Introduction: Why We’re All Missing the Point About AI

The conversation around AI is dominated by extremes. On one side, there are anxieties of mass job loss and uncontrollable superintelligence. On the other, there are utopian dreams of automated abundance. But this focus on AI’s “intelligence” is a distraction from its real, more profound impact. We are so busy asking if the machine is smart enough to replace us that we’re failing to see how it’s already changing the entire system we operate in.

This article distills five counter-intuitive truths from Sangeet Paul Choudary’s book, Reshuffle, to offer a new framework for understanding AI’s true power. These insights will shift your perspective from the tool to the system, revealing where the real opportunities and threats lie.

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1. It’s Not About Intelligence, It’s About the System

We mistakenly judge AI by how human-like it seems, a phenomenon Choudary calls the “intelligence distraction.” We debate its creativity or consciousness while overlooking the one thing that truly matters: its effect on the systems it enters.

Consider the parable of Singapore’s second COVID-19 wave in 2021. The nation was a global model of pandemic response, armed with precise tools like virus-tight borders and obsessive contact tracing. Yet, it was defeated not by a technological failure, but by systemic blind spots. An outbreak was traced to hostesses—colloquially known as “butterflies”—working illegally in discreet KTV lounges after entering the country on a “Familial Ties Lane” visa. With contact tracing ignored in the venues and a clientele of well-heeled men unwilling to risk their reputations by coming forward, the nation’s high-tech system was rendered useless. Singapore’s precise tools were no match for the hidden logic of the system.

This illustrates a crucial lesson: the real story of AI is not in the technology itself, but in the system within which it is deployed. Our focus should not be on the machine’s capabilities in isolation.

Instead of asking How smart is the machine?, we should shift our frame to ask What do our systems look like once they adopt this new logic of the machine?

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2. AI’s Real Superpower is Coordination, Not Automation

We often mistake AI’s impact for simple automation—making individual parts of a process faster. But its most transformative power lies in coordination: making all the parts work together in new and more reliable ways.

The shipping container provides a powerful analogy. Its revolution wasn’t just faster loading at ports (automation). Its true impact came from imposing a new, reliable logic of coordination across global trade. Innovations by entrepreneurs like Malcolm McLean, such as the single bill of lading that unified contracts across trucks, trains, and ships, and the push for standardization during the Vietnam War, were deliberate efforts to overcome systemic inertia. By standardizing how goods were moved, the container restructured entire industries, enabled just-in-time manufacturing, and redrew the map of economic power.

AI is the shipping container for knowledge work. Its most profound impact comes from its ability to coordinate complex activities and align fragmented players in ways previously impossible—what the book calls “coordination without consensus.” It can create a shared understanding from unstructured data, allowing teams, organizations, and even entire ecosystems to move in sync without rigid, top-down control.

This reveals a self-reinforcing flywheel of economic growth: better coordination drives deeper specialization, as companies can rely on external partners. This specialization leads to further fragmentation of industries, which in turn demands even more powerful forms of coordination to manage the complexity. AI is the engine of this modern flywheel.

The real leverage in connected systems doesn’t come from optimizing individual components, but from coordinating them.

This new power of system-level coordination is precisely why the old, task-focused view of job security is no longer sufficient.

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3. The “Someone Using AI Will Take Your Job” Trope is a Trap

The popular refrain, “AI won’t take your job, but someone using AI will,” is a dangerously outdated framework. It encourages a narrow, task-centric view of work that misses the bigger picture.

The book uses the Maginot Line as an analogy. In the 1930s, France built a chain of impenetrable fortresses to defend against a German invasion, perfecting its defense for the trench warfare of World War I. But Germany had changed the entire system of combat. The Blitzkrieg integrated mechanized infantry, tank divisions, and dive bombers, all of which were coordinated through two-way radio communication, to simply bypass the useless fortifications. The key wasn’t better weapons; it was a new coordination technology that changed the system of warfare itself.

Focusing on using AI to get better at your current tasks is like reinforcing the Maginot Line. The real threat isn’t that someone will perform your tasks better; it’s that AI is unbundling and rebundling the entire system of work. When the system changes, the economic logic that holds a job together can collapse, rendering the role obsolete even if the individual tasks remain.

When the system itself changes due to the effects of AI, the logic of the job can collapse, even if the underlying tasks remain intact.

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4. Stop Chasing Skills. Start Hunting for Constraints.

In a world where AI makes knowledge and technical execution abundant, simply “reskilling” is a losing game. It puts you in a constant race to learn the next task that AI can’t yet perform. A more strategic approach is to hunt for the new constraints that emerge in the system.

Take the surprising example of the sommelier. When information about wine became widely available online, the sommelier’s role as an information provider should have disappeared. Instead, their value increased. Why? Because they shifted from providing information to resolving new constraints for diners. With endless choice came new problems: the risk of making a bad selection and the desire for a curated, confident experience. The sommelier’s value migrated to managing risk. Furthermore, as one form of scarcity disappeared (information), they helped manufacture a new one: certified taste, created through elite credentialing bodies like the Court of Master Sommeliers.

The core lesson is that value flows to whoever can solve the new problems that appear when old ones are eliminated by technology. The key to staying relevant is not to accumulate more skills, but to identify and rebundle your work around solving the system’s new constraints, such as managing risk, navigating ambiguity, and coordinating complexity.

The assumption baked into most reskilling narratives is that skills are a scarce resource. But in reality, skills are only valuable in relation to the constraint they resolve.

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5. Using AI as a “Tool” Is a Path to Irrelevance

There is a crucial distinction between using AI as a “tool” versus using it as an “engine.” Using AI as a tool simply optimizes existing processes. It makes you faster or more efficient at playing the same old game, leading to short-term gains but no lasting advantage.

The book contrasts the rise of TikTok with early social networks to illustrate this. Platforms like Facebook and Instagram used AI as a tool to enhance their existing social-graph model, improving feed ranking and photo tagging. Their competitive logic remained centered on who you knew. TikTok, however, used AI as its core engine. It built an entirely new model based on a behavior graph—what you watch determines what you see. This was enabled by a brilliant positive constraint: the initial 60-second video limit forced a massive volume of rapid-fire user interactions, generating the precise data needed to train its behavior-graph engine at a speed competitors couldn’t match. This new logic made the old rules of competition irrelevant.

Companies that fall into the “tool integration trap” by becoming dependent on third-party AI to optimize tasks risk outsourcing their competitive advantage. The strategic choice is to move beyond simply applying AI and instead rebuild your core operating model around it.

A company that utilizes AI as a tool may improve efficiency, but it still competes on the same basis. A company that treats AI as an engine unlocks entirely new levels of performance and changes the basis of how it competes.

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Conclusion: Reshuffle or Be Reshuffled

To truly understand AI, we must shift our focus from its intelligence to its systemic impact. The five truths reveal a clear pattern: AI’s power isn’t in automating tasks but in reconfiguring the systems of work, competition, and value creation. It’s a force for coordination, a reshaper of constraints, and an engine for new business models.

True advantage comes not from reacting to AI with better skills or faster tools, but from actively using it to reshape the systems around us. It requires moving from a task-level view to a systems-level perspective.

The question is no longer “How will AI change my job?” but “What new systems can I help build with it?” What will your answer be?

Contact Factoring Specialist, Chris Lehens