In Light in August, Joe Christmas never knows his true origins, but his
assumptions, and the beliefs of others, lead to a dramatic portrayal of the effects of prejudice.

Often tragic, sometimes absurdly comic, Faulkner’s plots are frequently driven by forces
that cannot be controlled by his characters: the definition of classic tragedy. In As I Lay
Dying, the family set off on a journey to fulfill the dying wish of Addie Bundren, only to be
stymied by an almost biblical series of events: fire and flood among them. Benjy, Quentin, and
Jason Compton in The Sound and the Fury are each affected by something that happened to their
sister, which they could not or did not prevent, and perhaps by the effects of history itself. In
Light in August, the lives of two characters who never meet, Lena Grove and Joe Christmas, lead to
both horrifying tragedy and a small but significant ray of hope.

So, how do we approach Faulkner? We approach him through his language, letting ourselves hear the
poetry in it, stopping to savor a phrase (or look up an unfamiliar word!), or just reading until
the sound becomes familiar. We approach him through his characters, hating them or loving them,
fearing for them, hoping for them or merely wondering how they survive. We approach him through the
stories he tells, because they are familiar or strange, because they sound like history or myth or
just a good tale. We can even approach him through what we know about Faulkner’s own life and times
or through what we read in the newspaper every day or what we have experienced in our personal
lives. If the definition of classic literature is that it concerns things that we continue to want
(and need) to read about, then we can simply read Faulkner.
Text © 2005 by Alfred A. Knopf, Inc., a division of Random House, Inc., New York

ALSO BY WILLIAM FAULKNER

ABSALOM, ABSALOM!
One of Faulkner’s finest achievements, Absalom, Absalom! is the story of Thomas Sutpen and the
ruthless, single-minded pursuit of his grand design—to forge a dynasty in Jefferson, Mississippi,
in 1830—which is ultimately destroyed (along with Sutpen himself) by his two sons.
AS I LAY DYING
As I Lay Dying is the harrowing account of the Bundren family’s odyssey across the Mississippi
countryside to bury Addie, their wife and mother. Told by each of the family members—including
Addie herself—the novel ranges from dark comedy to deepest pathos.
A FABLE
Winner of the Pulitzer Prize and the National Book Award, this allegorical novel about World War I
is set in the trenches of France and deals with a mutiny in a French regiment.
FLAGS IN THE DUST
The complete text, published for the first time in 1973, of Faulkner’s third novel, written when he
was twenty-nine, which appeared, with his reluctant consent, in a much cut version in 1929 as
Sartoris.
LIGHT IN AUGUST
A novel about hopeful perseverance in the face of mortality, Light in August tells the tales of
guileless, dauntless Lena Grove, in search of the father of her unborn child; Reverend Gail
Hightower, who is plagued by visions of Confederate horsemen; and Joe Christmas, an enigmatic
drifter consumed by his mixed ancestry.
THE REIVERS
One of Faulkner’s comic masterpieces and winner of a Pulitzer Prize, The Reivers is a picaresque
tale that tells of three unlikely car thieves from rural Mississippi and their wild misadventures
in the fast life of

Memphis—from horse smuggling to bawdy houses.
REQUIEM FOR A NUN
The sequel to Faulkner’s most sensational novel Sanctuary, was written twenty years later but takes
up the story of Temple Drake eight years after the events related in Sanctuary. Temple is now
married to Gowan Stevens. The book begins when the death sentence is pronounced on the nurse Nancy
for the murder of Temple and Gowan’s child. In an attempt to save her, Temple goes to see the judge
to confess her own guilt. Told partly in prose, partly in play form, Requiem for a Nun is a
haunting exploration of the impact of the past on the present.
THE SOUND AND THE FURY
One of the greatest novels of the twentieth century, The Sound and the Fury is the tragedy of the
Compson family, featuring some of the most memorable characters in American literature: beautiful,
rebellious Caddy; the man-child Benjy; haunted, neurotic Quentin; Jason, the brutal cynic; and
Dilsey, their black servant.
THE UNVANQUISHED
The Unvanquished is a novel of the Sartoris family, who embody the ideal of Southern honor and its
transformation through war, defeat, and Reconstruction: Colonel John Sartoris, who is murdered by a
business rival after the war; his son Bayard, who finds an alternative to bloodshed; and Granny
Millard, the matriarch, who must put aside her code of gentility in order to survive.
Snopes Trilogy
THE HAMLET
The Hamlet, the first novel of Faulkner’s Snopes trilogy, is both an ironic take on classical
tragedy and a mordant commentary on the grand pretensions of the antebellum South and the depths of
its decay in the aftermath of war and reconstruction. It tells of the advent and the rise of the
Snopes family in Frenchman’s Bend, a small town built on the ruins of a once-stately plantation.
Flem Snopes—wily,

energetic, a man of shady origins—quickly comes to dominate the town and its people with his
cunning and guile.
THE TOWN
This is the second volume of Faulkner’s trilogy about the Snopes family, his symbol for the
grasping, destructive element in the post- bellum South. Like its predecessor The Hamlet, and its
successor The Mansion, The Town is completely self-contained, but it gains resonance from being
read with the other two. The story of Flem Snopes’ ruthless struggle to take over the town of
Jefferson, Mississippi, the book is rich in typically Faulknerian episodes of humor and of
profundidty.
THE MANSION
The Mansion completes Faulkner’s great trilogy of the Snopes family in the mythical county of
Yoknapatawpha, Mississippi, which also includes The Hamlet and The Town. Beginning with the murder
of Jack Houston and ending with the murder of Flem Snopes, it traces the downfall of the
indomitable post-bellum family who managed to seize control of the town of Jefferson within a
generation.
BIG WOODS
The best of William Faulkner’s hunting stories are woven together brilliantly in Big Woods. First
published in 1955 and now available in paperback for the first time, the volume includes Faulkner’s
most famous story, “The Bear” (in its original version), together with “The Old People,” “A Bear
Hunt,” and “Race at Morning.” Each of the stories is introduced by a prelude, and the final one is
followed by an epilogue, which serve as almost musical bridges between them. Together, these pieces
create a seamless whole, a work that displays the full eloquence, emotional breadth, and moral
complexity of Faulkner’s vision.
COLLECTED STORIES
“A Bear Hunt,” “A Rose for Emily,” “Two Soldiers,” “Victory,” “The Brooch,” “Beyond”—these are
among the forty-two stories that make up this magisterial collection by the writer who stands at
the pinnacle

of modern American fiction. Compressing an epic expanse of vision into narratives as hard and
wounding as bullets, William Faulkner’s stories evoke the intimate textures of place, the deep
strata of history and legend, and all the fear, brutality, and tenderness of which human beings are
capable. These tales are set not only in Yoknapatawpha County but in Beverly Hills and in France
during World War I; they are populated by such characters as the Faulknerian archetypes Flem Snopes
and Quentin Compson (“A Justice”) as well as ordinary men and women who emerge in these pages so
sharply and indelibly that they dwarf the protagonists of most novels.
GO DOWN, MOSES
Go Down, Moses is composed of seven interrelated stories, all of them set in Faulkner’s mythic
Yoknapatawpha County. From a variety of perspectives, Faulkner examines the complex, changing
relationships between blacks and whites, between man and nature, weaving a cohesive novel rich in
implication and insight.
INTRUDER IN THE DUST
Intruder in the Dust is at once engrossing murder mystery and unflinching portrait of racial
injustice: it is the story of Lucas Beauchamp, a black man wrongly arrested for the murder of
Vinson Gowrie, a white man. Confronted by the threat of lynching, Lucas sets out to prove his
innocence, aided by a white lawyer, Gavin Stevens, and his young nephew, Chick Mallison.
KNIGHT’S GAMBIT
Gavin Stevens, the wise and forbearing student of crime and the folk ways of Yoknapatawpha County,
Mississippi, plays the major role in these six stories of violence. In each, Stevens’ sharp
insights and ingenious detection uncover the underlying motives.
PYLON
One of the few of William Faulkner’s works to be set outside his fictional Yoknapatawpha County,
Pylon, first published in 1935, takes place at an air show in a thinly disguised New Orleans named
New Valois. An unnamed reporter for a local newspaper tries to understand

a very modern ménage a trois of flyers on the brainstorming circuit. These characters, Faulkner
said, “were a fantastic and bizarre phenomenon on the face of the contemporary scene.… That is,
there was really no place for them in the culture, in the economy, yet they were there, at that
time, and everyone knew that they wouldn’t last very long, which they didn’t.… That they were
outside the range of God, not only of respectability, of love, but of God too.” In Pylon Faulkner
set out to test their rootless modernity to see if there is any place in it for the old values of
the human heart that are the central concerns of his best fiction.
SANCTUARY
A powerful novel examining the nature of evil, informed by the works of T.S. Eliot and Freud,
mythology, local lore, and hardboiled detective fiction, Sanctuary is the dark, at times brutal,
story of the kidnapping of Mississippi debutante Temple Drake, who introduces her own form of
venality into the Memphis underworld where she is being held.
THREE FAMOUS SHORT NOVELS
In this book are three different approaches of Faulkner, each of them highly entertaining as well
as representative of his work as a whole. Spotted Horses is a hilarious account of a horse auction,
and pits the “cold practicality” of women against the boyish folly of men. The law comes in to
settle the dispute caused by the sale of “wild” horses, and finds itself up against a formidable
opponent, Mrs. Tull. Old Man is something of an adventure story. When a flood ravages the
countryside of the lower Mississippi, a convict finds himself adrift with a pregnant woman. His one
aim is to return the woman to safety and himself to prison, where he can be free of women. In order
to do this, he fights alligators and snakes, as well as the urge to be trapped once again by a
woman. Perhaps one of the best known of Faulkner’s shorter works, The Bear is the story of a boy
coming to terms with the adult world. By learning how to hunt, the boy is taught the real meaning
of pride and humility and courage, virtues that Faulkner feared would be almost impossible to learn
with the destruction of the wilderness.

UNCOLLECTED STORIES OF WILLIAM FAULKNER
This invaluable volume, which has been republished to commemorate the one-hundredth anniversary of
Faulkner’s birth, contains some of the greatest short fiction by a writer who defined the course of
American literature. Its forty-five stories fall into three categories: those not included in
Faulkner’s earlier collections; previously unpublished short fiction; and stories that were
later expanded into such novels as The Unvanquished, The Hamlet, and Go Down, Moses. With its
introduction and extensive notes by the biographer Joseph Blotner, Uncollected Stories of William
Faulkner is an essential addition to its author’s canon—as well as a book of some of the most
haunting, harrowing, and atmospheric short fiction written in this century.
THE WILD PALMS
In this feverishly beautiful novel—originally titled If I Forget Thee, Jerusalem by Faulkner, and
now published in the authoritative Library of America text—William Faulkner interweaves two
narratives, each wholly absorbing in its own right, each subtly illuminating the other. In New
Orleans in 1937, a man and a woman embark on a headlong flight into the wilderness of passions,
fleeing her husband and the temptations of respectability. In Mississippi ten years earlier, a
convict sets forth across a flooded river, risking his one chance at freedom to rescue a pregnant
woman. From these separate stories Faulkner composes a symphony of deliverance and damnation,
survival and self-sacrifice, a novel in which elemental danger juxtaposes with fatal injuries of
the spirit. The Wild Palms is grandly inventive, heart- stopping in its prose, and suffused on
every page with the physical presence of the country that Faulkner made his own.

VINTAGE INTERNATIONAL
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ACADEMIC RESOURCES FOR EDUCATORS

Committed to publishing intellectually important works and enduring classics, Vintage Books and
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Joyce, Thomas Mann, Ralph Ellison, Franz Kafka, and Toni Morrison, as well as Michel Foucault,
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Random House’s Academic Resources website— www.randomhouse.com/academic—presents over 5,000
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VINTAGE INTERNATIONAL

William Faulkner, Toni Morrison, Doris Lessing, V. S. Naipual, Eudora Welty, Joan Didion, and
Cormac McCarthy, among many others: VINTAGE INTERNATIONAL is a bold series of books devoted to
publishing the best writing of the last century from the world over. Offering both classic and
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generation of readers to world-class writing that has stood the test of time and essential works by
the preminent authors of today.

The Optimist: Sam Altman, OpenAI, and the Future Artificial Intelligence

Executive Summary

This document synthesizes key insights from Keach Hagey’s biography, The Optimist, which chronicles the life and career of Sam Altman, the CEO of OpenAI. The analysis reveals Altman as a brilliant dealmaker and a central figure in Silicon Valley, driven by an almost religious conviction in technological progress. His career is marked by a pattern of immense ambition, a talent for securing capital and influence, and a recurring tendency to move too fast for those around him, leading to internal conflicts at both his first startup, Loopt, and most consequentially, at OpenAI.

The Optimist chronicles the life and career of Sam Altman, the CEO of OpenAI. The analysis reveals Altman as a brilliant dealmaker and a central figure in Silicon Valley, driven by an almost religious conviction in technological progress. His career is marked by a pattern of immense ambition, a talent for securing capital and influence, and a recurring tendency to move too fast for those around him, leading to internal conflicts at both his first startup, Loopt, and most consequentially, at OpenAI.

The founding of OpenAI is presented as an effort to safely develop Artificial General Intelligence (AGI) for the benefit of humanity, a mission deeply influenced by the philosophies of Effective Altruism and fears of existential risk articulated by thinkers like Nick Bostrom and Eliezer Yudkowsky. However, the immense computational costs required to pursue AGI forced a pivotal shift from a pure nonprofit to a “capped-profit” model, leading to a foundational partnership with Microsoft and the departure of co-founder Elon Musk after a power struggle.

The narrative culminates in the November 2023 leadership crisis, or “the blip,” where the OpenAI board fired Altman. Contrary to public speculation, the ouster was not driven by fears of an imminent AGI breakthrough but by a loss of trust in Altman’s candor and what the board perceived as manipulative behavior. His swift return, orchestrated by overwhelming employee and investor support, solidified his position as the undisputed leader of the AI revolution but also intensified scrutiny of his character and ambitions. Altman’s vision extends far beyond OpenAI, encompassing a portfolio of “moonshot” investments in nuclear fusion (Helion), universal basic income (Worldcoin), and life extension (Retro Biosciences), all aimed at, in the words of his mentor Paul Graham, “making the whole future.”

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I. Profile of a Founder: Sam Altman

A. Formative Years and Family Background

Samuel Harris Altman, born April 22, 1985, demonstrated unusual precocity from a young age. His mother, dermatologist Connie Gibstine, noted he was “kind of born an adult,” grasping complex concepts like area codes at age three and fixing teachers’ computer problems in elementary school. His family history is rooted in St. Louis, with both sides involved in real estate. His father, Jerry Altman, was a real estate consultant specializing in low-income housing, driven by a desire to “do good in the world,” a value system that influenced Sam.

A pivotal experience was navigating his identity in the early 2000s. He knew he was gay by age twelve and later told The New Yorker that “finding AOL chat rooms was transformative” for a “gay [kid] in the Midwest.” This early reliance on technology for connection and self-discovery shaped his worldview. In high school, he was a standout student, bonding with his computer science teacher over AI and impressing the head of school, who noted, “It just seemed like he had read everything and had an interesting take on it.”

B. Core Philosophy and Personality

Altman embodies the Silicon Valley ethos of exponential growth, a mindset he attributes to his primary mentor, Y Combinator co-founder Paul Graham.

Sam Altman’s “Add a Zero” Philosophy: “It’s useful to focus on adding another zero to whatever you define as your success metric— money, status, impact on the world, whatever.”

This ambition is coupled with a distinct set of personality traits observed throughout his career:

  • Brilliant Dealmaker: He possesses an uncanny ability to raise capital and forge critical partnerships, from securing early carrier deals for Loopt to orchestrating OpenAI’s multi-billion dollar relationship with Microsoft.
  • Aversion to Confrontation: This trait has been cited as a source of conflict, as he sometimes operates independently or places his own wishes in the mouths of others to avoid direct disagreement.
  • Persuasive Power: Characterized by an intense, direct gaze, Altman is described as radiating confidence and making others feel like they are the most important person in the world. As Paul Graham noted, “Sam is extremely good at becoming powerful.”
  • Belief in Technological Progress: He views technology, particularly AI and cheap energy, as the primary engines for human advancement and the solution to societal ills, from poverty to mortality.
  • Interest in Unconventional Ideas: Peter Thiel, another key mentor, notes Altman’s sympathy for the simulation hypothesis—the idea that our reality is a computer simulation created by a higher intelligence. Altman brushes this off as “freshman dorm” talk but acknowledges, “you can’t be certain of anything other than your own awareness.”
The Optimist chronicles the life and career of Sam Altman, the CEO of OpenAI. The analysis reveals Altman as a brilliant dealmaker and a central figure in Silicon Valley, driven by an almost religious conviction in technological progress. His career is marked by a pattern of immense ambition, a talent for securing capital and influence, and a recurring tendency to move too fast for those around him, leading to internal conflicts at both his first startup, Loopt, and most consequentially, at OpenAI.

II. Career Trajectory Before OpenAI

A. Loopt: A Preview of Things to Come (2005–2012)

While an undergraduate at Stanford, Altman co-founded Loopt, a location-based social network for the flip-phone era. The company’s journey served as a microcosm of his future endeavors:

  • Y Combinator’s First Star: Loopt (then Viendo) was the first startup funded by Paul Graham’s Y Combinator. Graham recalled thinking upon meeting the 19-year-old Altman, “Ah, so this is what Bill Gates must have been like.”
  • Fundraising Success: Altman secured investment from top-tier venture capital firms Sequoia Capital and NEA, despite his youth.
  • Staff Mutinies: As at OpenAI later, Altman faced internal dissent. At Loopt, senior engineers grew concerned about his “shiny object syndrome,” lack of focus on profitability, and tendency to “start operating independently” on new projects without bringing others along.
  • Eventual Exit: After being eclipsed by rivals like Foursquare and turning down a reported $150 million acquisition offer from Facebook, Loopt was sold for parts to Green Dot in 2012 for $43.4 million. The experience solidified his relationship with Sequoia Capital, whose partner Michael Moritz praised Altman’s decision to pass on an early sale, noting he had passed Sequoia’s most important test.

B. Y Combinator Leadership: The Center of Silicon Valley (2014–2019)

In 2014, Paul Graham chose Altman as his successor to lead Y Combinator. In a blog post titled “Sam Altman for President,” Graham wrote, “Sam is one of the smartest people I know, and understands startups better than perhaps anyone I know, including myself.” Under Altman’s leadership, YC underwent a dramatic expansion:

  • Scaling Ambition: He grew YC from incubating dozens to hundreds of startups per year.
  • Push into “Hard Tech”: He expanded YC’s focus beyond software to include biotech, robotics, nuclear energy, and other “moonshots,” reflecting his belief that technological progress had stagnated.
  • YC Research: He created a nonprofit research arm to fund ambitious, long-term projects, including a study on universal basic income and, most significantly, a lab that would become OpenAI.

III. The OpenAI Saga

A. Genesis and Ideological Roots (2015)

OpenAI was founded in 2015 as a nonprofit research lab with a stated goal “to advance digital intelligence in a way that is most likely to benefit humanity as a whole, unconstrained by the need to generate financial return.”

  • Core Motivation: The founding was driven by fear, primarily articulated by Elon Musk and Sam Altman, that a competitive race to AGI could be catastrophic. Musk famously referred to the effort as “summoning the demon.”
  • Founding Team: The lab was co-founded by Altman, Musk, Greg Brockman (former CTO of Stripe), Ilya Sutskever (a protégé of AI pioneer Geoffrey Hinton), and others, backed by $1 billion in pledges.
  • Intellectual Influences: The organization’s charter was shaped by the AI safety movement and the Effective Altruism (EA) community. Key influences included:
    • Nick Bostrom’s Superintelligence: This book articulated the potential existential risks of a machine intelligence that vastly exceeds human capabilities.
    • Eliezer Yudkowsky’s LessWrong: This influential blog placed fear of existential risk at the heart of the rationalist and EA movements.
    • OpenAI Charter (2018): Declared a commitment to “stop competing with and start assisting” any “value-aligned” project that reaches AGI first, reflecting these safety concerns.

B. The Power Struggle and Pivot to Profit (2018–2019)

The nonprofit model quickly proved untenable due to the astronomical cost of computing power required for large-scale AI research.

  • Musk’s Departure: A power struggle ensued between Altman and Musk. Musk sought total control, but Altman, allied with Brockman and other researchers, resisted. In February 2018, Musk left OpenAI, citing a conflict of interest with Tesla’s AI development, and became a vocal critic and competitor.
  • The “Capped-Profit” Model: In 2019, Altman restructured OpenAI, creating a for-profit subsidiary controlled by the original nonprofit board. This unique structure allowed OpenAI to raise venture capital while capping investor returns, with any excess profit designated for the nonprofit’s mission.
  • The Microsoft Partnership: The new structure paved the way for a $1 billion investment from Microsoft in 2019, which provided crucial access to its Azure cloud computing platform. This partnership would deepen significantly over the following years.

C. Technical Milestones and Commercialization

Under Chief Scientist Ilya Sutskever’s research leadership, OpenAI shifted from reinforcement learning projects like Dota 2 to large language models (LLMs), a direction championed by researcher Alec Radford. This pivot, supercharged by Google’s 2017 “Transformer” paper, led to a series of groundbreaking models.

ModelYearKey Features and Impact
GPT-22019Generated such coherent text that OpenAI initially withheld the full model, fearing misuse. The move was widely mocked at the time.
GPT-32020With 175 billion parameters, it demonstrated remarkable “few-shot” learning, able to perform tasks with minimal examples.
OpenAI API2020The company’s first commercial product, allowing developers to build applications on top of GPT-3.
DALL-E 22022A powerful diffusion model that could generate photorealistic images from text prompts.
ChatGPT2022A fine-tuned version of a GPT model with a simple chat interface. Its accessibility led to viral adoption, setting a record for the fastest-growing user base and forcing competitors like Google to accelerate their own AI products.

D. The November 2023 “Blip”: Firing and Reinstatement

On November 17, 2023, the OpenAI board fired Sam Altman, citing that he “was not consistently candid in his communications.” The move shocked the tech world and triggered a five-day crisis.

  • Root Cause: The board’s decision was not about AI safety but a collapse of trust. Key board members Helen Toner and Tasha McCauley, along with Chief Scientist Ilya Sutskever, had grown concerned about a pattern of behavior they viewed as dishonest and manipulative.
  • Specific Incidents:
    1. Deployment Safety Board (DSB): Altman allegedly misrepresented to the board that new GPT-4 enhancements had received DSB approval when they had not.
    2. Manipulating Board Members: Altman allegedly told Sutskever that McCauley believed Toner should be removed from the board, a claim McCauley knew was false. This crystallized the board’s view of his methods.
  • The Aftermath:
    • Employee Revolt: Over 95% of OpenAI’s 700+ employees signed a letter threatening to quit and join a new Microsoft-led subsidiary unless the board resigned and reinstated Altman.
    • Microsoft’s Role: CEO Satya Nadella played a key role, offering to hire Altman and all departing employees while applying pressure on the board.
    • Altman’s Return: Altman was reinstated as CEO with a new initial board. The crisis solidified his control over the company and its trajectory.

IV. The Altman Doctrine: A Techno-Utopian Future

Altman’s work at OpenAI is one component of a broader, interconnected vision for civilizational transformation, funded by his personal investments. As his mentor Paul Graham stated, “I think his goal is to make the whole future.”

Key Investment Pillars:

CompanyArea of FocusAltman’s Role & InvestmentStated Goal
HelionNuclear FusionCo-founder, invested at least $375MProvide cheap, clean, abundant energy to power the future, including AI data centers.
OkloNuclear FissionBacker, ChairmanDevelop microreactors for clean energy.
WorldcoinCryptocurrency & UBICo-founderCreate a global currency distributed via iris scans, potentially as a mechanism for Universal Basic Income (UBI).
Retro BiosciencesLife ExtensionInvestor ($180M)Add a decade to the human lifespan by targeting the underlying causes of aging.

This portfolio reflects his core belief that “energy and intelligence are the two most important things” needed to unlock a future of health, abundance, and radical economic growth.

V. Politics, Scrutiny, and Personal Controversies

As his public profile has soared, Altman has become a political figure and the subject of intense scrutiny.

  • Political Ambitions: In 2016 and 2017, he explored running for President and Governor of California, drafting a national platform and seeking advice from political veterans. After ChatGPT’s launch, he embarked on a global tour, meeting with world leaders like Emmanuel Macron and Narendra Modi.
  • Regulatory Battles: Altman has publicly called for AI regulation, testifying before the U.S. Senate. However, a battle is emerging in Washington between OpenAI’s lobbying efforts and a well-funded network of EA-aligned organizations advocating for stricter safety measures, dubbed the “AI Doomer Industrial Complex.”
  • Family Conflict: His sister, Annie Altman, has publicly accused him and his brother Jack of “sexual, physical, emotional, verbal, financial and technological abuse.” She alleges he engaged in nonconsensual behavior when she was a child. The Altman family has stated the allegations are untrue and that Annie faces “mental health challenges.” The issue represents a significant and unresolved part of his personal story.

Contact Factoring Specialist, Chris Lehnes

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AI Value Creators – Audiobook Summary and Analysis

Briefing Document: Key Insights from “AI Value Creators”

Executive Summary

“AI Value Creators” presents a compelling argument that the current generative AI era represents a pivotal “Netscape moment”—a point of technological democratization that is not merely an opportunity but an economic imperative for businesses and governments alike. The central thesis is that sustained growth in a world of declining populations and expensive capital can only be achieved through massive productivity gains, for which AI is the primary catalyst.

The document advocates for a fundamental strategic shift from a +AI mindset (adding AI to existing processes) to an AI+ approach (reimagining business with an AI-first strategy). The ultimate goal is to become an AI Value Creator, an organization that leverages an AI platform to tune foundation models with its unique, proprietary data. This is identified as the only sustainable competitive advantage in a future where generic models will commoditize.

Success in this new era is defined by a core formula: AI Success = Foundation Models + Data + Governance + Use Cases. Navigating the inherent tension between progress and risk requires balancing the paradox that responsibility and disruption must coexist. This balance is achieved through a combination of Leadership, widespread Skills development, and a commitment to Openness (in platforms, data, and community). Organizations are urged to act with urgency, view AI as a value generator rather than a cost center, and begin their journey with safe, internal automation projects to build experience and confidence.

——————————————————————————–

1. The “Netscape Moment” of Generative AI

The emergence of generative AI is framed as a “Netscape moment,” an analogy to the 1994 debut of the first web browser which made the internet tangible, personal, and accessible to the masses.

  • Democratization of Technology: Generative AI, primarily through the natural language prompt, has taken AI “out of the hands of just the privileged few and democratized [it] for the many.” This accessibility is poised to unleash a wave of innovation and fundamentally change how data is stored, communication happens, and business is conducted.
  • A World-Changing, Not World-Ending, Technology: While acknowledging concerns about AI, the authors assert, “we don’t think a technology has to be world ending to be world changing.” It is positioned as a tool that will become an integral, “ambient” part of business operations, providing assistance in the background.
  • The Inevitable Divide: Just as the original Netscape moment created a divide, this new wave of AI will separate adopters from laggards. Those who embrace and integrate AI will reshape the future, while those who do not will face “hefty societal or business consequences.”
  • AI is Not Magic: Despite its seemingly magical capabilities, AI is fundamentally based on math and science. The document demystifies the technology, explaining that AI connects data points by guessing numerical sequences (vectors). An LLM is more accurately described as a “large number guessing model,” which operates on numerical representations of language, images, and sound.

2. The Strategic Imperative: From +AI to AI+

A core argument is the necessity of a profound mental model shift for organizations to thrive. This involves moving beyond simply incorporating AI into current operations and instead rebuilding processes around AI’s capabilities.

  • The +AI Mentality (The Past): This is the common approach of adding AI to existing business processes. While AI adoption has doubled in the last five years, most organizations remain in this mode, which limits potential gains.
  • The AI+ Mentality (The Future): This is an “AI first” strategy. It involves reimagining and creating entirely new workflows that leverage AI from the ground up. The document asserts that “the companies that adopt an AI+ mentality today… will be the winners of today’s Netscape moment.”
  • The Rebooted AI Ladder: This framework guides the transition from +AI to AI+.
    • Foundation: A robust, AI-infused Information Architecture (IA) to collect, organize, protect, and govern data.
    • Rung 1: Add AI to applications.
    • Rung 2: Automate workflows.
    • Rung 3: Reimagine and replace existing workflows with new AI and agentic workflows.
    • Top Rung: Let AI do the (rote) work, achieving a true AI+ state.

3. Becoming an AI Value Creator vs. an AI User

The document outlines three primary modes of AI consumption, drawing a critical distinction between passively using AI and actively creating unique value with it. The latter is presented as the only path to long-term differentiation.

Consumption ModelDescriptionStatusKey Considerations
Baked into SoftwareAI is embedded in off-the-shelf products (e.g., Grammarly, Adobe Photoshop).AI UserSets a new, higher baseline for productivity but offers no competitive differentiation, as it is available to everyone.
API Call to a ModelAn application calls an external, third-party generative AI service (e.g., ChatGPT).AI UserA viable approach, but entails significant risks: the model is an opaque black box; data privacy is a concern; the organization has no control over training data or governance; and value is disproportionately extracted by the service provider.
AI Platform ApproachAn organization uses a platform with tools to access, customize, and deploy various models (open source and proprietary) using its own data.AI Value CreatorThe most comprehensive and recommended model. It allows the business to create and accrue unique value, maintain control over data and governance, and build defensible, proprietary AI assets.

“The only sustainable competitive advantage will come from your data… the only AI that is differentiated in value from any other model for your business will be the AI that is further trained, steered, or tuned to your data on your business problems.”

4. A Framework for Execution and Investment

To ensure AI projects deliver tangible business value, a pragmatic two-dimensional framework for classification and strategy is proposed.

  • Dimension 1: Budget Intent
    • Spend Money to Save Money (Renovation): Using AI to improve efficiency and reduce costs. This includes projects focused on automation and optimization.
    • Spend Money to Make Money (Innovation): Using AI to generate new revenue streams, enter new markets, or transform the business model. This includes projects focused on prediction and transformation.
  • The Acumen Curve: A visual tool to plot AI initiatives along an x-axis of business impact (from cost reduction to transformation) and a y-axis of value. This helps organizations visualize their investment portfolio and focus on business outcomes, not just technology projects.
  • The “Shift Left, Shift Right” Strategy:
    • Shift Left: A concept borrowed from software development, redefined to mean using AI to address problems earlier in a process to reduce costs, defects, or negative outcomes (e.g., using AI for preventative maintenance, early disease detection, or streamlining internal HR processes). This is a “spend money to save money” activity.
    • Shift Right: Using the savings, experience, and confidence gained from “shifting left” to fund innovative, transformational projects that create new business models. This is a “spend money to make money” activity. Kodak’s failure to shift from film to digital photography is cited as a cautionary tale.

5. The Emergence of Agentic AI

Agentic AI is highlighted as a major breakthrough and the next frontier in enterprise productivity. Unlike task-oriented AI, agents are goal-oriented and autonomous.

  • Definition: An agent is a program where the flow logic is defined and controlled by the AI (an LLM) itself. Users provide a goal or desired outcome, and the agent independently plans and executes the necessary tasks to achieve it.
  • Examples of Agentic AI:
    • A team of agents (researcher, writer, social media poster) collaborating to create and distribute a blog post.
    • An agent tasked with improving a company’s Net Promoter Score (NPS) by 10 points, which would research, analyze, and propose an action plan.
    • AI shopping agents that navigate websites to find products and complete purchases autonomously.
  • Potential: Agents have the potential to unlock the next wave of productivity gains by automating complex, multi-step workflows.

6. The Economic Imperative and Persuasion Equations

Chapter 3 argues that AI adoption is not a choice but a necessity for economic survival and growth, based on current macroeconomic trends.

  • Equation 1: GDP Growth = ↑ Population + ↑ Productivity + ↑ Debt
    • With global populations declining and debt becoming more expensive, productivity is the only remaining lever for sustained economic growth. This creates an urgent, unavoidable imperative for AI.
  • The Core Paradox: Responsibility and disruption must coexist.
    • Organizations cannot afford to wait on the sidelines due to perceived risks. The economic need for productivity forces them to embrace the disruption of AI while simultaneously implementing it responsibly.
  • Equation 2: AI Success = Foundation Models + Data + Governance + Use Cases
    • This formula outlines the essential pillars for a successful AI strategy. Data is emphasized as the key long-term differentiator, while governance is critical for operating with confidence.
  • Equation 3: Finding the Balance = Leadership + Skills + Open
    • This formula provides the means to navigate the core paradox. Success requires:
      • Leadership: To guide the organization responsibly through disruption.
      • Skills: A massive, company-wide upskilling effort to create a workforce capable of leveraging AI.
      • Open: A commitment to open platforms that allow for model choice, transparency in data and training, and collaboration within the open-source community (e.g., Hugging Face, AI Alliance).

7. Key Principles and Recommendations

The document concludes with a set of actionable principles for organizations embarking on their generative AI journey.

  1. Act with Urgency: This is a transformative technological moment that demands bold, decisive action, guided by a smart and rehearsed plan.
  2. Bet on Community: One Model Will Not Rule Them All: The future is multi-model and will be driven by innovation from open-source communities. Businesses should build on open platforms that can accommodate a variety of open and proprietary models. Hugging Face is cited as a central hub for this community, with over a million models available.
  3. Prioritize Trust and Responsibility: Governance, fairness, and explainability must be foundational, not afterthoughts. Trust is described as the “ultimate license to operate.”
  4. Start with “Singles,” Not “Home Runs”: For organizations new to generative AI, the safest and most effective starting point is an internal automation use case that aims to “spend money to save money.” This approach allows the team to gain skills and confidence in a low-risk environment.
  5. View AI as a Value Generator, Not a Cost Center: A cultural shift is required to see technology investment not as a cost to be managed, but as a fundamental driver of business transformation and value creation.

Contact Factoring Specialist Chris Lehnes

AI Value Creators presents a compelling argument that the current generative AI era represents a pivotal "Netscape moment"—a point of technological democratization that is not merely an opportunity but an economic imperative for businesses and governments alike. The central thesis is that sustained growth in a world of declining populations and expensive capital can only be achieved through massive productivity gains, for which AI is the primary catalyst.

Study Guide for AI Value Creators

This study guide is designed to review and reinforce the core concepts presented in the initial chapters of AI Value Creators. It includes a short-answer quiz to test comprehension, suggested essay questions for deeper analysis, and a glossary of essential terms.

Short-Answer Quiz

Instructions: Answer the following questions in 2-3 sentences, drawing exclusively from the provided source material.

  1. What do the authors mean by a “Netscape moment” in the context of generative AI?
  2. How does the text define and differentiate agentic AI from task-oriented AI?
  3. Why do the authors assert that AI is not magic, and what do they claim is its fundamental operation?
  4. Explain the difference between a “+AI” and an “AI+” business mentality.
  5. According to the text, what are the two primary dimensions for classifying a generative AI project’s budget?
  6. Describe the concept of “shifting left” and how generative AI enables it.
  7. What are the three legs of the “AI stool” that are identified as crucial for generative AI?
  8. How does self-supervised learning differ from supervised learning, and why is this distinction significant for foundation models?
  9. Summarize the key differences between being an “AI User” and an “AI Value Creator.”
  10. What is the central economic paradox presented in Chapter 3, and what is its implication for businesses?

