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For many years, nuclear energy was one of the most challenging technological fields. The United States once investigated advanced nuclear reactor designs, but numerous projects were abandoned due to technical challenges, high costs, or political opposition. Now, China has revived and successfully implemented one of these discarded concepts—potentially achieving a breakthrough not only for clean energy but also for the future of artificial intelligence.
Recently, China launched the world’s first operational thorium molten salt reactor in Gansu Province, marking a significant milestone with far-reaching consequences. While most people are familiar with traditional uranium-based reactors, thorium reactors provide an alternative that is cleaner, safer, and possibly more sustainable.
Why Thorium Is Important:
– Safety Benefits: Unlike traditional uranium reactors, molten salt reactors operate at lower pressure and are much less likely to experience catastrophic failures.
– Simplified Waste Management: Uranium waste remains hazardous for tens of thousands of years, whereas thorium waste decays to safe levels within a few hundred years, greatly reducing long-term risks.
– Abundant Supply: China possesses vast thorium reserves, especially in Inner Mongolia. In theory, a single mine could supply the country’s energy needs for thousands of years.
– Strategic Advantage: Uranium supplies are becoming scarce. By 2040, experts anticipate a global shortage of 130 million pounds, driving up prices. Thorium avoids this supply constraint.
It’s worth remembering: the U.S. experimented with thorium molten salt reactors back in the 1960s but abandoned the effort due to technical challenges and a lack of immediate military applications. China’s researchers, however, spent decades poring over declassified American documents, testing, refining, and ultimately achieving what Washington gave up on—arguably surpassing the original designs.
The Link Between Nuclear Energy and AI
So, why does a nuclear breakthrough matter for artificial intelligence? The answer is simple: AI runs on power.
Training large-scale AI models is one of the most energy-intensive computing tasks ever attempted. Public data suggests that early GPT-4 training runs consumed as much electricity as the city of San Francisco over several days. Newer, even larger frontier models could require exponentially more power. But here’s the kicker: training is just the beginning.
Industry estimates show that 80–90% of AI’s electricity demand comes not from training, but from inference—the constant stream of queries, outputs, and agentic tasks once a model is deployed at scale. Every chat, every image generation, and every AI-driven application consumes energy.
To put it bluntly: no energy, no AI.
Already, data centers account for nearly 5% of U.S. electricity consumption, and global demand is growing by 12% per year. Stable, affordable electricity is fast becoming the ultimate bottleneck in the AI race.
America vs. China: Diverging Strategies
The U.S. has begun to realize this. Washington has loosened restrictions on nuclear development, and companies like OpenAI and Google are actively exploring partnerships with advanced nuclear startups to secure clean, long-term power. But China’s thorium reactor represents a fundamentally different technical path—one in which Beijing controls every core technology and has unmatched access to domestic fuel supplies.
By decoupling from uranium scarcity and building its own thorium-based infrastructure, China positions itself not just as an energy leader, but as a country uniquely capable of powering the vast AI compute farms of the future.
A Symbolic Milestone
Fittingly, China’s reactor achieved full power on June 17, 2024—the 57th anniversary of its first hydrogen bomb test. From nuclear weapons to peaceful nuclear energy, the symbolism is clear: China views mastery of nuclear technology as inseparable from its national strength.
By 2030, the experimental 10 MW reactor is expected to scale toward critical commercial capacity, with the potential to supply the enormous energy requirements of large data centers.
Energy = Compute = Intelligence
At the end of the day, AI is not just about clever algorithms. Algorithms are only as powerful as the hardware—and the hardware only runs if the electricity flows. The true foundation of the AI era is energy infrastructure.
The future of global AI competition may not hinge on who codes the best models in the short term, but on who controls the cheapest, most stable, and most scalable power supply.
China’s bet on thorium reactors is more than an energy story—it’s a statement about AI, sovereignty, and long-term advantage. In the emerging equation of Energy → Compute → Intelligence → Power, Beijing has just secured a crucial first-mover edge.