At Code Class 2026, Zhang Xun, Managing Director of Source Code Capital, delivered a talk titled “Accelerating Exponential Growth”. In his view, AI now sits on an exponential curve that is about to turn nearly vertical, while the human brain, shaped by two million years of evolution, is hardwired to underestimate the explosive effects of compounding. At the same time, value is concentrating in an ever-smaller number of companies as the power-law distribution becomes increasingly extreme.

Zhang moved from the persistence of Moore’s Law to a recurring industry pattern: general methods combined with more compute repeatedly outperform elaborate human engineering. He then turned to a power-law world in which fewer than 1% of companies account for net value creation, before returning to what compounding and long-termism really mean. The question he ultimately sought to answer was not whether AI is coming, but what capability is truly scarce in an era whose potential is being consistently underestimated. His answer: seeing the pattern earlier than others, and having the patience to back and stay with that tiny handful of exceptional companies over the long term.

Key Takeaways:

• At any given point in time, it can look as though growth has reached its limit. But zoom out, and the exponential curve has never stopped, with each successive stretch being steeper than the last.
• Two million years of evolution have made the human brain highly sensitive to relative change but poor at intuiting absolute magnitude, so we tend to underestimate exponential growth precisely when it matters most.
• More general methods, combined with greater compute, have repeatedly and decisively outperformed elaborate human engineering.
• This is a world with fewer and fewer winners—perhaps less than 0.5% of companies account for net wealth creation.
• Until a pattern is falsified, act as though it holds—remain skeptical, but place your bet.
• Compounding follows an exponential curve too, and like Moore’s Law, it is easy to underestimate in its early stages.
• The AI era may have given us the longest hill and the wettest snow yet. The only question is whether we have the stamina to keep the snowball rolling.

The following is an edited and abridged transcript of Zhang Xun’s talk:

The title of today’s talk is a bit of a mouthful—“Accelerating Exponential Growth”. But what I want to share comes down to something very simple, yet deeply counterintuitive: we may be approaching the point where an exponential curve begins to look almost vertical, while our brains are almost hardwired to underestimate it.

Beyond Exponential: The Underestimated AI Era

Take a look at this chart. It’s a visualization of Moore’s Law that I photographed at the Computer History Museum in Silicon Valley. The x-axis represents time, and the y-axis indicates the number of transistors on a single chip—a reasonable proxy for the computing power of that chip.

Let’s walk through a few milestones. In 1971, a single chip contained roughly 2,300 transistors. By 1994—23 years later—that figure had increased about 900-fold, equivalent to a compound annual growth rate of roughly 35%. If you had been a seasoned investor or semiconductor veteran in 1994, you would almost certainly have concluded that the previous 23 years had already been an extraordinary period of growth for semiconductors, especially leading-edge process technology, and that it would be difficult for the industry to accelerate much further.

But here’s what actually happened next. Over the 16 years from 1994 to 2010, transistor counts on a single chip ballooned to roughly 2 billion—about the level of Intel’s flagship processors at the time, when Intel was the world’s largest semiconductor company. Rather than slowing down, the compound annual growth rate actually accelerated to roughly 54%.

What is most striking is the chart itself. For visual effect, its creator placed the 2010 data point near the upper-right corner, almost as if growth had reached the limit of what the chart could contain. But extend the same chart to 2026, through the transition from serial to parallel and then accelerated computing, and the picture changes dramatically. The 2010 point that once looked almost “off the chart” ends up near the bottom of the expanded curve. From 2010 to 2026, the industry achieved another 168-fold increase, equivalent to roughly 37.75% annualized growth.

This is what makes exponential growth so counterintuitive: at any given point in time, it can look as though we’ve reached the limit. But extend the horizon, and the exponential trend continues, with the latter part of the curve often steeper than the previous part.

Anthropic CEO Dario Amodei has described AI as a tsunami, arguing that the public has very little intuition for how close we may be to “the end of the exponential”. By that, he does not mean the exponential curve literally ends. He means the point where a long, seemingly gradual phase gives way to an extremely steep rise. We may now be close to that inflection point, while most people still have very little sense of how near it is.

Why do people systematically underestimate this? The answer lies in how the human brain evolved.

Over the past two million years, our ancestors looked up and saw the sun, and looked around at prey such as pigs, cattle, and sheep. Our brains evolved to handle differences on the scale of a few units or perhaps a dozen–more than enough for hunting and gathering. Later, the German scientists Weber and Fechner formulated what became known as the Weber–Fechner law, and Daniel Kahneman extended this line of thinking in his own work, and was awarded the Nobel Prize in Economics. The basic idea is that the human brain is far more sensitive to changes in relative ratios than to absolute changes.

