Flashpoints | Economy | East Asia

The Real Key to the AI Race: Nanometers or Gigawatts?

By 2030, the China-U.S. AI competition may be decided less by who builds the most advanced chips than by who deploys the most computing power.

For years, the race for artificial intelligence leadership has been framed as a contest over advanced semiconductors. But the decisive advantage may belong not to the country that manufactures the best chips, but to the one that can deploy the most computing power across its economy. In the AI era, nanometers still matter. Yet increasingly, so do gigawatts.

China’s emerging strategy, including plans to connect AI data centers via a national computing network and expand supporting energy infrastructure, reflects a broader belief that AI leadership will be determined by large-scale deployment. Governments focusing mainly on semiconductor fabrication may be chasing past advantages instead of future ones. China’s reported plan to invest roughly 2 trillion yuan (i.e., $295 billion) over five years in a nationwide network of interconnected AI data centers underscores the scale of this strategic shift.

We can compare the global data center expansion trajectory. For the United States, McKinsey’s latest analysis puts U.S. data-center power capacity at 30-plus GW in 2025, rising to 90-plus GW by 2030. For China, Rystad estimates China’s total data-center capacity will rise from 32 GW at the end of 2025 to more than 60 GW in 2030. AI facilities are expected to increase from 39 percent of capacity in 2026 to 48 percent in 2030, implying roughly 29 GW by 2030. In the same study, it was estimated that global capacity would reach 155 GW, meaning the United States and China combined would account for roughly 77 percent of global AI capacity.

The computing landscape has evolved, with smaller transistors giving countries with advanced fabrication technologies a competitive edge. The U.S. and allies have traditionally led in semiconductor manufacturing, while China is increasing its own capabilities to reduce reliance on foreign technology. As new generations of semiconductors emerge, significant investment and innovation are needed to optimize the integration of processors, memory, data centers, and communication networks – particularly as artificial intelligence drives demand. 

Nvidia exemplifies this shift in the AI-driven economy. The U.S. firm is thriving not just through powerful processors but also by creating a robust ecosystem of networking technologies and software solutions. Analysts estimate Nvidia controls roughly 70 percent to 80 percent of the AI accelerator market, illustrating the value of ecosystem integration beyond chip design alone.

Chinese policymakers may have concluded that matching the United States at the frontier of semiconductor fabrication would be costly and time-consuming. Rather than waiting to close every technology gap, Beijing appears focused on building the infrastructure necessary to deploy AI widely using available hardware. The reported AI blueprint would connect computing hubs nationwide and rely heavily on domestic suppliers, reflecting a strategy centered on scale, coordination, and deployment.

In practical terms, China is attempting to industrialize artificial intelligence before it perfects it. History suggests that invention and large-scale economic adoption are often separate achievements. Transformative technologies generate their greatest impact when they become embedded throughout production systems, institutions, supply chains, and everyday economic activity. 

Another difference is that data center ownership and compute infrastructure in the U.S. are highly concentrated among the five hyperscalers (Amazon, Google, Microsoft, Meta, and Oracle) and fewer than 10 neocloud providers (CoreWeave as the........

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