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Answer Key

  1. A “Netscape moment” refers to a point in time when a technology becomes tangible, personal, and democratized for everyone, leading to significant innovation and societal change. The authors equate the current state of generative AI to the 1994 debut of the Netscape browser, which made the internet accessible to the many and reshaped the world.
  2. Agentic AI is goal-oriented, where an AI program’s flow logic is defined and controlled by the LLM itself to achieve a desired outcome without explicit guidance at each step. This contrasts with most current AI use, which is task-oriented and requires a user to prompt the AI for each specific action, like summarizing a document.
  3. The authors claim AI is not magic because its operations are based on math and science, not sorcery. Fundamentally, AI connects data points by guessing a number (a vector) using clues from previous numbers (vector sequences), effectively making it a “large number guessing model.”
  4. A “+AI” mentality involves adding AI to existing business processes as an afterthought, which is how most organizations currently operate. An “AI+” mentality means adopting an “AI first” strategy, where AI is foundational to how people are trained and how technology is put into production, with the goal of reimagining workflows.
  5. The first dimension is classifying the spend as either “spend money to save money” (renovation) or “spend money to make money” (innovation). The second dimension is categorizing how the AI helps the business, which falls into one of three categories: automation, optimization, or prediction.
  6. “Shifting left” is the concept of capturing defects or problems earlier in a cycle to make them less costly. The authors expand this definition to include using AI to reduce expenses, bugs, injuries, and illness, thereby compacting work, getting it done faster, and increasing productivity.
  7. The three legs of the AI stool are identified as model architecture, compute power, and data. The text emphasizes that you cannot discuss generative AI without considering all three components, especially data, which is called “maybe the most important ingredient.”
  8. Supervised learning is a traditional AI method that is expensive and time-consuming because it requires humans to manually label large datasets. Self-supervised learning, which powers foundation models, is a frictionless approach where an AI trains on vast amounts of unlabeled data by masking parts of the text and learning to fill in the blanks.
  9. An AI User consumes AI by using it embedded in software or by making an API call to someone else’s model, which provides a baseline of productivity but little differentiation. An AI Value Creator uses a platform approach to build their own tailored AI solutions, fine-tuning foundation models with their proprietary data to create unique, sustainable competitive advantages.
  10. The central paradox is that “Responsibility and disruption must coexist.” With global populations declining and debt becoming more expensive, productivity is the only path to economic growth, making AI adoption an imperative. Therefore, businesses and governments cannot afford to wait due to risks but must instead accept the disruption AI brings while simultaneously implementing it in a responsible and trustworthy manner.

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Essay Questions

Instructions: The following questions are designed for longer-form, analytical responses. Use the source material to construct a comprehensive argument for each prompt.

  1. Analyze the evolution of the “AI Ladder” from its original pre-generative AI form to the “rebooted” version. What do the changes in the ladder’s rungs signify about the strategic shift from a data-centric approach to an “AI+” methodology?
  2. The authors argue that “one model will not rule them all.” Construct an argument to support this claim, using evidence from the text regarding the open-source community (e.g., Hugging Face), the importance of proprietary data, and the platform approach of the AI Value Creator.
  3. Explain the framework of the “AI and Data Acumen Curve.” How does this tool help a business visualize and plan its AI strategy, moving from renovation projects (like cost reduction) to innovation projects (like business transformation)?
  4. Using the economic equations and macrodynamic trends presented in Chapter 3 (GDP Growth, population, debt, productivity), explain why the authors conclude that AI adoption is no longer a matter of choice for most businesses and countries.
  5. Define the difference between an “AI User” and an “AI Value Creator” as described in the text. Discuss the long-term strategic risks an organization faces by remaining solely an AI User, considering factors like data control, value accrual, competitive differentiation, and dependency on external models.

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Glossary of Key Terms

TermDefinition
+AIThe world of adding AI to existing business processes, as opposed to an AI-first approach.
AcumenAs used in “Data Acumen,” it refers to “skills related to putting data to work to help your business become data driven.”
Adaptable (AI)The ability of an AI to not only perform multiple tasks but also handle different use cases it wasn’t originally trained for.
Agentic AI / AI AgentsA program in which the flow logic is defined and controlled by the AI (an LLM) itself. Agents are goal-oriented, capable of planning and executing future actions without explicit guidance to achieve a desired outcome.
AI+An “AI first” mentality where companies train their people and put technology into production with AI as the foundation, reimagining new workflows.
AI Ladder (Rebooted)A reframed guiding strategy for the generative AI era that is built with AI in mind from the first rung, not as the destination. It guides organizations from data operations toward automating and replacing workflows with AI and agentic workflows.
AI Value CreatorAn entity that uses an AI platform to build its own AI solutions by fine-tuning foundation models with proprietary data, thereby creating and accruing unique business value.
AI UserAn entity that consumes AI when it is “baked into” off-the-shelf software or by prompting someone else’s model via an API call.
Foundation Model (FM)Large-scale, deep neural networks trained on broad data that can be easily adapted to perform various downstream tasks for which they were not originally designed. LLMs are a type of FM.
Generalizable (AI)The ability of an AI to perform well across a wide range of tasks and domains, often with little to no task-specific tuning.
High-dimensional spaceA state where data has so many dimensions (features or attributes) that it is hard for humans to visualize.
Information Architecture (IA)A platform that allows an organization to collect, organize, protect, govern, and store data, as well as build and govern generative AI models. The authors state, “You can’t have AI without an IA.”
Large Language Model (LLM)A type of foundation model that powers many generative AI programs. It is described as a “large number guessing model” that uses math to connect data points and predict sequences.
Netscape MomentA transformative moment when a technology is democratized and becomes tangible and personable for everyone, leading to widespread innovation and permanent changes in society.
ParametersIn the context of an LLM, parameters represent the overall knowledge of the model. A higher number of parameters generally means the model can perform more tasks.
PromptThe input, typically in natural language, given to an LLM to elicit a response or “completion.”
Self-supervised learningA type of frictionless learning where a model is trained on large amounts of unlabeled data by masking sections of the input and learning to predict the missing parts.
Shifting LeftA concept, originating from software development, of capturing defects or problems earlier in a cycle to make them less costly. The authors broaden it to mean using AI to reduce expenses, injuries, illness, and rote tasks.
Shifting RightThe ideation of new business models or a pivotal strategic move to transform an industry, often in response to technological change.
Supervised LearningA traditional AI training method that requires humans to manually annotate large datasets, a process described as expensive, error-prone, and time-consuming.
Transfer LearningThe ability of an AI model to apply information and skills it has learned about in one situation to another, different situation.

Upstream by Dan Heath: Dangers of Problem Blindness

Core Principles and Applications of Upstream Thinking

 the core principles of "upstream thinking," a framework for preventing problems rather than reacting to them. The central thesis is that society is disproportionately focused on downstream responses—addressing crises, emergencies, and failures after they occur. An upstream approach, conversely, involves proactively identifying and dismantling the systems that cause these problems in the first place. This shift is impeded by three primary barriers

This book synthesizes the core principles of “upstream thinking,” a framework for preventing problems rather than reacting to them. The central thesis is that society is disproportionately focused on downstream responses—addressing crises, emergencies, and failures after they occur. An upstream approach, conversely, involves proactively identifying and dismantling the systems that cause these problems in the first place. This shift is impeded by three primary barriers: Problem Blindness, the failure to see a problem or the belief that it is inevitable; Lack of Ownership, a mindset where those capable of fixing a problem believe it is not their responsibility; and Tunneling, a state of scarcity (of time, money, or bandwidth) that forces short-term, reactive thinking and precludes long-term planning. Successful upstream interventions require leaders to unite diverse teams, identify high-leverage points within complex systems, establish early warning signals, and secure funding for outcomes that are often invisible—the absence of problems. The analysis reveals that effective upstream work is not about finding a single “magic pill” solution but about creating data-rich “scoreboards” that enable continuous learning and systems-level change.

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1. The Upstream Philosophy: Prevention Over Reaction

The core concept of upstream thinking is captured in a public health parable: two friends rescuing an endless stream of drowning children from a river, until one goes upstream “to tackle the guy who’s throwing all these kids in the water.” This metaphor distinguishes between downstream actions, which react to problems, and upstream efforts, which aim to prevent them.

Defining Upstream vs. Downstream Action

  • Downstream Action: Reactive, tangible, and focused on restoration. Examples include a call center representative resolving a customer complaint, a doctor performing bypass surgery, or a police officer making an arrest after a crime. These actions are often demanded by circumstance.
  • Upstream Action: Proactive, preventative, and focused on systems change. It involves “systems thinking” to systematically reduce the harm caused by problems. Examples include redesigning a website so customers don’t need to call for help, promoting policies that support healthy lifestyles to prevent heart disease, or creating community opportunities that deter crime. These efforts are chosen, not demanded.

The further one moves upstream, the more complex, ambiguous, and slower the solutions become, but the potential for massive and long-lasting good increases significantly. An intervention can exist at many points along a spectrum; for example, swim lessons are further upstream than life preservers in preventing drowning.

The Case of Expedia: A Model for Upstream Intervention

The travel website Expedia provides a clear illustration of a successful upstream intervention.

  • The Downstream Problem: In 2012, 58 out of every 100 Expedia customers placed a support call after booking. The top reason, accounting for 20 million calls annually at a cost of roughly $100 million, was to request a copy of their itinerary.
  • The Downstream Mindset: The call center was managed for efficiency—minimizing call time—rather than questioning why the calls were necessary.
  • The Upstream Shift: A “war room” was created with a mandate to “Save customers from needing to call us.” They analyzed the root causes of the calls.
  • Upstream Solutions: For the itinerary issue, they implemented simple fixes: adding an automated voice-response option, changing email protocols to avoid spam filters, and creating an online self-service tool.
  • The Result: The 20 million itinerary-related calls were virtually eliminated. The overall percentage of customers needing to call for support dropped from 58% to approximately 15%. This success was achieved by integrating the work of different teams (product, tech, support) to solve a problem that no single group “owned.”

The Asymmetry of Attention: Why Society Favors Reaction

Despite the clear benefits of prevention, societal efforts are overwhelmingly skewed toward reaction.

  • Tangibility and Measurement: Downstream work is more tangible and easier to measure. A police officer who writes a stack of tickets has a visible output, while an officer whose presence on a dangerous corner prevents accidents has invisible victims and victories written only in declining data.
  • Funding and Resources: We spend billions to recover from disasters like hurricanes and earthquakes, while disaster preparedness is “perpetually starved for resources.” The U.S. healthcare system, a $3.5 trillion industry, is designed almost exclusively for reaction, functioning like a giant “Undo button” for ailments rather than a system for creating health.
  • Heroism: Society celebrates the rescue, the recovery, and the response. Upstream work creates a quieter breed of hero, one “actively fighting for a world in which rescues are no longer required.”

Case Study: Healthcare Spending in the U.S. vs. Norway

The contrast between U.S. and Norwegian healthcare spending illustrates the consequences of a downstream focus. While both nations spend a similar percentage of GDP on total health (combining formal healthcare with “social care” like housing, food, and childcare), their allocation is radically different.

Spending MetricUnited StatesNorway
Spending Ratio (Upstream:Downstream)For every 1** spent downstream, the U.S. spends roughly **1 upstream.For every 1** spent downstream, Norway spends roughly **2.50 upstream.
FocusWorld leader in downstream, high-tech treatments (e.g., knee replacements, cancer treatment).Focus on upstream support systems (e.g., free prenatal/delivery care, 49 weeks of paid parental leave, guaranteed high-quality daycare, free college).
Health Outcomes34th in infant mortality, 29th in life expectancy, 21st in stress levels.5th in infant mortality, 5th in life expectancy, 1st in stress levels.

The data suggests the U.S. is not necessarily spending “too much” on health, but that its allocation is radically different from its peers, prioritizing expensive cures over cost-effective prevention.

2. The Three Barriers to Upstream Thinking

Despite the logic of prevention, several powerful forces consistently push individuals and organizations downstream.

A. Problem Blindness: The Invisibility of Solvable Problems

Problem blindness is the belief that negative outcomes are natural, inevitable, or out of one’s control. It is treating a solvable problem like the weather.

  • Mechanism: It arises from inattentional blindness (intense focus on one task causing one to miss other information, like radiologists missing a gorilla in a CT scan) and habituation (growing accustomed to consistent stimuli until they become normal).
  • Example: Chicago Public Schools (CPS): In 1998, the 52.4% graduation rate was seen by many as an intractable problem caused by poverty and other societal ills—”that’s just how it is.” The problem was accepted as a regrettable but inevitable condition.
  • Example: Sexual Harassment: Before the term was coined in 1975 by Lin Farley, the behavior was so normalized that women were often encouraged to tolerate it. Giving the problem a name—”sexual harassment”—was an act of “problematizing the normal,” helping society awaken from problem blindness.
  • Example: C-Sections in Brazil: An 84% C-section rate in Brazil’s private health system was seen as normal by many doctors, driven by convenience and financial incentives. An activist movement led by mothers who felt pressured into the procedure successfully challenged this norm, reframing it as a public health problem.

B. Lack of Ownership: “Not My Problem to Fix”

This barrier exists when the people or groups best positioned to solve a problem declare, “That’s not mine to fix.” This can result from fragmented responsibilities, self-interest, or a perceived lack of legitimacy.

  • Fragmented Responsibility: At Expedia, no single team was measured on reducing customer calls, so no one “owned” the problem.
  • Lack of Psychological Standing: People may feel they lack the legitimacy to act on a problem that doesn’t affect them personally. Research shows that explicitly extending standing (e.g., naming a group “Men and Women Opposed to Proposition 174”) can dramatically increase participation from those without a direct vested interest.
  • Taking Ownership: Dr. Bob Sanders & Car Seats: Spurred by a 1975 article in Pediatrics that extended psychological standing to pediatricians on auto safety, Dr. Sanders took ownership of the issue. He successfully lobbied for Tennessee to become the first state to mandate child car seats in 1978. This micro-level action catalyzed a macro-level change, with all 50 states passing similar laws by 1985, saving an estimated 11,274 young lives by 2016.
  • Taking Ownership: Ray Anderson & Interface: The founder of carpet-tile firm Interface took ownership of his company’s environmental impact after reading Paul Hawken’s The Ecology of Commerce. He launched “Mission Zero,” a quest to eliminate the company’s negative environmental footprint by 2020. This was an optional, self-imposed burden that transformed the company’s culture and processes.

C. Tunneling: The Tyranny of Short-Term Crises

When experiencing scarcity of time, money, or mental bandwidth, people adopt “tunnel vision.” They stop long-term planning and focus solely on managing the immediate crisis, which prevents upstream thinking.

  • The Scarcity Trap: The experience of poverty reduces cognitive capacity more than a full night without sleep. It forces short-sighted decisions (like taking a payday loan) not because people are undisciplined, but because the tunnel of scarcity leaves no room for long-term considerations.
  • Organizational Tunneling: A study of nurses found they were constantly engaged in creative workarounds for recurring problems (e.g., missing equipment, lack of towels) but never engaged in fixing the underlying processes. Their scarce time and attention kept them in a reactive mode.
  • Escaping the Tunnel: Escaping requires creating slack—a reserve of time or resources dedicated to problem-solving. This can be structured, as with the “safety huddles” in hospitals or the “Freshman Success Teams” at CPS, which provide a guaranteed forum for emerging from the tunnel to address systems-level issues.
  • Co-opting the Tunnel: The Ozone Layer: To address the long-term threat of ozone depletion, advocates had to make an upstream problem feel downstream. They co-opted the power of tunneling by creating urgency through public advocacy, the memorable metaphor of an “ozone hole,” and negotiating international agreements like the Montreal Protocol that removed threats for opponents (like DuPont), thus reducing their need to fight the solution.

3. Key Strategies for Upstream Leaders

Successfully navigating the barriers requires addressing a series of fundamental questions.

A. How Will You Unite the Right People?

Upstream work is fundamentally collaborative, requiring leaders to “surround the problem” with all the necessary stakeholders.

  • Key Insight: Give every stakeholder a role. Progress hinges on voluntary effort, so maintaining a “big tent” is crucial.
  • Case Study: Iceland’s War on Teen Substance Abuse: In the 1990s, 42% of Icelandic teens reported being drunk in the past month. A coalition of researchers, policymakers, schools, parents, and community groups united to change the culture around teens.
    • Strategy: They focused on boosting “protective factors” (e.g., participation in formal sports, time spent with parents, “natural highs”) and reducing “risk factors” (unstructured, unsupervised time).
    • Tactics: They reinforced curfews, gave families “gift cards” for recreational activities, and professionalized coaching in sports clubs.
    • Result: Over 20 years, the percentage of teens getting drunk in the past 30 days fell from 42% to 5%. Daily smoking dropped from 23% to 3%.
  • Case Study: Domestic Violence in Newburyport, MA: After a woman was murdered by her estranged husband, the Jeanne Geiger Crisis Center united police, advocates, parole officers, and prosecutors to form a Domestic Violence High Risk Team.
    • Data-Driven Collaboration: The team meets monthly to review cases of women identified by the “Danger Assessment” tool as being at extreme risk of homicide. They use a by-name list to coordinate actions like police drive-bys and creating emergency plans.
    • Result: In the 14 years since the team’s formation, not one woman in the communities they serve has been killed in a domestic violence–related homicide, compared to 8 in the 10 years prior.
  • The Role of Data: In many successful upstream efforts, data is not used for top-down “inspection” but for frontline “learning.” Real-time, granular data (like a by-name list) becomes the centerpiece that unites diverse teams around a concrete and shared goal: “What are we going to do about Michael next week?”

B. How Will You Change the System?

Lasting upstream work must culminate in systems change, altering the “water” we swim in so that better outcomes happen by default.

  • Systems Determine Probabilities: A well-designed system makes success highly probable (e.g., fluoridated water preventing cavities). A flawed system rigs the game against certain people. As Dr. Anthony Iton discovered, disparities in life expectancy of up to 20 years between nearby ZIP codes are not caused by a few factors, but by entire systems (housing, education, crime, food access) that create “incubators of chronic stress.”
  • The California Endowment’s BHC Initiative: This $1 billion, 10-year program aims to fix these broken systems not by directly providing health services, but by empowering residents of 14 challenged communities to gain political power and win policy victories that reshape their environments.
  • The Danger of Enabling Bad Systems: Some well-intentioned downstream efforts can inadvertently prop up the flawed systems that create need. For example, while DonorsChoose provides vital classroom supplies, its success could excuse school districts from their funding obligations. The goal should be to push for a world where such crutches are no longer needed.

C. Where Can You Find a Point of Leverage?

In complex systems, the challenge is finding the right lever. This requires getting “proximate” to the problem.

  • Case Study: The UChicago Crime Lab & “Becoming a Man” (BAM): To understand youth violence, researchers read 200 consecutive homicide reports. They discovered that many deaths resulted not from strategic gang wars but from impulsive reactions to trivial disputes. This pointed to impulsivity as a leverage point.
    • The Intervention: They funded and studied “Becoming a Man” (BAM), a program that used small-group sessions and cognitive behavioral therapy (CBT) to help at-risk young men learn to manage anger and slow down their thinking in fraught situations.
    • The Result: A randomized controlled trial found that BAM participants had 45% fewer violent-crime arrests.
  • The Power of Proximity: Architects designing for the elderly donned an “age simulation suit” to experience navigation challenges firsthand. This direct experience revealed leverage points like the need for more benches, handrails, and three-step escalators.

D. How Will You Get Early Warning of the Problem?

Early warning signals provide the time and maneuvering room to prevent a problem or blunt its impact.

  • Predictive Analytics:
    • LinkedIn: Discovered that a customer’s product usage in the first 30 days could predict their likelihood of churning a year later. They shifted resources to intensive onboarding to ensure early engagement.
    • Northwell Health EMS: Uses historical data on 911 calls to predict where emergencies will occur (e.g., near nursing homes at mealtimes) and forward-deploys ambulances to reduce response times.
  • Human Sensors:
    • Sandy Hook Promise: After the 2012 school shooting, the organization realized that in most mass shootings, the perpetrator tells someone their plans in advance. They created the “Know the Signs” program to train students to spot warning signs and the “Say Something” anonymous tip line to report them. This system has averted multiple credible school shooting threats and led to hundreds of suicide interventions.
  • The Danger of False Positives: Early warning systems can backfire. An “epidemic” of thyroid cancer in South Korea was revealed to be an epidemic of overdiagnosis. Mass screening found huge numbers of slow-growing, nonlethal cancers (“turtles”), leading to unnecessary and harmful treatments for a problem that didn’t exist.

E. How Will You Measure Success and Avoid “Ghost Victories”?

Success in upstream work is often the absence of a negative event, making it hard to measure. This reliance on proxy measures can lead to “ghost victories”—superficial successes that cloak underlying failure.

  1. Mistaking Macro Trends for Success: In the 1990s, police chiefs across the U.S. claimed credit for falling crime rates, when in fact they were mostly benefiting from a nationwide trend.
  2. Misalignment of Measures and Mission: The City of Boston’s Public Works department measured its sidewalk repair success by spending per zone and 311 cases closed. This led them to fix sidewalks in wealthy neighborhoods (whose residents called 311) while neglecting crumbling sidewalks in poor neighborhoods, undermining their mission of equity and walkability.
  3. Measures Becoming the Mission: This is the most destructive form, where people “game” the metrics. The NYPD’s CompStat system, which held precinct leaders accountable for crime statistics, led to the widespread downgrading of crimes. In a chilling example, a reported rape of a prostitute was nearly reclassified as a “theft of service” to keep the numbers down.

To avoid ghost victories, leaders should use paired measures (balancing quantity with quality, as CPS did with graduation rates and ACT scores) and “pre-game” how measures could be misused.

F. How Will You Avoid Doing Harm?

Upstream interventions tinker with complex systems and can create unintended negative consequences, known as the “cobra effect.”

  • Case Study: Macquarie Island: A decades-long effort to eradicate invasive species on a subantarctic island created a cascade of problems. Killing rabbits (to stop erosion) led cats to eat rare birds. Killing the cats led to a rabbit population explosion. Killing all pests led to invasive weeds running rampant.
  • Anticipating Second-Order Effects: Wise interventions require seeing the whole system. The “cobra effect” is when an attempted solution makes the problem worse. Examples include an open-office plan meant to increase face-to-face collaboration actually causing it to plunge by 70%, or a ban on thin plastic bags leading retailers to offer thicker plastic bags.
  • The Need for Feedback Loops: Because not all consequences can be foreseen, upstream work requires experimentation and fast, reliable feedback loops. A business that creates a feedback loop for its staff meetings (rating each meeting on a 1-5 scale) can continuously improve them, whereas most meetings never get better because there is no mechanism for learning.

G. Who Will Pay for What Does Not Happen?

Funding prevention is notoriously difficult because success is invisible and payment models are designed for reaction.

  • The “Wrong Pocket Problem”: This occurs when the entity that pays for an intervention is not the one that reaps the financial benefits.
  • Case Study: The Nurse-Family Partnership (NFP): This program, which provides nurse home visits to first-time, low-income mothers, has been proven by multiple RCTs to produce significant long-term social benefits (e.g., reduced child abuse, preterm births, crime, and welfare payments), yielding a return of over $6 for every $1 invested. However, it struggles to get funding because the benefits are scattered across many “pockets” (Medicaid, criminal justice, social services), while a single entity is asked to bear the upfront cost.
  • Innovative Funding Models:
    • Pay for Success: A model being used in South Carolina to fund NFP, where private investors and foundations provide upfront capital. If the program meets pre-agreed success metrics, the government repays the investors. This shifts the financial risk away from the government.
    • Accountable Care Organizations (ACOs): A model where Medicare shares savings with groups of doctors who succeed in keeping their patients healthier and out of the hospital, creating a direct financial incentive for prevention.

4. Addressing Distant and Improbable Threats (“Far Upstream”)

Upstream thinking can also be applied to one-off, improbable, or unpreventable threats.

  • The Prophet’s Dilemma: This is a prediction that prevents what it predicts from happening. The massive global effort to fix the Y2K bug is a prime example. When disaster didn’t strike, many claimed it was a hoax, but it is likely the frantic preparations were what prevented the catastrophe.
  • The Power of Rehearsal: The “Hurricane Pam” simulation, conducted 13 months before Hurricane Katrina, convened 300 stakeholders to game-plan a response to a catastrophic New Orleans hurricane. While the eventual Katrina response was a national failure in many respects, the planning from Pam led to a drastically improved “contraflow” evacuation plan, which is credited with reducing the death toll from a projected 60,000 to approximately 1,700. The lesson is that preparing for disaster requires practice, but organizations in a state of “tunneling” often fail to invest in it.
  • Existential Risk & The “Black Ball” Hypothesis: Philosopher Nick Bostrom posits that technological invention is like pulling balls from an urn. So far we have pulled white (beneficial) and gray (mixed-blessing) balls. But what if there is a black ball—a technology that is easily accessible and allows a small group to cause mass destruction, thereby destroying civilization? The response to the remote threat of “Moon germs” in the 1960s, which led to the creation of NASA’s Planetary Protection Officer and strict quarantine protocols, provides an early model for how humanity can collectively address improbable but high-stakes risks.

5. Conclusion: You, Upstream

The principles of upstream thinking can be applied by individuals to solve personal and organizational problems.

  • Personal Application: Identify recurring problems in life—from finding parking to marital friction—and devise systems to prevent them. The creation of “Daddy Dolls” by a military spouse to ease her children’s pain during deployment is a powerful example of an individual creating an upstream solution.
  • Engaging in Societal Problems: When seeking to contribute to larger issues, one should:
    1. Be impatient for action but patient for outcomes: Upstream work is a long game of chipping away at a problem.
    2. Recognize that macro starts with micro: You cannot help a thousand people until you understand how to help one. Deep, proximate understanding is key.
    3. Favor “Scoreboards” over “Pills”: Prioritize initiatives that use real-time data for continuous learning and adaptation (a scoreboard) over those that seek a single, perfect, scalable solution that cannot be changed (a pill).
  • The Power of One Person: A single, retiring actuary at the Centers for Medicare & Medicaid Services wrote a “cry of the heart” letter to his boss, successfully arguing that the agency should not count “longer lives” as a cost when evaluating preventive programs. This quiet act of defiance changed a federal rule, unlocking funding for life-saving programs and demonstrating that even within vast bureaucracies, one person can achieve a profound upstream victory.
Upstream by Dan Heath. The core principles of "upstream thinking," a framework for preventing problems rather than reacting to them. The central thesis is that society is disproportionately focused on downstream responses—addressing crises, emergencies, and failures after they occur. An upstream approach, conversely, involves proactively identifying and dismantling the systems that cause these problems in the first place. This shift is impeded by three primary barriers

Upstream Thinking Study Guide

Quiz: Short-Answer Questions

Instructions: Answer the following questions in two to three sentences, drawing exclusively from the information provided in the source context.

  1. Describe the public health parable that opens the text. What is the core lesson it is meant to illustrate?
  2. Explain the problem Ryan O’Neill discovered at Expedia in 2012. What was the upstream solution the company implemented?
  3. What is “problem blindness”? How did this barrier manifest within the Chicago Public Schools (CPS) system regarding its low graduation rate?
  4. Define the barrier of “lack of ownership” and the related concept of “psychological standing.” How did the advocates for child car seat laws in the 1970s overcome this barrier?
  5. What is “tunneling”? How does this phenomenon, as described by Eldar Shafir and Sendhil Mullainathan, act as a barrier to upstream thinking?
  6. Summarize the core philosophy of the “Drug-free Iceland” campaign. What were the “risk factors” and “protective factors” it aimed to influence?
  7. What is a “ghost victory”? Using the example of Boston’s sidewalk repairs, explain how an organization can succeed on its metrics while failing its mission.
  8. How did the University of Chicago Crime Lab identify “impulsivity” as a key leverage point for reducing youth violence? Describe the “Becoming a Man” (BAM) program that addressed this.
  9. Explain the “cobra effect,” using the example of the British administrator’s attempt to reduce the cobra population in Delhi.
  10. What is the “wrong pocket problem”? How does the case of the Nurse-Family Partnership (NFP) illustrate this challenge in funding preventive programs?

Essay Questions

Instructions: The following questions are designed to provoke deeper thought and synthesis of the concepts presented in the text. Formulate a detailed response for each, citing specific examples and arguments from the source material.

  1. The text identifies three primary barriers to upstream thinking: Problem Blindness, Lack of Ownership, and Tunneling. Analyze how these three barriers were present in the Expedia case study and how the company’s leaders ultimately overcame them to implement a successful upstream intervention.
  2. Discuss the role of data in enabling upstream work, contrasting “data for the purpose of learning” with “data for the purpose of inspection.” Use the examples of the Chicago Public Schools’ Freshman On-Track metric, the Newburyport Domestic Violence High Risk Team’s Danger Assessment, and the Rockford homelessness team’s “by-name list” to illustrate your points.
  3. Compare and contrast the challenges of upstream interventions in the public sector versus the private sector, using the stories of Ray Anderson at Interface and Dr. Bob Sanders’s campaign for child car seats in Tennessee. What unique advantages and disadvantages did each leader face in trying to solve a problem they chose to own?
  4. Upstream interventions often create unintended consequences. Using the case studies of the Macquarie Island pest eradication program and the attempts to ban single-use plastic bags, discuss the importance of systems thinking, experimentation, and feedback loops in avoiding harm.
  5. The author argues that our society’s attention is “grossly asymmetrical” and skewed toward downstream reaction rather than upstream prevention. Using the detailed comparison between the United States and Norwegian healthcare systems, analyze the author’s argument. What are the demonstrated benefits and disadvantages of each country’s approach to “buying health”?

Quiz Answer Key

  1. The parable describes two friends rescuing drowning children from a river. While one continues the downstream work of pulling kids from the water, the other goes upstream to “tackle the guy who’s throwing all these kids in the water.” The lesson illustrates the difference between reacting to problems (downstream) and preventing them at their source (upstream).
  2. Ryan O’Neill found that for every 100 Expedia customers, 58 placed a call for help, with the number one reason being a request for their itinerary. The upstream solution was to prevent these calls by adding an automated voice-response option, improving email delivery to avoid spam filters, and creating an online tool for customers to retrieve their own itineraries.
  3. “Problem blindness” is the belief that negative outcomes are natural, inevitable, or out of one’s control. Within CPS, many staff members had come to accept the 50% dropout rate as “just how it is,” believing it was caused by intractable root causes like poverty or lack of student effort, which reinforced a sense of helplessness.
  4. “Lack of ownership” means that the parties capable of addressing a problem believe “that’s not mine to fix.” “Psychological standing” is the sense of legitimacy one feels in protesting or acting on an issue. Annemarie Shelness and Seymour Charles overcame this by publishing an article in Pediatrics, extending psychological standing to pediatricians and framing auto safety as a form of preventive medicine for them to own.
  5. “Tunneling” is a state of mind caused by scarcity of time, money, or bandwidth, where people adopt a narrow, short-term focus on immediate problems. It is a barrier to upstream thinking because it confines people to reactive problem-solving and prevents them from engaging in the long-term planning and systems thinking required to prevent future problems.
  6. The core philosophy was to change the community and cultural environment surrounding teenagers to make substance use feel abnormal. The campaign worked to reduce risk factors, such as unstructured time and friends who drink, while boosting protective factors, like participation in formal sports and spending more time with parents.
  7. A “ghost victory” is a superficial success that cloaks an underlying failure, often occurring when short-term measures do not align with the long-term mission. Boston’s Public Works department succeeded on its measures of closing 311 cases and spending its budget, but this system disproportionately repaired sidewalks in wealthy neighborhoods, failing the ultimate mission of equity and walkability for all citizens.
  8. By studying 200 homicide reports, the Crime Lab found that many deaths resulted not from strategic gang activity but from impulsive reactions to trivial disputes, like arguments over a bike or a basketball game. The “Becoming a Man” (BAM) program used cognitive behavioral therapy (CBT) and group mentoring to teach young men to slow down their thinking and manage anger in fraught situations.
  9. The “cobra effect” occurs when an attempted solution makes the problem worse. In colonial Delhi, a British administrator offered a bounty for dead cobras to reduce their population. In response, citizens began farming cobras to collect the bounty, and when the program was canceled, they released their now-worthless snakes, resulting in more cobras than before.
  10. The “wrong pocket problem” occurs when the entity that pays for a preventive intervention does not receive the primary financial benefit from its success. The Nurse-Family Partnership has been proven to save society money by reducing crime, preterm births, and welfare payments, but it struggles to get funding because these savings are scattered across many different government “pockets” (criminal justice, Medicaid, etc.), none of which want to bear the full upfront cost.

Glossary of Key Terms

TermDefinition
Accountable Care Organization (ACO)A model where a group of primary care doctors are incentivized by Medicare to keep their patient population healthy and out of the hospital, sharing in the savings generated from prevented hospital visits.
Backward ContaminationThe contamination of Earth by a returning spaceship, potentially carrying destructive alien life.
Becoming a Man (BAM)A program for at-risk youth in Chicago that uses group mentoring and cognitive behavioral therapy (CBT) to help young men learn to manage anger and impulsivity.
By-Name ListA real-time, regularly updated census of a specific population (e.g., all homeless veterans in a city), used by collaborative teams to coordinate services and track progress on an individual basis.
CapitationA healthcare payment model where providers are paid a flat, risk-adjusted fee per person to take care of all their health needs, incentivizing prevention and cost-effectiveness.
Cobra EffectAn unintended consequence where an attempted solution to a problem makes the problem worse.
Coordinated EntryA system where a single point of entry is established for people seeking a service (like housing for the homeless), allowing for thoughtful prioritization based on vulnerability rather than a “first-come, first-served” basis.
Data for the Purpose of LearningA model where real-time data is provided to frontline workers (e.g., teachers, nurses) to help them learn, adapt, and improve their own work, as opposed to “data for the purpose of inspection.”
Data for the Purpose of InspectionA model where data is used by superiors to hold subordinates accountable for hitting targets, which can create pressure to “game” the metrics.
Downstream ActionsEfforts that react to problems once they have already occurred, such as rescuing a drowning child, answering a customer complaint, or performing emergency surgery.
Forward ContaminationThe contamination of another planet with organisms from Earth during space exploration.
Freshman On-Track (FOT)A metric developed for Chicago Public Schools that predicts a student’s likelihood of graduation based on two factors: completing five full-year course credits and not failing more than one semester of a core course during freshman year.
Functional ZeroA state achieved when the number of people experiencing a problem (e.g., homelessness) is lower than the system’s proven monthly capacity to solve that problem for new cases.
Ghost VictoryA superficial success that cloaks an underlying failure. This can happen when short-term measures are misaligned with the long-term mission, when success is mistakenly attributed to one’s own efforts, or when the measures themselves become the mission in a way that undermines the work.
Housing FirstA strategy for addressing homelessness that prioritizes getting people into housing as the first step, providing a stable foundation from which they can then address other issues like substance abuse or unemployment.
Inattentional BlindnessA phenomenon where careful attention to one task leads people to miss important information that is unrelated to that task, such as radiologists missing a gorilla in a CT scan.
Lack of OwnershipA barrier to upstream thinking where the parties who are capable of addressing a problem declare, “That’s not mine to fix.”
Paired MeasuresA management principle of balancing a quantity-based metric with a quality-based metric to avoid a situation where improving one undermines the other (e.g., pairing “square feet cleaned” with “quality spot-checks”).
Problem BlindnessA barrier to upstream thinking characterized by the belief that negative outcomes are natural, inevitable, or out of one’s control.
Psychological StandingThe sense of legitimacy people feel they have to protest or take action on a problem, which is often tied to whether they feel personally affected by the issue.
Social CareA term for upstream spending on health, covering areas that keep people healthy such as housing, pensions, and childcare support.
TunnelingA third barrier to upstream thinking, caused by scarcity (of time, money, or bandwidth), where people adopt tunnel vision and focus only on short-term, reactive problem-solving, abandoning long-term planning.
Upstream EffortsEfforts intended to prevent problems before they happen or, alternatively, to systematically reduce the harm caused by those problems. Upstream work is characterized by systems thinking.
Wrong Pocket ProblemA situation that hinders funding for prevention, where the entity that bears the cost of an intervention does not receive the primary financial benefit, which is instead scattered across many other “pockets.”