A change from 100 to 200 is immediately recognized as a “doubling”, which feels dramatic. However, a change from 10,100 to 10,200 is perceived as “only 1%” and barely feels significant, even though the absolute increase is the same.

This perceptual system helped our ancestors survive in hunting and agrarian societies, but today it can mislead us. Technology does not progress linearly; it compounds, especially in the AI era. Once compounding reaches a later stage, the incremental growth added in each successive period can quickly exceed everything accumulated before it. Our intuition is simply not built to read that curve, so we repeatedly underestimate exponential growth precisely when we should take it most seriously.

The same pattern of underestimation is visible at the company level. Plot today’s largest companies by market capitalization, with years since founding on the x-axis and revenue or ARR on the y-axis, and a clear generational pattern emerges. At the same stage of maturity, the growth curves of the mobile internet generation, represented by Google and Meta, are markedly steeper than those of the PC and software generation, represented by Apple and Microsoft. Each successive generation has a steeper slope.

The AI generation has taken another step up in speed. Anthropic’s last publicly disclosed ARR was around $4.5–4.6 billion, and by May or June it could reach $5–6 billion. OpenAI’s current ARR is reportedly around $3–4 billion. And these curves are still accelerating, with both companies moving toward tens of billions of dollars in annualized revenue, potentially even approaching $100 billion, at a pace that is difficult to comprehend. If we use them as benchmarks for the AI era, the second derivative—“super-exponential” acceleration—appears even more extreme than in the mobile internet era. The speed at which this generation of companies goes from zero to scale would have been almost unimaginable in the previous generation.

Behind this acceleration, the evolution of the AI industry reveals another recurring pattern. Let me connect the dots through a series of milestones.

In 1997, Deep Blue used massive computing power to overpower generations of accumulated chess knowledge and carefully developed heuristics to defeat the best human chess player. The contrast between AlphaGo and AlphaZero is even more revealing. AlphaGo still learned from human game records; AlphaZero started from scratch, relied on no human game data, and learned entirely through self-play—yet ultimately became even stronger.

The same is true for machine translation. Earlier systems relied heavily on linguists parsing sentence structures and building phrase tables, and they often produced awkward or even absurd results. After the Transformer was introduced, neural networks that were not explicitly programmed with grammatical rules dramatically outperformed earlier linguistics-driven approaches. From ImageNet and AlexNet to speech recognition and then AlphaFold—which made a breakthrough on the protein-folding problem that had challenged biology for decades—these developments share a common underlying pattern.

Kai Yu, the founder of Horizon Robotics, has made a similar point from another angle. He argues that the point of autonomous driving is not to imitate human drivers because 99% of human drivers are not worth imitating. They corner too aggressively and brake too hard. The goal is to discard human driving habits and move toward the genuinely most efficient way of driving. At a fundamental level, that is exactly what AlphaZero and Tesla FSD are doing.

This entire sequence points to the same conclusion: at least in AI, more general methods combined with greater compute have repeatedly and decisively outperformed elaborate human engineering.

This idea may come as a shock to people working inside the industry. I remember an interview with Yang Zhilin, the founder of Moonshot AI, in which he was asked what he had gained most from his time at Google. He answered that as a CEO, especially in the AI era, you have to learn to step back from individual details and resist the temptation to over-engineer locally. This era rewards bets on general methods and scale, not endless refinement at the margins.

The Power Law: Fewer and Fewer Winners in the AI Era

If the previous section was about how fast growth happens, the next question is who ultimately captures that growth, and the answer is just as counterintuitive.

Value creation follows a power-law distribution, and that distribution is becoming increasingly concentrated.

Over the past 30 years, the top 1% of companies in the U.S. market have accounted for roughly 80% of public-market value creation; in China, the figure is about 76%. There is an even more striking way to visualize this. Put the number of companies on the x-axis and the total wealth created by listed companies worldwide on the y-axis, using U.S. Treasury returns as the benchmark. According to empirical research by Bessembinder and others, listed companies around the world created roughly $45 trillion in wealth between 1990 and 2018. But break that down and the distribution is startling: 60.9% of companies failed to outperform U.S. Treasuries and effectively destroyed value relative to that benchmark; 37.8% created just enough wealth to offset those losses; and only about 1.3% of companies accounted for virtually all net wealth creation.

