The AI Power Gap: China's Nuclear Strategy vs America's Gas Dependency

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The United States is building the most compute-intensive AI infrastructure in history. It is powering it with methane gas turbines.

China is building 28 nuclear reactors. Its stated target is 100.

The scale of American AI infrastructure investment is not in dispute.

OpenAI is constructing a one-gigawatt data center in Abilene, Texas, designed to house one million chips. Meta is building a supercomputer in Louisiana that draws half the average power demand of New York City. xAI's Colossus supercomputer is projected to reach two gigawatts of capacity by the end of 2026. The combined energy demand of these facilities, added to existing US cloud infrastructure, is straining a grid that was not designed for this load.

The solution American hyperscalers have deployed is natural gas. Some facilities are running on clusters of methane gas turbines, bypassing grid connection entirely to guarantee uptime. Microsoft restarted Three Mile Island to power its data centers. The nuclear option exists in America. It is being used to rescue a single facility, not to build a foundation.

China's approach is structural, not reactive.

China currently has 28 nuclear reactors under construction, more than any other country by a significant margin. Its long-term plan targets 100. The explicit purpose includes powering the next generation of AI data centers with stable, high-density energy that does not depend on commodity gas prices, grid capacity, or weather. By 2030, China is projected to overtake the United States as the world's largest nuclear power producer.

The energy infrastructure being built now determines who can afford to run frontier AI models at scale in five years. Compute requires power. Power requires infrastructure. Infrastructure requires decades of planning that started long before the current AI investment cycle.

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The chip restriction story runs in parallel and cuts the opposite direction.

US export controls limit Chinese companies to downgraded hardware. The most capable Nvidia chips, H100s and their successors, are restricted from Chinese buyers. Chinese labs work with H20s and H800s, hardware that is competitive but not equivalent to what American labs can access.

The restriction has a loophole that is currently operational.

Nebius Group, formerly Yandex, rebranded and redomiciled in Amsterdam after Russia's invasion of Ukraine. Amsterdam is a Tier-1 unrestricted export location. Nebius raised $700 million with Nvidia's backing in late 2025 and is building AI cloud infrastructure in Europe using unrestricted H100s. The compute is then made available as cloud services to customers in markets where direct chip purchase is restricted.

The arbitrage is legal. It is being watched closely by export control authorities. It is currently running.

Microsoft recently signed a $17.4 billion agreement to use Nebius infrastructure. The largest American cloud provider is a customer of the company providing compute access to markets US export controls were designed to restrict. The policy and the market are moving in opposite directions.

Jensen Huang, CEO of Nvidia, has stated publicly that the United States giving up its 95 percent market share in China to zero percent due to export controls is a severe strategic vulnerability. His argument is specific: American national security is tied to global adoption of American technology infrastructure. When US companies lose market share in AI infrastructure to Chinese alternatives, the pattern matches what happened in solar panels, telecommunications equipment, and rare earth magnets. Industries where American dominance was assumed and then lost, not through military action but through sustained investment and scale.

The energy gap is the same argument one layer down. The country that can run the most compute at the lowest cost over the longest period wins the infrastructure layer. Compute costs are predominantly energy costs. Energy costs are predominantly infrastructure decisions made fifteen to twenty years before they become relevant.

China started building nuclear capacity before the current AI investment cycle began. The United States is running methane gas turbines because the nuclear infrastructure was not there when the demand arrived.

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The market implication for builders operating across both ecosystems is specific.

Any product or service that depends on stable, low-cost AI inference at scale is exposed to energy infrastructure risk. The American cloud providers offering AI compute are paying energy costs that will rise as demand increases and grid capacity does not. Those costs pass through to API pricing.

The European alternative is being built. Mistral's announced computing platform runs 18,000 Nvidia chips on nuclear power. The energy foundation is sovereign and stable. The inference costs have a different trajectory than American cloud compute.

The builders who understand that AI infrastructure is an energy problem, not a software problem, will make different procurement decisions than the ones who do not.

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404 Found covers AI developments from a European Insider, three times a week. The full picture of what Europe is building to replace American tech infrastructure is in this week's archive at