Contact Factoring Specialist, Chris Lehnes

Click: How to Make What People Want by Jack Knapp

Key Insights on Creating Products That “Click”

Click!

Click: How to Make What People Want synthesizes a systematic methodology for developing successful products, services, and projects that “click” with customers. The core premise is that most new products fail due to a flawed, chaotic development process, which leads to a colossal waste of time, money, and energy. The proposed solution is a structured, focused system built around “sprints”—intensive, time-boxed work sessions that compress months of strategic debate and validation into a matter of days or weeks.

This document synthesizes a systematic methodology for developing successful products, services, and projects that click with customers. The core premise is that most new products fail due to a flawed, chaotic development process, which leads to a colossal waste of time, money, and energy. The proposed solution is a structured, focused system built around "sprints"—intensive, time-boxed work sessions that compress months of strategic debate and validation into a matter of days or weeks.

The centerpiece of this system is the Foundation Sprint, a two-day workshop designed to establish a project’s strategic core. On Day 1, teams define the Basics (customer, problem, advantage, competition) and craft their Differentiation. On Day 2, they generate and evaluate multiple Approaches before committing to a path. The output is a testable Founding Hypothesis, a single sentence that encapsulates the entire strategy.

Once a hypothesis is formed, the methodology advocates for rapid validation through Tiny Loops of experimentation, primarily using Design Sprints. These are weeklong cycles where teams build and test realistic prototypes with actual customers. This process allows teams to see how customers react and de-risk the project before investing in a full build, transforming product development from a high-stakes gamble into a series of manageable, low-cost experiments. The ultimate goal is to find what resonates with customers, pivot efficiently, and build with confidence.

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The Core Problem: Why Most New Products Fail

The source material identifies a fundamental challenge in product development: turning a big idea into a product that people genuinely want is exceedingly difficult. The conventional approach to launching new projects is described as chaotic, inefficient, and reliant on luck.

  • The “Old Way”: This process is characterized by endless meetings, debates, political maneuvering, and the creation of documents that are rarely read. Strategy development can take six months or more, often culminating in a decision based on a hunch, leading to a long-term commitment of resources with no real validation.
  • Cognitive Biases: Human psychology exacerbates the problem. Teams are tripped up by cognitive biases such as anchoring on first ideas, confirmation bias, overconfidence, and self-serving biases. These biases lead to a “tunnel vision” that prevents objective analysis of alternatives.
  • The Cost of Failure: The result is that most new products don’t “click”—they fail to solve an important problem, stand out from competition, or make sense to people. This failure represents a significant waste of time, energy, and resources.

The Solution: A System of Sprints

To counteract the chaos of the “old way,” the document proposes a systematic, focused approach centered on “sprints.” This method replaces prolonged, fragmented work with short, intense, and highly structured bursts of collaborative effort.

Lesson 1: Drop Everything and Sprint

The foundational principle is to clear the calendar and focus the entire team on a single, important challenge until it is resolved. This creates a “continent” of high-quality, uninterrupted time, which is more effective than scattered “islands” of focus.

  • Key Techniques for Sprinting:
    • Involve the Decider: The person with ultimate decision-making authority (e.g., CEO, project lead) must be part of the sprint team. This ensures decisions stick and eliminates the need for time-wasting internal pitches.
    • Form a Tiny Team: Sprints are most effective with five or fewer people with diverse perspectives (e.g., CEO, engineering, sales, marketing).
    • Declare a “Good Emergency”: The team should use “eject lever” messages to signal to the rest of the organization that they are completely focused and will be slow to respond to other matters.
    • Work Alone Together: To avoid the pitfalls of group brainstorming (which favors loud voices and leads to mediocre consensus), sprints utilize silent, individual work followed by structured sharing, voting, and debate.
    • Get Started, Not Perfect: The goal is not a perfect plan but a testable hypothesis that can be refined through experiments.

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The Foundation Sprint: Building a Strategic Core in Two Days

The Foundation Sprint is a new format designed to establish a project’s fundamental strategy in just ten hours over two days. It provides clarity on the core elements of a project and culminates in a Founding Hypothesis.

Day 1, Morning: Establishing the Basics

The sprint begins by answering four fundamental questions to create a shared understanding of the project’s landscape. The primary tool for this is the Note-and-Vote, a process where team members silently generate ideas on sticky notes, post them anonymously, vote, and then the Decider makes the final choice.

Lesson 2: Start with Customer and Problem

The most successful teams are deeply focused on their customers and the real problems they can solve. This requires moving beyond jargon-filled demographics to plain-language descriptions of real people and their challenges.

“It’s hard to make a product click if you don’t care about the person it’s supposed to click with.”

  • Example (Google Meet): The customer was “teams with people in different locations,” and the problem was that “it was difficult to meet.”

Lesson 3: Take Advantage of Your Advantages

Teams should identify and leverage their unique advantages, which fall into three categories:

  • Capability: What the team can do that few others can (e.g., world-class engineering know-how).
  • Insight: A deep, unique understanding of the problem or the customer.
  • Motivation: The specific fire driving the team, which can range from a grand vision to frustration with the status quo.
  • Example (Phaidra): The startup combined deep expertise in AI (Capability), real-world knowledge of industrial plants (Insight), and a drive to reduce energy waste (Motivation).

Lesson 4: Get Real About the Competition

A successful strategy requires an honest assessment of the alternatives customers have.

  • Types of Competition:
    • Direct Competitors: Obvious rivals solving the same problem (e.g., Nike vs. Adidas).
    • Substitutes: Workarounds customers use when no direct solution exists (e.g., manual adjustments in a factory before Phaidra’s AI).
    • Nothing: In some cases, customers are doing nothing about a problem. This is a risky but potentially high-reward opportunity.
  • Go for the Gorilla: Teams should focus on competing with the strongest, most established alternative (e.g., Slack positioning itself against email).

Day 1, Afternoon: Crafting Radical Differentiation

With the basics established, the focus shifts to creating a strategy that sets the solution far apart from the competition.

Lesson 5: Differentiation Makes Products Click

Successful products don’t just offer incremental improvements; they create radical separation by reframing how customers evaluate solutions.

  • The 2×2 Differentiation Chart: This visual tool is used to find two key factors where a new product can own the top-right quadrant, pushing competitors into “Loserville.” The axes should reflect customer perception, not internal technical details.
    • Example (Google Meet): Instead of competing on video quality or network size, the team differentiated on “Ease of Use” (just a browser link) and being “Multi-Way,” creating a new framework where they were the clear winner.

Lesson 6: Use Practical Principles to Reinforce Differentiation

To translate differentiation into daily decisions, teams create a short list of practical, actionable principles.

  • “Differentiate, Differentiate, Safeguard”: A recommended formula is to create one principle for each of the two differentiators and a third “safeguard” principle to prevent unintended negative consequences.
  • Example (Google): Early principles like “Focus on the user and all else will follow” and “Fast is better than slow” were not vague platitudes but concrete decision-making guides that reinforced Google’s differentiation.
  • The Mini Manifesto: The 2×2 chart and the project principles are combined into a one-page “Mini Manifesto” that serves as a strategic guide for the entire project.

Day 2: Choosing the Right Approach

The second day is dedicated to ensuring the team pursues the best possible path to executing its strategy, rather than simply defaulting to the first idea.

Lesson 7: Seek Alternatives to Your First Idea

First ideas are often flawed. Before committing, teams should generate multiple alternative approaches to force a more measured decision. This “pre-pivot” can save months or years of wasted effort.

  • Example (Genius Loci): The founders’ first idea was a GPS-based app. By considering alternatives like a website and physical QR-code signs, they realized the app was a “fragile” solution. They ultimately chose the more robust website-and-sign combination, which proved successful.

Lesson 8: Consider Conflicting Opinions Before You Commit

To evaluate options rigorously, teams should simulate a “team of rivals” by looking at the approaches through different lenses.

  • Magic Lenses: This technique uses a series of 2×2 charts to plot the various approaches against different criteria. This makes complex trade-offs visual and easier to debate.
    • Classic Lenses: Customer (dream solution), Pragmatic (easiest to build), Growth (biggest audience), Money (most profitable).
    • Custom Lenses: Teams also create lenses specific to their project’s risks and goals.
  • Example (Reclaim): The AI scheduling startup used Magic Lenses to evaluate three potential features. The exercise revealed that “Smart Scheduling Links,” an idea that was not initially the team’s favorite, consistently scored highest across all lenses. They built it, and it became their fastest-growing feature.

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From Hypothesis to Validation

The Foundation Sprint does not produce a final plan but rather a well-reasoned, testable hypothesis. The final phase of the methodology is about proving that hypothesis through rapid experimentation.

Lesson 9: It’s Just a Hypothesis Until You Prove It

A strategy is an educated guess until it makes contact with customers. Framing it as a hypothesis encourages a mindset of learning and adaptation, helping teams avoid the “Vulcan” trap—becoming so attached to a belief that they ignore conflicting evidence, as astronomer Urbain Le Verrier did.

  • The Founding Hypothesis Sentence: All the decisions from the sprint are distilled into one Mad Libs-style statement:

Lesson 10: Experiment with Tiny Loops Until It Clicks

Instead of embarking on a long-loop project (which takes a year or more), teams should use “tiny loops” of experimentation to test their Founding Hypothesis quickly.

  • Design Sprints as the Tool for Tiny Loops: The recommended method is the Design Sprint, a five-day process to prototype and test ideas with real customers.
    • Monday: Map the problem.
    • Tuesday: Sketch competing solutions.
    • Wednesday: Decide which to test.
    • Thursday: Build a realistic prototype.
    • Friday: Test with five customers.
  • The Power of Prototypes: Prototypes allow teams to get genuine customer reactions and test core strategic questions in days, not years. This allows for hyper-efficient pivots before significant resources are committed.
  • When to Stop Sprinting: A solution is ready to be built when customer tests show a clear “click”—unguarded, genuine reactions of excitement, where customers lean forward, ask to use the solution immediately, or try to pull the prototype out of the facilitator’s hands.
Click: How to Make What People Want synthesizes a systematic methodology for developing successful products, services, and projects that "click" with customers. The core premise is that most new products fail due to a flawed, chaotic development process, which leads to a colossal waste of time, money, and energy.

Study Guide for “Click”

This study guide provides a review of the core concepts, methodologies, and case studies presented in the source material. It includes a short-answer quiz with an answer key, a set of essay questions for deeper analysis, and a comprehensive glossary of key terms.

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Short-Answer Quiz

Instructions: Answer the following ten questions in two to three sentences each, based on the information provided in the source context.

  1. What are the three essential characteristics of a product that “clicks” with customers?
  2. What is the primary goal of the two-day Foundation Sprint?
  3. Explain the concept of “working alone together” and why it is preferred over traditional group brainstorming.
  4. What are the three distinct types of “advantages” a team can possess, as outlined in the text?
  5. According to the source, what does it mean for a product to be “competing against nothing,” and what are the risks associated with this situation?
  6. What is the purpose of creating a 2×2 differentiation chart, and what is the ideal outcome for a project on this chart?
  7. Describe the “Differentiate, differentiate, safeguard” formula for creating practical project principles.
  8. What is the purpose of the “Magic Lenses” exercise performed on Day 2 of the Foundation Sprint?
  9. Why is a project’s strategy referred to as a “hypothesis” rather than a “plan,” and what cognitive biases does this mindset help overcome?
  10. Explain the concept of “tiny loops” and how they contrast with the “long loop” of a traditional product launch or Minimum Viable Product (MVP).

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Answer Key

  1. A product that “clicks” solves an important problem for a customer, stands out from the competition, and makes sense to people. These elements must fit together like two LEGO bricks, creating a simple, compelling promise that customers will pay attention to.
  2. The primary goal of the Foundation Sprint is to create a “Founding Hypothesis” in just ten hours over two days. This process helps a team gain clarity on fundamentals, define a differentiation strategy, and choose a testable approach, compressing what would normally take six months of chaotic meetings into a short, focused workshop.
  3. “Working alone together” is a method where team members generate ideas and proposals silently and in parallel before sharing and voting. It is preferred over group brainstorming because it produces more higher-quality solutions, ensures participation from everyone regardless of personality, and leads to faster, better-considered decisions by avoiding the pitfalls of groupthink.
  4. The three types of advantages are capability (what a team can do that few can match, like technical know-how), motivation (the specific reason or frustration driving the team to solve a problem), and insight (a deep understanding of the problem and customers that others lack).
  5. “Competing against nothing” occurs when customers have a real problem, but no reasonable solution exists yet, so they currently do nothing. This is the riskiest type of opportunity because it is difficult to overcome customer inertia, but it can also be the most exciting if the new solution offers enough value.
  6. A 2×2 differentiation chart is a visual tool used to state a project’s strategy by plotting it against competitors on two key differentiating factors. The ideal outcome is to find differentiators that place the project alone in the top-right quadrant, pushing all competitors into the other three quadrants (referred to as “Loserville”), thus making the choice easy for customers.
  7. The “Differentiate, differentiate, safeguard” formula is a method for writing three practical project principles. The first two principles are derived directly from the project’s two main differentiators to reinforce the strategy, while the third is a “safeguard” principle designed to protect against the unintended negative consequences of a successful product.
  8. The “Magic Lenses” exercise uses a series of 2×2 charts to evaluate multiple project approaches through different perspectives, such as the customer, pragmatic, growth, and money lenses. This structured argument helps the team consider conflicting opinions and make a well-informed decision on which approach to pursue without getting into political dogfights.
  9. A strategy is called a “hypothesis” because, until it clicks with customers, it is just an educated guess that is intended to be tested, proven wrong, and updated. This mindset helps overcome cognitive biases like anchoring bias (loving the first idea) and confirmation bias (seeking only data that confirms a belief), encouraging a scientific process of learning and adaptation.
  10. “Tiny loops” are rapid, experimental cycles, such as one-week Design Sprints, where teams test prototypes with customers to get feedback before committing to building a product. This contrasts with a “long loop,” which is the year-or-more timeline it typically takes to build and launch even a Minimum Viable Product (MVP), making it too slow for effective learning.

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Essay Questions

Instructions: The following questions are designed for longer-form answers that require synthesizing multiple concepts from the source material. No answers are provided.

  1. Describe the complete system proposed in the text, from the initial Foundation Sprint through multiple Design Sprints. Explain how each stage addresses specific challenges in product development and how the ten key lessons are integrated into this overall process.
  2. Using the case study of Phaidra, analyze how the startup embodied the principles of defining advantages, using “tiny loops,” and testing a Founding Hypothesis. How did their sprint-based approach allow them to de-risk their ambitious project before fully building their AI software?
  3. The text uses the story of astronomer Urbain Le Verrier and his search for the planet Vulcan as a cautionary tale about cognitive biases. Explain the specific biases Le Verrier fell prey to and detail how the methodologies of the Foundation Sprint and Design Sprint are explicitly designed to counteract these human tendencies.
  4. Compare the strategic challenges faced by Nike in the movie Air with those faced by the startup Genius Loci. How did each entity use differentiation and the evaluation of alternative approaches to craft a winning strategy against very different types of competition?
  5. The author states, “Differentiation makes products click.” Argue why differentiation (covered in Day 1 of the Foundation Sprint) is the most critical element for a project’s success, more so than choosing the right approach (covered in Day 2). Use examples like Google Meet, Slack, and Orbital Materials to support your argument.

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Glossary of Key Terms

TermDefinition
AdvantageA unique strength a team possesses, composed of three elements: Capability (what you can do that few can match), Insight (a deep understanding of the problem and customers), and Motivation (the specific reason or frustration driving you to solve the problem).
BasicsThe foundational questions addressed on Day 1 of the Foundation Sprint: defining the target Customer, the Problem to be solved, the team’s unique Advantage, and the strongest Competition.
ClickThe moment a product and customer fit together perfectly. A product that “clicks” solves an important problem, stands out from the competition, and makes sense to people.
Cognitive BiasesPredictable patterns of mistakes humans make when thinking, such as Anchoring bias (falling in love with the first idea) and Confirmation bias (seeking only data that confirms our beliefs). Sprint methods are designed to counteract these.
CompetitionThe alternatives a customer has to a product. This includes Direct competitors (similar products), Substitutes (work-arounds), and “Do nothing” (customer inertia).
DeciderThe person on the sprint team responsible for making final decisions on the project. Their presence is mandatory for a sprint’s decisions to be effective and stick.
Design SprintA five-day process for solving big problems and testing new ideas. It involves mapping a problem, sketching solutions, deciding on an approach, building a realistic prototype, and testing it with customers. It serves as the primary method for testing a Founding Hypothesis.
DifferentiationWhat makes a product or service radically different from the alternatives in the customer’s perception. It is the essence of a strategy and the reason a customer will choose a new solution.
Foundation SprintA two-day, ten-hour workshop designed to create a team’s foundational strategy. It compresses months of debate into a structured process that results in a testable Founding Hypothesis.
Founding HypothesisA single, Mad Libs-style sentence that distills a team’s complete strategy: “For [CUSTOMER], we’ll solve [PROBLEM] better than [COMPETITION] because [APPROACH], which delivers [DIFFERENTIATION].” It is an educated guess intended to be tested.
Long LoopThe extended timeframe (often a year or more) required to build and launch a real product, including a Minimum Viable Product (MVP). This lengthy cycle makes learning from real-world data slow and expensive.
Magic LensesA decision-making exercise using a series of 2×2 charts to evaluate multiple project approaches from different perspectives (e.g., customer, pragmatic, growth, money). It facilitates a structured argument to help a team make a well-informed choice.
Mini ManifestoA document created at the end of Day 1 of the Foundation Sprint that combines the project’s 2×2 differentiation chart and its three practical principles. It serves as an easy-to-understand guide for future decision-making.
Minimum Viable Product (MVP)A simpler version of a product that is just enough to be useful to customers, launched to test product-market fit. The text argues that even MVPs typically constitute a “long loop.”
Note-and-VoteA core sprint technique for “working alone together.” Team members silently write down ideas on sticky notes, post them anonymously, and then vote on their favorites before the Decider makes a final choice.
Practical PrinciplesA set of three-ish project-specific rules designed to guide decision-making and reinforce differentiation. They are practical and action-oriented, not abstract corporate values.
PrototypeA realistic but non-functional fake version of a product created rapidly (often in one day) during a Design Sprint. It is used to test a hypothesis with customers without the time and expense of building a real product.
Skyscraper RobotA metaphor from the movie Big for a product idea that focuses on company metrics (like market share) or creator ego, rather than what is actually fun or useful for the customer.
Tiny LoopsShort, rapid cycles of experimentation, like a one-week Design Sprint, that allow a team to test a hypothesis with a prototype and get customer reactions quickly. This allows for hyperefficient pivots before committing to a long development cycle.
Work Alone TogetherA core collaboration principle in sprints where individuals are given time to think and generate ideas in silence before sharing them with the group. It is designed to produce higher-quality ideas and avoid the pitfalls of group brainstorming.
2×2 Differentiation ChartA visual tool consisting of a two-axis grid used to map a project’s key differentiators against the competition. The goal is to define axes that place the project alone in the top-right quadrant.

Contact Factoring Specialist Chris Lehnes

Superagency: What Could Go Right with Our AI Future by Reid Hoffman 

The Techno-Humanist Compass: Shaping a Better AI Future

Superagency: What Could Possibly Go Right with Our AI Future written by Reid Hoffman 

Hoffman argues that humanity is in the early stages of an “existential reckoning” with AI, akin to the Industrial Revolution. While new technologies have historically sparked fears of dehumanization and societal collapse, the author maintains a “techno-humanist compass” is essential to navigate this era. This compass prioritizes human agency – our ability to make choices and exert influence – and aims to broadly augment and amplify individual and collective agency through AI.

Key Themes & Ideas:

  • Historical Parallelism: New technologies throughout history (printing press, automobile, internet) have faced skepticism and opposition before becoming mainstays. Similarly, current fears surrounding AI, including job displacement and extinction-level threats, echo past anxieties.
  • The Inevitability of Progress: “If a technology can be created, humans will create it.” Attempts to halt or prohibit technological advancement are ultimately futile and counterproductive.
  • Techno-Humanism: Technology and humanism are “integrative forces,” not oppositional. Every new invention redefines and expands what it means to be human.
  • Human Agency as the Core Concern: Most concerns about AI, from job displacement to privacy, are fundamentally questions about human agency. The goal of AI development should be to broadly augment and amplify individual and collective agency.
  • Iterative Deployment: A key strategy, pioneered by OpenAI, for developing and deploying AI is “iterative deployment.” This involves incremental releases, gathering user feedback, and adapting as new evidence emerges. It prioritizes flexibility over a grand master plan.
  • Beyond Doom and Gloom: The author categorizes perspectives on AI into “Doomers” (extinction threat), “Gloomers” (near-term risks, top-down regulation), “Zoomers” (unfettered innovation, skepticism of regulation), and “Bloomers” (optimistic, mass engagement, iterative deployment). Hoffman aligns with the “Bloomer” perspective.

Important Facts:

  • Unemployment rates are lower today than in 1961, despite widespread automation in the 1950s.
  • ChatGPT, launched with “zero marketing dollars,” attracted “one million users in five days” and “100 million users in just two months.”
  • Some AI models, even “state-of-the-art” ones, “hallucinate”—generating false information or misleading outcomes. This occurs because LLMs “never know a fact or understand a concept in the way that we do,” but rather “make a prediction about what tokens are most likely to follow” in a contextually relevant way.
  • US public opinion on AI is generally cautious: “only 15 percent of U.S. adults said they were ‘more excited than concerned’” in a 2023 Pew Research Center survey.

II. Big Knowledge, Private Commons, and Networked Autonomy

The book elaborates on how AI can convert “Big Data into Big Knowledge,” transforming various aspects of society, from mental health to governance, and fostering a “private commons” that expands individual and collective agency.

Key Themes & Ideas:

  • The “Light Ages” of Data: In contrast to George Orwell’s dystopian vision in “1984,” where technology enables “God-level techno-surveillance,” Hoffman argues that big knowledge, enabled by computers and AI, leads to a “Light Ages of data-driven clarity and growth.”
  • Beyond “Extraction Operations”: The author refutes the notion that Big Tech’s use of data is primarily “extractive.” Instead, he views it as “data agriculture” or “digital alchemy,” where repurposing and synthesizing data creates tremendous value for users and society, a “mutualistic ecosystem.”
  • The Triumph of the Private Commons: Platforms like Google Maps, YouTube, and LinkedIn, though privately owned, function as “private commons,” offering free or near-free “life-management resources that effectively function as privatized social services and utilities.”
  • Consumer Surplus: The value users derive from these private commons often far exceeds the explicit costs, creating significant “consumer surplus.”
  • Informational GPS: LLMs act as “informational GPS,” helping individuals navigate complex and expanding informational environments, enhancing “situational fluency” and enabling better-informed decisions.
  • Upskilling and Democratization: AI, particularly LLMs, can rapidly upskill beginners and democratize access to high-value services (education, healthcare, legal advice) for underserved communities.
  • Networked Autonomy and Liberating Limits: The historical evolution of automobiles demonstrates how regulation, when thoughtfully applied and coupled with innovation, can expand individual freedom and agency by creating safer, more predictable, and scalable systems. Similarly, new regulations and norms for AI will emerge to manage its power while ultimately expanding autonomy.
Superagency: What Could Possibly Go Right with Our AI Future written by Reid Hoffman 

Important Facts:

  • In 1963, the IRS collected $700,000 in unpaid taxes after announcing it would use an IBM 7074 to process returns.
  • Vance Packard’s 1964 bestseller, “The Naked Society,” expressed fears of “giant memory machines” recalling “every pertinent action” of citizens.
  • The median compensation Facebook users were willing to accept to give up the service for one month was $48, while Meta’s average annual revenue per user (ARPU) in 2023 was $44.60, suggesting a significant “consumer surplus.”
  • The amount of data produced globally in 2024 is “roughly 402 billion gigabytes per day,” enough to fill “2.3 billion books per second.”
  • Studies in 2023 showed that professionals using ChatGPT completed tasks “37 percent faster,” with “the quality boost bigger for participants who received a low score on their first task.” Less experienced customer service reps saw productivity increases of “14 percent.”
  • The US federal government passed the Infrastructure Investment and Jobs Act in 2021, which includes a provision for mandatory “Driver Alcohol Detection System for Safety (DADSS)” in new cars, potentially by 2026.
  • The US Interstate Highway System (IHS), initially authorized for 41,000 miles in 1956, now encompasses over 48,000 miles and creates “annual economic value” of “$742 billion.”

III. Innovation, Safety, and the Social Contract

Hoffman posits that innovation itself is a form of safety, and that successful AI integration will require a renewed social contract and active citizen participation in shaping its development and governance.

Key Themes & Ideas:

  • Innovation as Safety: Rapid, adaptive development with short product cycles and frequent updates leads to safer products. “Innovation is safety” in contrast to the “precautionary principle” (“guilty until proven innocent”) favored by some critics.
  • Competition as Regulation: Benchmarks and public leaderboards (like Chatbot Arena) serve as “dynamic mechanisms for driving progress” and promote transparency and accountability in AI development, effectively “regulation, gamified.”
  • Law Is Code: Lawrence Lessig’s thesis that “code is law” is more relevant than ever as AI-enabled “perfect control” becomes possible in physical spaces (e.g., smart cars, instrumented public venues).
  • The Social Contract and Consent of the Governed: The successful integration of AI, especially agentic systems, requires a robust “social contract” and the “consent of the governed.” Voluntary compliance and public acceptance are crucial for legitimacy and stability.
  • Rational Discussion at Scale: AI can be used to enhance civic participation and collective decision-making, moving beyond traditional surveillance models to enable “rational discussion at scale” and build consensus.
  • Sovereign AI: Nations will increasingly seek to “own the production of their own intelligence” to protect national security, economic competitiveness, and cultural values.

Important Facts:

  • The Future of Life Institute’s letter called for a pause on AI development until systems were “safe beyond a reasonable doubt,” reversing the standard of criminal law.
  • Chatbot Arena, an “open-source platform,” allows users to “vote for the one they like best” between two unidentified LLMs, creating a public leaderboard.
  • MSG Entertainment uses facial recognition to deny entry to attorneys from firms litigating against it.
  • South Korea’s Covid-19 response relied on extensive data collection (mobile GPS, credit card transactions, travel records) and transparent sharing, demonstrating how “public outrage has been nearly nonexistent” due to “a radically transparent version of people-tracking.”
  • Jensen Huang (Nvidia CEO) stated that models are likely to grow “1,000 to 10,000 times more powerful over the next decade,” leading to “highly skilled virtual programmers, engineers, scientists.”

Conclusion: A Path to Superagency

Hoffman concludes by reiterating the core principles: designing for human agency, leveraging shared data as a catalyst for empowerment, and embracing iterative deployment for safe and inclusive AI. The ultimate goal is “superagency,” where individuals and institutions are empowered by AI, leading to compounding benefits across society, from mental health to scientific discovery and economic opportunity. This future requires an “exploratory, adaptive, forward-looking mindset” and a collective commitment to shaping AI with a “techno-humanist compass” that prioritizes human flourishing.

Contact Factoring Specialist, Chris Lehnes

Superagency: What Could Possibly Go Right with Our AI Future written by Reid Hoffman 

The Superagency Study Guide

This study guide is designed to help you review and deepen your understanding of the provided text, “Superagency: Our AI Future” by Reid Hoffman and Greg Beato. It covers key concepts, arguments, historical examples, and debates surrounding the development and adoption of Artificial Intelligence.

I. Detailed Study Guide

A. Introduction: Humanity Has Entered the Chat (pages xi-24)

  • The Nature of Technological Fear: Understand the historical pattern of new technologies (printing press, power loom, telephone, automobile, automation) sparking fears of dehumanization and societal collapse.
  • AI’s Unique Concerns: Identify why current fears about AI are perceived as different and more profound (simulating human intelligence, potential for autonomy, extinction-level threats, job displacement, human obsolescence, techno-elite cabals).
  • The “Future is Hard to Foresee” Argument: Grasp the authors’ skepticism about accurate predictions, both pessimistic and optimistic, and their argument against stopping progress.
  • Coordination Problem and Global Competition: Understand why banning or containing new technology is difficult due to inherent human competition and diverse global interests.
  • Techno-Humanist Compass: Define this guiding principle, emphasizing the integration of humanism and technology to broaden and amplify human agency.
  • Iterative Deployment: Explain this approach (OpenAI’s method) for developing and deploying AI, focusing on equitable access, collective learning, and continuous feedback.
  • Authors’ Background and Perspective: Recognize Reid Hoffman’s experience as a founder/investor in tech companies (PayPal, LinkedIn, Microsoft, OpenAI, Inflection AI) and how it shapes his optimistic, “Bloomer” perspective. Understand the counter-argument that his involvement might bias his views.
  • The Printing Press Analogy: Analyze the comparison between the printing press’s initial skepticism and its ultimate role in democratizing knowledge and expanding agency, serving as an homage to transformative technologies.
  • Key AI Debates and Constituencies: Differentiate between the four main schools of thought regarding AI development and risk:
  • Doomers: Believe in extinction-level threats from superintelligent AIs.
  • Gloomers: Critical of AI and Doomers; focus on near-term risks (job loss, disinformation, bias, undermining agency); advocate for prohibitive, top-down regulation.
  • Zoomers: Optimistic about AI’s productivity gains; skeptical of precautionary regulation; desire complete autonomy to innovate.
  • Bloomers (Authors’ Stance): Optimistic, believe AI can accelerate human progress but requires mass engagement and active participation; favor iterative deployment.
  • Individual vs. National Agency: Understand the argument that individual agency is increasingly tied to national agency in the 21st century, making democratic leadership in AI crucial.

B. Chapter 1: Humanity Has Entered the Chat (continued)

  • The “Swipe-Left” Month for Tech (November 2022): Understand the context of layoffs and cryptocurrency bankruptcies preceding ChatGPT’s launch, challenging the “Big Tech’s complete control” narrative.
  • ChatGPT’s Immediate Impact: Describe its capabilities (knowledge, versatility, human-like responses, “hallucinations”) and rapid adoption rate.
  • Industry Response to ChatGPT: Note the “code-red alerts” and new generative AI groups formed by tech giants.
  • The Pause Letter: Explain the call for a 6-month pause on AI training (Future of Life Institute) and the shift in sentiment from “too slow” to “too fast.”
  • Understanding LLM Mechanics:Neural Network Architecture: How layers of nodes and mathematical operations process language.
  • Parameters: Their role as “tuning knobs” determining connection strength.
  • Pretraining: How LLMs learn associations and correlations from vast text amounts.
  • Statistical Prediction vs. Human Understanding: Crucial distinction: LLMs predict next tokens, they don’t “know facts” or “understand concepts” like humans.
  • LLM Limitations and Challenges:Hallucinations: Define and provide examples (incorrect facts, fabricated information, contextual irrelevance, logical inconsistencies).
  • Bias: How training data (scraped from the internet) can lead to sexist or racist outputs.
  • Black Box Phenomenon: The opacity of complex neural networks, making it hard to explain decisions.
  • Lack of Commonsense Reasoning/Lived Experience: LLMs’ fundamental inability to apply knowledge across domains like humans.
  • Slowing Performance Gains: Critics’ argument that bigger models don’t necessarily lead to Artificial General Intelligence (AGI).
  • AI Hype Cycle: Recognize the shift from “Public Enemy No. 1” to “dud” in public perception of LLMs.
  • Hoffman’s Long-Term Optimism: His belief that AI is still in early stages and will overcome limitations through new architectures (multimodal, neurosymbolic AI) and continued breakthroughs.
  • Public Concerns about AI: Highlighting survey data on American skepticism, linking fears to the question of human agency.

C. Chapter 2: Big Knowledge (pages 25-46)

  • Orwell’s 1984 and Techno-Surveillance: Understand the influence of Orwell’s dystopian vision (Big Brother, telescreens, Thought Police) on fears about technology.
  • Mainframe Computers of the 1960s: Describe their impact and the initial “doomcasting” they inspired (e.g., IRS use, “giant memory machines”).
  • The National Data Center Proposal: Explain its purpose (consolidating government data for research and policy) and the strong backlash it received from Congress and the public, driven by privacy fears (Vance Packard, Myron Brenton, Cornelius Gallagher).
  • Griswold v. Connecticut: Connect this Supreme Court ruling to the emergence of a constitutional “right to privacy” and its impact on the data center debate.
  • Packard’s Predictions and Historical Reality: Contrast Packard’s fears of “humanity in chains” with the eventual outcome of increased freedoms and individual agency, particularly for marginalized groups.
  • The Rise of the Personal Computer: Emphasize its role in promoting individualism and self-actualization, challenging the mainframe’s image of totalitarianism.
  • Big Business vs. Big Brother: Argue that commercial enterprises used data to “make you feel seen” through personalization, leading to a more diverse and inclusive world.
  • Privacy vs. Public Identity: Discuss the evolving balance between the right to privacy (“right to be left alone”) and the benefits of public identity (discoverability, trustworthiness, influence, social/financial capital) in a networked world.
  • LinkedIn as a Trust Machine: Explain how LinkedIn used networks and public professional identity to scale trust and facilitate new connections and opportunities.
  • The “Update Problem”: How LinkedIn solved the issue of manually updating contact information.
  • Early Resistance to LinkedIn: Understand why individuals and employers were initially wary of sharing professional information publicly.
  • Collective Value of Shared Information: How platforms like LinkedIn, by making formerly siloed information accessible, empower users and companies.
  • The Information Deluge: Explain Hal Varian’s and Ithiel de Sola Pool’s observations about “words supplied” vs. “words consumed,” and how AI is crucial for converting “Big Data into Big Knowledge.”

