AI and Beyond Pareto

At the end of the nineteenth century, Vilfredo Pareto noticed a stubborn pattern in the data.

Studying land ownership and income in Italy, he found that wealth did not distribute evenly. A small minority held a disproportionate share, while the majority held comparatively little. He looked elsewhere and saw the same shape again and again. The exact percentages changed. The asymmetry didn't.

Pareto wasn't making a moral claim. He wasn't offering a theory of how societies should work. He was documenting how they do work: left to their own dynamics, human systems concentrate value, influence, and outcomes in the hands of a few.

Much later, we turned that observation into a slogan: the 80/20 rule. The numbers were never the point. The curve was.

And for a remarkably long time, the curve held.

Across organizations and markets, a minority reliably produced the majority of visible progress. The names changed, the industries rotated, the technologies evolved—but the distribution stayed familiar: steep, persistent, and crucially, bounded.

For decades, Pareto's shadow stretched across modern life.

The Resilience of the 80/20 Curve

From Pareto's time onward, the distribution he described proved stubbornly resilient.

Across more than a century of industrialization, digitization, and globalization, the pattern bent but rarely broke. A small fraction generated most of the value, but the imbalance stayed bounded. It appeared in companies, products, markets, and institutions. Some people mattered more than others. Some firms dominated entire industries. But the skew rarely exceeded what our intuition could tolerate. Teams still mattered. Institutions still required bodies. Work still demanded many hands.

Even in public equities—one of the cleanest laboratories for concentration—the curve rarely went vertical.

Markets are already designed to reward scale: capital compounds, winners attract more capital, and the biggest firms get the cheapest financing, the best talent, and the strongest distribution. If any domain should collapse into "a few own everything," it's this one.

And yet, even here, the skew stayed bounded.

In the late 2010s, investors started naming the concentration because a small set of technology companies began to explain an outsized share of index movement. "FAANG" wasn't a theory—it was a coping mechanism: a way to point at five tickers and say, watch these; they move the market. By 2018, those five alone were already more than 11% of the S&P 500. By early 2020, the largest U.S. tech names (the exact membership shifts depending on who's counting) approached nearly 18% of the index's total value.

That's dramatic. But it's not total capture. Even at those peaks, the majority of market value still lived outside the winners. The curve steepened, but it held. Hundreds of other companies still made up most of the index. The distribution remained recognizably Pareto-shaped: persistent, asymmetric, but bounded.

The same pattern appeared inside organizations.

When Elon Musk acquired Twitter in 2022 and mass layoffs began, the instinctive prediction was collapse. A system of that scale, it was assumed, could not function without its human bulk. Yet the platform continued to operate, with a smaller fraction of contributors carrying most visible activity.

What stuck with me wasn't the drama of the cuts. It was the familiarity of the outcome. I kept expecting the curve to finally break—expecting some clean proof that we could leave 80/20 behind. And instead, it was like watching a rubber band stretch and then settle back into the same shape.

Because the contour was enforced by limits. Not moral limits—mechanical ones. The finite bandwidth of an individual. The managerial capacity of a company. The capital constraints that slow expansion. The operational drag that appears whenever something scales. These weren't inconveniences—they were the boundary conditions of modern work. They kept the distribution steep, but they kept it from going vertical.

For more than a century, systems resisted true extremes. You could optimize toward concentration, but the distribution rarely collapsed into shapes like 95/5 or 99/1—not because the world was fair, but because the physics of scale pushed back. Those asymmetries were theoretically imaginable, but practically unreachable.

Until very recently.

For most of my life, I’ve favoured the term machine learning over AI. Not out of pedantry—more out of instinct. The outcomes rarely felt satisfying enough to justify the word intelligence. Useful, yes. Sometimes impressive. But AI sounded like a claim, and for a long time the results didn’t earn it.

Then ChatGPT arrived, and the abstract became operational. Not a breakthrough in a lab, but a capability in the hands of everyone. And almost immediately, the market responded in the only language it truly speaks: capital. Nvidia’s valuation didn’t just rise—it signaled a regime change. Suddenly, the “AI story” wasn’t a niche narrative. It was the organizing thesis of the entire economy.

You can see it in how quickly a new concentration emerged—not just in consumer apps, but across the entire AI supply chain. Compute. Memory. Networking. Foundries. Accelerators. And then the layer everyone used to treat as “background”: infrastructure and energy—generation, transmission, grid upgrades, data-center construction, cooling, and the physical footprint that makes the machines real. The stack didn’t just grow; it hardened into an ecosystem where value pulls inward, toward the few chokepoints that turn electrons into intelligence.

And you can feel it in productivity, too. In the day-to-day reality of work, the ceiling that used to enforce the old curve is weakening. The constraints that once kept output distributed—time, headcount, managerial capacity—are being replaced by something else: tool leverage, iteration speed, and the ability to turn intent into execution without dragging an entire organization behind you.

The curve didn't just bend.

It began to tip.

Stretching the Pareto

The curve is starting to behave like it’s no longer bounded—not because Pareto stopped being true, but because one sector is pulling disproportionate gravity: AI.

This is what Pareto stretching looks like in the economy: capital doesn’t distribute evenly across “innovation.” It concentrates into the few places where returns can compound fastest. And right now, the market is treating AI less like a category and more like a new layer—one that could sit underneath everything else.

The first signal is capital, and it’s not subtle.

In most cycles, money follows traction. In this one, money is trying to manufacture advantage. It’s pouring into AI not as a single bet, but as a full-stack land grab—financing the model layer, the application layer, the tooling layer, the data layer, the infrastructure layer. It’s an attempt to own the chokepoints where value will pool: compute, distribution, proprietary data, and the workflows where human labor gets converted into outcomes.

That concentration is the point. When capital compresses this hard into one sector, it doesn’t just accelerate innovation—it reshapes the distribution of winners. It turns a modest lead into a compounding one. It makes “dominant” outcomes more likely. The top of the stack stops looking like a thousand companies competing. It starts looking like a small number of gateways through which the rest of the economy has to pass.

And that’s why the venture and private markets feel almost unnatural right now.

You’re seeing multiple contenders in the same arenas funded in parallel—because investors aren’t sure which company becomes the gateway, but they’re increasingly sure that a gateway exists. Once you believe the loop compounds—usage → data → product → distribution → usage—“too much capital” stops being a coherent objection. Redundancy and overcapacity become rational, because missing the winner isn’t a missed return. It’s missing the interface.

But the second signal is even stranger: productivity itself is starting to stretch Pareto.

We’re watching founders in their early and mid-twenties build companies that look like empires on timelines that would have sounded absurd a few years ago. Not “in a decade.” In months. Companies like Cursor, Mercor, Lovable, same underlying pattern: extremely small teams, insanely fast iteration, and a growth curve that feels less like a startup and more like a compression algorithm applied to an industry.

This is not just “people working harder.” It’s a fundamental change in the economics of execution.

When model capability rises, the cost of producing a first pass collapses. When retrieval is cheap, learning speed spikes. When iteration loops tighten, output compounds. The people who can run that loop—specify clearly, delegate aggressively, evaluate ruthlessly—start operating on a different slope of the curve. And once that happens, the distribution doesn’t just get more unequal. It gets more extreme.

Capital is concentrating into AI.

Productivity is concentrating into a smaller set of builders.

And together, they’re not just bending Pareto.

They’re pushing it toward the limit.