At Code Class 2026, Han Guang, Managing Director at Source Code Capital, who leads the firm’s growth-stage and AI-focused investments, delivered a talk titled “Standing at 1769 of the Intelligence Revolution”. In his view, this wave of AI is not an extension of the previous revolution in communications, but another revolution in productivity—one in which “intelligence itself” is being industrialized. Scaling laws continue to hold; “one year in AI is like three years in the human world.” Given that 68% of the global population of 8.3 billion has yet to use AI, we are not at the peak of this wave—we are in its earliest days.

From Silicon Valley’s “three months is too long” mindset and “wish-driven work”, to new frameworks like “tokens are not equal” and “to-human versus to-agent”, and finally to the organizational questions facing CEOs and Source Code Capital’s own investment thinking, Han Guang’s point is not to ask whether AI is coming, but to recognize that it is already here. The flagship of this new era set sail three years ago. For entrepreneurs, the choice is whether to stay on the old continent and watch it sail away, or jump aboard.

Key Takeaways:

• Believers and skeptics of scaling laws have made very different choices over the past few years—and seen very different returns.
• The CEOs of frontier labs aren’t hyping things up; they’re presenting facts.
• The Industrial Revolution commoditized mechanical power; this time, we may be industrializing intelligence itself.
• We must build a new world designed for intelligence.
• Not all tokens are created equal—some are as valuable as diamonds, while others are worth little more than water.
• We used to categorize companies as B2C or B2B. Now that boundary is blurring: are you building for humans or for agents?
• The flagship of this new era set sail three years ago. Do we stay on the old continent and watch it sail away, or jump aboard?

The following is an edited and abridged transcript of Han Guang’s talk:

The theme of my talk today is “Standing at 1769 of the Intelligence Revolution”. I’d like to share a few thoughts with you. AI is not a continuation of the previous communications revolution; it represents a fundamentally different kind of productivity revolution. For the first time since the invention of the steam engine in 1769, humanity is once again thoroughly industrializing a primary factor of production. The Industrial Revolution industrialized mechanical power; this time, we are industrializing intelligence itself.

Over the past six months, chatter about AI “hitting a wall”, “being in a bubble”, or “needing a cool-down” has resurfaced regularly. But my view is that scaling laws continue to hold. One year of AI progress is equivalent to three years of human development. We are not at the peak of this wave—we are in its earliest days. So, what I want to discuss today is not whether AI is coming. It’s already here. For entrepreneurs, the question is: do we stand on the shore and watch the ship sail away, or do we jump aboard?

A World with Scaling Laws Is a Very Different World

Let’s start with the “g-force” we’ve all felt over the past six months.

I imagine many of you have felt the same way I have over the past six months: as if we’re trapped on a bullet train accelerating wildly, pinned to our seats and holding our breath. All we can do is hold on tight to the handrails so we don’t get thrown off.

Looking back over the past few years, if you asked me what mattered most, I would undoubtedly say scaling laws.

A world in which scaling laws hold and one in which they don’t are two entirely different worlds. Believers and skeptics of scaling laws made completely different decisions and saw very different returns.

While scaling laws have a formal academic definition, there’s also a simpler way to understand them: an AI model’s intelligence scales predictably with the logarithm of the compute and data resources invested in training and running it. There are two key points: first, the resources being deployed grow exponentially over time; second, the gain in model intelligence is strictly predictable. This “predictability” is paramount. When you listen to what Sam Altman or Dario Amodei have been saying lately, it essentially boils down to one phrase: “We could already see this coming several years ago.”

Standing here today, a natural question arises: Have scaling laws hit a wall? How can we tell? The best way, of course, would be to be inside a frontier lab. But for those of us on the outside, there are still a few ways to judge.

First, listen directly to those who see the reality firsthand—people who are “one degree of separation from the truth”. Information degrades as it is disseminated; by the time you’re two degrees removed, the signal gets drowned out by noise. The individuals closest to ground truth are the CEOs and leading researchers at frontier labs. Over the past six months, they have repeatedly stated that scaling laws continue to hold in various forms of communication, including essays, blog posts, interviews, and talks. In January, Dario wrote “The Adolescence of Technology”, arguing that every few months, public sentiment either becomes convinced that AI is “hitting a wall” or becomes excited about a new breakthrough while AI’s capabilities continue to improve steadily and resolutely. In an interview last month, he reiterated that current progress was broadly in line with what he had expected as early as 2017.