D. Chapter 3: What Could Possibly Go Right? (pages 47-69)

  • Solutionism vs. Problemism: Define these opposing viewpoints on technology’s role in addressing societal challenges.
  • Solutionism: Belief that complex challenges have simplistic technological fixes (authors acknowledge this criticism).
  • Problemism: Default mode of Gloomers, viewing technology as inherently suspect, anti-human, and capitalist; emphasizes critique over action.
  • The “Existential Threat of the Status Quo”: Introduce the idea that inaction on long-standing problems (like mental health) is itself a significant risk.
  • AI in Mental Health Care: Explore the potential of LLMs to:
  • Address the shortage of mental health professionals and expand access.
  • Bring “Big Knowledge” to psychotherapy’s “black box” by analyzing millions of interactions to identify effective evidence-based practices (EBPs).
  • Enhance agency for both care providers and recipients.
  • The Koko Controversy:Describe Rob Morris’s experiment with GPT-3-driven responses in Koko’s peer-based mental health messaging service.
  • Explain the public backlash due to misinterpretations and perceived unethical behavior (lack of transparency).
  • Clarify Koko’s actual transparency (disclaimers) and the “copilot” approach.
  • Highlight this as a “classic case of problemism” where hypothetical risks overshadowed actual attempts to innovate.
  • Mental Health Crisis Statistics: Provide context on rising rates of depression, anxiety, and suicide, and the chronic shortage of mental health professionals.
  • Existing Tech in Mental Health: Briefly mention crisis hotlines, teletherapy, apps, and their limitations (low engagement, attrition rates).
  • Limitations of Specialized Chatbots (Woebot, Wysa): Explain their reliance on “frames” and predefined structures, making them less nuanced and adaptable than advanced LLMs; contrast with human empathy.
  • AI’s Transformative Potential in Mental Health: How LLMs can go beyond replicating human skills to reimagine care, making it abundant and affordable.
  • Clinician, Know Thyself:Discuss the challenges of data collection and assessment in traditional psychotherapy.
  • How digital technologies (smartphones, wearables) and AI can provide objective, continuous data.
  • The Lyssn.io/Talkspace study: AI-driven analysis of therapy transcripts to identify effective therapist behaviors (e.g., complex reflections, affirmations) and less effective ones (e.g., “giving information”).
  • Stages of AI Integration in Mental Health (Stade et al.):Stage 1: Simple assistive uses (drafting notes, administrative tasks).
  • Stage 2: Collaborative engagements (assessing trainee adherence, client homework).
  • Stage 3: Fully autonomous care (clinical LLMs performing all therapeutic interventions).
  • The “Therapy Mix” Vision: Envision a future of affordable, accessible, personalized, and data-informed mental health care, with virtual and human therapists, diverse approaches, and user reviews.
  • Addressing Problemist Tropes:The concern that accessible care trivializes psychotherapy (authors argue against this).
  • The worry about overreliance on therapeutic LLMs leading to reduced individual agency (authors compare to eyeglasses, pacemakers, seat belts, and propose a proactive wellness model).
  • Superhumane: Explore the idea of forming bonds with nonhuman intelligences, drawing parallels to relationships with deities, pets, and imaginary friends.
  • AI’s Empathy and Kindness:Initial discourse claimed LLMs lacked emotional intelligence.
  • The AskDocs/ChatGPT study demonstrating AI’s ability to provide more empathetic and higher-rated responses than human physicians.
  • The “always on tap” availability of kindness and support from AI, potentially increasing human capacity for kindness.
  • The “superhumane” world where AI interactions make us nicer and more patient.

E. Chapter 4: The Triumph of the Private Commons (pages 71-98)

  • Big Tech Critique: Understand the arguments that Big Tech innovations disproportionately benefit the wealthy and lead to job displacement (MIT Technology Review, Ted Chiang).
  • The Age of Surveillance Capitalism (Shoshana Zuboff):Big Other: Zuboff’s term for the “sensate, networked, computational infrastructure” that replaces Big Brother.
  • Total Certainty: Technology weaponizing the market to predict and manipulate behavior.
  • Behavioral Value Reinvestment Cycle: Google’s initial virtuous use of data to improve services.
  • Original Sin of Surveillance Capitalism: Applying behavioral data to make ads more relevant, leading to “behavioral surplus” and “behavior prediction markets.”
  • “Abandoned Carcass” Metaphor: Zuboff’s view that users are exploited, not product.
  • Authors’ Counter-Arguments to Zuboff:Value Flows Two Ways: Billions of users for Google/Apple products indicate mutual value exchange.
  • “Extraction” Misconception: Data is non-depletable and ever-multiplying, not like natural resources.
  • Data Agriculture/Digital Alchemy: Authors’ preferred metaphor for repurposing dormant data to create new value.
  • AI Dataset Creation and Copyright Concerns:How LLMs are trained on massive public repositories (Common Crawl, The Pile, C4) without explicit copyright holder consent.
  • The ongoing lawsuits by copyright holders (New York Times, Getty Images, authors/artists).
  • The need for novel solutions for licensing at scale if courts rule against fair use.
  • The Private Commons Defined:Resources characterized by shared open access and communal stewardship.
  • Shift from natural resources to public parks, libraries, and creative works.
  • Elinor Ostrom’s narrower definition of “common-pool resources” with defined communities and governance.
  • Authors’ concept of “private commons” for commercial platforms (Google Maps, Yelp, Wikipedia, social media) that enlist users as producers/stewards and offer free/near-free life-management resources.
  • Consumer Surplus:The difference between what people pay and what they value.
  • Erik Brynjolfsson and Avinash Collis’s research on consumer surplus in the digital economy (e.g., Facebook, search engines, Wikipedia).
  • Argument that digital products can be “better products” (more articles, easier access) while being free.
  • Digital Free-for-All:Hal Varian’s photography example: shift from 80 billion photos costing 50 cents each to 1.6 trillion costing zero, enabling new uses (note-taking).
  • YouTube as a “visually driven, applied-knowledge Wikipedia,” transforming from “fluff” to a comprehensive storehouse of human knowledge.
  • Algorithmic Springboarding: The positive counterpart to algorithmic radicalization, where recommendation algorithms lead to education, self-improvement, and career advancement (e.g., learning Python).
  • The synergistic contributions of private commons elements (YouTube, GitHub, freeCodeCamp, LinkedIn) to skill development and professional growth.
  • “Tragedy of the Commons” in the Digital World:Garrett Hardin’s original concept: overuse of shared resources leads to depletion.
  • Authors’ argument that data is nonrivalrous and ever-multiplying, so limiting its creation/sharing is the real tragedy in the digital world.
  • Example of Waze: more users increase value, not deplete it.
  • Fairness and Value Distribution:The argument that users want their “cut” of Big Tech’s profits.
  • Meta’s ARPU vs. users’ willingness to pay (Brynjolfsson and Collis’s research) suggests mutual value.
  • Distinction between passive data generation and active content creation.
  • Data as a “quasi-public good” that, when shared, benefits users more than platform operators capture.
  • Universal Networked Intelligence:AI’s capacity to analyze and synthesize data dramatically increases the value of the private commons.
  • Multimodal LLMs (GPT-4o): Define their native capabilities (input/output of text, audio, images, video) and the impact on interaction speed and expressiveness.
  • Smartphones as the ideal portal for multimodal AI, extending benefits of the private commons.
  • Future driving apps, “Stairway to Heaven” guitar tutorials, AI travel assistants, and their personalized value.

F. Chapter 5: Testing, Testing 1, 2, ∞ (pages 99-120)

  • “AI Arms Race” Critique: Challenge the common media narrative, arguing it misrepresents AI development as reckless.
  • Temporal Component of AI Development: Acknowledge rapid progression similar to the Space Race (Sputnik to Apollo 11).
  • AI Development Culture: Emphasize the prevalence of “extreme data nerds” and “eye-glazingly comprehensive testing.”
  • Turing Test: Introduce its historical significance as an early method for evaluating machine intelligence.
  • Competition as Regulation:Benchmarks: Define as standardized tests created by third parties to measure system performance (e.g., IBM Deep Blue, Watson).
  • SuperGLUE: Example of a benchmark testing language understanding (reading comprehension, word sense disambiguation, coreference resolution).
  • Public Leaderboards: How they promote transparency, accountability, and continuous improvement, functioning as a “communal Olympics.”
  • Benchmarks vs. Regulations: Benchmarks are dynamic, incentivize improvement, and are “regulation, gamified,” unlike static, compliance-focused laws.
  • Measuring What Flatters? (Benchmark Categories):Beyond accuracy/performance: benchmarks for fairness, reliability, consistency, resilience, explainability, safety, privacy, usability, scalability, accessibility, cost-effectiveness, commonsense reasoning, dialogue.
  • Examples: RealToxicityPrompts, StereoSet, HellaSwag, A12 Reasoning Challenge (ARC).
  • How benchmarks track progress (e.g., InstructGPT vs. GPT-3 vs. GPT-4 on toxicity).
  • Benchmark Obsolescence: How successful benchmarks can inspire so much improvement that models “saturate” them.
  • “Cheating” and Data Contamination:Skeptics’ argument that large models “see the answers” due to exposure to test data during training.
  • Developers’ efforts to prevent data contamination and ensure genuine progress.
  • Persistent Errors vs. True Understanding:Gloomers’ argument that errors (hallucinations, logic problems, “brittleness”) indicate a lack of true generalizable understanding (e.g., toaster-zebra example).
  • Authors’ counter: humans also make errors; focus should be on acceptable error rates and continuous improvement, not perfection.
  • Interpretability and Explainability:Define these concepts (predicting model results, explaining decision-making).
  • Authors’ argument: while important, absolute interpretability/explainability is unrealistic and less important than what a model does, especially its scale.
  • Societal Utility over Technical Capabilities: Joseph Weizenbaum’s argument that “ordinary people” ask “is it good?” and “do we need these things?” emphasizing usefulness.
  • Chatbot Arena:An open-source platform for public evaluation of LLMs through blind, head-to-head comparisons.
  • How it drives improvement through “general customer satisfaction” and a public leaderboard.
  • “Regulation, the Internet Way”: Nick Grossman’s concept of shifting from “permission” to “accountability” through transparent reputation scores.
  • Its resistance to gaming, and potential for granular assessment and data aggregation (factual inaccuracies, toxicity, emotional intelligence).
  • Its role in democratizing AI governance and building trust through transparency.

G. Chapter 6: Innovation Is Safety (pages 121-141)

  • Innovation vs. Prudence: The dilemma of balancing rapid development with safety.
  • Innovation as Safety: The argument that rapid, adaptive development (shorter cycles, frequent updates) leads to safer products, especially in software.
  • Global Context of AI: Maintaining America’s “innovation power” is a key safety priority, infusing democratic values into AI.
  • Precautionary Principle vs. Permissionless Innovation:Precautionary Principle: “Guilty until proven innocent” for new technologies; shifts burden of proof to innovators; conservative, “better safe than sorry” approach (e.g., GMOs, GDPR, San Francisco robot ban, Portland facial recognition ban, NYC autonomous vehicle rule, Virginia facial recognition ban).
  • Permissionless Innovation: Ample breathing room for experimentation, adaptation, especially when harms are unproven or covered by existing regulations.
  • Government’s Role in Permissionless Innovation:The intentional policy choices in the 1990s that fostered the internet’s growth (National Science Foundation relaxing commercial use restrictions, Section 230, “Framework for Global Economic Commerce”).
  • The economic and job growth that followed.
  • Public Sentiment Shift: How initial excitement for tech eventually led to scrutiny and calls for precautionary measures (e.g., #DeleteFacebook, Cambridge Analytica scandal).
  • Critique of “Beyond a Reasonable Doubt” for AI: The Future of Life Institute’s call for a pause until AI is “safe beyond a reasonable doubt” is an “illogical extreme,” flipping legal standards and inhibiting progress.
  • Iterative Deployment and Learning: Reinforce that iterative deployment is a mechanism for rapid learning, progress, and safety, by engaging millions of users in real-world scenarios.
  • Automobility as a Historical Analogy:Cars as “personal mobility machines” and “Ferraris of the mind.”
  • Early harms (fatalities) but also solutions (electric starters, road design, traffic signals, driver’s licenses) driven by innovation and iterative regulation.
  • The role of “unfettered experimentation” (speed tests, races) in driving safety improvements.
  • The Problem Cars Solved: Horse manure, accidents, limited travel.
  • Early Opposition: “Devil wagons,” “death cars,” opposition from farmers and in Europe.
  • Network Effects of Automobility: How increased adoption led to infrastructure development, economic growth, and expanded choices.
  • Fatality Rate Reduction: Dramatic improvement in driving safety over the century.
  • AI and Automobility Parallel: The argument that AI, like cars, will introduce risks but ultimately amplify individual agency and life choices, making a higher tolerance for error and risk reasonable.

H. Chapter 7: Informational GPS (pages 143-165)

  • Evolution of Maps and GPS:Paper Maps: Unwieldy, hard to update, dangerous.
  • GPS Origin: Department of Defense project, made available for civilian use by Ronald Reagan (Korean passenger jet incident).
  • Selective Availability: Deliberate scrambling of civilian GPS signals for national security, later lifted by Bill Clinton to boost private-sector innovation.
  • FCC Requirement: Mandating GPS in cell phones for 911 calls, accelerating adoption.
  • “Map Every Meter” Prediction (James Spohrer): Initial fears of over-legibility vs. actual benefits (environmental protection, planned travel, discovering new places).
  • Economic Benefits of GPS: Trillions in economic benefits.
  • Informational GPS Analogy for LLMs:Leveraging Big Data for Big Knowledge: How GPS turns spatial/temporal data into context-aware guidance.
  • Enhancing Individual Agency: LLMs as tools to navigate complex informational environments and make better-informed decisions.
  • Decentralized Development: Contrast GPS’s military-controlled development with LLMs’ global, diverse origins (open-source, proprietary, APIs).
  • “Informational Planet” Concept: Each LLM effectively creates a unique, human-constructed “informational planet” and map, which can change.
  • LLMs for Navigating Informational Environments:Upskilling: How LLMs offer “accelerated fluency” in various domains, acting as a democratizing force.
  • Productivity Gains: Studies showing LLMs increase speed and quality, especially for less-experienced workers (e.g., MIT study on writing tasks, customer service study).
  • Democratizing Effect of Machine Intelligence: Bridging access gaps for those lacking traditional human intelligence clusters (e.g., college applications, legal aid, non-native speakers, dyslexia, vision/hearing impairments).
  • Screenshots (Google Pixel 9): AI making photographic memory universal.
  • Challenging “Band-Aid Fixes” Narrative: Countering the idea that automated services for underserved communities are low-quality or misguided.
  • LLMs as Accessible, Patient, Grudgeless Tutors/Advisors: Their unique qualities for busy executives and under-resourced individuals.
  • Agentic AI Systems:Beyond Question-Answering: LLMs that can autonomously plan, write, run, and debug code (Code Interpreter, AutoGPT).
  • Multiply Human Productivity: The ability of agentic AIs to work on multiple complex tasks simultaneously.
  • Multi-Turn Dialogue Remains Key: Emphasize that better agentic AIs will also improve listening and interaction in one-to-one conversations, leading to more precise control.
  • User Intervention and Feedback: How users can mitigate weaknesses (hallucinations, bias) by challenging/correcting outputs, distinguishing LLMs from earlier AIs.
  • Custom Instructions: Priming LLMs with values and desired responses.
  • “Steering Toward the Result You Desire”: Users’ unprecedented ability to redirect content and mitigate bias.
  • “Latent Expertise”: How experts, through specific prompts, unlock deeper knowledge within LLMs.
  • Providing “Coordinates”: The importance of specific instructions (what, why, who, role, learning style) for better LLM responses.
  • GPS vs. LLM Risks: While GPS has risks, its overall story is massively beneficial. The argument for broadly distributed, hands-on AI to achieve similar value.
  • Accelerating Adoption: Clinton’s decision to accelerate GPS access as a model for AI.

I. Chapter 8: Law Is Code (pages 167-184)

  • Google’s Mission Statement: “To organize the world’s information and make it universally accessible and useful.”
  • “The Net Interprets Censorship as Damage”: John Gilmore’s view of the internet’s early resistance to control.
  • Code, and Other Laws of Cyberspace (Lawrence Lessig):Central Thesis: Code is Law: How software developers, through architecture, determined the rules of engagement in early internet.
  • Four Constraints on Behavior: Laws, norms, markets, and architecture.
  • Commercialization as Trojan Horse: How online commerce, requiring identity and data (credit card numbers, mailing addresses, user IDs, tracking cookies), led to centralization and “architectures of control.”
  • Lessig’s Perspective: Not opposed to regulation, but highlighting trade-offs and political nature of internet development.
  • Cyberspace vs. “Real World”: How the internet has become ubiquitous, making “code as law” apply to physical devices (phones, cars, appliances).
  • DADSS (Driver Alcohol Detection System for Safety) Scenario (2027 Chevy Equinox EV):Illustrates “code as law” in a physical context, where a car (NaviTar, LLM-enabled) prevents drunk driving.
  • Debate: dystopian vs. utopian, individual autonomy vs. public safety.
  • Congressional mandate for DADSS.
  • Other Scenarios of Machine Agency and “Perfect Control”:AI in workplace (focus mode, HR notification).
  • Home insurance (smart sensors, decommissioning furnace).
  • Lessig’s concept of “perfect control”: architecture displacing liberty by making compliance unavoidable.
  • “Laws are Dependent on Voluntary Compliance”: Contrast with automated enforcement (sensorized parking meter).
  • “Architectures emerge that displace a liberty that had been sustained simply by the inefficiency of doing anything different.”
  • Shoshana Zuboff’s “Uncontracts”:Self-executing agreements where automated procedures replace promises, dialogue, and trust.
  • Critique: renders human capacities (judgment, negotiation, empathy) superfluous.
  • Authors’ Counter to “Uncontracts”:Consensual automated contracts (smart contracts on blockchain) can be beneficial, ensuring fairness and transparency, reducing power imbalances.
  • Blockchain Technology: Distributed digital ledgers for tamper-resistant transactions (blocks, nodes, consensus mechanisms).
  • Machine Learning in Smart Contracts:Challenges: determinism required for blockchain consensus.
  • Potential: ML algorithms can make code-based rules dynamic and adaptive, replicating human legal flexibility.
  • Example: AI-powered crop insurance dynamically adjusting payouts based on real-time data.
  • New challenges: ambiguity, interpretability (black box), auditability, discrimination.
  • Drafting a New Social Contract:Customers vs. Members (Lessig): Arguing for citizens as “members” with control over architectures shaping their lives.
  • Physical Architecture and Perfect Control: MSG Entertainment’s facial recognition policy to ban litigating attorneys, illustrating AI-enabled physical regulation.
  • Voluntary Compliance and Social Contract Theory (Locke, Rousseau, Jefferson):“Consent of the governed” as an eternal, earned validation.
  • Expressed through civic engagement and embrace/resistance of new technologies.
  • Internet amplifies this process.
  • Pluralism and Dissent: Acknowledging that 100% consensus on AI is neither likely nor desirable in a democracy.
  • Legitimizing AI: Citizen participation (permissionless innovation, iterative deployment) as crucial for building public awareness and consent.

J. Chapter 9: Networked Autonomy (pages 185-204)

  • Future of Autonomous Vehicles: VW Buzz as a vision of fully autonomous (and possibly constrained) travel.
  • Automobility as Collective Action and Liberation through Regulation:Network Effects: Rapid scaling of car ownership leading to consensus and infrastructure.
  • Balancing Act of Freedom: Desiring freedom to act and freedom from harm/risk.
  • Regulation Enabling Autonomy: Driver’s licenses, standardized road design, traffic lights making driving safer and more scalable.
  • The Liberating Limits of Freedom:Freedom is Relational: Not immutable, correlated with technology.
  • 2025 Road Trip vs. Donner Party (1846):Contrast modern constraints (laws, surveillance) with the “freedoms” but extreme risks/hardship of historical travel.
  • Argument that modern regulations and infrastructure enable extraordinary freedom and safety.
  • Printing Press and Freedom of Speech Analogy:Early book production controlled by Church/universities.
  • Printing press led to censorship laws, but also the concept of free speech and laws protecting it (First Amendment).
  • More laws prohibiting speech now, but greater freedom of expression overall.
  • AI and New Forms of Regulation:AI’s parallel processing power can free us from “sluggish neural architecture.”
  • “Democratizing Risk” (Mustafa Suleyman): Growing availability of dual-use devices (drones, robots) gives bad actors asymmetric power, necessitating new surveillance/regulation.
  • Biden’s EO on AI: Mandates for cloud providers to report foreign entities training large AI models.
  • Potential New Security Measures: AI licenses, cryptographic IDs, biometric data, facial recognition.
  • The “Absurd Bargain”: Citizens asked to accept new identity/security measures for machines they view as a threat.
  • “What’s in It for Us?”:Importance of AI benefiting society as a whole, not just individuals.
  • South Korea’s Covid-19 Response: A model of rapid testing, contact tracing, and broad data sharing (GPS, credit card data) over individual privacy, enabled by AI.
  • “Radically Transparent Version of People-Tracking”: Government’s willingness to share data reinforced civic trust and participation.
  • Intelligent Epidemic Early Warning Systems: Vision for future AI-powered public health infrastructure, requiring national consensus.
  • U.S. Advantage: Strong tech companies, academic institutions, government research, large economy.
  • U.S. Challenge: Political and cultural polarization hindering such projects.
  • Networked Autonomy (John Stuart Mill):Individual freedom contributes to societal well-being.
  • Thriving individuals lead to thriving communities, and vice versa.
  • The Interstate Highway System (IHS): A “pre-moonshot moonshot” unifying the nation, enabling economic growth, and directly empowering individual drivers, despite initial opposition (“freeway revolts”).
  • A powerful example of large-scale, coordinated public works shaping a nation’s trajectory.

K. Chapter 10: The United States of A(I)merica (pages 205-217)

  • Donner Party as Avatars of American Dream: Epitomizing exploration, adaptation, self-improvement, and the pursuit of a brighter future.
  • The Luddites (Early 1800s England):Context: Mechanization of textile industry, economic hardship, war with France, wage cuts.
  • Resistance: Destruction of machines, burning factories, targetting exploitative factory system, perceived loss of liberty.
  • Government Response: Frame Breaking Act (death penalty for machine destruction), military deployment.
  • “Loomers FTW!” (Alternate History):Hypothetical scenario where Luddites successfully gained broad support and passed the “Jobs, Safety, and Human Dignity Act (JSHDA),” implementing a strong precautionary mandate for technology.
  • Initial “positive reversal” (factories closed, traditional crafts revived).
  • Long-Term Consequences: England falling behind technologically and economically, brain drain, diminished military power, social stagnation compared to industrialized nations.
  • Authors’ Conclusion from Alternate History: Technologies depicted as dehumanizing often turn out to be humanizing and liberating; lagging in AI adoption has significant negative national and individual impacts (health care, food, talent drain).
  • “Sovereign Scramble”:Eric Schmidt’s Prediction: AI models growing 1,000-10,000 times more powerful, leading to productivity doubling for nations.
  • Non-Zero-Sum Competition: AI benefits are widely available, but relative winners/losers based on adoption speed/boldness.
  • Beyond US vs. China: Democratization of computing power leading to a wider global AI race.
  • Jensen Huang (Nvidia CEO) on “Sovereign AI”: Every country needs to “own the production of their own intelligence” because data codifies culture, society’s intelligence, history.
  • Pragmatic Value of Sovereign AI: Compliance with laws, avoiding sanctions/supply chain disruptions, national security.
  • CHIPS and Science Act: U.S. investment in semiconductor manufacturing for computational sovereignty.
  • AI for Cultural Preservation: Singapore, France using AI to reflect local cultures, values, and norms, and avoid “biases inherited from the Anglo-Saxons.”
  • “Imagined Orders” (Yuval Noah Harari): How national identity is an informational construct, and AI can encompass these.
  • U.S. National AI Strategy:Existing “national champions” (OpenAI, Microsoft, Alphabet, etc.)
  • Risk of turning champions into “also-rans” through antitrust actions and anti-tech sentiments.
  • Need for a “techno-humanist compass” in government, with more tech/engineering expertise.
  • Government for the People:David Burnham’s Concerns (1983): Surveillance poisoning the soul of a nation.
  • Big Other vs. Big Brother: Tech companies taking on the role of technological bogeyman, diverting attention from government surveillance.
  • Harvard CAPS/Harris Poll (2023): Amazon and Google rated highly for favorability, outranking government institutions, due to personal, rewarding experiences.
  • “IRS Prime,” “FastPass”: Vision for convenient, trusted, and efficient government services leveraging AI.
  • South Korea’s Public Services Modernization: Consolidating services and using AI to notify citizens of benefits.
  • Opportunity for Civic Participation: Using AI to connect citizens to legislative processes.
  • Rational Discussion at Scale:Orwell’s Telescreens: Two-way devices, but citizens didn’t speak back; authors argue screens can be communication devices if government commits to listening.
  • “Government 2.0” (Tim O’Reilly): Government as platform/facilitator of civic action.
  • Remesh (UN tool): Using AI for rapid assessment of needs/opinions in conflict zones, enabling granular and actionable feedback.
  • Polis (Computational Democracy Project): Open-source tool for large-scale conversations, designed to find consensus (e.g., Uber in Taiwan).
  • AI for Policymaking: Leading to bills reflecting public will, increasing trust, reducing polarization, allowing citizens to propose legislation.
  • Social Media vs. Deliberation Platforms: Social media rewards provocation; Polis/Remesh emphasize compromise and consensus.
  • Ambitious Vision: Challenges lawmakers to be responsive, citizens to engage in good faith, and politics to be pragmatic.
  • The Future Vision: AI as an “extension of individual human wills” and a force for collective benefit (mental health, education, legal advice, scientific discovery, entrepreneurship), leading to “superagency.”

L. Chapter 11: You Can Get There from Here (pages 229-232)

  1. Four Fundamental Principles:Designing for human agency for broadly beneficial outcomes.
  2. Shared data and knowledge as catalysts for empowerment.
  3. Innovation and safety are synergistic, achieved through iterative deployment.
  4. Superagency: compounding effects of individual and institutional AI use.
  • Uncharted Frontiers: Acknowledge current uncertainty about the future due to machine learning advances.
  • Technology as Key to Human Flourishing: Contrast a world without technology (smaller numbers, shorter lives, less agency) with one empowered by it.
  • “What Could Possibly Go Right” Mindset Revisited:Historical examples (automobiles, smartphones) demonstrate that focusing on potential benefits, despite risks, leads to profound improvements.
  • Iterative deployment, market economies, and democratic oversight steer technologies towards human agency.
  • AI as a Strategic Asset for Existential Threats:AI can reduce risks and mitigate impacts of pandemics, climate change, asteroid strikes, supervolcanoes.
  • Encourage an “exploratory, adaptive, forward-looking mindset” to leverage AI’s upsides.
  • Techno-Humanist Compass and Consent of the Governed: Reiterate these guiding principles for a future of greater human manifestation.

II. Quiz: Short Answer Questions

Answer each question in 2-3 sentences.

  1. What is the “techno-humanist compass” and why do the authors believe it’s crucial for navigating the AI future?
  2. Explain the concept of “iterative deployment” as it relates to OpenAI and AI development.
  3. How do the authors differentiate between “Doomers,” “Gloomers,” “Zoomers,” and “Bloomers” in their views on AI?
  4. What is a key limitation of Large Language Models (LLMs) regarding their understanding of facts and concepts?
  5. Describe the “black box phenomenon” in LLMs and why it presents a challenge for human overseers.
  6. How do the authors use the historical example of the personal computer to counter Vance Packard’s dystopian predictions about data collection?
  7. Define “consumer surplus” in the context of the digital economy and how it helps explain the value derived from “private commons.”
  8. Why do the authors argue that “innovation is safety,” challenging the precautionary principle in AI development?
  9. Provide two examples of how Informational GPS (LLMs) can democratize access to high-value services for underserved communities.
  10. How does Lessig’s concept of “code is law” become increasingly relevant as the physical and virtual worlds merge with AI?

III. Answer Key (for Quiz)

  1. The techno-humanist compass is a dynamic guiding principle that aims to orient technology development towards broadly augmenting and amplifying individual and collective human agency. It’s crucial because it ensures that technological innovations, like AI, actively enhance what it means to be human, rather than being presented as oppositional forces.
  2. Iterative deployment is OpenAI’s method of introducing new AI products incrementally, without advance notice or excessive hype, and then using continuous public feedback to inform ongoing development efforts. This approach allows society to adapt to changes, builds trust through exposure, and gathers diverse user input for improvement.
  3. Doomers fear extinction-level threats from superintelligent AI, while Gloomers focus on near-term risks like job loss and advocate for prohibitive regulation. Zoomers are optimistic about AI’s benefits and want innovation without government intervention, whereas Bloomers (the authors’ stance) are optimistic but believe mass engagement and continuous feedback are essential for safe, equitable, and useful AI.
  4. LLMs do not “know a fact” or “understand a concept” in the human sense. Instead, they make statistically probable predictions about what tokens (words or fragments) are most likely to follow others in a given context, based on patterns learned from their training data.
  5. The “black box phenomenon” refers to the opaque way complex neural networks operate, identifying patterns that human overseers struggle to discern, making it hard or impossible to explain a model’s outputs or trace its decision-making process. This presents a challenge for building trust and ensuring accountability.
  6. Packard feared that mainframe computers would lead to “humanity in chains” due to data collection, but the authors argue the personal computer actually liberated individuals by enabling self-expression and diverse lifestyles. Big Business used data to personalize services, making people feel “seen” rather than oppressed, which led to a more diverse and inclusive world.
  7. Consumer surplus is the difference between what people pay for a product or service and how much they value it. In the digital economy, free “private commons” services (like Wikipedia or Google Maps) generate massive consumer surplus because users place a high value on them despite paying nothing.
  8. The authors argue that “innovation is safety” because rapid, adaptive development, with shorter product cycles and frequent updates, allows for quicker identification and correction of issues, leading to safer products more effectively than static, precautionary regulations. This approach is exemplified by how the internet fosters continuous improvement through feedback loops.
  9. Informational GPS (LLMs) can democratize access by providing: 1) context and guidance for college applications to low-income students who lack access to expensive human tutors, and 2) immediate explanations of complex legal documents (like “rent arrearage”) in a non-native speaker’s own language, potentially even suggesting next steps or legal aid.
  10. As the physical and virtual worlds merge, code as law means that physical devices (like cars with alcohol-detection systems or instrumented national parks) are increasingly embedded with software that dictates behavior and enforces rules automatically. This level of “perfect control” extends beyond cyberspace, directly impacting real-world choices and obligations in granular ways.

IV. Essay Format Questions (Do not supply answers)

  1. The authors present a significant debate between the “precautionary principle” and “permissionless innovation.” Discuss the core tenets of each, providing historical and contemporary examples from the text. Argue which approach you believe is more suitable for managing the development of advanced AI, supporting your stance with evidence from the reading.
  2. “Human agency” is a central theme throughout the text. Analyze how different technological advancements, from the printing press to AI, have been perceived as both threats and amplifiers of human agency. Discuss the authors’ “techno-humanist compass” and evaluate how effectively they argue that AI can ultimately enhance individual and collective agency.
  3. The concept of the “private commons” is introduced as a new way to understand value creation in the digital age. Explain what the authors mean by this term, using examples like LinkedIn, Google Maps, and YouTube. Contrast this perspective with Shoshana Zuboff’s “surveillance capitalism” and the “extraction operation” metaphor, assessing the strengths and weaknesses of each argument based on the text.
  4. The text uses several historical analogies (the printing press, the automobile, GPS) to frame the challenges and opportunities of AI. Choose two of these analogies and discuss how effectively they illuminate specific aspects of AI development, adoption, and regulation. What are the strengths of these comparisons, and where do they fall short in fully capturing the unique nature of AI?
  5. “Law is code” and the notion of “perfect control” are explored through scenarios like Driver Alcohol Detection Systems and smart contracts. Discuss the implications of AI-enabled “perfect control” on traditional concepts of freedom, voluntary compliance, and the “social contract.” How do the authors balance the potential benefits (e.g., safety, fairness) with the risks (e.g., loss of discretion, human judgment) in a society increasingly governed by code?

