Trade tensions between the United States and China have eased for now, but the technology race is accelerating. While Washington and Beijing have relaxed export controls on semiconductors and rare earths, both have announced new AI models, new applications, and new deals. 

This week, world leaders are meeting in New Delhi for the India AI Impact Summit. And technology competition is, unsurprisingly, high on the list of discussion topics. Ever since last year’s “DeepSeek moment,” policymakers and executives have continued to debate: Will the U.S. or China win the AI race? 

But that question may simplify more than it clarifies. What does AI leadership mean? What are we racing toward? What are the tradeoffs? Where are global supply chains too connected to derisk? And where can countries other than the U.S. and China compete? Without understanding the nuance of these questions, leaders risk advancing short-sighted solutions to today’s most pressing technology problems and may be pursuing flawed strategies in the long game of geopolitics.

The many different AI races 

The truth is that AI isn’t a single race. There are multiple. Competition itself is creating new arenas of contestation, driving countries and companies to push the boundaries of innovation. And states beyond the U.S.-China binary are reshaping technology, capital investment, and geopolitics.

For instance, there is a race to develop a dominant open-source AI model and a race to develop the top closed-source AI model. America’s frontier AI models are predominantly closed-source, protecting weights and methods. China’s are mostly open-source, diffusing quickly and cheaply, making them attractive to the Global South. While the U.S. does have open models, many of its biggest open-source developers are reportedly pivoting in favor of closed-source models.

The distinction between open and closed is often more commercial than technical. The U.S. leads in AI monetization, and its companies have held their market share. But open-source models may be narrowing the performance gap with closed-source models.

However, the pursuit of top AI models isn’t the only contest that counts. The U.S.’s leading AI labs are driving toward artificial........

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