Everyone has a theory of artificial intelligence. Some believe the technology is progressing toward superintelligence—powerful AI that will bring epochal changes beyond any previous technology. Others expect that it will boost productivity and scientific discovery but will follow a more uneven and potentially less dramatic path.

People also disagree about how easily breakthroughs can be replicated. Some argue that rivals will fast-follow (that is, quickly imitate), whereas others believe catching up will become slower and costlier, giving first movers lasting advantage. And whereas many are sure China is determined to beat the United States at the frontier, others insist it is focused on deployment of existing technology while seeking to distill and reproduce leading-edge American innovations once they appear.

Every confident policy argument rests on hidden assumptions about which of these stories is true. Those prioritizing frontier innovation assume breakthroughs will compound and be difficult to replicate, whereas those focused on spreading American systems abroad often assume the opposite. If those assumptions are wrong, the strategies built on them will waste resources and could cost the United States its lead.

Betting everything on a single story is tempting but dangerous. Washington does not need another prediction about the AI age. It needs a way to make choices under uncertainty—one that secures the United States’ advantage across multiple possible futures and adapts as the shape of the AI era comes into view.

However the AI future ultimately unfolds, U.S. strategy should begin with a clear definition of success. Washington should use AI to strengthen national security, broad-based prosperity, and democratic values both at home and among allies. When aligned with the public good, AI can drive scientific and technological progress to improve lives; help address global challenges such as public health, development, and climate change; and sustain and extend American military, economic, technological, and diplomatic advantages vis-à-vis China. The United States can do all of this while responsibly managing the very real risks that AI creates.

The challenge is how to get there. To make hidden assumptions explicit and to test strategies against different futures, those thinking about AI strategy should consider a simple framework. It turns on three questions: Will AI progress accelerate toward superintelligence, or plateau for an extended period? Will breakthroughs be easy to copy, or will catching up become difficult and costly? And is China truly racing for the frontier, or is it putting its resources elsewhere on the assumption that it can imitate and commodify later? Each question has two plausible answers. Considering every combination yields a three-dimensional matrix—a 2×2×2 diagram with eight possible worlds.

The first axis is the nature of AI progress. At one end lies superintelligence: an AI that far outpaces humans and is capable of recursive self-improvement, teaching itself to become ever smarter and inventing ever more new things. At the other end lies bounded and jagged intelligence: impressive scientific, economic, and military applications, but not a singular break with history. It is bounded because the progress it makes eventually hits limits, at least for a while. And it is jagged because it is uneven; systems may reach incredible performance in areas such as math or coding but struggle with judgment, creativity, or certain physical applications. If progress leads to superintelligence, even a narrow lead could prove decisive, justifying massive frontier investments. If it is bounded and jagged, channeling unlimited resources to moonshots is less compelling than prioritizing adoption and diffusion.

The second axis is the ease of catching up—the fast-follow problem. In one world, catching up is easy. Breakthroughs can be copied quickly through espionage; leaked weights, in which a trained model’s internal parameters are stolen or released; innovative training on older hardware; or model distillation, in which a less capable system is trained to imitate a more advanced one. In the other, catching up is hard: frontier capability depends on the full technological stack—proprietary hardware, institutional expertise, vast and often unique datasets, a vibrant ecosystem of talent, and structural factors that cannot be foreseen. The model, or software layer, may be easy to copy, but the quality and scale of hardware, infrastructure, and human capital behind training and inference may be far more difficult to reproduce. When catching up is easy, the contest is more about diffusion, embedding American systems abroad before rivals can spread their own. When it is hard, diffusion still matters, but strategy places greater emphasis on defending the underlying foundations of frontier capability—that is, the inputs and know-how that allow advances to compound over time. Across the whole axis, the question is not whether AI spreads, but how quickly, to whom, and on what terms.

The third axis is China’s strategy. At one extreme, Beijing is racing aggressively to the frontier, funding massive training runs and competing labs. At the other extreme, Beijing is not racing but prioritizing adoption and diffusion and occasionally producing large models to signal progress and spur the United States into focusing on the frontier. China may not have a perfectly coherent national plan—indeed, different institutions within the country may act differently—but at the system level, China’s behavior will still approximate either racing or not racing. This dimension of the framework focuses on China because, at present, it is the United States’ dominant competitor at the frontier. If other actors emerge, the matrix would need to adjust to reflect their racing calculus, as well.

Reality is, of course, more complicated than any diagram. More axes could be added, and each axis could be treated as a spectrum. China may pursue a middle path in frontier R & D. Catching up may be only somewhat hard. AI may be truly powerful but still have certain limitations. Although considering binary outcomes can make strategic planning easier, policymakers can still account for the intermediate possibilities by thinking probabilistically along each axis. A partial Chinese investment strategy, for instance, increases the odds that Beijing narrowly follows the United States or even unexpectedly closes the gap.

Finally, policymakers’ own decisions can shape which AI future emerges, at least on the margins. U.S. actions can make catching up harder or easier, particularly by tightening or loosening export controls. Whether China races or holds back will depend in part on how Beijing judges the pace of AI progress and the difficulty of catching up. Still, by making uncertainty part of the policy framework, policymakers will at least be forced to confront their own assumptions and plan for multiple futures rather than one.

Before turning to that planning exercise, it is worth pausing to ask two questions: Who actually sets U.S. AI strategy? And what tools does Washington have to shape the trajectory of AI? After all, the government doesn’t own the country’s leading labs or decide what they build. It can’t set production targets or direct investment flows the way Beijing can. Yet Washington’s policy choices and signaling significantly influence the AI ecosystem, even if indirectly.

Many American policies amount to an implicit subsidy for the domestic AI industry. Export controls and investment restrictions have limited China’s access to advanced chips and U.S. capital. They have raised the value of American and allied firms by constraining their strongest competitors and channeling private capital toward them.

Expectations amplify that effect. When senior officials describe AI leadership as a national priority, companies and investors anticipate favorable rulemaking, administrative streamlining, and closer coordination with government. Those assumptions influence how much risk firms take on and where investors place their bets—perhaps even more than a slow-to-deploy congressional appropriation would.

Washington’s direct support complements these signals. R & D tax credits,........

© Foreign Affairs