In July, the Trump administration released an artificial intelligence action plan titled “Winning the AI Race,” which framed global competition over AI in stark terms: whichever country achieves dominance in the technology will reap overwhelming economic, military, and geopolitical advantages. As it did during the Cold War with the space race or the nuclear buildup, the U.S. government is now treating AI as a contest with a single finish line and a single victor.

But that premise is misleading. The United States and China, the world’s two AI superpowers, are not converging on the same path to AI leadership, nor are they competing across a single dimension. Instead, the AI competition is fragmenting across many domains, including the development of the most advanced large language and multimodal models; control over computing infrastructure such as data centers and top-of-the-line chips used to train and run models; influence over which technologies and standards are used throughout the world; and integration of AI into physical systems such as robots, factories, vehicles, and military platforms. Having an edge in one area does not automatically translate into an advantage in the others. As a result, it is plausible that Washington and Beijing could each emerge as leaders in different parts of the AI ecosystem rather than one side decisively outpacing the other across the board.

This outcome is even more likely in the wake of the Trump administration’s decision to lift some export controls on advanced AI chips to China. In December, President Donald Trump announced that the U.S. government would permit the sale of Nvidia’s H200—the company’s second most powerful AI chip—to approved customers in China. The decision reflects a belief that allowing China access to “good enough” computing power can generate revenue for U.S. companies and reinforce American technological standards without risking the United States’ edge in AI innovation. But the danger of selling high-end U.S. chips to China is that it could lead to a more divided AI landscape—one in which U.S. firms maintain a lead in providing advanced AI-based services, but Chinese companies gain ground in disseminating their slightly less advanced but cheaper technology around the world and applying AI to machines, factories, and infrastructure.

The most plausible outcome of the AI race, then, may not be decisive American or Chinese victory, but something more complex and more consequential: an asymmetric form of AI bipolarity. In a world without a clear winner, the United States will need to adapt to a longer-term competition while engaging China to manage the shared risks that advanced AI is likely to produce.

The United States still enjoys a clear advantage at the cutting edge of AI. The world’s most capable large language models and multimodal systems are produced by U.S. firms such as OpenAI, Google, and Anthropic. These models demonstrate superior reasoning and tool-use capabilities—such as autonomously writing and debugging code, querying live databases, and analyzing spreadsheets—and anchor the most commercially valuable AI services, including AI assistants that help manage cloud platforms, productivity software, and customer service.

But the United States’ lead at the frontier is narrower than it once appeared. Chinese firms including DeepSeek, Alibaba (through its Qwen models), and Moonshot AI (with its Kimi series) are catching up. For many practical applications, such as drafting text, summarizing and translating documents, writing routine code, or powering customer service chatbots, the difference between the best U.S. models and the best Chinese ones is already marginal.

For now, the United States’ most significant advantage lies not in models but in compute—the quality and quantity of computing resources to train and run AI models. U.S. companies design the world’s most advanced AI chips, primarily through Nvidia, and the United States is far ahead of China in the scale of AI data centers. U.S. firms control roughly 70 percent of global AI compute, whereas Chinese companies control around ten percent. This capacity allows U.S. companies to train larger and more capable models and absorb the enormous computational costs of customers making requests of models in ways that Chinese competitors cannot easily match. U.S. companies, such as Amazon, Google, Meta, and Microsoft, plan to spend........

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