Second, look at the effective compute going into frontier models. If scaling laws had hit a wall, that compute would no longer be growing exponentially. If it is still expanding exponentially, that suggests the people closest to the frontier still don’t see a wall ahead. There’s a piece of work called “Situational Awareness” that plotted a chart in 2024 tracking the effective compute invested in frontier models, with time on the horizontal axis and effective compute on a logarithmic vertical axis, each tick representing an order of magnitude (OOM). The chart extended through GPT-4 and made a number of predictions that looked crazy at the time and that almost no one believed. Two years later, these predictions have proved broadly accurate, which is striking. With the help of AI, we updated the chart and were surprised to find that the trend line was still climbing linearly on the log scale. After the release of OpenAI o1, there was even a slight inflection toward faster growth. There is a good chance that we are still firmly on the scaling-law trajectory.

There’s another interesting observation: AI progress is proceeding at roughly three times the pace of a child’s cognitive development—one year of AI progress is roughly equivalent to three years of human intellectual development. In 2019, GPT-2 was like a preschooler. In 2020, GPT-3 was like an elementary schooler. By 2023, we had a smart high schooler; and by the second half of 2025, it suddenly seemed more like a PhD student. The ratio is roughly 1:3. Extrapolate that trajectory forward. What kind of intelligence might we have by 2030? Perhaps we only began to grasp how formidable AI had become by the end of last year. This was not because its progress suddenly accelerated—it had been advancing steadily all along—but because we finally crossed our perceptual threshold.

A year or two ago, I thought Dario, Sam Altman, and Demis Hassabis were exaggerating because the world they described sounded like science fiction. Looking at it now, however, I realize that they weren’t hyping it at all. They were describing facts.

Dario has said that “powerful AI”, smarter than a Nobel laureate across most relevant fields, could emerge within one or two years, and is highly likely to arrive within the next few years. He has spoken of “a country of geniuses in a data center”, predicted that half the world’s population will be talking about AI by the end of this year, and recommended The Making of the Atomic Bomb because he believes advanced AI could prove comparable in historical significance. Sam Altman has said that we may be only a couple of years away from an early form of true superintelligence, by which he means an AI capable of serving as the CEO of a large company or conducting research beyond the level of the world’s best scientists. Demis is the most conservative of the three. In January, he estimated a 50% chance that AGI would arrive by the end of the decade, but his bar is much higher: train an AI on everything we knew by 1911 and see if it can independently derive general relativity. Even a 50% chance of that is both alarming and exciting.

Are we already witnessing the first glimmers of an AI scientist? A few days ago, a general-purpose reasoning model from OpenAI, with no specialized training, independently produced a proof concerning the unit-distance conjecture first posed in 1946. The result was reviewed and validated by four mathematicians. The proof itself is not exceptionally difficult; it is constructive in nature, but it offers an early glimmer of the AI scientist. It suggests that the world they have been describing may indeed be possible.

The 1769 Revolution Industrialized Mechanical Power. This Time, It’s Intelligence Itself

1769 was the year James Watt improved the steam engine, marking a turning point in human history. Before then, nearly all the mechanical power behind the goods around us came from human and animal labor, supplemented by a small amount of wind and water power. After that, machines began to supply most of it—first with steam engines, then with internal combustion engines, and today, increasingly, with electric motors. Over the course of two centuries, the percentage of mechanical power supplied by humans and animals decreased from 98% to less than 1%.

So, what if we are facing a computing revolution this time?

Today, most cognitive activity is still performed by the human brain, supplemented marginally by mechanical and electronic computation (PCs, smartphones) and now, neural networks. If this really is a computing revolution, could the human brain’s share of general cognitive work shrink dramatically over time? What will supply tomorrow’s general intelligence? Perhaps neural networks, some other architecture, or something beyond our current imagination. But the direction is unmistakable.

What is remarkable is that the Industrial Revolution commoditized and industrialized mechanical power; this time, we may be doing the same to general human cognition, or intelligence itself.

This may be the fastest wave in history. It took Apple and Microsoft about two decades to reach $10–20 billion in revenue. The mobile internet moved much faster, with leading companies reaching $100–200 billion over a similar time span. Yet the two breakout AI companies have taken only three or four years to reach annualized revenue in the tens of billions, with a path toward $100 billion. This pace is unprecedented in business history and far exceeds that of earlier technological generations.

So how large is this market, really?

One heuristic is labor substitution. Global GDP stands at roughly $110 trillion, with around $60 trillion allocated to wages. Suppose that AI could replace 50% to 80% of that labor and that machine replacement is priced at a discount to human labor. For simplicity, let’s use $10 trillion as a round number. How much of that opportunity have we captured so far? Only about $80 billion—a tiny fraction.

But is substitution really the right way to size the market?

