The United States and China are often said to be in a “race” with one another with respect to artificial intelligence. In a sense this is true, but the metaphor manages to miss almost all that is interesting about US-China dynamics in emerging technology. Today I’d like to offer some brief thoughts about how I see this “race” and where it might be headed.

All metaphors are lossy approximations of reality. But “race” is an especially inapt metaphor for this context. A race is a competition with clear boundaries and a clearly defined finish line. There are no such luxuries to be found here. Beyond the rhyme, “the Space Race” made intuitive sense because the objective was clear: landing humans on the Moon.

Stating that there is an “AI race” underway invites the obvious follow-up question: the AI race to where? And no one—not you, not me, not OpenAI, not the U.S. government, and not the Chinese government—knows where we are headed.

The U.S. and China are more like ships on the open seas, voyaging toward some unknown, only dimly imagined destination. Perhaps we think it is India we will find, though more likely it is a new continent altogether. We do not know that we are headed in the right direction, though neither are we stabbing entirely in the dark. And we both have the intuition that it is probably to beneficial to “arrive” (my metaphor is breaking down) before the other. That intuition is likely correct. It would be more accurate to describe this state of affairs as an “unbounded, multi-dimensional, technological, scientific, and economic competition.”

Now, you might say, “didn’t you work on a national AI strategy called ‘Winning The Race: America’s AI Action Plan’?” And you would be right to point out this tension. The reality is I don’t love the title. We settled on it for many reasons, and one of the best ones is that “Winning The Unbounded, Multi-Dimensional, Technological, Scientific, and Economic Competition: America’s AI Action Plan” does not roll off the tongue, nor does it fit very well on a title page. But rest assured: I believe, with high confidence, that the relevant figures within the Trump Administration understand these subtleties well.

Rhetorical affordances aside, the other major problem with the “race” metaphor is that it implies that the U.S. and China understand what we are racing toward in the same way. In reality, however, I believe our countries conceptualize this competition in profoundly different ways.

The U.S. economy is increasingly a highly leveraged bet on deep learning. This has been true for a couple years now, though it is more explicit and extreme today than it was two years ago. Most of this is because of decisions made by private actors (AI companies, hyperscalers, banks and other large sources of capital, etc.), but on the margin the policy and posture of the Trump Administration has heightened this dynamic as well.

Of all the bets to stake one’s economy on, deep learning is a very good one. Sam Altman’s mantra is true: deep learning works. It is, at the very least, the most important macroinvention of our lifetime so far. There are not many good reasons to expect deep learning to stop working, though of course there are many questions regarding timelines, economic implications, risks, whether “full automation of the economy” is really feasible, and much else.

Another way of putting this is that America is “bitter-lesson pilled.” Our strategy rests on the presumption that advanced AI is both possible in the near-term and hugely consequential, and that compute is the high-order bit to advancing AI (as opposed to data, scaffolding, clever architectures, and the like). This is not so much the government’s strategy (though at least in the Biden Administration it is true that the senior AI policy planners mostly believed this) as it is the strategy of the leading AI companies and hyperscalers. As such we have pivoted with an alacrity that has been lacking recently in the West.

We are, as it were, “all in” on deep learning and the bitter lesson. This will basically remain true until there is a major shift in vibes.

China, on the other hand, does not strike me as especially “AGI-pilled,” and certainly not “bitter-lesson-pilled”—at least not yet. There are undoubtedly some elements of their government and AI firms that prefer the strategy I’ve laid out above, but their thinking has not won the day. Instead China’s AI strategy is based, it seems to me, on a few pillars:

1. Embodied AI—robotics, advanced sensors, drones, self-driving cars, and a Cambrian explosion of other AI-enabled hardware;

2. Fast-following in AI, especially with open-source models that blunt the impact of U.S. export controls (because inference can be done by anyone in the world if the models are desirable) while eroding the profit margins of U.S. AI firms;

3. Adoption of AI in the here and now—building scaffolding, data pipelines, and other tweaks to make models work in businesses, and especially factories.