V. Glossary of Key Terms

  • AGI (Artificial General Intelligence): A hypothetical type of AI capable of understanding, learning, and applying intelligence across a wide range of tasks and domains at a human-like level or beyond, rather than being limited to a specific task.
  • Algorithmic Radicalization: A phenomenon where recommendation algorithms inadvertently or intentionally lead users down spiraling paths of increasingly extreme and destructive viewpoints, often associated with social media.
  • Algorithmic Springboarding: The positive counterpart to algorithmic radicalization, where recommendation algorithms guide users towards educational, self-improvement, and career advancement content.
  • “Arms Race” (AI): A common, but critiqued, metaphor in media to describe the rapid, competitive development of AI, often implying recklessness and danger. The authors argue against this characterization.
  • Benchmarks: Standardized tests developed by a third party (often academic institutions or industry consortia) to objectively measure and compare the performance of AI systems on specific tasks, promoting transparency and driving improvement.
  • “Behavioral Surplus”: A term used by Shoshana Zuboff to describe the excess data collected from user behavior beyond what is needed to improve a service, which she argues is then used by surveillance capitalism for prediction and manipulation.
  • “Behavioral Value Reinvestment Cycle”: Zuboff’s term for the initial virtuous use of user data to improve a service, which she claims was abandoned by Google for ad monetization.
  • “Big Other”: Shoshana Zuboff’s term for the “sensate, networked, computational infrastructure” of surveillance capitalism, which she views as replacing Orwell’s “Big Brother.”
  • Bloomers: One of the four key constituencies in the AI debate; fundamentally optimistic, believing AI can accelerate human progress but requires mass engagement and active participation, favoring iterative deployment.
  • “Black Box” Phenomenon: The opacity of complex AI systems, particularly neural networks, where even experts have difficulty understanding or explaining how decisions are made or outputs are generated.
  • Blockchain: A decentralized, distributed digital ledger that records transactions across many computers (nodes) in a secure, transparent, and tamper-resistant way, grouping transactions into “blocks.”
  • “Code is Law”: Lawrence Lessig’s central thesis that the architecture (code) of cyberspace sets the terms for online experience, regulating behavior by determining what is possible or permissible. The authors extend this to physical devices enabled by AI.
  • “Commons”: Resources characterized by shared open access and communal stewardship for individual and community benefit. Traditionally referred to natural resources but expanded to digital ones.
  • “Consent of the Governed”: An Enlightenment-era concept, elaborated by Thomas Jefferson, referring to the implicit agreement citizens make to trade some potential freedoms for the order and security a state can provide, constantly earned and validated through civic engagement.
  • Consumer Surplus: The economic benefit derived when the value a consumer places on a good or service is greater than the price they pay for it. Especially relevant in the digital economy where many services are free.
  • “Data Agriculture” / “Digital Alchemy”: Authors’ metaphors for the process of repurposing, synthesizing, and transforming dormant, underutilized, or narrowly relevant data in novel and compounding ways, arguing it is resourceful and regenerative rather than extractive.
  • Data Contamination (Data Leaking): The phenomenon where an AI model is inadvertently exposed to its test data during training, leading to artificially inflated performance metrics and an inaccurate assessment of its true capabilities.
  • Democratizing Risk: Mustafa Suleyman’s concept that making highly capable AI widely accessible also means distributing its potential risks more broadly, especially with dual-use technologies.
  • Doomers: One of the four key constituencies in the AI debate; believe in worst-case scenarios where superintelligent, autonomous AIs may destroy humanity.
  • Dual-Use Devices: Technologies (like drones or advanced AI models) that can be used for both beneficial and malicious purposes.
  • Evidence-Based Practices (EBPs): Approaches or interventions that have been proven effective through rigorously designed clinical trials and data analysis.
  • “Extraction Operations”: A pejorative term used by critics like Shoshana Zuboff to describe how Big Tech companies allegedly “extract” value from users’ data, implying depletion and exploitation.
  • Explainability (AI): The ability to explain, in understandable terms, how an AI system arrived at a particular decision or output, often after the fact, aiming to demystify its “black box” nature.
  • “Frames”: Predefined structures or scripts used by traditional chatbots (like early mental health chatbots) that give them a somewhat rigid and predictable quality, limiting their nuanced responses.
  • “Freeway Revolts”: Protests that occurred in U.S. cities, primarily in the mid-20th century, against the construction of urban freeways that bisected established neighborhoods, leading to significant alterations or cancellations of proposed routes.
  • Generative AI: Artificial intelligence that can produce various types of content, including text, images, audio, and more, in response to prompts.
  • Gloomers: One of the four key constituencies in the AI debate; highly critical of AI and Doomers, focusing on near-term risks (job loss, disinformation, bias); advocating for prohibitive, top-down regulation.
  • GPUs (Graphic-Processing Units): Specialized electronic circuits designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer for output to a display device; crucial for training and running large AI models.
  • Hallucinations (AI): When AI models generate false information or misleading outcomes that do not accurately reflect the facts, patterns, or associations grounded in their training data. (The text notes “confabulation” as an alternative term.)
  • Human Agency: The capacity of individuals to make their own choices, act independently, and exert influence over their lives, endowing life with purpose and meaning.
  • Informational GPS: An analogy used by the authors to describe how LLMs function as infinitely applicable and extensible maps that help users navigate complex and ever-expanding informational environments with greater certainty and efficiency.
  • Innovation Power: A nation’s capacity to develop and deploy new technologies effectively, which the authors argue is a key safety priority for maintaining democratic values and global influence.
  • Interpretability (AI): The degree to which a human can consistently predict an AI model’s results, focusing on the transparency of its structures and inputs.
  • Iterative Deployment: An approach to AI development (championed by OpenAI) where products are released incrementally, with continuous user feedback informing ongoing refinements, allowing society to adapt and trust to build over time.
  • “Latent Expertise”: Knowledge absorbed implicitly by LLMs through their training that is not immediately apparent, but can be unlocked through specific and expert user prompts.
  • Large Language Models (LLMs): A specific kind of machine learning construct designed for language-processing tasks, using neural network architecture and massive datasets to predict and generate human-like text.
  • “Law is Code”: Lawrence Lessig’s concept that the underlying code or architecture of digital systems (and increasingly physical systems embedded with AI) effectively functions as a regulatory mechanism, setting the rules of engagement and influencing behavior.
  • Multimodal Learning: An AI capability that allows models to process and generate information using multiple forms of media simultaneously, such as text, audio, images, and video.
  • National Data Center: A proposal in the 1960s to consolidate various government datasets into a single, accessible repository for research and policymaking, which faced strong public and congressional opposition due to privacy concerns.
  • Network Effects: The phenomenon where a product or service becomes more valuable as more people use it, exemplified by the automobile and the internet.
  • Networked Autonomy: John Stuart Mill’s philosophical concept that individual freedom, when fostered, contributes to the overall well-being of society, leading to thriving communities that, in turn, strengthen individuals.
  • Neurosymbolic AI: Hybrid AI systems that integrate neural networks (for pattern recognition) with symbolic reasoning (based on explicit, human-defined rules and logic) to overcome limitations of purely connectionist models.
  • Parameters (AI): In a neural network, these function like “tuning knobs” that determine the strength of connections between nodes, adjusted during training to reinforce or reduce associations in data.
  • “Perfect Control”: A concept describing a state where technology, through its architecture and automated enforcement, can compel compliance with rules and laws with uncompromising precision, potentially eliminating human leeway or discretion.
  • Permissionless Innovation: An approach to technology development that advocates for ample breathing space for experimentation and adaptation, without requiring prior approval from official regulators, especially when tangible harms don’t yet exist.
  • Precautionary Principle: A regulatory approach that holds new technologies “guilty until proven innocent,” shifting the burden of proof to innovators to demonstrate safety before widespread deployment, especially when potential harms are uncertain.
  • Pretraining (LLMs): The initial phase of LLM training where the model scans a vast amount of text data to learn associations and correlations between “tokens” (words or word fragments).
  • “Private Commons”: The authors’ term for privately owned or administrated digital platforms that enlist users as producers and stewards, offering free or near-free life-management resources that function as privatized social services and utilities.
  • Problemism: The default mode of “Gloomers,” viewing technology as a suspect, anti-human force, emphasizing critique, precaution, and prohibition over innovation and action.
  • Selective Availability: A U.S. Air Force policy (active from 1990-2000) that deliberately scrambled the signal of GPS available for civilian use, making it ten times less accurate than the military version, due to national security concerns.
  • Smart Contract: A self-executing program stored on a blockchain, containing the terms of an agreement as code. It automatically enforces, manages, and verifies the negotiation or performance of a contract.
  • Solutionism: The belief that even society’s most vexing challenges, including those involving deep political, economic, and cultural inequities, have a simplistic technological fix.
  • “Sovereign AI”: The idea that every country needs to develop and control its own AI infrastructure and models, to safeguard national data, codify its unique culture, and maintain economic competitiveness and national security.
  • Superagency: A new state achieved when a critical mass of individuals, personally empowered by AI, begin to operate at levels that compound through society, leading to broad societal abundance and growth.
  • Superhumane: A future vision where constant interactions with emotionally attuned AI models help humans become nicer, more patient, and more emotionally generous versions of themselves.
  • Surveillance Capitalism: Shoshana Zuboff’s term for an economic system where companies (like Google and Facebook) profit from the pervasive monitoring of users’ behavior and data to predict and modify their actions, particularly for advertising.
  • “Techno-Humanist Compass”: A dynamic guiding principle suggesting that technological innovation and humanism are integrative forces, and that technology should be steered towards broadly augmenting and amplifying individual and collective human agency.
  • Telescreens: Fictional two-way audiovisual devices in George Orwell’s 1984 that broadcast state propaganda while simultaneously surveilling citizens, serving as a powerful symbol of dystopian technological control.
  • “The Tragedy of the Commons”: Garrett Hardin’s concept that individuals, acting in their own self-interest, will deplete a shared, open-access resource through overuse. The authors argue this doesn’t apply to nonrivalrous digital data.
  • Tokens: Words or fragments of words that LLMs process and generate, representing the basic units of language in their models.
  • Turing Test: A test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
  • “Uncontracts”: Shoshana Zuboff’s term for self-executing agreements mediated by code that manufacture certainty by replacing human elements like promises, dialogue, shared meaning, and trust with automated procedures.
  • Zoomers: One of the four key constituencies in the AI debate; argue that AI’s productivity gains and innovation will far exceed negative impacts, generally skeptical of precautionary regulation, desiring complete autonomy to innovate.

“Artificial Intelligence: A Guided Tour” by Melanie Mitchell

Executive Summary

Melanie Mitchell’s Artificial Intelligence: A Guided Tour offers a comprehensive and critical examination of the current state of AI, highlighting its impressive advancements in narrow domains while robustly arguing that true human-level general intelligence remains a distant goal. The author, a long-time AI researcher, frames her exploration through the lens of a pivotal 2014 Google meeting with AI legend Douglas Hofstadter, whose “terror” at the shallow nature of modern AI’s achievements sparked Mitchell’s deeper investigation.

The book traces the history of AI from its symbolic roots to the current dominance of deep learning and machine learning. It delves into key AI applications such as computer vision, game-playing, and natural language processing, showcasing successes but consistently emphasizing their limitations. A central theme is the “barrier of meaning” – the profound difference between human understanding, grounded in common sense, abstraction, and analogy, and the pattern-matching capabilities of even the most sophisticated AI systems. Mitchell expresses concern about overestimating AI’s current abilities, its brittleness, susceptibility to bias and adversarial attacks, and the ethical implications of deploying such systems without full awareness of their limitations. Ultimately, she posits that general human-level AI is “really, really far away” and will likely require a fundamental shift in approach, potentially involving embodiment and more human-like cognitive mechanisms.

Main Themes and Key Ideas/Facts

1. The Enduring Optimism and Recurring “AI Winters”

  • Early Optimism and Overpromising: From its inception at the 1956 Dartmouth workshop, AI has been characterized by immense optimism and bold predictions of imminent human-level intelligence. Pioneers like Herbert Simon predicted machines would “within twenty years, be capable of doing any work that a man can do” (Chapter 1).
  • The Cycle of Hype and Disappointment: AI’s history is marked by a “repeating cycle of bubbles and crashes.” New ideas generate optimism, funding pours in, but “the promised breakthroughs don’t occur, or are much less impressive than promised,” leading to “AI winter” (Chapter 1).
  • Current “AI Spring”: The last decade has seen a resurgence, dubbed “AI spring,” driven by deep learning’s successes, with tech giants investing billions and experts once again predicting near-term human-level AI (Chapter 3).

2. The Distinction Between Narrow/Weak AI and General/Strong AI

  • Narrow AI’s Successes: Current AI, even in its most impressive forms like AlphaGo or Google Translate, is “narrow” or “weak” AI, meaning it “can perform only one narrowly defined task (or a small set of related tasks)” (Chapter 3). Examples include:
  • IBM’s Deep Blue defeating Garry Kasparov in chess (1997), and later its Watson program winning Jeopardy! (2011).
  • DeepMind’s AlphaGo mastering Go (2016).
  • Advances in speech recognition, Google Translate, and automated image captioning (Chapter 3, 11, 12).
  • Lack of General Intelligence: “A pile of narrow intelligences will never add up to a general intelligence. General intelligence isn’t about the number of abilities, but about the integration between those abilities” (Chapter 3). These systems cannot “transfer” what they’ve learned from one task to a different, even related, task (Chapter 10).
  • The “Easy Things Are Hard” Paradox: Tasks easy for young children (e.g., natural language conversation, describing what they see) have proven “harder for AI to achieve than diagnosing complex diseases, beating human champions at chess and Go, and solving complex algebraic problems” (Chapter 1). “In general, we’re least aware of what our minds do best” (Chapter 1).

3. Deep Learning: Its Power and Limitations

  • Dominant Paradigm: Since the 2010s, deep learning (deep neural networks) has become the “dominant AI paradigm” and is often inaccurately equated with AI itself (Chapter 1).
  • How Deep Learning Works (Simplified): Inspired by the brain’s visual system, Convolutional Neural Networks (ConvNets) use layers of “units” to detect increasingly complex features in data (e.g., edges, then shapes, then objects in images). Recurrent Neural Networks (RNNs) process sequences like sentences, “remembering” context through recurrent connections (Chapter 4, 11).
  • Supervised Learning and Big Data: Deep learning’s success heavily relies on “supervised learning,” where systems are trained on massive datasets of human-labeled examples (e.g., ImageNet for computer vision, sentence pairs for translation). This requires “a huge amount of human effort… to collect, curate, and label the data, as well as to design the many aspects of the ConvNet’s architecture” (Chapter 6).
  • The “Alchemy” of Hyperparameter Tuning: Optimizing deep learning systems is not a science but “a kind of alchemy,” requiring specialized “network whispering” skills to tune “hyperparameters” (e.g., number of layers, learning rate) (Chapter 6).
  • Lack of Human-like Learning: Unlike children who learn from few examples, deep learning requires millions of examples and passive training. It doesn’t learn “on its own” in a human-like sense or infer abstractions and connections between concepts (Chapter 6).
  • Brittleness and Vulnerability: Even successful AI systems are “brittle” and prone to errors when inputs deviate slightly from training data.
  • Overfitting: ConvNets “overfitting to their training data and learning something different from what we are trying to teach them,” leading to poor performance on novel, slightly different images (Chapter 6).
  • Long-tail Problem: Real-world scenarios have a “long tail” of unlikely but possible situations not present in training data, making systems vulnerable (e.g., self-driving cars encountering unusual road conditions) (Chapter 6).
  • Adversarial Examples: Deep neural networks are “easily fooled” by “adversarial examples” – minuscule, human-imperceptible changes to inputs that cause confident misclassification (e.g., school bus as ostrich, modified audio transcribing to malicious commands) (Chapter 6, 13).

4. The “Barrier of Meaning”: What AI Lacks

  • Absence of Understanding: A core argument is that no AI system “yet possesses such understanding” that humans bring to situations. This lack is revealed by “un-humanlike errors,” “difficulties with abstracting and transferring,” “lack of commonsense knowledge,” and “vulnerability to adversarial attacks” (Chapter 14).
  • Common Sense (Intuitive Knowledge): Humans possess innate and early-learned “core knowledge” or “common sense” in intuitive physics, biology, and psychology. This allows understanding of object behavior, living things, and other people’s intentions (Chapter 14). This is “missing in even the best of today’s AI systems” (Chapter 7).
  • Efforts like Douglas Lenat’s Cyc project to manually encode common sense have been “heroic” but ultimately “not led to an AI system being able to master even a simple understanding of the world” (Chapter 15).
  • Abstraction and Analogy: These are “two fundamental human capabilities” crucial for forming concepts and understanding new situations. Abstraction involves recognizing specific instances as part of a general category, while analogy is “the perception of a common essence between two things” (Chapter 14). Current AI systems, including ConvNets, “do not have what it takes” for human-like abstraction and analogy-making, even in idealized problems like Bongard puzzles (Chapter 15).
  • The author’s own work, like the Copycat program, aimed to model these abilities but “only scratched the surface” (Chapter 15).
  • The Role of Embodiment: The “embodiment hypothesis” suggests that human-level intelligence requires a body that interacts with the world. Without physical experience, a machine may “never be able to learn all that’s needed” for robust understanding (Chapter 3, 15).

5. Ethical Considerations and Societal Impact

  • The Great AI Trade-Off: Society faces a dilemma: embrace AI’s benefits (e.g., health care, efficiency) or be cautious due to its “unpredictable errors, susceptibility to bias, vulnerability to hacking, and lack of transparency” (Chapter 7).
  • Bias in AI: AI systems reflect and can magnify biases present in their training data (e.g., face recognition systems being less accurate on non-white or female faces; word vectors associating “computer programmer” with “man” and “homemaker” with “woman”) (Chapter 6, 11).
  • Explainable AI: The “impenetrability” of deep neural networks, making it difficult to understand how they arrive at decisions, is “the dark secret at the heart of AI.” This lack of transparency hinders trust and makes predicting/fixing errors difficult (Chapter 6).
  • Moral AI: Programming machines with a human-like sense of morality for autonomous decision-making (e.g., self-driving car “trolley problem” scenarios) is incredibly challenging, requiring the very common sense that AI lacks (Chapter 7).
  • Regulation: There’s a growing call for AI regulation, but challenges include defining “meaningful information” for explanations and who should regulate (Chapter 7).
  • Job Displacement: While AI has historically automated undesirable jobs, the potential for massive unemployment, especially in fields like driving, remains a significant, though uncertain, concern (Chapter 7, 16).
  • “Machine Stupidity” vs. Superintelligence: The author argues that the immediate worry is “machine stupidity” – machines making critical decisions without sufficient intelligence – rather than an imminent “superintelligence” that “will take over the world” (Chapter 16).

6. The Turing Test and the Singularity

  • Turing Test Controversy: Alan Turing’s “imitation game” proposes that if a machine can be indistinguishable from a human in conversation, it should be considered to “think.” However, experts largely dismiss recent “wins” (like Eugene Goostman) as “publicity stunts” based on superficial trickery and human anthropomorphism (Chapter 3).
  • Ray Kurzweil’s Singularity: Kurzweil, a prominent futurist and Google engineer, predicts an “AI Singularity” by 2045, where AI “exceeds human intelligence” due to “exponential progress” in technology (Chapter 3).
  • Skepticism of the Singularity: Mitchell, like many AI researchers, is “dismissively skeptical” of Kurzweil’s predictions, arguing that software progress hasn’t matched hardware, and he vastly underestimates the complexity of human intelligence (Chapter 3). Hofstadter also expressed “terror” that this vision trivializes human depth (Prologue).
  • “Prediction is hard, especially about the future”: The timeline for general AI is highly uncertain, with estimates ranging from decades to “never” among experts (Chapter 16).

Conclusion

Melanie Mitchell’s book serves as a vital call for realism in the discourse surrounding AI. While acknowledging the remarkable utility and commercial success of deep learning in specific domains, she persistently underscores that these achievements do not equate to human-level understanding or general intelligence. The “barrier of meaning,” rooted in AI’s lack of common sense, abstraction, and analogy-making abilities, remains a formidable obstacle. The book urges a cautious and critical approach to AI deployment, emphasizing the need for robust, transparent, and ethically considered systems, and reminds readers that the true complexity and subtleties of human intelligence are often underestimated.

Contact Factoring Specialist, Chris Lehnes

Melanie Mitchell's Artificial Intelligence: A Guided Tour offers a comprehensive and critical examination of the current state of AI, highlighting its impressive advancements in narrow domains while robustly arguing that true human-level general intelligence remains a distant goal. The author, a long-time AI researcher, frames her exploration through the lens of a pivotal 2014 Google meeting with AI legend Douglas Hofstadter, whose "terror" at the shallow nature of modern AI's achievements sparked Mitchell's deeper investigation.

The Landscape of Artificial Intelligence: A Study Guide

I. Detailed Study Guide

This study guide is designed to help you review and deepen your understanding of the provided text on Artificial Intelligence by Melanie Mitchell.

Part 1: Foundations and Early Development of AI

  1. The Genesis of AI
  • Dartmouth Workshop (1956): Understand its purpose, key figures (McCarthy, Minsky, Shannon, Rochester, Newell, Simon), the origin of the term “Artificial Intelligence,” and the initial optimism surrounding the field.
  • Early Predictions: Recall the bold forecasts made by pioneers like Herbert Simon and Marvin Minsky about the timeline for achieving human-level AI.
  • The “Suitcase Word” Problem: Grasp why “intelligence” is a “suitcase word” in AI and how this ambiguity has influenced the field’s growth.
  • The Divide: Symbolic vs. Subsymbolic AI:Symbolic AI: Define its core principles (human-understandable symbols, explicit rules), recall examples like the General Problem Solver (GPS) and MYCIN, and understand its strengths (interpretable reasoning) and weaknesses (brittleness, difficulty with subconscious knowledge).
  • Subsymbolic AI: Define its core principles (brain-inspired, numerical operations, learning from data), recall early examples like the perceptron, and understand its strengths (perceptual tasks) and weaknesses (hard to interpret, limited problem-solving initially).
  1. The Perceptron and Early Neural Networks
  • Inspiration from Neuroscience: Understand how the neuron’s structure and function (inputs, weights, threshold, firing) inspired the perceptron.
  • Perceptron Mechanism: Describe how a perceptron processes numerical inputs with weights to produce a binary output (1 or 0).
  • Supervised Learning and Perceptrons: Explain supervised learning in the context of perceptrons (training examples, labels, supervision signal, adjustment of weights and threshold). Differentiate between training and test sets.
  • The Perceptron-Learning Algorithm: Summarize its process (random initialization, iterative adjustment based on error, gradual learning).
  • Limitations and the “AI Winter”:Minsky & Papert’s Critique: Understand their mathematical proof of perceptron limitations and their skepticism about multilayer neural networks.
  • Impact on Research and Funding: Explain how Minsky and Papert’s work, combined with overpromising, led to a decrease in neural network research and contributed to the “AI Winter.”
  • Recurring Cycles: Recognize the “AI spring” and “AI winter” pattern in AI history, driven by optimism, hype, and unfulfilled promises.
  1. The “Easy Things Are Hard” Paradox:
  • Minsky’s Observation: Understand this paradox in AI, where tasks easy for humans (e.g., natural language, common sense) are difficult for machines, and vice versa (e.g., complex calculations).
  • Implications: Reflect on how this paradox highlights the complexity and subtlety of human intelligence.

Part 2: The Deep Learning Revolution and Its Implications

  1. Rise of Deep Learning:
  • Multilayer Neural Networks: Define them and differentiate between shallow and deep networks (number of hidden layers). Understand the role of “hidden units” and “activations.”
  • Back-Propagation: Explain its role as a general learning algorithm for multilayer neural networks (propagating error backward to adjust weights).
  • Connectionism: Understand its core idea (knowledge in weighted connections) and its contrast with symbolic AI (expert systems’ brittleness due to lack of subconscious knowledge).
  • The “Deep Learning” Gold Rush:Key Catalysts: Identify the factors that led to the resurgence of deep learning (big data, increased computing power/GPUs, improved training methods).
  • Pervasive AI: Recall examples of how deep learning has become integrated into everyday technologies and services (Google Translate, self-driving cars, virtual assistants, facial recognition).
  • Acqui-Hiring: Understand the trend of tech companies acquiring AI startups for their talent.
  1. Computer Vision and ImageNet:
  • Challenges of Object Recognition: Detail the difficulties computers face in recognizing objects (pixel variations, lighting, occlusion, diverse appearances).
  • Convolutional Neural Networks (ConvNets):Biological Inspiration: Understand how Hubel and Wiesel’s discoveries about the visual cortex (hierarchical organization, edge detectors, receptive fields) inspired ConvNets (e.g., neocognitron).
  • Mechanism: Describe how ConvNets use layers of units and “activation maps” to detect increasingly complex features through “convolutions.”
  • Training: Explain how ConvNets learn features and weights through back-propagation and the necessity of large labeled datasets.
  • ImageNet and Its Impact:Creation: Understand the role of WordNet and Amazon Mechanical Turk in building ImageNet, a massive labeled image dataset.
  • Competitions: Describe the ImageNet Large Scale Visual Recognition Challenge and AlexNet’s breakthrough win in 2012, which signaled the dominance of ConvNets.
  • “Surpassing Human Performance”: Critically analyze claims of machines surpassing human performance in object recognition, considering caveats like top-5 accuracy, limited human baselines, and correlation vs. understanding.
  1. Limitations and Trustworthiness of Deep Learning:
  • “Learning on One’s Own” – A Misconception: Understand the significant human effort (data collection, labeling, hyperparameter tuning, “network whispering”) required for ConvNet training, challenging the idea of autonomous learning.
  • The Long-Tail Problem: Explain this phenomenon in real-world AI applications (e.g., self-driving cars), where rare but possible “edge cases” are difficult to train for with supervised learning, leading to fragility.
  • Overfitting and Brittleness: Understand how ConvNets can overfit to training data, leading to poor performance on slightly varied or “out-of-distribution” images (e.g., robot photos vs. web photos, slight image perturbations).
  • Bias in AI: Discuss how biases in training data (e.g., face recognition datasets skewed by race/gender) can lead to discriminatory outcomes in AI systems.
  • Lack of Explainability (“Show Your Work”):”Dark Secret”: Understand why deep neural networks are often “black boxes” and why their decisions are hard for humans to interpret.
  • Trust and Prediction: Explain why this lack of transparency makes it difficult to trust AI systems or predict their failures.
  • Explainable AI: Recognize this as a growing research area aiming to make AI decisions more understandable.
  • Adversarial Examples: Define and illustrate how subtle, human-imperceptible changes to input data can drastically alter a deep neural network’s output, highlighting the systems’ superficiality and vulnerability to attack (e.g., school bus to ostrich, patterned eyeglasses, traffic sign stickers).

Part 3: Learning Through Reinforcement and Natural Language Processing

  1. Reinforcement Learning:
  • Operant Conditioning Inspiration: Understand how this psychological concept (rewarding desired behavior) is foundational to reinforcement learning.
  • Contrast with Supervised Learning: Differentiate reinforcement learning (intermittent rewards, no labeled data, exploration) from supervised learning (labeled data, direct error signal).
  • Key Concepts:Agent: The learning program.
  • Environment: The simulated world where the agent acts.
  • Rewards: Feedback from the environment.
  • State: The agent’s perception of its current situation.
  • Actions: Choices the agent can make.
  • Q-Table / Q-Learning: A table storing the “value” of performing actions in different states, updated through trial and error.
  • Exploration vs. Exploitation: The balance between trying new actions and sticking with known good ones.
  • Deep Q-Learning:Integration with Deep Neural Networks: Explain how a ConvNet replaces the Q-table to estimate action values in complex, infinite state spaces (e.g., Atari games).
  • Temporal Difference Learning: Understand how “learning a guess from a better guess” works to update network weights without explicit labels.
  • Game-Playing Successes:Atari Games (DeepMind): Describe how deep Q-learning achieved superhuman performance on many Atari games, discovering clever strategies (e.g., Breakout tunneling).
  • Go (AlphaGo):Grand Challenge: Understand why Go was harder for AI than chess (larger game tree, lack of good evaluation function, reliance on human intuition).
  • AlphaGo’s Approach: Explain the combination of deep Q-learning and Monte Carlo Tree Search, and its self-play learning mechanism.
  • “Kami no itte”: Recall AlphaGo’s “divine moves” and their impact.
  • Transfer Limitations: Emphasize that AlphaGo’s skills are not generalizable to other games without retraining (“idiot savant”).
  1. Natural Language Processing (NLP):
  • Challenges of Human Language: Highlight the inherent ambiguity, context dependence, and reliance on vast background knowledge in human language.
  • Early Approaches: Recall the limitations of rule-based NLP.
  • Statistical and Deep Learning Approaches: Understand the shift to data-driven methods and the current focus on deep learning.
  • Speech Recognition:Deep Learning’s Impact: Recognize its significant improvement since 2012, achieving near-human accuracy in quiet environments.
  • Lack of Understanding: Emphasize that this achievement occurs without actual comprehension of meaning.
  • “Last 10 Percent”: Discuss the remaining challenges (noise, accents, unknown words, ambiguity, context) and the potential need for true understanding.
  • Sentiment Classification: Explain its purpose (determining positive/negative sentiment) and commercial applications, noting the challenge of gleaning sentiment from context.
  • Recurrent Neural Networks (RNNs):Sequential Processing: Understand how RNNs process variable-length sequences (words in a sentence) over time, using recurrent connections to maintain context.
  • Encoder Networks: Describe how they encode an entire sentence into a fixed-length vector representation.
  • Long Short-Term Memory (LSTM) Units: Understand their role in preventing information loss over long sentences.
  • Word Vectors (Word Embeddings):Limitations of One-Hot Encoding: Explain why arbitrary numerical assignments fail to capture semantic relationships.
  • Distributional Semantics (“You shall know a word by the company it keeps”): Understand this core linguistic idea.
  • Semantic Space: Conceptualize words as points in a multi-dimensional space, where proximity indicates semantic similarity.
  • Word2Vec: Describe this method for automatically learning word vectors from large text corpora, and how it captures relationships (e.g., country-capital analogies).
  • Bias in Word Vectors: Discuss how societal biases in language data are reflected and amplified in word vectors, leading to biased NLP outputs.
  1. Machine Translation and Image Captioning:
  • Early Approaches: Recall the rule-based and statistical methods for machine translation.
  • Neural Machine Translation (NMT):Encoder-Decoder Architecture: Explain how an encoder RNN creates a sentence representation, which is then used by a decoder RNN to generate a translation.
  • “Human Parity” Claims: Critically evaluate these claims, considering limitations like averaging ratings, focus on isolated sentences, and use of carefully written text.
  • “Lost in Translation”: Illustrate with examples (e.g., “Restaurant” story) how NMT struggles with ambiguous words, idioms, and context, due to lack of real-world understanding.
  • Automated Image Captioning: Describe how an encoder-decoder system can “translate” images into descriptive sentences, and its limitations (lack of understanding, focus on superficial features).
  1. Question Answering and the Barrier of Meaning:
  • IBM Watson on Jeopardy!:Achievement: Describe Watson’s success in interpreting pun-laden clues and winning against human champions.
  • Mechanism: Briefly outline its use of diverse AI methods, rapid search through databases, and confidence scoring.
  • Limitations and Anthropomorphism: Discuss how Watson’s un-humanlike errors and carefully designed persona masked a lack of true understanding and generality.
  • “Watson” as a Brand: Understand how the name “Watson” evolved to represent a suite of AI services rather than a single coherent intelligent system.
  • Reading Comprehension (SQuAD):SQuAD Dataset: Describe this benchmark for machine reading comprehension, noting its design for “answer extraction” rather than true understanding.
  • “Surpassing Human Performance”: Again, critically evaluate claims, highlighting the limited scope of the task (answer present in text, Wikipedia articles) and the lack of “reading between the lines.”
  • Winograd Schemas:Purpose: Understand these as tests requiring commonsense knowledge to resolve pronoun ambiguity.
  • Machine Performance: Note the limited success of AI systems, which often rely on statistical co-occurrence rather than understanding.
  • Adversarial Attacks on NLP Systems: Extend the concept of adversarial examples to text (e.g., image captions, speech recognition, sentiment analysis, question answering), showing how subtle changes can fool systems.
  • The “Barrier of Meaning”: Summarize the overarching idea that current AI systems lack a deep understanding of situations, leading to errors, poor generalization, and vulnerability.

Part 4: The Quest for Understanding, Abstraction, and Analogy

  1. Core Knowledge and Intuitive Thinking:
  • Human Core Knowledge: Detail innate or early-learned common sense (object permanence, cause-and-effect, intuitive physics, biology, psychology).
  • Mental Models and Simulation: Understand how humans use these models to predict and imagine future scenarios, supporting the “understanding as simulation” hypothesis.
  • Metaphors We Live By: Explain Lakoff and Johnson’s theory that abstract concepts are understood via metaphors grounded in physical experiences, and how this supports the simulation hypothesis.
  • The Cyc Project:Goal: Describe Lenat’s ambitious attempt to manually encode all human commonsense knowledge.
  • Approach: Understand its symbolic nature (logic-based assertions and inference rules).
  • Limitations: Discuss why it has had limited impact and why encoding subconscious knowledge is inherently difficult.
  1. Abstraction and Analogy Making:
  • Central to Human Cognition: Recognize these as fundamental human capabilities underlying concept formation, perception, and generalization.
  • Bongard Problems:Purpose: Understand these visual puzzles as idealized tests for abstraction and analogy making.
  • Challenges for AI: Explain why ConvNets and other current AI systems struggle with them (limited examples, need to perceive “subtlety of sameness,” irrelevant attributes, novel concepts).
  • Letter-String Microworld (Copycat):Idealized Domain: Understand how this simple domain (e.g., changing ‘abc’ to ‘abd’) reveals principles of human analogy.
  • Conceptual Slippage: Explain this core idea in analogy making, where concepts are flexibly remapped between situations.
  • Copycat Program: Recognize it as an AI system designed to emulate human analogy making, integrating symbolic and subsymbolic aspects.
  • Metacognition: Define this human ability to reflect on one’s own thinking and note its absence in current AI systems (e.g., Copycat’s inability to recognize unproductive thought patterns).
  1. The Embodiment Hypothesis:
  • Descartes’s Influence: Recall the traditional AI assumption of disembodied intelligence.
  • The Argument: Explain the hypothesis that human-level intelligence requires a physical body interacting with the world to develop concepts and understanding.
  • Implications: Consider how this challenges current AI paradigms and the “mind-boggling” complexity of human visual understanding (e.g., Karpathy’s Obama photo example).

Part 5: Future Directions and Ethical Considerations

  1. Self-Driving Cars Revisited:
  • Levels of Autonomy: Understand the six levels defined by the U.S. National Highway Traffic Safety Administration.
  • Obstacles to Full Autonomy (Level 5): Reiterate the long-tail problem, need for intuitive knowledge (physics, biology, psychology of other drivers/pedestrians), and vulnerability to malicious attacks and human pranks.
  • Geofencing and Partial Autonomy: Understand this intermediate solution and its limitations.
  1. AI and Employment:
  • Uncertainty: Acknowledge the debate and lack of clear predictions about AI’s impact on jobs.
  • “Easy Things Are Hard” Revisited: Apply this maxim to human jobs, suggesting many may be harder for AI to automate than expected.
  • Historical Context: Consider how past technologies created new jobs as they displaced others.
  1. AI and Creativity:
  • Defining Creativity: Discuss the common perception of creativity as non-mechanical.
  • Computer-Generated Art/Music: Recognize that computers can produce aesthetically pleasing works (e.g., Karl Sims’s genetic art, EMI’s music).
  • Human Collaboration and Understanding: Argue that true creativity, involving judgment and understanding of what is created, still requires human involvement.
  1. The Path to General Human-Level AI:
  • Current State: Reiterate the consensus that general AI is “really, really far away.”
  • Missing Links: Emphasize the continued need for commonsense knowledge, abstraction, and analogy.
  • Superintelligence Debate:”Intelligence Explosion”: Describe I. J. Good’s theory.
  • Critique: Argue that human limitations (bodies, emotions, “irrationality”) are integral to general intelligence, not just shortcomings.
  • Hofstadter’s View: Recall his idea that intelligent programs might be “slothful in their adding” due to “extra baggage” of concepts.
  1. AI: How Terrified Should We Be?
  • Misconceptions: Challenge the science fiction portrayal of AI as conscious and malevolent.
  • Real Worries (Near-Term): Focus on massive job losses, misuse, unreliability, and vulnerability to attack.
  • Hofstadter’s Terror: Recall his specific fear that human creativity and cognition would be trivialized by superficial AI.
  • The True Danger: “Machine Stupidity”: Emphasize the “tail risk” of brittle AI systems making spectacular failures in “edge cases” they weren’t trained for, and the danger of overestimating their trustworthiness.
  • Ethical AI: Reinforce the need for robust ethical frameworks, regulation, and a diverse range of voices in discussions about AI’s impact.

Part 6: Unsolved Problems and Future Outlook

  1. AI’s Enduring Challenges: Reiterate that most fundamental questions in AI remain unsolved, echoing the original Dartmouth proposal.
  2. Scientific Motivation: Emphasize that AI is driven by both practical applications and deep scientific questions about the nature of intelligence itself.
  3. Human Intelligence as a Benchmark: Conclude that understanding human intelligence is key to further AI progress.

II. Quiz

Instructions: Answer each question in 2-3 sentences.

  1. What was the primary goal of the 1956 Dartmouth workshop, and what lasting contribution did it make to the field of AI?
  2. Explain the “suitcase word” problem as it applies to the concept of “intelligence” in AI, and how this ambiguity has influenced the field.
  3. Describe the fundamental difference between “symbolic AI” and “subsymbolic AI,” providing a brief example of an early system for each.
  4. What was the main criticism Minsky and Papert’s book Perceptrons leveled against early neural networks, and how did it contribute to an “AI Winter”?
  5. Summarize the “easy things are hard” paradox in AI, offering examples of tasks that illustrate this principle.
  6. How did the creation of the ImageNet dataset, facilitated by Amazon Mechanical Turk, contribute to the “deep learning revolution” in computer vision?
  7. Explain why claims of AI “surpassing human-level performance” in object recognition on ImageNet should be viewed with skepticism, according to the text.
  8. Define “adversarial examples” in the context of deep neural networks, and provide one real-world implication of this vulnerability.
  9. What is the core distinction between “supervised learning” and “reinforcement learning,” particularly regarding the feedback mechanism?
  10. Beyond simply playing Go, what fundamental limitation does AlphaGo exhibit that prevents it from being considered truly “intelligent” in a human-like way?

III. Answer Key (for Quiz)

  1. The primary goal of the 1956 Dartmouth workshop was to explore the possibility of creating thinking machines, based on the conjecture that intelligence could be precisely described and simulated. Its lasting contribution was coining the term “artificial intelligence” and outlining the field’s initial research agenda.
  2. “Intelligence” is a “suitcase word” because it’s packed with various, often ambiguous meanings (emotional, logical, artistic, etc.), making it hard to define precisely. This lack of a universally accepted definition has paradoxically allowed AI to grow rapidly by focusing on practical task performance rather than philosophical agreement.
  3. Symbolic AI programs use human-understandable words or phrases and explicit rules to process them, like the General Problem Solver (GPS) for logic puzzles. Subsymbolic AI, inspired by neuroscience, uses numerical operations and learns from data, with the perceptron for digit recognition as an early example.
  4. Minsky and Papert mathematically proved that simple perceptrons had very limited problem-solving capabilities and speculated that multilayer networks would be “sterile.” This criticism, alongside overpromising by AI proponents, led to funding cuts and a slowdown in neural network research, known as an “AI Winter.”
  5. The “easy things are hard” paradox means that tasks effortlessly performed by young children (e.g., natural language understanding, common sense) are extremely difficult for AI, while tasks difficult for humans (e.g., complex calculations, chess mastery) are easy for computers. This highlights the hidden complexity of human cognition.
  6. ImageNet provided a massive, human-labeled dataset of images for object recognition, which was crucial for training deep convolutional neural networks. Amazon Mechanical Turk enabled the efficient and cost-effective labeling of millions of images, overcoming a major bottleneck in data collection.
  7. Claims of AI surpassing humans on ImageNet are often based on “top-5 accuracy,” meaning the correct object is just one of five guesses, rather than the single top guess. Additionally, the human error rate benchmark was derived from a single researcher’s performance, not a representative human group, and machines may rely on superficial correlations rather than true understanding.
  8. Adversarial examples are subtly modified input data (e.g., altered pixels in an image, a few changed words in text) that are imperceptible to humans but cause a deep neural network to misclassify with high confidence. A real-world implication is the potential for malicious attacks on self-driving car vision systems by placing inconspicuous stickers on traffic signs.
  9. Supervised learning requires large datasets where each input is explicitly paired with a correct output label, allowing the system to learn by minimizing error. Reinforcement learning, in contrast, involves an agent performing actions in an environment and receiving only intermittent rewards, learning which actions lead to long-term rewards through trial and error without explicit labels.
  10. AlphaGo is considered an “idiot savant” because its superhuman Go-playing abilities are extremely narrow; it cannot transfer any of its learned skills to even slightly different games or tasks. It lacks the general ability to think, reason, or plan beyond the specific domain of Go, which is fundamental to human intelligence.