Back in 1700, before the Industrial Revolution, total global mechanical power from humans and animals was just 10 gigawatts. Two hundred years later, that surged 50-fold to 500 gigawatts. When the power loom was invented, the market it created was far larger than the hand-weaving activity it displaced; it dramatically expanded the market itself. Any forecast bounded by “substitution” is likely to be constrained by our lack of imagination.

And we are still in the earliest days. Across the global population of 8.3 billion, 68% have never touched AI. Paying users make up only 1.2%, and coding users just 0.3%. Yet this tiny fraction drives staggering consumption: a developer consumes 300 times more tokens than a free user, and 10 times more than a standard paying user, and that trajectory is still climbing. Over the past two years, as we shifted from chatbots to AI agents, token consumption per task exploded by 1,000 times.

We need to build a new world for intelligence, structured in two layers.

The first layer is enabling intelligence generation, which immediately hits physical walls in energy and hardware. If you chart the AI industry’s compute demand as Dario describes it, you find that in three to four years, meeting all of that projected demand could require power equivalent to roughly half of average U.S. electricity generation. This is a systemic physical-infrastructure bottleneck across power, chips, storage, interconnects, long-distance networking, power electronics, liquid cooling, and even construction capacity, all facing full-stack shortages. Fundamentally, growth in compute and scaling in intelligence demands exponential investment, but physical infrastructure can only expand linearly.

The second layer is building a native ecosystem for agents. Today, virtually all of the infrastructure of the digital world—identity systems, payment rails, security checks, authentication protocols—was designed for humans. Some of that infrastructure already exists for agents; much of it does not. In the future, agents may have their own communication protocols, authentication systems, and payment tools, and perhaps one day even their own economic systems and markets. It will be an entirely new and exciting world, though perhaps a bit unsettling as well.

We’re All Novices: Did You Spend Time at the Keyboard Yourself Today?

In March, we spent some time on the ground in Silicon Valley. Being there in person gave us a completely different sense of what was happening, and of the magnitude of the change. I’d like to share three key phrases that left a lasting impact:

First, “Three months is too long.” I asked a frontier researcher what to expect by year-end. He retorted: “Year-end is nine months away. That’s far too long; so much could happen. How could I possibly predict that?” Inside frontier labs, people no longer spend much time discussing what might happen four months out, because time itself feels compressed. The very time horizons we rely on today must be challenged.

Second, “Make wishes.” I asked another researcher, “You’re as close to AI as anyone. How do you direct its work?” He said, “Direct it? I’m learning from it. Every morning I come to work with three wishes: ‘Dear AI, please grant these three wishes today.’ And by the end of the day, it delivers.” “Making wishes” has become one of my favorite phrases lately.

Third, “Results in two hours.” One researcher noted a shift in how his manager assigns tasks: “This is very promising—I want results in two hours.” Not “when you finish”, and certainly not tomorrow. Two hours. So, what should be the new baseline for the speed at which an organization moves and iterates?

So how do we become super-individuals in the AI era? We came away with four words for ourselves.

First, dive in—Don’t stand idly on the shore—jump into the water.

Second, unlearn—Let go of old standards, old metrics, and familiar tools and processes. Start again from facts and first principles.

Third, be open—In this era, we’re all beginners. No one has an inherent head start, and mindset shapes action.

Fourth, enjoy—This is an age of abundance, full of new things to explore and play with. The whole world feels like one enormous playground. We should enjoy it.

I often ask myself: Is AI always on? We’re all managers, spending our days managing people and organizations. But have you spent time at the keyboard yourself today? When you run into a problem, is your first instinct to ask AI, or to ask another person? Do you reach for the tool you already know, or first see if a new tool can solve the problem better? There is a downside, of course. If you become obsessed with coding or with trying every new tool, you may end up neglecting the things that matter most—and exhausting yourself in the process. Are you falling into a counterproductive loop?

We need to prepare for a new world in which humans and agents work side by side. The duration of tasks agents can complete autonomously is doubling roughly every four months. Two years ago, in the era of chatbots and tab completion, the model was essentially one human working with one AI assistant. Today, some people already work with ten agents at once. Before long, perhaps one of those ten will be “promoted” to “CEO” and manage the others for you. You could then be running an agent-native company, with one person overseeing fifty agents. Eventually, we may not need to manage individual agents at all. They could operate autonomously like a “lights-out factory”—a workforce running inside a data center. You give it an objective, and it gives you the result. In 1937, Ronald Coase wrote “The Nature of the Firm”. In the agent era, might we see the emergence of a new “nature of the firm”? I’m looking forward to finding out.

