The Leverage Gap

Winners, losers, and the 'Average is Over' reality

Part 4 of 4 in the "The Allocation Economy" series

In 2013, economist Tyler Cowen published Average is Over, a prophetic warning that intelligent machines would bifurcate the economy. He argued that the middle ground—the domain of average skills, average execution, and average cognition—was being hollowed out. Today, as we stand in the midst of the Allocation Economy, Cowen’s thesis reads less like a prediction and more like a history book.

The Knowledge Economy was built on a bell curve. Most people fell in the middle: solid, reliable contributors who traded time for money. They weren't geniuses, but they weren't unskilled. They were the "median" workers—the accountants, the mid-level managers, the copywriters, the Java developers. They were the backbone of the corporate structure.

The Allocation Economy is destroying the bell curve. It is replacing it with a barbell.

On one end, we have the displaced: those whose primary value was "execution"—doing the thing that has now been commoditized by AI. On the other end, we have the leveraged: the "super-individuals" who have learned to direct the infinite supply of synthetic labor.

The gap between these two groups is not linear; it is exponential. This is the Leverage Gap.

The Death of "Good Enough"

In the pre-AI world, being "good enough" was a viable career strategy. You didn't need to be the best writer in the world to be a copywriter; you just needed to be better than the client. You didn't need to be a 10x engineer to get a job; you just needed to know the syntax and show up.

But AI is "good enough" at almost everything now. It is the ultimate median worker. It is the average copywriter, the average paralegal, the average coder—and it works for free, instantly, 24/7.

This creates a brutal reality: If your output is indistinguishable from the median output of an LLM, your economic value is trending toward zero.

This is the manifestation of Skill-Biased Technological Change (SBTC) on steroids. Historically, SBTC meant that technology favored those with higher education. Computers made college graduates more productive than high school graduates. But AI-driven SBTC is more specific. It doesn't just favor the "educated"; it favors the allocators.

It favors those with:

1. Taste: The ability to discern quality when the cost of generating quantity is zero.
2. Context: The ability to understand the specific nuance of a problem that an AI, trained on general data, might miss.
3. Agency: The ability to take responsibility for outcomes, not just outputs.

The One-Person Unicorn

The most striking symbol of the Leverage Gap is the theoretical "One-Person Unicorn"—a billion-dollar company built by a single human founder.

Sam Altman, CEO of OpenAI, has predicted this will happen by the late 2020s. "We’re going to see a 10-person billion-dollar company," he said, and later revised it to a single person.

This sounds like science fiction, but the trajectory is clear. In the industrial age, scaling a business meant scaling people. To double your output, you roughly had to double your headcount. In the Allocation Economy, scaling means scaling compute.

Consider the evolution:

• The 1x Engineer: Writes code. Limited by typing speed and mental stack.
• The 10x Engineer: Understands systems, writes better code, automates tasks.
• The 1000x Allocator: Describes the system to an army of AI agents, reviews the architecture, and deploys.

The 1000x Allocator is not working 1000 times harder. They simply have 1000 times more leverage. They have moved from "doing" the work to "allocating" the resources (AI agents, APIs, automation) to get the work done.

For this individual, the marginal cost of execution has collapsed. They can launch a marketing campaign, build a full-stack app, and manage customer support simultaneously. They are the beneficiaries of the Leverage Gap.

Redefining Meritocracy

This shift forces us to redefine "merit."

For decades, meritocracy was tied to effort and retention. The student who memorized the most facts (medical school) or the employee who worked the longest hours (investment banking) was rewarded.

In the Allocation Economy, effort is decoupled from output. You can spend 10 hours writing a report that an AI can generate in 10 seconds. Does your 10 hours of effort have value? The market says no.

Merit is shifting from Execution to Judgment.

• Old Merit: "I wrote this code from scratch."
• New Merit: "I architected this solution and verified that the AI's implementation is secure and scalable."
• Old Merit: "I spent weeks researching this topic."
• New Merit: "I asked the right questions to synthesize this insight in minutes."

This is a terrifying shift for many. It feels unfair. It feels like "cheating." But the economy does not care about our feelings of fairness; it cares about value. And value has moved up the stack.

The Socioeconomic Rift: Servants vs. Architects

If Cowen is right, and the middle is hollowed out, where do the people go?

They go to the edges. Some will climb the ladder of leverage, learning to use AI to become super-individuals. But many will slide down into roles that AI cannot yet do—roles that require physical presence, emotional labor, or high-liability human accountability.

We risk creating a "Servant Economy" to support the "Allocation Economy." The Allocators will live in a world of digital abundance, while the rest fight for the remaining physical-world scraps—service jobs, trades, and care work.

This is the dark side of the Leverage Gap. When capital (software/AI) becomes infinitely more productive than labor, the share of wealth going to labor drops. Unless we find a way to democratize leverage itself—to make every worker an allocator—inequality will explode.

Survival Strategy: Build Personal Leverage

How do you survive the Leverage Gap? You must cross the chasm. You must stop identifying as a "worker" and start identifying as a "manager of resources."

1. Abandon "Average" Skills: If you are "average" at Python, stop writing Python. Start learning system architecture. Let AI write the Python.
2. Cultivate "Taste": AI can generate infinite variations of a design, a text, or a codebase. It cannot tell you which one is good. Your value is your taste. Train it.
3. Own the Outcome: AI cannot take responsibility. It cannot go to jail. It cannot be fired (effectively). The human who puts their name on the final product owns the risk. Charge for that risk.
4. Become a Generalist: The specialist is vulnerable because their narrow slice of the pie can be automated. The generalist—who can connect marketing to engineering to sales—is harder to replace because their value lies in the synthesis of domains, which is currently harder for AI to replicate than isolated tasks.

Conclusion: The Allocation Imperative

The Allocation Economy is not coming; it is here. The Leverage Gap is widening every day.

We are entering an era where the difference between a junior employee and a senior executive is not just experience—it is the magnitude of leverage they can wield. One is using a shovel; the other is directing a fleet of autonomous excavators.

The "average" career is over. But for those willing to adapt, the ceiling has never been higher. The tools of creation are in your hands. The question is no longer "What can I do?" but "What can I command?"

Welcome to the Allocation Economy.

This series has explored the shift from the Knowledge Economy to the Allocation Economy. For more insights on navigating this transition, explore the XPS Institute Solutions column for practical guides on AI implementation.