IV. Essay Format Questions (No Answers Provided)

  1. Discuss the cyclical nature of optimism and skepticism in the history of AI, specifically referencing the “AI Spring” and “AI Winter” phenomena. How have deep learning’s recent successes both mirrored and potentially diverged from previous cycles?
  2. Critically analyze the claims of AI systems achieving “human-level performance” in domains like object recognition (ImageNet) and machine translation. What caveats and limitations does Melanie Mitchell identify in these claims, and what do they reveal about the difference between statistical correlation and genuine understanding?
  3. Compare and contrast symbolic AI and subsymbolic AI as fundamental approaches to achieving artificial intelligence. Discuss their respective strengths, weaknesses, and the impact of Minsky and Papert’s Perceptrons on the trajectory of subsymbolic research.
  4. Melanie Mitchell dedicates a significant portion of the text to the “barrier of meaning.” Explain what she means by this phrase and how various limitations of current AI systems (e.g., adversarial examples, long-tail problem, lack of explainability, struggles with Winograd Schemas) illustrate AI’s inability to overcome this barrier.
  5. Douglas Hofstadter and other “Singularity skeptics” express terror or concern about AI, but for reasons distinct from those often portrayed in science fiction. Describe Hofstadter’s specific anxieties about AI progress and contrast them with what Melanie Mitchell identifies as the “real problem” in the near-term future of AI.

V. Glossary of Key Terms

  • Abstraction: The ability to recognize specific concepts and situations as instances of a more general category, forming the basis of human concepts and learning.
  • Activation Maps: Grids of units in a convolutional neural network (ConvNet), inspired by the brain’s visual system, that detect specific visual features in different parts of an input image.
  • Activations: The numerical output values of units (simulated neurons) in a neural network, often between 0 and 1, indicating the unit’s “firing strength.”
  • Active Symbols: Douglas Hofstadter’s conception of mental representations in human cognition that are dynamic, context-dependent, and play a crucial role in analogy making.
  • Adversarial Examples: Inputs that are intentionally perturbed with subtle, often human-imperceptible changes, designed to cause a machine learning model to make incorrect predictions with high confidence.
  • AI Winter: A period in the history of AI characterized by reduced funding, diminished public interest, and slowed research due to unfulfilled promises and overhyped expectations.
  • AlexNet: A pioneering convolutional neural network that achieved a breakthrough in the 2012 ImageNet competition, demonstrating the power of deep learning for computer vision.
  • Algorithm: A step-by-step “recipe” or set of instructions that a computer can follow to solve a particular problem.
  • AlphaGo: A Google DeepMind program that used deep Q-learning and Monte Carlo tree search to achieve superhuman performance in the game of Go, notably defeating world champion Lee Sedol.
  • Amazon Mechanical Turk: An online marketplace for “crowdsourcing” tasks that require human intelligence, such as image labeling for AI training datasets.
  • Analogy Making: The perception of a common essence or relational structure between two different things or situations, fundamental to human cognition and concept formation.
  • Anthropomorphize: To attribute human characteristics, emotions, or behaviors to animals or inanimate objects, including AI systems.
  • Artificial General Intelligence (AGI): Also known as general human-level AI or strong AI; a hypothetical form of AI that can perform most intellectual tasks that a human being can.
  • Back-propagation: A learning algorithm used in neural networks to adjust the weights of connections between units by propagating the error from the output layer backward through the network.
  • Barrier of Meaning: Melanie Mitchell’s concept describing the fundamental gap between human understanding (which involves rich meaning, common sense, and abstraction) and the capabilities of current AI systems (which often rely on statistical patterns without true comprehension).
  • Bias (in AI): Systematic errors or unfair preferences in AI system outputs, often resulting from biases present in the training data (e.g., racial or gender imbalances).
  • Big Data: Extremely large datasets that can be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Essential for deep learning.
  • Bongard Problems: A set of visual puzzles designed to challenge AI systems’ abilities in abstraction and analogy making, requiring the perception of subtle conceptual distinctions between two sets of images.
  • Brittleness (of AI systems): The tendency of AI systems, especially deep learning models, to fail unexpectedly or perform poorly when presented with inputs that deviate even slightly from their training data.
  • Chatbot: A computer program designed to simulate human conversation, often used in Turing tests.
  • Cognitron/Neocognitron: Early deep neural networks developed by Kunihiko Fukushima, inspired by the hierarchical organization of the brain’s visual system, which influenced later ConvNets.
  • Common Sense: Basic, often subconscious, knowledge and beliefs about the world, including intuitive physics, biology, and psychology, that humans use effortlessly in daily life.
  • Conceptual Slippage: A key idea in analogy making, where concepts from one situation are flexibly reinterpreted or replaced by related concepts in a different, analogous situation.
  • Connectionism/Connectionist Networks: An approach to AI, synonymous with neural networks in the 1980s, based on the idea that knowledge resides in weighted connections between simple processing units.
  • Convolution: A mathematical operation, central to convolutional neural networks, where a “filter” (array of weights) slides over an input (e.g., an image patch), multiplying corresponding values and summing them to detect features.
  • Convolutional Neural Networks (ConvNets): A type of deep neural network particularly effective for processing visual data, inspired by the hierarchical structure of the brain’s visual cortex.
  • Core Knowledge: Fundamental, often innate or very early-learned, common sense about objects, agents, and their interactions, forming the bedrock of human understanding.
  • Cyc Project: Douglas Lenat’s ambitious, decades-long symbolic AI project aimed at manually encoding a vast database of human commonsense knowledge and logical rules.
  • Deep Learning: A subfield of machine learning that uses deep neural networks (networks with many hidden layers) to learn complex patterns from large amounts of data.
  • Deep Q-Learning (DQN): A combination of reinforcement learning (specifically Q-learning) with deep neural networks, used by DeepMind to enable AI systems to learn to play complex games from scratch.
  • Deep Neural Networks: Neural networks with more than one hidden layer, allowing them to learn hierarchical representations of data.
  • Distributional Semantics: A linguistic theory stating that the meaning of a word can be understood (or represented) by the words it tends to occur with (“you shall know a word by the company it keeps”).
  • Edge Cases: Rare, unusual, or unexpected situations (the “long tail” of a probability distribution) that are difficult for AI systems to handle because they are not sufficiently represented in training data.
  • Embodiment Hypothesis: The philosophical premise that a machine cannot attain human-level general intelligence without having a physical body that interacts with the real world.
  • EMI (Experiments in Musical Intelligence): A computer program that generated music in the style of classical composers, capable of fooling human experts.
  • Encoder-Decoder System: An architecture of recurrent neural networks used in natural language processing (e.g., machine translation, image captioning) where one network (encoder) processes input into a fixed-length representation, and another (decoder) generates output from that representation.
  • Episode: In reinforcement learning, a complete sequence of actions and states, from an initial state until a goal is reached or the learning process terminates.
  • Epoch: In machine learning, one complete pass through the entire training dataset during the learning process.
  • Exploration versus Exploitation: The fundamental trade-off in reinforcement learning between trying new, potentially higher-reward actions (exploration) and choosing known, reliable high-value actions (exploitation).
  • Expert Systems: Early symbolic AI programs that relied on human-programmed rules reflecting expert knowledge in specific domains (e.g., MYCIN for medical diagnosis).
  • Explainable AI (XAI): A research area focused on developing AI systems, particularly deep neural networks, that can explain their decisions and reasoning in a way understandable to humans.
  • Exponential Growth/Progress: A pattern of growth where a quantity increases at a rate proportional to its current value, leading to rapid acceleration over time (e.g., Moore’s Law for computer power).
  • Face Recognition: The task of identifying or verifying a person’s identity from a digital image or video of their face, often powered by deep learning.
  • Game Tree: A conceptual tree structure representing all possible sequences of moves and resulting board positions in a game, used for planning and search in AI game-playing programs.
  • General Problem Solver (GPS): An early symbolic AI program designed to solve a wide range of logic problems by mimicking human thought processes.
  • Geofencing: A virtual geographic boundary defined by GPS or RFID technology, used to restrict autonomous vehicle operation to specific mapped areas.
  • GOFAI (Good Old-Fashioned AI): A disparaging term used by machine learning researchers to refer to traditional symbolic AI methods that rely on explicit rules and human-encoded knowledge.
  • Graphical Processing Units (GPUs): Specialized electronic circuits designed to rapidly manipulate and alter memory to accelerate the creation of images, crucial for training deep neural networks due to their parallel processing capabilities.
  • Hidden Units/Layers: Non-input, non-output processing units or layers within a neural network, where complex feature detection and representation learning occur.
  • Human-Level AI: See Artificial General Intelligence.
  • Hyperparameters: Parameters in a machine learning model that are set manually by humans before the training process begins (e.g., number of layers, learning rate), rather than being learned from data.
  • IBM Watson: A question-answering AI system that famously won Jeopardy! in 2011; later evolved into a suite of AI services offered by IBM.
  • ImageNet: A massive, human-labeled dataset of over a million images categorized into a thousand object classes, used as a benchmark for computer vision challenges.
  • Imitation Game: See Turing Test.
  • Intuitive Biology: Humans’ basic, often subconscious, knowledge and beliefs about living things, how they differ from inanimate objects, and their behaviors.
  • Intuitive Physics: Humans’ basic, often subconscious, knowledge and beliefs about physical objects and how they behave in the world (e.g., gravity, collision).
  • Intuitive Psychology: Humans’ basic, often subconscious, ability to sense and predict the feelings, beliefs, goals, and likely actions of other people.
  • Long Short-Term Memory (LSTM) Units: A type of specialized recurrent neural network unit designed to address the “forgetting” problem in traditional RNNs, allowing the network to retain information over long sequences.
  • Long Tail Problem: In real-world AI applications, the phenomenon where a vast number of rare but possible “edge cases” are difficult to train for because they appear infrequently, if at all, in training data.
  • Machine Learning: A subfield of AI that enables computers to “learn” from data or experience without being explicitly programmed for every task.
  • Machine Translation (MT): The task of automatically translating text or speech from one natural language to another.
  • Mechanical Turk: See Amazon Mechanical Turk.
  • Metacognition: The human ability to perceive and reflect on one’s own thinking processes, including recognizing patterns of thought or self-correction.
  • Metaphors We Live By: A book by George Lakoff and Mark Johnson arguing that human understanding of abstract concepts is largely structured by metaphors based on concrete physical experiences.
  • Monte Carlo Tree Search (MCTS): A search algorithm used in AI game-playing programs that uses a degree of randomness (simulated “roll-outs”) to evaluate possible moves from a given board position.
  • Moore’s Law: The observation that the number of components (and thus processing power) on a computer chip doubles approximately every one to two years.
  • Multilayer Neural Network: A neural network with one or more hidden layers between the input and output layers, allowing for more complex function approximation.
  • MYCIN: An early symbolic AI expert system designed to help physicians diagnose and treat blood diseases using a set of explicit rules.
  • Narrow AI (Weak AI): AI systems designed to perform only one specific, narrowly defined task (e.g., AlphaGo for Go, speech recognition).
  • Natural Language Processing (NLP): A subfield of AI concerned with enabling computers to understand, interpret, and generate human (natural) language.
  • Neural Machine Translation (NMT): A machine translation approach that uses deep neural networks (typically encoder-decoder RNNs) to translate between languages, representing a significant advance over statistical methods.
  • Neural Network: A computational model inspired by the structure and function of biological neural networks (brains), consisting of interconnected “units” that process information.
  • Object Recognition: The task of identifying and categorizing objects within an image or video.
  • One-Hot Encoding: A simple method for representing categorical data (e.g., words) as numerical inputs to a neural network, where each category (word) has a unique binary vector with a single “hot” (1) value.
  • Operant Conditioning: A learning process in psychology where behavior is strengthened or weakened by the rewards or punishments that follow it.
  • Overfitting: A phenomenon in machine learning where a model learns the training data too well, including its noise and idiosyncrasies, leading to poor performance on new, unseen data.
  • Perceptron: An early, simple model of an artificial neuron, inspired by biological neurons, that takes multiple numerical inputs, applies weights, sums them, and produces a binary output based on a threshold.
  • Perceptron-Learning Algorithm: An algorithm used to train perceptrons by iteratively adjusting their weights and threshold based on whether their output for training examples is correct.
  • Q-Learning: A specific algorithm for reinforcement learning that teaches an agent to find the optimal action to take in any given state by learning the “Q-value” (expected future reward) of actions.
  • Q-Table: In Q-learning, a table that stores the learned “Q-values” for all possible actions in all possible states.
  • Reading Comprehension (for machines): The task of an AI system to process a text and answer questions about its content; often evaluated by datasets like SQuAD.
  • Recurrent Neural Networks (RNNs): A type of neural network designed to process sequential data (like words in a sentence) by having connections that feed information from previous time steps back into the current time step, allowing for “memory” of context.
  • Reinforcement Learning (RL): A machine learning paradigm where an “agent” learns to make decisions by performing actions in an “environment” and receiving intermittent “rewards,” aiming to maximize cumulative reward.
  • Semantic Space: A multi-dimensional geometric space where words or concepts are represented as points (vectors), and the distance between points reflects their semantic similarity or relatedness.
  • Sentiment Classification (Sentiment Analysis): The task of an AI system to determine the emotional tone or overall sentiment (e.g., positive, negative, neutral) expressed in a piece of text.
  • Singularity: A hypothetical future point in time when technological growth becomes uncontrollable and irreversible, resulting in unfathomable changes to human civilization, often associated with AI exceeding human intelligence.
  • SQuAD (Stanford Question Answering Dataset): A large dataset used to benchmark machine reading comprehension, where questions about Wikipedia paragraphs are designed such that the answer is a direct span of text within the paragraph.
  • Strong AI: See Artificial General Intelligence. (Note: John Searle’s definition differs, referring to AI that literally has a mind.)
  • Subsymbolic AI: An approach to AI that takes inspiration from biology and psychology, using numerical, brain-like processing (e.g., neural networks) rather than explicit, human-understandable symbols and rules.
  • Suitcase Word: A term coined by Marvin Minsky for words like “intelligence,” “thinking,” or “consciousness” that are “packed” with multiple, often ambiguous meanings, making them difficult to define precisely.
  • Superhuman Intelligence (Superintelligence): An intellect that is much smarter than the best human brains in virtually every field, including scientific creativity, general wisdom, and social skills.
  • Supervised Learning: A machine learning paradigm where an algorithm learns from a “training set” of labeled data (input-output pairs), with a “supervision signal” indicating the correct output for each input.
  • Symbolic AI: An approach to AI that attempts to represent knowledge using human-understandable symbols and manipulate these symbols using explicit, logic-based rules.
  • Temporal Difference Learning: A method used in reinforcement learning (especially deep Q-learning) where the learning system adjusts its predictions based on the difference between successive estimates of the future reward, essentially “learning a guess from a better guess.”
  • Test Set: A portion of a dataset used to evaluate the performance of a machine learning model after it has been trained, to assess its ability to generalize to new, unseen data.
  • Theory of Mind: The human ability to attribute mental states (beliefs, intentions, desires, knowledge) to oneself and others, and to understand that these states can differ from one’s own.
  • Thought Vectors: Vector representations of entire sentences or paragraphs, analogous to word vectors, intended to capture their semantic meaning.
  • Training Set: A portion of a dataset used to train a machine learning model, allowing it to learn patterns and relationships.
  • Transfer Learning: The ability of an AI system to transfer knowledge or skills learned from one task to help it perform a different, related task. A key challenge for current AI.
  • Turing Test (Imitation Game): A test proposed by Alan Turing to determine if a machine can exhibit intelligent behavior indistinguishable from that of a human.
  • Unsupervised Learning: A machine learning paradigm where an algorithm learns patterns or structures from unlabeled data without explicit guidance, often through clustering or anomaly detection.
  • Weak AI: See Narrow AI. (Note: John Searle’s definition differs, referring to AI that simulates a mind without literally having one.)
  • Weights: Numerical values assigned to the connections between units in a neural network, which determine the strength of influence one unit has on another. These are learned during training.
  • Winograd Schemas: Pairs of sentences that differ by only one or two words but require commonsense reasoning to resolve pronoun ambiguity, serving as a challenging test for natural-language understanding in AI.
  • Word Embeddings: See Word Vectors.
  • Word Vectors (Word2Vec): Numerical vector representations of words in a multi-dimensional semantic space, where words with similar meanings are located closer together, learned automatically from text data.
  • WordNet: A large lexical database of English nouns, verbs, adjectives, and adverbs, grouped into sets of cognitive synonyms (synsets) and organized in a hierarchical structure, used extensively in NLP and for building ImageNet.
Melanie Mitchell's Artificial Intelligence: A Guided Tour offers a comprehensive and critical examination of the current state of AI, highlighting its impressive advancements in narrow domains while robustly arguing that true human-level general intelligence remains a distant goal. The author, a long-time AI researcher, frames her exploration through the lens of a pivotal 2014 Google meeting with AI legend Douglas Hofstadter, whose "terror" at the shallow nature of modern AI's achievements sparked Mitchell's deeper investigation.

Never Split the Distance by Chris Voss – Summary and Analysis

Executive Summary

“Never Split the Difference” by Chris Voss, a former FBI lead international kidnapping negotiator, fundamentally challenges traditional negotiation theories, particularly those advocating for rational problem-solving and compromise. Drawing from decades of high-stakes experience, Voss argues that effective negotiation is deeply rooted in human psychology, emotional intelligence, and active listening. The book introduces a system of “tactical empathy” and practical psychological tactics designed to gain the upper hand by understanding and influencing the emotional, often irrational, drivers of counterparts. These methods, proven in life-or-death scenarios, are presented as universally applicable to business, career, and personal interactions, emphasizing that “Life is negotiation.”

Main Themes and Key Concepts

1. The Primacy of Emotion Over Logic

Traditional negotiation, often taught in business schools, emphasizes rational problem-solving and logical arguments. Voss, however, vehemently argues that this approach is flawed because humans are fundamentally “crazy, irrational, impulsive, emotionally driven animals.”

  • Rejection of Pure Rationality: Voss contends that theories built on “intellectual power, logic, authoritative acronyms like BATNA and ZOPA, rational notions of value, and a moral concept of what was fair and what was not” are based on a “false edifice of rationality.”
  • System 1 vs. System 2 Thinking: Drawing on Daniel Kahneman’s work, Voss highlights that our “animal mind” (System 1) is “fast, instinctive, and emotional” and “far more influential” than our “slow, deliberative, and logical” mind (System 2). To influence System 2 rationality, one must first affect System 1 feelings.
  • Emotional Intelligence is Key: The FBI’s shift in negotiation strategy, after failures like Ruby Ridge and Waco, moved from problem-solving to focusing “on the animal, emotional, and irrational.” This made “Emotions and emotional intelligence… central to effective negotiation, not things to be overcome.”

2. Tactical Empathy: Listening as a Martial Art

Tactical Empathy is the cornerstone of Voss’s approach, described as “listening as a martial art.” It’s not about agreement or sympathy, but about profound understanding.

  • Definition: Tactical empathy is “the ability to recognize the perspective of a counterpart, and the vocalization of that recognition.” It involves “understanding the feelings and mindset of another in the moment and also hearing what is behind those feelings so you increase your influence in all the moments that follow.”
  • Core Premise: “It all starts with the universally applicable premise that people want to be understood and accepted. Listening is the cheapest, yet most effective concession we can make to get there.”
  • Benefits of Feeling Understood: Psychotherapy research shows that “when individuals feel listened to, they tend to listen to themselves more carefully and to openly evaluate and clarify their own thoughts and feelings. In addition, they tend to become less defensive and oppositional and more willing to listen to other points of view.”

3. Key Tactical Empathy Tools

Voss introduces several practical techniques to implement tactical empathy:

  • Mirroring: This is “the art of insinuating similarity.” It involves repeating the “last three words (or the critical one to three words) of what someone has just said.” This triggers a neurobehavioral instinct to copy, establishing rapport and encouraging the counterpart to elaborate, revealing more information.
  • Example: In a bank robbery, Voss mirrored a kidnapper’s statement: “We chased your driver away?” which led the kidnapper to “vomit information.”
  • Labeling: Giving a name to a counterpart’s emotions or perceptions. It almost always begins with “It seems like…”, “It sounds like…”, or “It looks like…”.
  • Purpose: Labeling “disrupts its raw intensity” by applying “rational words to a fear.” It’s used to “neutralize the negative, reinforce the positive.”
  • Accusation Audit: A proactive form of labeling where you “list every terrible thing your counterpart could say about you” and say them first. This disarms negative dynamics and can often lead the other person to deny the accusation, thus revealing common ground.
  • Example: In a Harlem standoff, Voss repeatedly stated, “It looks like you don’t want to come out. It seems like you worry that if you open the door, we’ll come in with guns blazing. It looks like you don’t want to go back to jail,” leading to the fugitives’ surrender.

4. Mastering “No” and Striving for “That’s Right”

Voss radically redefines the significance of “Yes” and “No” in negotiation.

  • “No” as an Asset: Contrary to common belief, “No” is “pure gold” because “it provides a temporary oasis of control” for the speaker. It often means “I am not yet ready to agree,” “I do not understand,” or “I need more information,” rather than outright rejection.
  • Strategy: “Great negotiators seek ‘No’ because they know that’s often when the real negotiation begins.” It offers safety and control, making the environment more collaborative.
  • Example: Asking “Is now a bad time to talk?” is preferable to “Do you have a few minutes to talk?” because it offers the counterpart an easy “No” or full focus.
  • Beware of “Yes”: There are three types of “Yes”: Counterfeit (a polite dodge), Confirmation (a simple affirmation without commitment), and Commitment (the real deal). Most people give counterfeit “yes” to end an uncomfortable conversation.
  • “That’s Right” as the Breakthrough: The “sweetest two words in any negotiation are actually ‘That’s right.'” This signifies that the counterpart feels truly understood, leading to a subtle epiphany and genuine behavioral change.
  • How to Achieve: A good summary, combining paraphrasing and labeling, is the best way to trigger a “That’s right.”
  • Contrast with “You’re Right”: “You’re right” is often a dismissive phrase meaning “just shut up and go away,” leading to no real change.

5. Bending Reality and Leveraging Cognitive Biases

Voss advocates for understanding and using predictable human irrationality, particularly cognitive biases like loss aversion and framing effects, to one’s advantage.

  • Don’t Compromise: “Compromise is often a ‘bad deal'” because it satisfies neither side and can lead to absurd outcomes. “No deal is better than a bad deal.”
  • Deadlines as Allies: Deadlines are “the bogeymen of negotiation, almost exclusively self-inflicted figments of our imagination.” They often make people rush into bad deals. By revealing your deadline, you reduce impasse risk and speed up concessions from the other side. Understanding the counterpart’s hidden deadlines (e.g., kidnappers wanting “party money” by Friday) provides significant leverage.
  • “Fair” is a Weapon: The word “Fair” is “a tremendously powerful word that you need to use with care.” It’s often used defensively (“We just want what’s fair”) or manipulatively (“We’ve given you a fair offer”).
  • Counter-Tactic: If accused of unfairness, ask, “Okay, I apologize. Let’s stop everything and go back to where I started treating you unfairly and we’ll fix it.” To preempt, state early, “I want you to feel like you are being treated fairly at all times. So please stop me at any time if you feel I’m being unfair, and we’ll address it.”
  • Anchoring Emotions: Emotionally “anchor them by saying how bad it will be” (an accusation audit) to prepare them for a loss, then make your offer seem reasonable.
  • Extreme Anchors & Ranges: When talking numbers, letting the other side anchor first can be beneficial. However, if you must anchor, set an extreme anchor to shift their perception or use a range where the low end is your desired price (“bolstering range”).
  • Odd Numbers: Use “precise, nonround numbers like, say, $37,893 rather than $38,000” to give offers “credibility and weight.”
  • Loss Aversion: “People will take more risks to avoid a loss than to achieve gains.” To gain leverage, “persuade them that they have something concrete to lose if the deal falls through.”

6. Calibrated Questions: The Illusion of Control

Calibrated questions are open-ended questions designed to subtly guide the conversation and encourage the counterpart to develop your desired solution.

  • Mechanism: They “remove aggression from conversations by acknowledging the other side openly, without resistance.” They start with “What” or “How” (and sometimes “Why” strategically).
  • “How am I supposed to do that?”: This is a powerful, gentle “No” that invites collaboration and forces the other side to “expend their energy on devising a solution” to your problem.
  • “Art of letting someone else have your way”: These questions give the “illusion of control” to the counterpart while you “are framing the conversation.”
  • Guaranteeing Execution: Asking “How will we know we’re on track?” and “How will we address things if we find we’re off track?” forces the counterpart to articulate implementation in their own words, making them more invested in the solution.
  • Red Flags: Beware of “You’re right” and “I’ll try,” as they often signal a lack of buy-in or an intention to fail.

7. Finding Black Swans: Uncovering Unknown Unknowns

Black Swans are “hidden and unexpected pieces of information—those unknown unknowns—whose unearthing has game-changing effects on a negotiation dynamic.”

  • Definition: Unlike “known knowns” (what we know) and “known unknowns” (what we know we don’t know), Black Swans are “pieces of information we’ve never imagined but that would be game changing if uncovered.”
  • Leverage Multipliers: Black Swans provide the most potent forms of leverage:
  • Positive Leverage: The ability to give (or withhold) something the counterpart wants.
  • Negative Leverage: The ability to make the counterpart suffer (based on threats, but used carefully and subtly, e.g., “It seems like you strongly value the fact that you’ve always paid on time”).
  • Normative Leverage: Using the other party’s “norms and standards to advance your position” by showing inconsistencies between their beliefs and actions.
  • “Know Their Religion”: Delving into a counterpart’s “worldview, their reason for being, their religion” (their deeply held beliefs, values, and motivations). This provides normative leverage.
  • Example: In the Dwight Watson standoff, uncovering his identity as a “devout Christian” allowed negotiators to use the concept of “the Dawn of the Third Day” to facilitate his surrender.
  • Overcoming “They’re Crazy!”: What seems irrational is usually a clue. Counterparts might be “ill-informed,” “constrained” by unstated factors (e.g., internal politics), or have “other interests” (hidden agendas).
  • Method: Get face time, observe unguarded moments (before/after meetings, during interruptions), and relentlessly ask questions to uncover these underlying realities.

8. The Negotiation One Sheet: Preparation for Agility

Voss proposes a simplified preparation tool, the “Negotiation One Sheet,” contrasting it with traditional methods that can lead to rigidity.

  • Rejection of BATNA as a Primary Focus: While BATNA (Best Alternative To a Negotiated Agreement) is useful, obsessing over it “tricks negotiators into aiming low” and “sets the upper limit of what you will ask for.”
  • Focus on High-End Goal: Instead, set an “optimistic but reasonable goal and define it clearly,” writing it down and discussing it to commit.
  • Dynamic Preparation: The one-sheet includes sections for:
  • Goal: Best-case scenario (optimistic but realistic).
  • Summary: Known facts leading to the negotiation.
  • Labels/Accusation Audit: Anticipated negative perceptions or accusations from the counterpart.
  • Calibrated Questions: To reveal value, identify deal-killers, and influence behind-the-table players.
  • Noncash Offers: Ideas for valuable non-monetary concessions.

Most Important Ideas/Facts

  • Negotiation is primarily emotional, not rational. All decisions are ultimately governed by emotion (Kahneman’s System 1).
  • Tactical Empathy is the core skill. It’s about profoundly understanding, not necessarily agreeing with, the other side.
  • “That’s right” is the ultimate goal, not “Yes.” “That’s right” signals genuine understanding and buy-in, while “Yes” can be a counterfeit or confirmation without commitment.
  • “No” is not a failure; it’s the start of the negotiation. It provides safety and control for the counterpart, opening up the dialogue.
  • Calibrated Questions (starting with “How” or “What”) give the illusion of control. They subtly guide the counterpart to solve your problems, leading to solutions they “own.” “How am I supposed to do that?” is a powerful, gentle “No.”
  • Compromise often leads to bad deals. Never “split the difference.”
  • Loss aversion is a powerful motivator. People will take greater risks to avoid a loss than to achieve an equal gain.
  • Black Swans are “unknown unknowns” that are leverage multipliers. Uncovering these hidden pieces of information—often related to underlying motivations, constraints, or “religion” (worldview)—can be game-changing.
  • “Fair” is a highly emotional and manipulative word. Use it with caution or strategically to disarm or set boundaries.
  • Preparation should focus on anticipating emotional responses and crafting flexible questions, rather than rigid scripts or aiming low (avoiding BATNA as a primary focus).
  • It’s crucial to influence the “behind the table” players. Few negotiations are solo; many hidden individuals can be deal makers or deal killers.

This briefing highlights the transformative power of a psychological and empathetic approach to negotiation, emphasizing that by understanding and addressing the emotional landscape, one can achieve superior and lasting outcomes in any interaction.

Contact Factoring Specialist, Chris Lehnes

"Never Split the Difference" by Chris Voss, a former FBI lead international kidnapping negotiator, fundamentally challenges traditional negotiation theories, particularly those advocating for rational problem-solving and compromise. Drawing from decades of high-stakes experience, Voss argues that effective negotiation is deeply rooted in human psychology, emotional intelligence, and active listening. The book introduces a system of "tactical empathy" and practical psychological tactics designed to gain the upper hand by understanding and influencing the emotional, often irrational, drivers of counterparts. These methods, proven in life-or-death scenarios, are presented as universally applicable to business, career, and personal interactions, emphasizing that "Life is negotiation."

A Study Guide to Chris Voss’s Never Split the Difference

This study guide is designed to help you review and deepen your understanding of Chris Voss’s negotiation principles as outlined in Never Split the Difference.

I. Quiz: Short Answer Questions

Answer each question in 2-3 sentences.

  1. What is the core difference between the FBI’s approach to negotiation and the traditional Harvard Law School approach, as described by Voss?
  2. Explain the “Late-Night FM DJ Voice” and its primary purpose in a negotiation.
  3. How does Voss define “Tactical Empathy” and what is its goal?
  4. Why does Voss advocate for striving for “That’s right” instead of “Yes” in a negotiation?
  5. Describe the concept of an “Accusation Audit” and why it is an effective negotiation tactic.
  6. According to Voss, why is “No” often considered “pure gold” in a negotiation, rather than a negative outcome?
  7. What are “Calibrated Questions” and how do they create the “illusion of control” for the counterpart?
  8. Explain the “Rule of Three” and how it helps a negotiator guarantee execution.
  9. What is an “extreme anchor” in the context of bargaining, and what psychological effect does it aim to achieve?
  10. Define a “Black Swan” in negotiation and explain its significance.

II. Answer Key

  1. What is the core difference between the FBI’s approach to negotiation and the traditional Harvard Law School approach, as described by Voss? The FBI’s approach, rooted in experiential learning from high-stakes crisis situations, emphasizes emotional intelligence, psychology, and crisis intervention to understand and influence irrational human behavior. In contrast, the traditional Harvard approach, exemplified by “Getting to Yes,” focuses on rational problem-solving, logic, and intellectual power to achieve mutually beneficial outcomes.
  2. Explain the “Late-Night FM DJ Voice” and its primary purpose in a negotiation. The “Late-Night FM DJ Voice” is characterized by a deep, soft, slow, and reassuring tone, often with a downward inflection. Its primary purpose is to convey calm, control, and authority without triggering defensiveness, thereby making the counterpart feel safe and encouraging them to open up.
  3. How does Voss define “Tactical Empathy” and what is its goal? Tactical Empathy is defined as the ability to recognize and vocalize a counterpart’s perspective and underlying feelings in the moment, and to understand what drives those feelings. Its goal is to increase influence by acknowledging emotions, creating trust, and guiding the conversation toward a desired outcome.
  4. Why does Voss advocate for striving for “That’s right” instead of “Yes” in a negotiation? Voss argues that “Yes” can often be superficial (“Counterfeit Yes” or “Confirmation Yes”) and doesn’t guarantee genuine agreement or action. “That’s right,” however, indicates that the counterpart feels truly understood and has assessed and confirmed the negotiator’s summary of their world, leading to a deeper level of buy-in and a breakthrough in the negotiation.
  5. Describe the concept of an “Accusation Audit” and why it is an effective negotiation tactic. An “Accusation Audit” involves proactively listing and vocalizing all the negative things the counterpart could say about the negotiator or their position before the counterpart can voice them. This tactic disarms the counterpart by addressing their fears and potential criticisms head-on, reducing defensiveness and fostering a sense of empathy and trust.
  6. According to Voss, why is “No” often considered “pure gold” in a negotiation, rather than a negative outcome? “No” is “pure gold” because it gives the speaker a feeling of safety, security, and control, allowing them to define their boundaries and true desires. It’s often a temporary decision to maintain the status quo, opening the door for clarification, reevaluation, and further negotiation, rather than ending the discussion.
  7. What are “Calibrated Questions” and how do they create the “illusion of control” for the counterpart? Calibrated Questions are open-ended questions, typically starting with “How” or “What” (avoiding “Why”), that force the counterpart to think deeply about the problem and articulate solutions. They create the “illusion of control” because the counterpart feels they are providing the answers and driving the conversation, while the negotiator is subtly framing the discussion and guiding them toward the desired outcome.
  8. Explain the “Rule of Three” and how it helps a negotiator guarantee execution. The “Rule of Three” is a tactic to ensure genuine commitment by getting the counterpart to agree to the same thing three different ways within the same conversation. This helps to uncover any hidden objections or insincerity, as it’s difficult to repeatedly lie or fake conviction, thereby increasing the likelihood of successful implementation.
  9. What is an “extreme anchor” in the context of bargaining, and what psychological effect does it aim to achieve? An “extreme anchor” is a deliberately high or low initial offer made at the beginning of a monetary negotiation. Its psychological effect is to “bend the reality” of the counterpart, unconsciously adjusting their expectations and moving their perceived range of possible outcomes closer to the extreme anchor, making subsequent, more reasonable offers seem highly attractive.
  10. Define a “Black Swan” in negotiation and explain its significance. A “Black Swan” is an unknown unknown—a piece of game-changing information that was previously unimagined or thought impossible, and whose discovery fundamentally alters the negotiation dynamic. Its significance lies in its power to unlock breakthroughs and provide immense leverage, transforming seemingly intractable situations.

III. Essay Format Questions (No Answers Provided)

  1. Compare and contrast the influence of emotional intelligence and logical reasoning in negotiation, drawing on specific examples or theories presented in the text to support your argument.
  2. Analyze how the different bargaining styles (Accommodator, Assertive, Analyst) impact negotiation dynamics and what strategies Voss suggests for effectively dealing with each type.
  3. Discuss the critical role of “listening as a martial art” and “Tactical Empathy” in information gathering and relationship building. How do these concepts challenge traditional notions of negotiation?
  4. Examine the psychological significance of “Yes” and “No” in negotiation according to Voss. How does understanding these words, particularly the power of “No,” transform a negotiator’s approach and potential outcomes?
  5. Explain the concept of “bending their reality” through various tactics like anchoring, loss aversion, and the strategic use of numbers. How does this approach leverage human irrationality to achieve desired results?