So, I’d ask the CEOs in the room to think about a few questions. If, three years from now, every employee is working with five to ten agents, does your current organizational structure still make sense? Which roles in your company are fundamentally about shuttling information around and moving workflows forward? What kind of senior person will you hire next? If AI takes over most junior-level work, how will you develop the next generation of senior talent? Of your ten most important workflows, which can AI already handle end to end, or close to it, and where is the remaining bottleneck? And if the cost and difficulty of execution fall dramatically, what becomes your moat?

Not All Tokens Are Created Equal: From Monetizing Attention to Commodifying Intelligence

Times are changing, and so is investing. Jensen Huang has described AI as a five-layer cake: energy, chips, infrastructure, models, and applications. Startups founded over the past three years rushed to fill out every layer of that stack, yet the total cake remains vast. This is a multi-trillion-dollar frontier, and we should be competing for our piece.

At the same time, we are convinced this represents a fundamentally distinct kind of revolution. Most of us in this room, including those of my generation, have never really lived through a productivity revolution. What we have experienced has largely been a revolution in communications. Communications accelerated information distribution. The AI revolution crafts entirely new “intelligence products”, transforming intelligence from something scarce, precious, and artisanal into something industrialized. We need a different lens to understand it.

In the communications era, we monetized attention. Whether I’m a billionaire or an ordinary knowledge worker, an hour of our attention is broadly comparable—“attention is more or less equal.” Business models were built around advertising and transaction fees, with the goal of keeping users on the platform for as long as possible. But today, “tokens are not equal”. Some tokens carry enormous value, like diamonds, while others are highly commoditized and worth little more than water. Some generate high margins; some create strong retention; others are purely transactional.

That probably means our evaluation metrics need to change as well. In the communications era, we focused on DAU, time spent, and cohort retention. Today, we track token volume. Tomorrow, will we track “token value density”—or metrics we haven’t even conceived yet? I think that shift is inevitable.

We used to classify companies as B2C or B2B, but that boundary is starting to blur. In both cases, a human may be the one using the product—so where exactly does B2B end and B2C begin? Increasingly, people are asking a different question: are you a “to-human” company or a “to-agent” company?

There’s another difficult question we don’t yet have an answer to: if model companies become this powerful, where does that leave application-layer companies? Here are a few ways to think about it.

First, AI capabilities are advancing almost vertically, unlocking new capabilities at extraordinary speed, while real-world adoption lags behind. It is this gap that presents an opportunity for application-layer companies. Speed is everything.

Second, good products speak for themselves. It’s an old principle, but it still holds. Even my two-year-old daughter instinctively knows to put four fingers through a mug handle and steady the mug with her thumb. A well-designed product requires almost no explanation. Turn the handle ninety degrees and anyone can tell that it’s poorly designed. While many products may look similar on paper, users can intuitively sense quality.

Third, the last mile matters. Model companies will capture some vertical use cases, but they will not capture all of them. In a market measured in the tens of trillions of dollars, there will always be verticals where intelligence has to be embedded deeply into workflows before it can create real value. For serial entrepreneurs, the winning playbook is to go deep before going wide.

Fourth, building a moat is especially difficult when the market is moving this fast; every part of the stack is in motion. It may therefore be more important to think from the user’s perspective than from the technology’s perspective. How deeply can you embed your product into customers’ workflows? How do you accumulate domain knowledge? And what softer organizational capabilities do you have, especially the organizational learning velocity? Those may matter more than ever.

Fifth, be imaginative. When electricity first emerged in the late nineteenth century, the most obvious early applications were the light bulb (1879) and the telephone (1876). Who could have imagined that the next hundred years would bring a world filled with electrical appliances of every kind? Today, we can readily imagine chatbots and coding agents. Why shouldn’t the next fifty years bring an explosion of intelligent applications we cannot yet conceive of? This is an era for pioneers, and for visionary product managers.

So what are we investing in? First, intelligence itself—one of the largest value pools in this cycle, with the competitive landscape in the U.S. already beginning to take shape. Second, hardware: because of the tension between exponential demand and the linear expansion of physical capacity, hardware may be the most important near-term theme. Beyond that will come the infrastructure needed to produce, distribute, and support intelligence—to build this new world for AI. And finally, applications. They will emerge more gradually. This is not a two- or three-year cycle; it is a ten- or twenty-year opportunity. We will continue looking for brilliant product managers who know how to put intelligence to work and turn it into high-value solutions for customers.

The ship of this new era set sail three years ago. That is a fact. Do we remain in the old continent and watch it fade into the horizon, or do we jump aboard? The choice is entirely in our hands.