In other words, virtually all net wealth creation came from an extraordinarily thin tail of companies. That was already true in the mobile-internet era.

In the AI era, this tail may become even thinner. This next part is a hypothesis rather than an established fact. If we take 2023—the first full year of the ChatGPT era—as the starting point, and the world goes on to create another $200 trillion, $300 trillion, or even $500 trillion in wealth, the blue distribution curve could become an even steeper red one: perhaps 80% of companies fail to outperform Treasuries and destroy value relative to that benchmark, another 19.5% merely offset those losses, and fewer than 0.5%, perhaps even 0.3%, account for almost all net wealth creation.

There is already some evidence of this playing out in front of us. At the close of U.S. trading on May 29, the evening before this talk, all ten of the largest companies by market capitalization in the U.S. were technology companies. In addition to the familiar Magnificent Seven, the list included TSMC, Micron, and Broadcom. Look back at snapshots from 1880, 1920, 1940, or 2000, and it is hard to find another moment when the world’s ten largest companies were so uniformly concentrated in one sector.

Put simply, this is a world with fewer and fewer winners: a tiny handful of companies account for the vast majority of incremental wealth creation.

Of course, no matter how powerful a pattern appears, we have to be honest about its limits. In The Logic of Scientific Discovery, Karl Popper argued that scientific theories cannot ultimately be proven; they can only be falsified. Moore’s Law is fundamentally an empirical pattern fitted to historical experience, not a scientific axiom. Newtonian mechanics works within certain bounds, but ceases to apply at sufficiently small scales. Likewise, the power law, this apparent pattern of super-exponential growth, is likely subject to the same limitation: it cannot be proven once and for all, only falsified.

That is precisely what makes this interesting: until the pattern is falsified, acting as though it holds may be the more rational way to make decisions.

As Dario has put it, we believe that scaling laws will continue to hold. We question them every time, yet every time training scale increases by another order of magnitude, they continue to work. So, we “bet on scaling” with a healthy respect for uncertainty: remain skeptical, but keep placing the bet. This may be a better mental model for navigating a power-law era.

Long-Termism: Investing Through AI’s Steep Curve

Put these forces together and the implication for long-term investors is clear: technology is accelerating at a super-exponential pace that human intuition tends to underestimate, while value is becoming increasingly concentrated in a very small number of companies.

What can be deduced from these facts? This means that the truly scarce capability is identifying those few companies on the thin tail early, while most people are still underestimating them, and having the patience to stay with them for the long term as they climb that steep curve.

The power law tells a cold, unforgiving story: most companies destroy value. But viewed from another angle, that concentration is also where the greatest opportunity lies. Precisely because so much value accrues to so few companies, the potential returns from identifying the right winners early and staying with them for the long term may be greater than in any previous era. The steeper the curve and the fewer the winners, the greater the potential reward for those who get in early and have the patience to stay.

Time itself is another severely underestimated force, and Warren Buffett is perhaps the clearest illustration of this. There is a widely circulated set of figures: 99.6% of Buffett’s wealth was created after age 52, 99.976% after age 40, and 99.9993% after age 30. In other words, almost all of his wealth came later in life, not because he suddenly became smarter, but because compounding takes time. A snowball needs a sufficiently long hill before it can grow to extraordinary size. Compounding is itself an exponential process, and like Moore’s Law, it is easy for our intuition to underestimate it in the early stages.

This is exactly what Source Code Capital has been doing for the past twelve years. Since our founding in the spring of 2014, we have focused on the same basic task: identifying exceptional companies early and staying with them as they grow over the long term. The AI era has not changed the nature of that work; it has simply increased both the potential payoff and the difficulty. The curve is steeper, so seeing it early matters more. There are fewer winners, so the cost of being wrong is higher. And the cycle is longer, so patience is more valuable than ever.

In an era like this, we increasingly come back to a few simple principles. Respect exponential growth; do not use linear intuition to measure an accelerating world. Bet on general methods and long-term trends rather than becoming absorbed in local optimization. Accept the reality of power laws and focus your attention on the very small number of companies that truly matter, rather than spreading your effort evenly across mediocre opportunities. And always remain humble in the face of any apparent pattern, because even the strongest trend may eventually be falsified.

In the end, this is a discipline of patience. Buffett has a famous metaphor: investing is like rolling a small snowball down a very, very long hill. The secret is to find a hill long enough and snow wet enough.

The AI era may have provided us with the longest hill and the wettest snow yet. The only question is whether we have the patience to keep the snowball rolling all the way down the hill.