IV. Glossary of Key Terms

  • Accusation Audit: A proactive negotiation tactic where you list and verbalize all the negative things your counterpart could say about you or your position to disarm them and build trust.
  • Accommodator (Bargaining Style): A negotiator type primarily focused on building and maintaining relationships, often prioritizing agreement and harmonious exchange of information over concrete outcomes.
  • Ackerman Model: A structured, six-step offer-counteroffer bargaining system (65%, 85%, 95%, 100% of target price) that incorporates psychological tactics like extreme anchors, reciprocity, and diminishing increments to achieve a desired price.
  • Active Listening: A core component of tactical empathy, involving intense focus on the other person, observing verbal, paraverbal, and nonverbal cues, and demonstrating a sincere desire to understand their perspective.
  • Analyst (Bargaining Style): A methodical, diligent negotiator type focused on minimizing mistakes, thorough preparation, and data. They are typically reserved, less emotional, and hypersensitive to reciprocity.
  • Anchoring: The psychological tendency to rely heavily on the first piece of information offered (the “anchor”) when making decisions. In negotiation, it refers to setting a strong initial offer or statement to influence the perceived value of a deal.
  • Assertive (Bargaining Style): A negotiator type driven by winning and achieving results quickly. They are direct, candid, and often aggressive in their communication, focusing on their own goals rather than primarily on relationships.
  • BATNA (Best Alternative To a Negotiated Agreement): (Coined by Fisher and Ury) Your best option if a negotiation fails. Voss critiques its overuse as it can lead to aiming low by becoming the negotiator’s psychological target.
  • Behavioral Change Stairway Model (BCSM): A five-stage model (active listening, empathy, rapport, influence, and behavioral change) developed by the FBI’s Crisis Negotiation Unit to guide negotiators from understanding to influencing behavior.
  • Black Swan: An “unknown unknown”—a powerful, unexpected piece of information or event that, if discovered, fundamentally changes the entire negotiation dynamic and provides significant leverage.
  • Calibrated Questions: Open-ended questions, usually starting with “How” or “What” (and generally avoiding “Why”), designed to make the counterpart think and articulate solutions, giving them the “illusion of control” while subtly guiding the conversation.
  • Certainty Effect: A concept from Prospect Theory stating that people are drawn to sure things over probabilities, even when the probability is a statistically better choice.
  • Commitment “Yes”: A genuine agreement from the counterpart that leads to action and a signed deal.
  • Confirmation “Yes”: A simple, reflexive affirmation in response to a black-or-white question, without a promise of action.
  • Counterfeit “Yes”: A “yes” given by the counterpart who intends to say “no” but uses “yes” as an easier escape route or to gather more information.
  • “Chris Discount”: A personal tactic where the negotiator uses their own first name in a friendly, humanizing way to establish rapport and potentially secure a small concession.
  • Deadlines: Time constraints that can create pressure and anxiety in negotiations. Voss argues many are arbitrary and negotiable, and revealing your deadline can lead to better deals.
  • Extreme Anchor: A deliberately high or low initial offer intended to psychologically shift the counterpart’s perception of value and range of possible agreement.
  • “Fair”: A highly emotional and often manipulative word in negotiation. Voss advises caution when using or encountering it, suggesting strategies to either preempt accusations of unfairness or deflect them.
  • “Forced Empathy”: A dynamic created by calibrated “How” questions, where the counterpart is implicitly made to consider and understand the negotiator’s situation, often leading them to offer solutions.
  • Framing Effect: A cognitive bias where people respond differently to the same choice depending on how it is presented or “framed.”
  • “How Am I Supposed To Do That?”: A powerful calibrated question used as a gentle way to say “No” and force the counterpart to consider the negotiator’s constraints and propose solutions.
  • “I” Messages: Statements using the first-person singular pronoun (“I feel X when you Y because Z”) to set boundaries or express a viewpoint without escalating confrontation.
  • Isopraxism (Mirroring): The unconscious or conscious imitation of another person’s speech patterns, body language, vocabulary, tempo, or tone of voice. Consciously used as a negotiation tactic to build rapport and encourage elaboration.
  • Labeling: A tactical empathy technique where you verbalize the emotions or assumptions you perceive in your counterpart (“It sounds like…”, “It seems like…”, “It looks like…”). This diffuses negative emotions and reinforces positive ones.
  • Late-Night FM DJ Voice: A deep, soft, slow, and reassuring vocal tone used to project calm, control, and authority, making the counterpart feel safe and open.
  • Loss Aversion: A psychological principle (from Prospect Theory) where people are statistically more motivated to avoid a loss than to achieve an equal gain. Effective negotiators leverage this by framing proposals in terms of what the counterpart stands to lose.
  • Mirroring: The act of repeating the last one to three critical words your counterpart has just said to encourage them to elaborate and build rapport.
  • Negative Leverage: The ability of a negotiator to make their counterpart suffer, often based on threats of negative consequences. Used with extreme caution.
  • Negotiation One Sheet: A concise preparatory document used by negotiators to outline their goal, summarize known facts, prepare labels/accusation audits, formulate calibrated questions, and list noncash offers.
  • “No”: Voss argues that “No” is a powerful word in negotiation, signifying autonomy, safety, and a desire to maintain the status quo. It often marks the beginning of true negotiation, clarifying boundaries and paving the way for creative solutions.
  • Noncash Offers: Non-monetary items or terms that can be valuable to one party in a negotiation, offering a way to create value without directly adjusting the price.
  • Nonround Numbers: Specific, precise numbers (e.g., $37,263) used in offers to convey thoughtfulness, credibility, and firmness, in contrast to rounded numbers (e.g., $38,000) which can feel like temporary placeholders.
  • Normative Leverage: Using the other party’s norms, standards, or moral framework to advance your position, highlighting inconsistencies between their beliefs and actions.
  • “Paradox of Power”: The phenomenon where the harder one pushes in a negotiation, the more likely they are to be met with resistance from the other party.
  • Paraphrase: Restating what the other person has said in your own words to demonstrate understanding and clarify meaning.
  • Pinocchio Effect: A linguistic indicator of deception, where liars tend to use more words and more third-person pronouns to distance themselves from the lie, and often more complex sentences.
  • Positive Leverage: The ability of a negotiator to provide or withhold things that their counterpart wants.
  • Positive/Playful Voice: The default voice tone recommended for negotiators, characterized by an easygoing, good-natured, and encouraging attitude, often accompanied by a smile, to promote collaboration and mental agility.
  • Prospect Theory: A theory by Kahneman and Tversky describing how people choose between options involving risk, highlighting biases like Loss Aversion and the Certainty Effect.
  • “Religion” (of your counterpart): A metaphor for your counterpart’s worldview, their reason for being, their core beliefs, values, and what truly matters to them. Understanding this helps uncover Black Swans and build influence.
  • Rule of Three: A technique to ensure genuine commitment by getting the counterpart to affirm an agreement or idea three different ways in a conversation (e.g., “Yes,” “That’s right,” and a “How” question about implementation).
  • 7-38-55 Percent Rule: Albert Mehrabian’s rule stating that in communication, 7% of a message is conveyed by words, 38% by tone of voice, and 55% by body language. It emphasizes the importance of nonverbal cues.
  • “Sixty Seconds or She Dies”: An introductory exercise Voss uses in his negotiation classes to highlight the urgency and difficulty of high-stakes negotiations and the need for learned skills.
  • Similarity Principle: The psychological tendency for people to trust and like those they perceive as similar or familiar to themselves. Negotiators can leverage this by finding common ground.
  • “Slow. It. Down.”: A crucial negotiation principle advocating for deliberate pacing to calm the situation, allow for thorough listening, and prevent impulsive decisions.
  • Strategic Umbrage: A well-timed expression of (real, controlled) anger directed at a proposal (not the person) to make a counterpart realize their offer is unreasonable and shift their perspective.
  • Summarize: A powerful active listening technique combining paraphrasing and labeling to rearticulate the meaning of what was said and acknowledge the underlying emotions.
  • System 1 Thinking: (From Kahneman’s Thinking, Fast and Slow) Our fast, instinctive, and emotional thought process.
  • System 2 Thinking: (From Kahneman’s Thinking, Fast and Slow) Our slow, deliberative, and logical thought process. Voss argues System 1 often guides System 2.
  • Tactical Empathy: The ability to understand and verbalize the feelings and mindset of another person in the moment, and to hear what is behind those feelings, to increase influence. It’s empathy as a deliberate tool.
  • “That’s Right”: A powerful affirmation from the counterpart indicating that they feel truly understood and have embraced the negotiator’s summary of their perspective, signifying a breakthrough in the negotiation.
  • Ultimatum Game: A game theory experiment demonstrating human irrationality and the powerful role of perceived fairness in decision-making, where responders often reject offers they deem unfair, even if it means getting nothing.
  • Unconditional Positive Regard: A concept from Carl Rogers, suggesting that real change occurs when a person feels completely accepted and understood, without judgment or conditions. In negotiation, it fosters trust and openness.
  • “Unbelief”: (From Kevin Dutton) Active resistance and complete rejection of what the other side is saying. The goal in negotiation is to suspend this unbelief to open the path to persuasion.
  • “Wimp-Win” Mentality: A negotiation mindset where individuals set modest goals to protect their self-esteem, leading to easily claimed victories but ultimately mediocre outcomes.
  • “You’re Right”: An affirmation from the counterpart that Voss identifies as generally ineffective, often used as a polite way to dismiss or shut down the negotiator without genuine agreement or commitment to action.
  • ZOPA (Zone of Possible Agreement): (Coined by Fisher and Ury) The overlap between the buyer’s and seller’s acceptable price ranges in a negotiation. Voss downplays its importance in real-world “bare-knuckle bargaining.”
"Never Split the Difference" by Chris Voss, a former FBI lead international kidnapping negotiator, fundamentally challenges traditional negotiation theories, particularly those advocating for rational problem-solving and compromise. Drawing from decades of high-stakes experience, Voss argues that effective negotiation is deeply rooted in human psychology, emotional intelligence, and active listening. The book introduces a system of "tactical empathy" and practical psychological tactics designed to gain the upper hand by understanding and influencing the emotional, often irrational, drivers of counterparts. These methods, proven in life-or-death scenarios, are presented as universally applicable to business, career, and personal interactions, emphasizing that "Life is negotiation."

Unreasonable Hospitality – Will Guidara – Summary and Analysis

Unreasonable Hospitality – Will Guidara

I. The Core Philosophy: Unreasonable Hospitality

At the heart of Guidara’s work is the concept of “Unreasonable Hospitality,” which he defines as “the remarkable power of giving people more than they expect.” This goes beyond mere “service,” which Guidara describes as “black and white”—competent and efficient. Hospitality, in contrast, is “color”—making people feel great about the service they receive and creating an authentic connection.

  • Service vs. Hospitality: “Service is black and white; hospitality is color.” Service is doing your job with competence and efficiency; hospitality is genuinely engaging to make an authentic connection.
  • Challenging the Status Quo: The term “unreasonable” was initially used to shut down Guidara’s ambitious ideas but became a “call to arms.” He argues that “no one who ever changed the game did so by being reasonable.”
  • Beyond Restaurants: Guidara believes this philosophy is applicable across all service industries, from retail and finance to healthcare and education. He posits that America has transitioned into a “service economy,” where intentional and creative hospitality offers “an incredible opportunity.”
  • The Power of Feeling Good: While the financial impact of making someone feel good may be hard to quantify, Guidara asserts, “it matters more.” He describes hospitality as a “selfish pleasure” because “it feels great to make other people feel good.”
  • Can Hospitality Be Taught? Guidara firmly believes it can, contrary to some leaders. He co-founded the Welcome Conference to evolve the craft of dining room professionals, noting that attendees quickly expanded beyond the restaurant industry, demonstrating a broader recognition of the value of a hospitality-first culture.

II. Building a Foundation for Greatness: Early Lessons and Principles

Guidara’s upbringing and early career experiences profoundly shaped his approach to leadership and hospitality.

  • The Magic of Experience: His twelfth birthday dinner at the Four Seasons, where a server “expertly carved my duck on a gleaming cart” and replaced a dropped napkin, left an indelible mark. This experience taught him that a restaurant “could create magic.” This aligns with Maya Angelou’s (attributed) quote: “People will forget what you do; they’ll forget what you said. But they’ll never forget how you made them feel.”
  • The Power of Intentionality: His father, Frank Guidara, instilled in him the importance of “intentionality”—making every decision thoughtfully, with “clear purpose and an eye on the desired result.” His father’s selflessness in caring for his ailing mother also taught Guidara “what it’s like to feel truly welcomed.”
  • The Nobility of Service: A profoundly moving experience at Daniel with his father after his mother’s death revealed “how important, how noble, working in service can be.” Chef Daniel Boulud’s “ray of light” provided “an oasis of comfort and restoration, an island of delight and care in the sea of our grief.”
  • Enlightened Hospitality (Danny Meyer’s Influence): Working for Danny Meyer’s Union Square Hospitality Group (USHG) introduced Guidara to “Enlightened Hospitality,” which prioritized employees, believing that “if he wanted his frontline teams to obsess about how they made their customers feel, he had to obsess about how he made his employees feel.” Key tenets included:
  • Go Above and Beyond: Exemplified by a sommelier rescuing a guest’s champagne from a freezer and leaving caviar and a card. This evolved into “grace notes” like feeding parking meters, showing that small, seemingly non-essential acts of hospitality could “blow people’s minds.”
  • Enthusiasm is Contagious: Randy Garutti, Guidara’s general manager at Tabla, demonstrated unwavering positivity and instilled a “sense of ownership” by entrusting young managers with responsibility.
  • Language Creates Culture: Danny Meyer’s brilliance in coining phrases like “constant, gentle pressure,” “athletic hospitality,” and “be the swan” helped build a strong, shared culture. Guidara’s favorite was “Make the charitable assumption,” a reminder to “assume the best of people, even when (or perhaps especially when) they weren’t behaving particularly well.”
  • “Cult” is Short for “Culture”: Guidara embraced the “cult” label given by outsiders, recognizing it as a sign of a deeply invested and positive company culture.

III. Navigating Business Acumen and Creative Freedom

Guidara’s journey involved understanding the balance between strict business controls and creative hospitality.

  • Restaurant-Smart vs. Corporate-Smart: His father introduced him to this distinction: restaurant-smart companies offer autonomy and human connection but may lack corporate support, while corporate-smart companies have strong back-end systems but can stifle creativity. Guidara’s goal was to build a company that was “corporate-smart and restaurant-smart.”
  • Control Doesn’t Have to Stifle Creativity: His time at Restaurant Associates (RA) as an assistant purchaser and controller, tracking the financial impact of daily decisions, taught him the power of systems. He realized that corporate controls could “return [chefs] to their creativity” by freeing them from financial worries.
  • Trust the Process: His mentor at RA, Hani Ichkhan, meticulously guided him through financial reporting, withholding the “big picture” P&L until Guidara had a strong foundational understanding. This taught Guidara to “trust the process” and the importance of a “solid base.”
  • When Control Stifles Creativity: However, he also experienced the negative side of excessive corporate control when he was reprimanded for moving a vase at Nick + Stef’s Steakhouse and when HR rehired a disruptive employee (Felix) he had fired. This taught him that “corporate-smart could be restaurant-dumb” and the importance of trusting “the people on the ground.” As former navy captain David Marquet says, “the people at the top have all the authority and none of the information, while the people on the front line have all the information and none of the authority.”
  • The Rule of 95/5: Guidara’s time at MoMA, managing the museum’s cafés, led to the development of this principle: “Manage 95 percent of your business down to the penny; spend the last 5 percent ‘foolishly.'” This “foolish” 5% has an “outsize impact on the guest experience” and can create unforgettable moments, such as the custom tiny blue gelato spoons or a rare, expensive glass of wine in a pairing.

IV. The Eleven Madison Park Transformation: Pursuing a Vision

Guidara’s leadership at EMP was defined by a relentless pursuit of a unique vision.

  • A True Partnership: Guidara’s condition for taking the GM role at EMP was a true partnership with Chef Daniel Humm, where “what happens in the dining room doesn’t matter as much as what happens in the kitchen.” This led to the foundational decision that EMP would be “a restaurant run by both sides of the wall.”
  • Setting Expectations: Upon arriving at EMP, Guidara found a “bad bad” situation with internal factions and disorganization. His strategy involved:
  • Inviting the Team Along: Bridging the gap between the “old guard” and the “fine-dining squad” by improving communication and establishing clear systems.
  • Leaders Listen: Spending weeks “sitting down with every single member of the team and hearing them out” to understand the restaurant’s true state.
  • Finding the Hidden Treasures: Identifying and leveraging individual strengths, as he did with Eliazar Cervantes, transforming him from a struggling food runner to a brilliant expeditor.
  • Keep Emotions Out of Criticism: Emphasizing constructive feedback (“Criticize the behavior, not the person. Praise in public; criticize in private. Praise with emotion, criticize without emotion.”) and implementing initiatives like the “Made Nice Award.”
  • Thirty Minutes a Day Can Transform a Culture: Implementing mandatory, structured daily pre-meal meetings to “fill the gas tank” of employees, communicate standards, and “speak to the spirit of the restaurant.”
  • Set Them Up to Succeed: Cutting back on overwhelming demands (like extensive wine knowledge) to allow staff to build a solid foundation, embracing the mantra “slow down to speed up.”
  • Breaking Rules and Building a Team: Guidara’s “four-star inexperience” allowed him to critically examine fine-dining rules, questioning those that didn’t serve the guest. This led to abandoning norms like not touching the table, serving soufflés “wrong,” and having cooks kneel when describing dishes. They also changed their goodbye gift from elaborate canelés to a jar of granola, focusing on “what our guests might actually want to eat.”
  • Hire the Person, Not the Résumé: Guidara prioritized attitude and a “philosophy of hospitality” over fine-dining experience. New hires started as kitchen servers, immersing them in the culture and Daniel’s food before interacting with guests.
  • Every Hire Sends a Message: Emphasizing that hiring is a “sobering responsibility” because new hires impact the entire team. He advocated for “hire slow” to ensure cultural fit and to reward “A players” by surrounding them with other “A players.”
  • Build a Cultural Bonfire: To combat negativity and foster enthusiasm, he started hiring groups of new employees simultaneously, creating a “bonfire no one could put out.”
  • Make It Cool to Care: Drawing inspiration from a college friend, Brian Canlis, Guidara fostered an environment where genuine passion and effort were celebrated, transforming EMP into a place where “it had become cool to care.”
  • Working with Purpose, On Purpose:Don’t Try to Be All Things to All People: While open to criticism, Guidara believed in having a clear “point of view” and not changing everything based on a few negative opinions.
  • Articulate Your Intentions: Inspired by Miles Davis’s “endless reinvention” and collaborative spirit, Guidara and Humm developed a list of eleven words (Cool, Endless Reinvention, Inspired, Forward Moving, Fresh, Collaborative, Spontaneous, Vibrant, Adventurous, Light, Innovative) to guide their vision.
  • Strategy is for Everyone: Breaking the industry norm, they involved all staff, “from the assistant general manager and the chef de cuisine all the way to the dishwashers, prep cooks, and assistant servers,” in strategic planning to identify core values (Education, Passion, Excellence, Hospitality).
  • Choose Conflicting Goals: Embracing “integrative thinking” by choosing seemingly contradictory goals like “hospitality and excellence” forced innovation and ensured a balanced approach.
  • Know Why Your Work is Important: Guidara aimed to instill a sense of “nobility” in service, encouraging employees to understand that they “make a difference in someone’s life” and “make the world a better place.”

V. Continuous Improvement and Crisis Navigation

EMP’s journey to the top involved constant adaptation and strategic responses to challenges.

  • Leveraging Affirmation: Guidara actively sought and amplified external praise to boost team morale. He ensured credit went to those responsible, even if it meant risking them being “poached.” He believed “Persistence and determination alone are omnipotent” (Calvin Coolidge).
  • Restoring Balance (The Nuclear Reactor was Melting Down): The relentless pursuit of perfection led to staff burnout, highlighted by a cook showing up ten hours early due to stress. Guidara recognized the need to “slow down to speed up” and encouraged staff to find their “oxygen” for self-restoration.
  • The Deep Breathing Club (DBC): Inspired by a friend’s work with agitated youth, Guidara introduced “DBC” as a code word for overwhelmed staff to signal they needed to pause and receive support, de-stigmatizing asking for help.
  • Touch the Lapel: A staff-generated sign language gesture meaning “I need help,” which streamlined support during busy services and further destigmatized asking for assistance.
  • The Best Offense is Offense (Navigating the 2008 Recession):Adversity is a Terrible Thing to Waste: Facing financial desperation, Guidara and Humm decided to “play offense” rather than just cut costs.
  • Raindrops Make Oceans: They meticulously cut “invisible” expenses (e.g., dishwashing detergent, paper toques) but protected the guest experience. Guidara’s father encouraged him to journal these cuts to remember “the best of them” for future profitability.
  • Building the Top Line: Introduced a $29 two-course lunch to fill seats and attract new demographics. They also introduced a dessert trolley, increasing dessert sales by 300%.
  • Keep the Team Engaged: They hosted an elaborate Kentucky Derby party, which, while breaking even, “invigorated the team” and “broadened” EMP’s community.
  • It Doesn’t Have to Be Real to Work: To prepare for Frank Bruni’s anticipated four-star review during a long and stressful waiting period, they designated a “Critic of the Night” table, where every detail of service was flawlessly executed. This “ruse” allowed the team to practice and perfect their performance without the pressure of a real critic, making them ready for the actual review.

VI. Scaling, Evolution, and the Ultimate Achievement

Guidara’s principles extended beyond EMP to new ventures and ultimately led to global recognition.

  • Earning Informality: After earning four New York Times stars, EMP faced new expectations for formality. Guidara emphasized “earning informality” by initially amping up formality, then gradually building trust to offer a more casual, connected experience. This involved being “present” and focusing on relationships.
  • Learning to Be Unreasonable: After being ranked 50th on the World’s 50 Best Restaurants list, Guidara used his father’s quote, “What would you attempt to do if you knew you could not fail?” to inspire the team to aim for number one. This involved “radical” changes to hospitality, removing transactional elements (e.g., podiums, coat check tags) to create a more personal “welcome.”
  • Hospitality is a Dialogue, Not a Monologue: Inspired by Rao’s, Guidara sought to make the dining experience a true “dialogue.” They introduced a menu listing only the main ingredient (beef, duck, lobster), allowing guests choice while still enjoying an element of surprise. They also started asking guests about disliked ingredients, fostering vulnerability by first sharing his own dislike of sea urchin.
  • Treat Everyone Like a VIP: Unreasonable Hospitality meant extending “thoughtful, high-touch gestures for every one of our guests.” This included kitchen tours for all, not just VIPs, and the “hospitality solution” of leaving a bottle of cognac with the check at the end of the meal, eliminating the “rushed out” feeling.
  • Improvisational Hospitality: Guidara championed “one-off hospitality,” like serving a street hot dog to guests who mentioned they hadn’t had one. This led to the creation of the “Dreamweaver” role, a dedicated staff member to execute these spontaneous, personalized “Legends” (e.g., a watercolor of a new home, a Nerf gun game for a chef). The true gift of a Legend was “the story that made a Legend a legend.”Creating a Tool Kit: To scale these moments, they developed a “tool kit” of readily deployable gestures for recurring situations (e.g., “Plus One” cards with local recommendations, engagement flutes from Tiffany, hangover kits). He noted, “the value of a gift isn’t about what went into giving it, but how the person receiving it feels.”
  • Scaling a Culture (The NoMad): When opening the NoMad, Guidara aimed to “rejuvenate a New York neighborhood” and demonstrate that their hospitality culture could be scaled. They brought EMP staff to “seed the new spot with our culture” and made a rare external hire for GM, Jeff Tascarella, for his volume experience and “coolness.” Training was given an “outrageous” budget to ensure cultural transfer, resulting in a “Field Manual” of core values.
  • Leaders Say Sorry: Guidara admitted to one of his biggest mistakes: trying to manage both EMP and the NoMad simultaneously, leading to a decline in morale at EMP. He publicly apologized to his team and promoted Kirk Kelewae to GM, demonstrating the “power of vulnerability” and reinforcing that “Sometimes the best time to promote people is before they are ready.”
  • No Guest Left Behind: The NoMad allowed EMP to evolve its elaborate tasting menu without abandoning loyal regulars, offering a more casual yet still exceptional option nearby.
  • Back to Basics: After a drop on the 50 Best list and a realization that their meals had become “too much,” Guidara and Humm returned to first principles. They cut the menu from fifteen to seven courses, doubled down on Dreamweavers, and eliminated the script-like menu presentations, returning to a menu-less “conversation” about preferences. Their new mission: “To be the most delicious and gracious restaurant in the world.”
  • The Ultimate Achievement: In 2017, after “seven years of hard work, creativity, a maniacal attention to detail, and a truly unreasonable dedication to hospitality,” Eleven Madison Park was named the best restaurant in the world. Guidara noted it was the “pursuit of excellence that brought us to the table, but it was our pursuit of Unreasonable Hospitality that took us to the top.”

VII. Post-EMP and Future Vision

Guidara’s journey continued beyond EMP, reinforcing his core beliefs.

  • Doing What’s “Right”: His split with Daniel Humm was guided by his father’s advice to “ask yourself what ‘right’ looks like, then do that,” even if it meant personal sacrifice.
  • Continuing the Mission: Despite leaving EMP, Guidara remains dedicated to the industry, co-founding the Independent Restaurant Coalition and continuing to advocate for hospitality in various fields. He concludes by inviting leaders across industries to join “the hospitality economy.”

Contact Factoring Specialist, Chris Lehnes

At the heart of Will Guidara's work is the concept of Unreasonable Hospitality  which he defines as "the remarkable power of giving people more than they expect." This goes beyond mere "service," which Guidara describes as "black and white"—competent and efficient. Hospitality, in contrast, is "color"—making people feel great about the service they receive and creating an authentic connection.

Unreasonable Hospitality: A Comprehensive Study Guide

I. Quiz

Instructions: Answer each question in 2-3 sentences, drawing upon the provided source material.

  1. What was the initial “crazy idea” Will Guidara had for transforming Eleven Madison Park into the best restaurant in the world, and how did it differ from the typical approach to fine dining?
  2. Explain the distinction between “service” and “hospitality” as described in the text, using the “black and white” and “color” analogy.
  3. Describe the “Rule of 95/5” and provide an example of how Eleven Madison Park applied this principle in its operations.
  4. Why did Will Guidara initially decide against accepting the General Manager position at Eleven Madison Park, and what persuaded him to take the role?
  5. What was the significance of Daniel Humm and Will Guidara’s decision to run Eleven Madison Park as a “restaurant run by both sides of the wall”?
  6. How did Will Guidara address the issue of inconsistent service standards and communication among staff in the early days at Eleven Madison Park?
  7. Explain the concept of “making the charitable assumption” as taught by Danny Meyer and how it was applied to both employees and guests.
  8. What were the four core values that emerged from Eleven Madison Park’s first strategic planning meeting, and which two were considered to be in “inherent conflict”?
  9. Describe how the “Deep Breathing Club (DBC)” and the “touch the lapel” sign helped the team at Eleven Madison Park manage high-pressure situations and foster a culture of support.
  10. How did Will Guidara leverage external affirmation for his team at Eleven Madison Park, and what was his philosophy regarding staff members receiving media attention?

Answer Key

  1. Will Guidara’s “crazy idea” was to approach hospitality with the same passion, attention to detail, and rigor as the food. This differed from the typical approach which primarily focused on culinary innovation, aiming instead to prioritize connection and graciousness for both staff and guests.
  2. “Service is black and white; hospitality is color.” Service refers to doing a job with competence and efficiency, like delivering the right plate. Hospitality, however, means genuinely engaging with the person being served to make them feel great and establish an authentic connection.
  3. The “Rule of 95/5” means managing 95% of the business down to the penny, and spending the last 5% “foolishly” on details that have an outsized impact on the guest experience. An example at EMP was splurging on a rare and expensive glass of wine for one course in a pairing, or sending a family on a sledding trip after their meal.
  4. Will Guidara initially hesitated because he didn’t want to work for a chef who didn’t respect the dining room, insisting on a true partnership. He was persuaded when Danny Meyer allowed him to propose a one-year commitment, after which he could transition to Shake Shack, and Daniel Humm committed to a partnership between kitchen and dining room.
  5. The decision to run EMP as a “restaurant run by both sides of the wall” meant that both the chef and the restaurateur would make decisions together. This ensured that choices prioritized the restaurant’s overall best interest, rather than solely focusing on food (chef-driven) or service (restaurateur-driven), creating a more balanced and collaborative environment.
  6. Guidara addressed inconsistent service by reinstituting printed line-up notes with clear standards and information for servers, holding daily mandatory 30-minute pre-meal meetings to communicate expectations and inspire the team, and implementing food and wine tests. He also actively listened to staff feedback to understand underlying issues.
  7. “Making the charitable assumption” meant assuming the best of people, even when they were behaving poorly. For employees, it meant asking if everything was okay when they were late, rather than immediately reprimanding. For guests, it meant considering they might be having a difficult personal experience, and therefore needed more love and hospitality, even if dismissive.
  8. The four core values were Education, Passion, Excellence, and Hospitality. The two considered in “inherent conflict” were Excellence and Hospitality, as achieving both simultaneously required constant innovation and attention to balancing meticulous standards with genuine warmth and connection.
  9. The “Deep Breathing Club (DBC)” encouraged overwhelmed colleagues to take deep breaths during crises, implicitly communicating support. The “touch the lapel” sign provided a discreet and efficient way for staff to signal to a manager or colleague that they needed help, removing the stigma from asking for assistance in a fast-paced environment.
  10. Will Guidara leveraged external affirmation by sharing good press, gushing emails from guests, and compliments from other restaurateurs directly with his staff. His philosophy was to turn the spotlight on those who deserved it, giving credit to staff members like Kirk Kelewae for the beer program, even if it meant risking them being “poached,” as it inspired the team and attracted new talent.

II. Essay Questions (No Answers Supplied)

  1. Analyze the role of intentionality in shaping the culture and success of Eleven Madison Park, drawing examples from both Will Guidara’s personal life and the restaurant’s operational decisions.
  2. Compare and contrast the “restaurant-smart” and “corporate-smart” approaches to business, as described by Will Guidara’s father. Discuss how Guidara aimed to integrate both philosophies at MoMA and later at Eleven Madison Park, and the challenges he faced in doing so.
  3. Discuss the significance of “unreasonable hospitality” as a guiding principle for Eleven Madison Park. How did Guidara and his team operationalize this concept, and what impact did it have on both the guest experience and the internal culture of the restaurant?
  4. Examine the evolution of Eleven Madison Park’s mission and menu over time, including the introduction of the “New York theme” tasting menu and its eventual reevaluation. What lessons did Guidara learn about balancing creativity, tradition, and guest preferences in the pursuit of greatness?
  5. Reflect on the various leadership strategies employed by Will Guidara throughout his career, particularly during moments of adversity or significant change (e.g., the 2008 recession, the Michelin snub, or the separation from Daniel Humm). How did his approach to communication, feedback, and team empowerment contribute to the resilience and growth of his organizations?