This strategy is sensible. And it is worth noting that (1) and (2) are complementary. Highly capable open-weight models, designed to be run cheaply under compute constraints, can give a “brain” to a wide range of devices whose manufacturers may not themselves be equipped to train a frontier model. This is a classic example of an economic benefit unique to open-source and open-weight models, and part of the reason I have been supportive of open source since the earliest days of this newsletter.

I find it intriguing that both countries seem to have converged on the strategies that best suit their respective strengths. Advanced AI is, at its core, software-as-a-service delivered through high-end semiconductors, cloud computing platforms, charismatic user interfaces, and enabled by clever financial and legal engineering. Every one of those things is America’s civilizational bread and butter. Embodied AI is, at its core, enabled by mass manufacturing excellence, thick trade networks, and other characteristics that fundamentally tilt in China’s advantage.

It is likely that we both have things to learn from one another. China’s focus on adoption is sound—though one can easily waste time engineering the scaffolding required to make current systems work in industrial applications, only to find that the next generation of models work without any scaffolding. Indeed, one of the core themes of the U.S. AI Action Plan is adoption rather than pure development (though it does not discount the importance of advancing the frontier).

And of course there is manufacturing. China’s industrial base puts them at a serious advantage when it comes to the development of robotics, self-driving cars, and the rest. It may well be the case that American robots will be smarter and safer, because we train superior neural networks, but China’s robots will be stronger, more flexible, and more durable because they manufacture superior actuators, batteries, and other components. Even today this is the case.

It is worth saying it explicitly: America is probably behind in many important areas of robotics, and it seems very possible that the area where we hold an advantage—software—will soon also become an area where China bests us. This is a very serious problem. To the extent the Trump Administration is channeling investment from trade deals into strategic industries, robotics should be toward the top of the list of priorities. Data centers, which are already amply funded, should probably be toward the bottom of the list.

Similarly, by most accounts American self-driving cars are better drivers but worse cars to be driven in, because China—with its vast complex of automobile manufacturing firms and expertise—has learned how to make Mercedes-level luxury at Kia prices. There is no immediate fix to this problem. As I have written before, China enjoys these advantages because they have mastered and achieved immense economies of scale in the unsexy basics of manufacturing. These basics were once useful outsourcing targets for American firms, until China built upon that foundation to begin manufacturing more advanced, and more strategic, goods. Now these advantages are threatening to America. The only solution is for the U.S. to re-build the manufacturing prowess it once enjoyed. This is underway, but it will take many years.

Fundamentally, however, I remain bullish on the U.S. strategy. Advanced AI is the most important technology of our era. Our companies enjoy the lead in models, chips, and cloud computing infrastructure. But even more importantly, American firms are historically far better than Chinese firms at complex software systems, financial engineering, and other technical and business mechanics required to market what will amount to a new kind of operating system to the world.

Chinese fast-following on AI model benchmarks is probably overrated. So too is open-weight model distribution as a source of geopolitical strength. Sticky consumer preferences, network effects, platform and ecosystem advantages, form factor, user interface, ergonomics, and similar are probably underrated. These are the “operating system-like” factors where the US is currently thriving.

The U.S. and China may well end up racing toward the same thing—“AGI,” “advanced AI,” whatever you prefer to call it. That would require China to become “AGI-pilled,” or at least sufficiently threatened by frontier AI that they realize its strategic significance in a way that they currently do not appear to. If that happens, the world will be a much more dangerous place than it is today. It is therefore probably unhelpful for prominent Americans to say things like “our plan is to build AGI to gain a decisive military and economic advantage over the rest of the world and use that advantage to create a new world order permanently led by the U.S.” Understandably, this tends to scare people, and it is also, by the way, a plan riddled with contestable presumptions (all due respect to Dario and Leopold).

The sad reality is that the current strategies of China and the U.S. are complementary. There was a time when it was possible to believe we could each pursue our strengths, enrich our respective economies, and grow together. Alas, such harmony now appears impossible. We are locked into a structural conflict, and tempting as it may be to look away, we must accept this bitter lesson, too.