III. Glossary of Key Terms

  • 95/5 Rule: A principle of business management where 95% of a budget or operation is managed meticulously down to the penny, while the remaining 5% is spent “foolishly” on details that have a disproportionately large impact on customer experience or employee morale.
  • “Anchor”: An employee positioned discreetly behind the podium at the entrance of Eleven Madison Park, in communication with the dining room, to signal to the maître d’ whether a guest’s table is ready.
  • “Athletic Hospitality”: A concept within Enlightened Hospitality referring to actively seeking opportunities to improve the guest experience (“playing offense”) or effectively resolving issues (“playing defense”).
  • “Being Present”: A state of deep engagement where one focuses entirely on the current interaction or task, putting aside thoughts of future responsibilities. In hospitality, it means being fully with the guest.
  • “Black and White” (Service): Refers to the competent and efficient execution of job duties, the technical aspects of service.
  • “Charitable Assumption”: The practice of assuming the best intentions or circumstances for another person’s behavior, especially when they are being difficult or late, rather than immediately judging or criticizing.
  • “CGS” (China, Glass, and Silver): An abbreviation referring to the department or responsibility for managing and maintaining all tableware.
  • “Color” (Hospitality): Refers to the emotional and connective aspects of service that make people feel great, going beyond mere competence.
  • “Conflicting Goals”: The strategic decision to pursue two seemingly opposing objectives simultaneously, such as hospitality and excellence, forcing innovation and deeper understanding to achieve both.
  • “Constant, Gentle Pressure”: Danny Meyer’s version of kaizen, emphasizing continuous, incremental improvement by everyone in the organization.
  • “Corporate-Smart”: A business approach characterized by strong back-end systems, controls, and profitability, often with centralized decision-making and less autonomy for frontline staff.
  • “Critic of the Night”: An internal practice at Eleven Madison Park where one random table each night was treated with the same meticulous attention and heightened service as if a real New York Times food critic were dining there.
  • “Cult is Short for Culture”: A phrase used to describe companies with strong, immersive cultures, suggesting that outsiders might perceive their shared language and dedication as cult-like due to their unconventional commitment to shared values.
  • “DBC” (Deep Breathing Club): A cultural initiative at Eleven Madison Park (inspired by a juvenile psychiatric hospital) where taking a few deep breaths was used as a rescue remedy for overwhelmed staff in high-pressure situations, fostering a sense of mutual support.
  • “Dreamweavers”: A dedicated team at Eleven Madison Park (and later Make It Nice) responsible for executing “improvisational hospitality” and creating bespoke, memorable “Legends” for guests based on overheard conversations or prior knowledge.
  • “Earning Informality”: The strategy of starting with a more formal approach to service to gain a guest’s respect and trust, gradually transitioning to a more relaxed and personal interaction as the meal progresses, rather than imposing informality from the start.
  • Eleven Madison Park (EMP): The New York City restaurant co-owned by Will Guidara and Daniel Humm, which transformed from a two-star brasserie to the number one restaurant in the world through a focus on “Unreasonable Hospitality.”
  • “Endless Reinvention”: One of the core values inspired by Miles Davis, emphasizing continuous and radical evolution in the restaurant’s offerings and approach to stay authentic and at the forefront of the industry.
  • Enlightened Hospitality: Danny Meyer’s philosophy that prioritizes employees first, believing that if employees are well-treated, they will then take excellent care of customers, leading to investor satisfaction.
  • Expeditor: A crucial kitchen role responsible for coordinating the timing of dishes, ensuring each plate reaches the correct table in a timely manner, and communicating between the kitchen and dining room.
  • “Fire Fast”: A management principle advocating for quickly dismissing employees who are a negative influence or poor fit, to prevent damage to team morale and culture.
  • First Principles: Fundamental truths or beliefs upon which an organization’s mission and operations are built; a return to these principles helps clarify decisions and refocus efforts.
  • “Four-Star Restaurant for the Next Generation”: The initial mission statement of Eleven Madison Park, aiming to combine the excellence and luxury of classic fine dining with contemporary fun and informality.
  • Grace Note: A sweet but nonessential addition or gesture that enhances an experience, often unexpected and delightful.
  • Happy Hour: Weekly meetings at Eleven Madison Park, led by staff members, dedicated to learning about wine, beer, cocktails, and other topics relevant to the restaurant and broader culture, fostering a culture of teaching and shared knowledge.
  • “Hire Slow”: A management principle advocating for a thorough and unhurried hiring process to ensure the right cultural fit and talent are brought into the organization.
  • Hospitality Economy: A term suggesting a shift in the broader economy where all businesses, not just traditional hospitality sectors, can differentiate themselves by intentionally focusing on making people feel seen, valued, and welcome.
  • “Important to Me” Card: A verbal or implied signal used in discussions between Will Guidara and Daniel Humm, indicating that a particular issue was of higher personal importance to one partner, leading the other to concede for the sake of partnership.
  • Improvisational Hospitality: The art of creating spontaneous, personalized, and unexpected gestures of care and delight for guests, often based on overheard conversations or prior knowledge.
  • Kaizen: A Japanese philosophy of continuous improvement, involving everyone in an organization making small, incremental changes. (Referenced as “constant, gentle pressure.”)
  • “Keep Your Eyes Peeled”: Frank Guidara’s advice to his son, emphasizing the importance of staying observant, listening, noticing, and learning in all situations.
  • “Legends”: A term coined at Eleven Madison Park for extraordinary, personalized acts of improvisational hospitality that create memorable stories for guests.
  • Make It Nice: The name of the company founded by Will Guidara and Daniel Humm, reflecting Daniel’s signature phrase for meticulous execution and embodying both excellence (“make”) and hospitality (“nice”).
  • “Making Magic”: The ability of a restaurant or service experience to create an enchanting, immersive atmosphere that makes everything else fade away, leaving a lasting positive impression.
  • Maître d’: The head of the dining room staff in a restaurant, responsible for welcoming guests, managing reservations, and overseeing service.
  • Molecular Gastronomy: A style of cooking that explores the physical and chemical transformations of ingredients, often using scientific techniques to create new textures and flavors.
  • NoMad Hotel: A luxury hotel opened by Will Guidara and Daniel Humm (under their company Make It Nice), aiming to reintegrate high-quality dining and hospitality as a central part of the hotel experience.
  • “Nobility in Service”: The belief that serving other human beings, through genuine hospitality, is an inherently important and dignified profession.
  • One-Inch Rule: A metaphor for maintaining focus and precision through the very last step of any task, emphasizing that a lapse in the final “inch” can compromise all preceding efforts.
  • Optimism Press: An imprint of Penguin Random House LLC, publishing “Unreasonable Hospitality.”
  • “Perception is Our Reality”: A mantra at Eleven Madison Park meaning that a guest’s subjective experience or belief, even if technically inaccurate, is the restaurant’s reality and must be addressed with hospitality.
  • “Plus One Cards”: Index cards at Eleven Madison Park containing answers to frequently asked guest questions (e.g., about purveyors, floral arrangements), used to provide “a little extra” information effortlessly.
  • Podium: A stand or desk typically used by a maître d’ at the entrance of a restaurant. Eleven Madison Park sought to eliminate the “transactional” feeling associated with it.
  • Pre-meal Meeting (Line-up): A daily meeting held before service in restaurants to review menu changes, wine pairings, and service standards, and to inspire and align the team.
  • Prix Fixe Menu: A menu offering a complete meal at a fixed price, with limited choices for each course.
  • Rao’s: An iconic, exclusive Italian American restaurant in Harlem, known for its lack of menus and personalized, conversational ordering.
  • Reconnaissance: The act of gathering information or intelligence, particularly before starting a new role or project, to understand the current situation and challenges.
  • Relais & Châteaux: A prestigious international association of independent luxury hotels and restaurants, known for its stringent acceptance guidelines.
  • “Restaurant-Smart”: A business approach where decision-making and creative latitude are largely held by staff working directly in the restaurants, prioritizing human connection over rigid corporate systems.
  • Rising Star Chef of the Year Award: A James Beard Award recognizing chefs under the age of thirty.
  • Roulade: A dish made by rolling a filling inside a piece of meat or pastry.
  • Rubin Museum: A New York City museum focusing on the art and cultures of the Himalayas, India, and neighboring regions.
  • Rule of 95/5: See 95/5 Rule.
  • Sabat’s (Sabrett’s): A brand of hot dogs commonly sold by street vendors in New York City.
  • Scaling a Culture: The process of successfully expanding an organization while preserving and transmitting its core values and unique way of operating to new locations or teams.
  • Seder: A Jewish ceremonial dinner, typically held on the first or second night of Passover, characterized by a specific order of prayers, rituals, and readings.
  • Service Bubble: A metaphorical concept referring to the immersive, undistracted atmosphere created around a dining table when all elements of service (timing, lighting, music) are perfectly executed.
  • Side Work: Behind-the-scenes maintenance tasks required to keep a restaurant running smoothly, such as polishing glassware, folding napkins, or restocking.
  • Siphon System (Vacuum Pot): A method of brewing coffee that uses vacuum and vapor pressure to draw water through grounds.
  • Sky Chefs: American Airlines’ catering arm, where Will Guidara’s parents met.
  • Skybox: A luxurious, glass-enclosed private dining room overlooking the kitchen at Daniel.
  • “Slow Down to Speed Up”: A mantra emphasizing that taking the time to solidify foundations, train thoroughly, or restore balance will ultimately lead to more efficient and sustainable progress.
  • Sous Vide: A cooking method where food is vacuum-sealed in a bag and then cooked in a precisely temperature-controlled water bath.
  • Spago: Wolfgang Puck’s famous restaurant, known for popularizing California cuisine.
  • Speakeasy: An illicit establishment that sells alcoholic beverages, especially during Prohibition. Also used to describe bars with hidden entrances or exclusive atmospheres.
  • Spiel: To give a detailed, often enthusiastic, description or explanation, typically of a dish or wine.
  • Spidey Sense: An intuitive or instinctive awareness, akin to Spider-Man’s ability to sense danger.
  • Stained-Glass Yuengling Lamps: Decorative lamps, often found in casual bars, featuring the logo of Yuengling beer.
  • Stalemate: A situation in which further action or progress by opposing parties seems impossible.
  • Stages (Stagiare): Unpaid or low-paid internships in a kitchen or dining room, common in the culinary world, where individuals gain experience and learn skills.
  • Strategic Planning Sessions: Long-form meetings where groups from across an organization brainstorm and define goals for future growth and development.
  • “Superstition” (song): A hit song by Stevie Wonder, referenced as a song Will Guidara played in his band.
  • Tasting Menu: A series of small, artfully presented courses, often chosen by the chef, designed to showcase a range of flavors and techniques.
  • “Their Perception Is Our Reality”: A mantra at Eleven Madison Park emphasizing that the guest’s subjective experience of a dish or service, even if technically “incorrect,” is the truth that the restaurant must address.
  • “Touch the Lapel”: A non-verbal signal used by staff at Eleven Madison Park to discreetly indicate to a colleague or manager that they needed help during a busy service.
  • “Transactional Feeling”: An impersonal, business-like exchange that lacks genuine human connection, often associated with routine customer service.
  • Tribeca Grill: A New York City restaurant owned by Drew Nieporent, where Will Guidara worked as a server.
  • Unreasonable Hospitality: The core philosophy of Will Guidara’s approach to service, defined as giving people more than they expect, going above and beyond what is reasonable or customary to create profound human connections and memorable experiences.
  • Union Square Hospitality Group (USHG): Danny Meyer’s restaurant company, known for its Enlightened Hospitality philosophy and for owning several celebrated New York City restaurants, including Eleven Madison Park and Gramercy Tavern.
  • Wasting Adversity: The idea that challenging times or setbacks should not be passively endured but actively leveraged as opportunities for creativity, growth, and innovation.
  • Welcome Conference: An annual symposium co-founded by Will Guidara and Anthony Rudolf, designed to foster community, trade ideas, and evolve the craft of dining room professionals and, later, leaders across various industries.
  • “What would you attempt to do if you knew you could not fail?”: A quote that served as a significant inspiration for Will Guidara and his team, encouraging ambitious goal-setting and overcoming fear of failure.
  • Win/Win/Win: A situation where all parties involved (e.g., employees, customers, the business itself) benefit from a particular decision or initiative.
  • World’s 50 Best Restaurants: A prestigious international award and ranking system for restaurants, influencing global culinary trends and industry recognition.
  • Zagat: A popular restaurant guide known for its survey-based ratings and reviews.
At the heart of Will Guidara's work is the concept of Unreasonable Hospitality  which he defines as "the remarkable power of giving people more than they expect." This goes beyond mere "service," which Guidara describes as "black and white"—competent and efficient. Hospitality, in contrast, is "color"—making people feel great about the service they receive and creating an authentic connection.

Zero to One – By Peter Thiel – Summary and Analysis

Executive Summary: The Imperative of “Zero to One”

Peter Thiel’s “Zero to One” challenges conventional wisdom in business and entrepreneurship, arguing that true progress comes not from incremental improvements (going from 1 to n), but from creating something entirely new (going from 0 to 1). This “vertical progress” is synonymous with technology and is essential for a sustainable and prosperous future, especially in a world grappling with the limitations of globalization without innovation. The book emphasizes that successful ventures achieve a temporary monopoly by solving unique problems, requiring bold planning, focused execution, and a contrarian mindset that seeks out “secrets” overlooked by the mainstream.

II. Main Themes and Core Ideas

A. The Challenge of the Future: 0 to 1 vs. 1 to n Progress

Thiel posits that progress can take two forms:

  • Horizontal or Extensive Progress (1 to n): Copying things that work. This is globalization, taking existing ideas and spreading them. China’s economic growth is cited as a paradigmatic example.
  • Vertical or Intensive Progress (0 to 1): Doing new things, creating something nobody else has ever done. This is technology, broadly defined as “any new and better way of doing things.”
  • Key Idea: The future of the world will be defined by technology more than globalization. “Without technological change, if China doubles its energy production over the next two decades, it will also double its air pollution… In a world of scarce resources, globalization without new technology is unsustainable.”
  • The Post-1970 Stagnation: Thiel argues that despite rapid IT advancements, overall technological progress has stalled since the 1970s. Earlier generations expected moon vacations and cheap energy, but this didn’t materialize.
  • Startup Thinking: New technology typically originates from startups – small groups “bound together by a sense of mission.” Big organizations struggle with innovation due to bureaucracy and risk aversion. Startups provide “space to think” and “question received ideas and rethink business from scratch.”
Peter Thiel - Zero to One challenges conventional wisdom in business and entrepreneurship, arguing that true progress comes not from incremental improvements (going from 1 to n), but from creating something entirely new (going from 0 to 1). This "vertical progress" is synonymous with technology and is essential for a sustainable and prosperous future, especially in a world grappling with the limitations of globalization without innovation. The book emphasizes that successful ventures achieve a temporary monopoly by solving unique problems, requiring bold planning, focused execution, and a contrarian mindset that seeks out "secrets" overlooked by the mainstream.

B. The Myth of Competition: Why Monopolies are Good

Thiel fundamentally refutes the conventional belief that “competition is healthy.”

  • Capitalism and Competition are Opposites: “Capitalism is premised on the accumulation of capital, but under perfect competition all profits get competed away.”
  • Monopoly as the Goal: A “monopoly” in Thiel’s view is “the kind of company that’s so good at what it does that no other firm can offer a close substitute.” Google, with its dominance in search, is a prime example.
  • The Benefits of Monopoly:Sustainable Profits: Monopolies can “capture lasting value” and afford to think beyond daily margins.
  • Ethical Operation: “Monopolists can afford to think about things other than making money; non-monopolists can’t.” Google’s “Don’t be evil” motto is cited.
  • Innovation: “Monopolies drive progress because the promise of years or even decades of monopoly profits provides a powerful incentive to innovate.”
  • Lies Companies Tell: Both monopolists (to avoid scrutiny) and competitive firms (to exaggerate uniqueness) distort their market positions. Startups’ biggest mistake is “to describe your market extremely narrowly so that you dominate it by definition.”
  • Competition as a Destructive Ideology: Competition is portrayed as “allegedly necessary, supposedly valiant, but ultimately destructive.” It leads to “ruthlessness or death” (e.g., the intense restaurant market) and causes people and companies to “lose sight of what matters and focus on their rivals instead” (e.g., Microsoft vs. Google’s rivalry benefited Apple).

C. Definite Optimism and the Rejection of Chance

Thiel criticizes the modern world’s “indefinite optimism,” where people expect the future to be better but have no concrete plans, relying on diversification and optionality rather than design.

  • Controlling the Future: The key distinction is between treating the future as “definite” (understand it, shape it) or “hazily uncertain” (ruled by randomness, give up on mastering it).
  • Four Views of the Future:Indefinite Pessimism: Bleak future, no idea what to do (e.g., Europe since the 1970s).
  • Definite Pessimism: Bleak future, known and prepared for (e.g., China’s rapid copying of Western methods).
  • Definite Optimism: Future will be better if planned and worked for. This characterized the Western world from the 17th to mid-20th century (e.g., Empire State Building, Apollo Program).
  • Indefinite Optimism: Future will be better, but no specific plans; profit from it without designing it (e.g., modern finance, law, consulting, and the “lean startup” methodology).
  • The Problem with Indefinite Optimism: “How can the future get better if no one plans for it?” It leads to “progress without planning is what we call ‘evolution’,” which Thiel argues is insufficient for startups.
  • The Return of Design: “Darwinism may be a fine theory in other contexts, but in startups, intelligent design works best.” Steve Jobs is lauded for his multi-year plans to create new products, rejecting “minimum viable products” and focus group feedback.
  • You Are Not a Lottery Ticket: Rejecting the “unjust tyranny of Chance” means taking definite mastery over one’s endeavors.

D. The Power Law and Focused Investment

Thiel highlights the pervasive “power law” distribution, where a small minority radically outperforms all others, especially in venture capital.

  • Unequal Distributions: “Small minorities often achieve disproportionate results.” This applies to earthquakes, cities, and businesses.
  • Venture Capital and the Power Law: “The biggest secret in venture capital is that the best investment in a successful fund equals or outperforms the entire rest of the fund combined.”
  1. Implications for VCs:“Only invest in companies that have the potential to return the value of the entire fund.”
  2. “Because rule number one is so restrictive, there can’t be any other rules.”
  • Beyond VCs: This principle applies to everyone. Entrepreneurs must consider whether their company will become overwhelmingly valuable. Individuals should “focus relentlessly on something you’re good at doing, but before that you must think hard about whether it will be valuable in the future.” Diversification in life and career is rejected as a “source of strength.”

E. Secrets: The Foundation of New Value

To create something new, one must discover “secrets”—important and unknown truths.

  • Contrarian Question Link: “Contrarian thinking doesn’t make any sense unless the world still has secrets left to give up.” A valuable company nobody is building is necessarily a secret.
  • Why People Don’t Look for Secrets:Incrementalism: Taught to take small, safe steps.
  • Risk Aversion: Fear of being wrong or “lonely and wrong.”
  • Complacency: Elites benefit from the status quo.
  • Flatness (Globalization): Belief that if something new were possible, someone smarter would have found it already.
  • The Case for Secrets: “There are many more secrets left to find, but they will yield only to relentless searchers.” Examples include curing diseases, new energy sources, and efficient transportation.
  • Types of Secrets:Secrets of Nature: Undiscovered aspects of the physical world.
  • Secrets About People: Things people don’t know about themselves, or hide. For example, the hidden opportunities in unused capacity (Airbnb, Uber, Lyft).
  • Finding and Using Secrets: The best place to look is “where no one else is looking.” Once found, a secret should be shared carefully within a “conspiracy to change the world” – a company.

III. Building a Monopoly: Last Mover Advantage and Key Characteristics

A durable monopoly is built on specific qualitative characteristics and a strategic approach to market entry and expansion.

  • Last Mover Advantage: “It’s much better to be the last mover—that is, to make the last great development in a specific market and enjoy years or even decades of monopoly profits.” This requires focusing on future cash flows.
  1. Characteristics of Monopoly (The Four Pillars):Proprietary Technology: Must be at least “10 times better than its closest substitute” to escape competition.
  2. Network Effects: Product becomes “more useful as more people use it.” Requires starting with “especially small markets” where the product is valuable to early users (e.g., Facebook starting with Harvard).
  3. Economies of Scale: Fixed costs spread over greater sales. Software startups particularly benefit from near-zero marginal costs.
  4. Branding: A strong brand helps claim a monopoly, but must be built on “strong underlying substance” (proprietary technology, network effects, scale). Apple is the prime example.
  • Building a Monopoly Strategy:Start Small and Monopolize: Dominate a “very small market” (e.g., PayPal targeting eBay PowerSellers, Amazon starting with books). Avoid large, competitive markets.
  • Scaling Up: “Gradually expand into related and slightly broader markets” (e.g., Amazon from books to other retail, eBay from Beanie Babies).
  • Don’t Disrupt: Avoid direct confrontation with large competitors. Instead, “expand the market for payments overall,” as PayPal did with Visa. “If your company can be summed up by its opposition to already existing firms, it can’t be completely new and it’s probably not going to become a monopoly.”

IV. Foundational Decisions and Company Culture

Getting the initial decisions right is paramount, as “a startup messed up at its foundation cannot be fixed.”

  • Founding Matrimony: Choosing co-founders is like “getting married,” requiring a shared “prehistory” and strong working relationships.
  • Ownership, Possession, and Control: Clear alignment between who owns the equity, who runs the company, and who governs it is crucial to avoid misalignment and bureaucracy (e.g., the DMV as an example of extreme misalignment).
  • On the Bus or Off the Bus: Everyone involved with the company should be “full-time” to ensure alignment. Remote work is discouraged.
  • Cash is Not King: High cash compensation incentivizes short-term thinking and value-claiming. Low CEO salaries (under $150,000/year for early-stage startups) and equity compensation (part ownership) foster long-term commitment and value creation.
  • The Mechanics of Mafia (Company Culture): A good company culture is a “team of people on a mission.”
  • Beyond Professionalism: Hire people who genuinely “enjoy working together” and envision a long-term future, not just transactional relationships.
  • Recruiting Conspirators: Specific answers about a unique mission and team are essential to attract top talent, not generic promises or perks. “The opportunity to do irreplaceable work on a unique problem alongside great people.”
  • Do One Thing: Each employee should be responsible for “just one thing,” reducing internal conflict and fostering long-term relationships. “Internal conflict is like an autoimmune disease.”
  • Cults and Consultants: The best startups can resemble “slightly less extreme kinds of cults,” where members are “fanatically right about something those outside it have missed.” Consultants, lacking a distinctive mission and long-term connection, are ineffective.

V. The Importance of Sales and Distribution (“Everybody Sells”)

Even the best product won’t sell itself; effective distribution is crucial and often underestimated, especially by engineers.

  • Nerds vs. Salesmen: Engineers often view sales as “superficial and irrational,” failing to recognize the “hard work to make sales look easy.”
  • Sales is Hidden: Good sales works best when hidden. Job titles are often obfuscated (e.g., “account executives” for salespeople).
  • The Bad Business: “If you’ve invented something new but you haven’t invented an effective way to sell it, you have a bad business—no matter how good the product.”
  • Key Metrics: Customer Lifetime Value (CLV) must exceed Customer Acquisition Cost (CAC).
  • Distribution Channels (Continuum):Complex Sales: For high-priced products ($1M+), requires close personal attention, often from the CEO (e.g., SpaceX, Palantir).
  • Personal Sales: For mid-priced products ($10K-$100K), requires a sales team to establish a process (e.g., Box, ZocDoc).
  • Marketing and Advertising: For low-priced, mass-appeal products without viral potential (e.g., Warby Parker). Startups should avoid competing on ad budgets with large companies.
  • Viral Marketing: Product’s core functionality encourages users to invite others, leading to “exponential growth” (e.g., Facebook, PayPal’s early strategy). The goal is to “dominate the most important segment of a market with viral potential.”
  • Power Law of Distribution: “One of these methods is likely to be far more powerful than every other for any given business.” Focus on mastering one channel; a “kitchen sink approach” fails.
  • Selling to Non-Customers: Companies must also “sell” themselves to employees and investors, and a public relations strategy is vital for attracting talent and funding.

VI. Man and Machine: Complementarity, Not Substitution

Thiel challenges the widespread fear that computers will replace human workers, arguing that the future lies in human-computer collaboration.

  • Computers as Complements: “Computers are complements for humans, not substitutes.” They excel at fundamentally different things. Humans have “intentionality” and make “basic judgments” where computers struggle. Computers excel at “efficient data processing.”
  • Gains from Working with Computers: “Much higher than gains from trade with other people.” Computers are tools, not rivals for resources.
  • Complementary Businesses: Examples include PayPal’s “Igor” fraud detection system (human operators making final judgments on flagged transactions) and Palantir (software empowering human analysts to identify terrorist networks and fraud).
  • Ideology of Computer Science: The fields of “machine learning” and “big data” often lean towards substitution, mistakenly believing “more data always creates more value.”
  • The Future: “The most valuable companies in the future won’t ask what problems can be solved with computers alone. Instead, they’ll ask: how can computers help humans solve hard problems?”

VII. Case Study: Cleantech Failure vs. Tesla’s Success

The cleantech bubble serves as a cautionary tale of widespread failure due to neglecting key business questions, contrasting with Tesla’s success.

  • Cleantech’s Failure (The Seven Questions Unanswered): Most cleantech companies failed because they had “zero good answers” to the seven critical questions:
  1. Engineering: Rarely 10x better; often incremental or worse (e.g., Solyndra’s cylindrical cells).
  2. Timing: Entered a slow-moving market without a definite plan (e.g., solar’s linear vs. microprocessors’ exponential growth).
  3. Monopoly: Focused on “trillion-dollar markets” which meant “ruthless, bloody competition,” failing to dominate a small niche.
  4. People: Run by “shockingly nontechnical teams” (salesman-executives) who prioritized fundraising over product.
  5. Distribution: Forgot about customers, assuming technology would sell itself (e.g., Better Place’s complex battery swapping).
  6. Durability: Failed to anticipate competition (especially from China) or market changes (e.g., fracking making fossil fuels cheaper).
  7. Secrets: Justified themselves with “conventional truths” about a cleaner world, lacking specific, unique insights.
  • Tesla: 7 for 7: Tesla thrived by answering all seven questions correctly:
  • Technology: Superior integrated design (Model S), relied on by other car companies.
  • Timing: Seized a “one-time-only opportunity” for a large government loan.
  • Monopoly: Dominated a tiny submarket (high-end electric sports cars) before expanding.
  • Team: Elon Musk, a “consummate engineer and salesman,” built a “Special Forces” team.
  • Distribution: Owned the entire distribution chain, controlling the customer experience.
  • Durability: Head start, fast movement, strong brand, founder still in charge.
  • Secrets: Understood that “fashion drove interest in cleantech,” building a brand around cars that “made drivers look cool, period.”

VIII. The Founder’s Paradox and the Pursuit of a Singular Future

Thiel explores the unique, often paradoxical nature of successful founders and the importance of individual vision for a better future.

  • Extreme Traits: Founders often exhibit an “inverse normal distribution” of traits—simultaneously insider/outsider, praised and blamed (e.g., Richard Branson, Sean Parker, Steve Jobs). They are “unusual people” who become more unusual.
  • The Scapegoat Analogy: Historically, extreme figures (kings, deities, scapegoats) served to resolve societal conflict. Modern celebrities and tech founders share this dynamic, experiencing intense adulation and demonization.
  • The Irreplaceable Value of Founders: Companies that create new technology often resemble “feudal monarchies” rather than impersonal bureaucracies. A unique founder can make authoritative decisions, inspire loyalty, and plan decades ahead.
  • The Need for Founders: We need founders who are “strange or extreme” to lead companies beyond “mere incrementalism.”
  • Caution for Founders: Avoid becoming “so certain of his own myth that he loses his mind.” Recognize that individual prominence is often a reflection of societal needs and can be fleeting.
  • Conclusion: Stagnation or Singularity?: Humanity faces a choice between stagnation (leading to conflict or extinction) or “accelerating takeoff toward a much better future” through new technology (the Singularity). “The future won’t happen on its own.” It’s up to us to “find singular ways to create the new things that will make the future not just different, but better—to go from 0 to 1.” This begins with thinking for oneself.

Contact Factoring Specialist, Chris Lehnes

Peter Thiel's Zero to Onechallenges conventional wisdom in business and entrepreneurship, arguing that true progress comes not from incremental improvements (going from 1 to n), but from creating something entirely new (going from 0 to 1). This "vertical progress" is synonymous with technology and is essential for a sustainable and prosperous future, especially in a world grappling with the limitations of globalization without innovation. The book emphasizes that successful ventures achieve a temporary monopoly by solving unique problems, requiring bold planning, focused execution, and a contrarian mindset that seeks out "secrets" overlooked by the mainstream.

Zero to One Study Guide

Quiz

  1. Zero to One vs. One to N: Explain the fundamental difference between “going from 0 to 1” and “going from 1 to n” in the context of business progress. Why does the author argue that going from 0 to 1 is more crucial for the future?
  2. The Contrarian Question: What is the “contrarian question” that Peter Thiel frequently asks, and why does he consider it a crucial indicator of brilliant thinking and potential for future success? Provide an example of a “bad” answer and explain why.
  3. Monopoly vs. Competition: According to the author, why is it more advantageous for a company to strive for a monopoly rather than compete in a perfectly competitive market? Explain the negative consequences of intense competition for businesses.
  4. Lessons from the Dot-Com Crash: List and briefly explain two of the “dogmas” that emerged from the dot-com crash, and then state the author’s contrarian perspective on each.
  5. Characteristics of a Monopoly: Identify and briefly describe two of the four key characteristics that contribute to a company’s ability to maintain a durable monopoly.
  6. Definite vs. Indefinite Views of the Future: Distinguish between a “definite” and an “indefinite” view of the future. How does each perspective influence an individual’s or society’s approach to planning and action?
  7. The Power Law in Venture Capital: Explain the “power law” as it applies to venture capital investments. How does understanding this principle influence a VC’s investment strategy?
  8. Why People Don’t Look for Secrets: Discuss two reasons why, according to the author, most people act as if there are no secrets left to find, leading to a lack of innovation.
  9. Founding Matrimony and Company Alignment: Why does the author compare choosing a co-founder to getting married? Explain how this initial decision is critical for a startup’s long-term alignment and success, and discuss the impact of misalignment.
  10. Sales is Hidden: Explain the author’s concept that “sales is hidden.” Why do people in roles involving distribution often use job titles that obscure their sales function, and why do engineers often underestimate the importance of sales?

Answer Key

  1. Zero to One vs. One to N: “Going from 0 to 1” refers to creating something entirely new, an act of singular innovation that produces something fresh and strange. “Going from 1 to n” means copying things that already work, adding more of something familiar (horizontal progress or globalization). The author argues that 0 to 1 is crucial because relying on existing practices (1 to n) will eventually lead to stagnation and failure, especially in a world with scarce resources.
  2. The Contrarian Question: The “contrarian question” is: “What important truth do very few people agree with you on?” It’s a crucial indicator because knowledge everyone is taught is by definition agreed upon, and it takes courage to articulate an unpopular truth. A bad answer merely takes one side in a familiar debate or states something many people already agree with, rather than revealing a hidden truth.
  3. Monopoly vs. Competition: The author argues that monopolies are more advantageous because under perfect competition, all profits are competed away, leading to an undifferentiated commodity business. Intense competition pushes companies toward ruthlessness, prevents long-term planning, and destroys profits, making it difficult to innovate or care for employees.
  • Lessons from the Dot-Com Crash:Dogma 1: Make incremental advances. The author’s contrarian view is: It is better to risk boldness than triviality. Grand visions might have fueled the bubble, but small, incremental steps lead to dead ends.
  • Dogma 2: Stay lean and flexible. The author’s contrarian view is: A bad plan is better than no plan. While flexibility is good, treating entrepreneurship as agnostic experimentation without a concrete plan is flawed.
  • (Other possible answers: Dogma 3: Improve on the competition – Contrarian: Competitive markets destroy profits. Dogma 4: Focus on product, not sales – Contrarian: Sales matters just as much as product.)
  • Characteristics of a Monopoly:Proprietary Technology: Technology that is at least 10 times better than its closest substitute, making the product difficult or impossible to replicate (e.g., Google’s search algorithms).
  • Network Effects: A product becomes more useful as more people use it, creating a natural barrier to entry for competitors (e.g., Facebook).
  • Economies of Scale: A business gets stronger as it gets bigger because fixed costs can be spread over greater quantities of sales, leading to higher margins (e.g., software startups with near-zero marginal costs).
  • Branding: A strong brand creates a perception of uniqueness and quality that is difficult for competitors to replicate, reinforcing other underlying monopolistic advantages (e.g., Apple).
  1. Definite vs. Indefinite Views of the Future: A “definite” view assumes the future can be known and shaped through specific plans and actions, fostering a sense of agency. An “indefinite” view treats the future as uncertain and random, leading to a portfolio approach where individuals try to keep options open without committing to a specific path. The former encourages creation, the latter leads to process-oriented work and stagnation.
  2. The Power Law in Venture Capital: The power law states that in venture capital, a small handful of companies (e.g., the top investment) will radically outperform all others, often returning more than the entire rest of the fund combined. This understanding leads VCs to focus on identifying and heavily investing in a very few companies with the potential for overwhelming value, rather than diversifying broadly (“spray and pray”).
  • Why People Don’t Look for Secrets:Incrementalism: Education systems teach people to take small steps and conform to existing knowledge, discouraging exploration beyond established boundaries.
  • Risk Aversion: People are afraid of being wrong or being lonely in their convictions, making them hesitant to pursue unvetted or unpopular truths.
  • Complacency: Social elites, comfortable with their current standing, may not see the need to search for new secrets, content to collect rents on existing achievements.
  • “Flatness” / Globalization: The perception of a globalized, highly competitive marketplace can lead individuals to doubt their ability to discover something unique, assuming someone else would have found it already.
  1. Founding Matrimony and Company Alignment: The author compares choosing a co-founder to getting married because it’s the most crucial initial decision, and founder conflict can be as destructive as divorce. A good founding team should have a shared prehistory, complementary skills, and strong working relationships to ensure alignment. Misalignment, especially between ownership, possession, and control, can lead to internal conflicts, slow decision-making, and ultimately jeopardize the company’s future.
  2. Sales is Hidden: “Sales is hidden” means that effective sales often operate subtly and without overt labeling. People in sales, marketing, or advertising roles frequently have job titles that don’t explicitly state their sales function (e.g., “account executive,” “business development”). Engineers often underestimate sales because they value transparency and objective technical merit, seeing sales as superficial or dishonest, while failing to recognize the hard work and persuasion involved in making sales appear effortless.

Essay Format Questions (No Answers Supplied)

  1. Peter Thiel argues that “capitalism and competition are opposites.” Discuss this assertion by explaining his definitions of perfect competition and monopoly, the incentives each creates for businesses, and why he believes creative monopolies are beneficial for society.
  2. Analyze the concept of “indefinite optimism” as presented in the text. How does this mindset manifest in various aspects of modern American society (finance, politics, philosophy, life sciences), and what are its perceived consequences for progress and innovation?
  3. Thiel posits that “every great business is built around a secret that’s hidden from the outside.” Explore the nature of secrets (natural vs. about people), the societal reasons why people tend not to look for them, and how founders can identify and leverage secrets to build valuable companies.
  4. The author dedicates a significant portion to the “lessons learned” from the dot-com crash and the subsequent failure of cleantech companies. Compare and contrast the common mistakes made by businesses in these two periods, focusing on how a misunderstanding of key business questions (e.g., timing, monopoly, distribution) contributed to their downfalls.
  5. Examine the “Founder’s Paradox” and the idea that “we need founders.” Discuss the extreme traits often associated with successful founders, how these traits contribute to their ability to build companies that “go from 0 to 1,” and the potential dangers or downsides of such individuality.

Glossary of Key Terms

  • 0 to 1 (Vertical Progress/Intensive Progress): The act of creating something entirely new, a singular innovation that results in something fresh and strange. This is contrasted with “1 to n” progress.
  • 1 to N (Horizontal Progress/Extensive Progress): Copying things that already work, adding more of something familiar. This is also referred to as globalization.
  • Contrarian Question: Peter Thiel’s signature interview question: “What important truth do very few people agree with you on?” It’s used to identify original thinkers who can see beyond conventional wisdom.
  • Perfect Competition: An economic model where many firms sell identical products, have no market power, and thus make no economic profit in the long run. The author views this as a destructive state for businesses.
  • Monopoly: A company that is so good at what it does that no other firm can offer a close substitute. The author advocates for “creative monopolies” that innovate and provide unique value.
  • Creative Monopoly: A company that creates entirely new categories of abundance in the world through innovation, rather than by unfairly eliminating rivals or exploiting customers.
  • Last Mover Advantage: The concept that it is better to be the last great developer in a specific market, dominating a small niche and scaling up, to enjoy long-term monopoly profits, rather than just being the first (first mover advantage).
  • Cash Flow: The movement of money into and out of a business. The author emphasizes that the value of a business is the sum of its future discounted cash flows, making durability crucial.
  • Proprietary Technology: Technology that is difficult or impossible for others to replicate, offering a substantive advantage (e.g., being 10x better than substitutes).
  • Network Effects: A phenomenon where a product or service gains additional value as more people use it.
  • Economies of Scale: The cost advantages that enterprises obtain due to their size, with fixed costs spread over a larger volume of production, leading to lower per-unit costs.
  • Branding: The process of creating a unique name, image, and identity for a product or company. A strong brand can reinforce a monopoly by creating a perception of unique value.
  • Definite Optimism: A belief that the future can be made better through specific plans and hard work. Characterized by active creation and long-term vision.
  • Indefinite Optimism: A belief that the future will be better, but without specific plans on how to make it so. Characterized by keeping options open, process over substance, and diversification.
  • Definite Pessimism: A belief that the future will be bleak but can be prepared for through known actions (e.g., relentless copying).
  • Indefinite Pessimism: A belief that the future will be bleak, with no idea what to do about it. Characterized by undirected bureaucratic drift and waiting for things to happen.
  • Power Law: An exponential distribution pattern where a small number of instances account for a disproportionately large share of the total, especially relevant in venture capital returns.
  • Secrets: Important, unknown, and hard-but-doable truths about the natural world or about people. Great companies are built on these hidden insights.
  • Customer Lifetime Value (CLV): The total net profit a company expects to earn from a customer over the course of their relationship.
  • Customer Acquisition Cost (CAC): The average cost to acquire one new customer. For a sustainable business, CLV must exceed CAC.
  • Complex Sales: A distribution method for high-value products (e.g., seven figures or more) that requires extensive personal attention, relationship building, and often involves the CEO.
  • Personal Sales: A distribution method for products with average deal sizes (e.g., $10,000 to $100,000) that relies on a sales team to build relationships and move the product to a wide audience.
  • Marketing and Advertising: Distribution methods for relatively low-priced products with mass appeal, often used when other viral or personal sales channels are uneconomical.
  • Viral Marketing: A distribution method where a product’s core functionality encourages users to invite others, leading to exponential growth.
  • Complementarity (Man and Machine): The idea that humans and computers are fundamentally good at different things and can achieve dramatically better results by working together, rather than computers simply replacing humans.
  • Founding Matrimony: The analogy used to describe the critical importance of selecting co-founders, emphasizing that this relationship is as crucial and potentially fraught with conflict as a marriage.
  • Ownership, Possession, and Control: Three distinct aspects of a company’s structure: ownership (equity holders), possession (day-to-day management), and control (board of directors). Misalignment among these can lead to dysfunction.
  • PayPal Mafia: The term used to describe the closely-knit team from PayPal, many of whom went on to found and invest in other highly successful tech companies, demonstrating the power of strong company culture and relationships.
  • Founder’s Paradox: The phenomenon where successful founders often exhibit extreme and contradictory traits (e.g., insider/outsider, brilliant/crazy), which are both powerful for innovation and potentially dangerous for the individual.
  • Singularity: A theoretical future point where technological growth becomes uncontrollable and irreversible, resulting in unfathomable changes to human civilization.