Is the United States losing its AI hegemony? China's nuclear power expansion may become a critical turning point in the artificial intelligence race.

In the global technology hegemony competition, a widely overlooked key factor is coming to the fore—energy supply. As AI data centers become a pillar of the U.S. economy, contributing over 1% of GDP, a disturbing question begins to emerge: Is the U.S. losing the artificial intelligence race, not due to technological lag, but because of fatal flaws in its energy strategy?

Nuclear Energy Race: Amazing Numerical Gap

The author of "The Bitcoin Age," Adam Levinson, presents a shocking viewpoint: "The game is over: China is far ahead, not because it has surpassed the United States in coding, but because it has quietly monopolized the frontier resources that artificial intelligence needs most—energy, especially nuclear energy."

The data behind this conclusion is shocking:

"China is constructing approximately half of the new reactors being built globally," noted an energy policy expert. "They plan to reach a nuclear power installed capacity of 65 GW by the end of this year, expanding to 200 GW by 2040. In contrast, the United States' nuclear expansion plans remain on paper."

The dilemma of nuclear energy development in the United States is evident: the Vogtle Units 3 and 4 were completed only after long delays and cost overruns, and currently, there are no new large nuclear power projects in the groundwork stage. Although Westinghouse Electric announced plans to build 10 large reactors before 2030, the regulatory hurdles, public concerns, and construction complexities cast uncertainty on these plans.

Energy: The Invisible Bottleneck of AI Development

The development of artificial intelligence relies on energy to a degree far beyond most people's imagination. Training cutting-edge models like GPT-4 requires tens of megawatts of electricity, and the data centers running these models are even greater energy consumers.

"In 2024, global data center energy consumption will reach 415 terawatt-hours, and it is expected to double by 2030," said an expert studying AI energy consumption. "Among them, the energy demand for AI-related applications is growing the most rapidly."

The power demand of data centers in the United States is expected to more than double in the next decade, reaching 78 gigawatts by 2035. This astonishing energy demand makes energy supply a key limiting factor for the development of AI.

China's Energy Strategy Advantages

China's advantages in the energy sector are not only reflected in the number of nuclear power plants being built, but also in its industrial policy guidelines.

· Direct and top-down decision-making mechanism: able to quickly mobilize resources and accelerate the construction of energy infrastructure.

· Long-term planning and execution ability: The time from planning to completion of nuclear power construction is much shorter than that in the United States.

· Integrating energy and technology strategy: Viewing energy security and technological development as two complementary aspects of national strategy.

"China's industrial policy allows them to expand nuclear power construction at a pace that is hard for the United States to match," noted an international energy policy analyst. "This is not just about financial investment; it also concerns decision-making efficiency and execution capability."

U.S. Response Strategy: Is It Too Late?

In the face of China's rapid expansion in the energy sector, the United States is not completely inactive. However, its response strategy appears relatively passive and slow:

· Improve the efficiency of existing facilities: Focus on upgrading and renovating existing nuclear power facilities to extend the license period.

· Development of Small Modular Reactors (SMR): Investing in next-generation nuclear technology, but the commercialization process is slow.

· Renewable Energy Supplementation: Vigorously developing solar and wind energy, but intermittent issues limit their support capability for AI data centers.

"The United States still has significant advantages in foundational AI research, chip design, cloud infrastructure, and venture capital," said a technology policy expert. "However, if energy becomes a bottleneck, these advantages may be gradually eroded."

Energy Wars: The New Battlefield of AI Competition

Livingston's perspective reveals a severely underestimated dimension in the global technological competition. In past discussions, the AI race has primarily focused on talent, algorithms, data, and computing power, while the fundamental element of energy supply has often been overlooked.

"Energy wars may become as important as software or data," pointed out a scholar researching international technological competition. "In the age of AI, whoever can provide stable, large-scale, and reasonably priced clean energy holds the key to competitive advantage."

However, the outcome of this competition is not solely determined by the number of nuclear power plants. Innovations such as efficiency improvements, smart grid technology, and distributed computing can also change the game. The technological advantages of the United States in these areas may partially compensate for its deficiencies in energy infrastructure.

Conclusion: The competition is not over yet, but the alarm has already sounded

Livingston's assertion that "the funeral has already taken place" may be premature, but his warning should not be taken lightly. China's proactive measures in nuclear energy expansion do indeed lay a solid energy foundation for its future AI development, while the relative lag of the United States in this field could pose a potential threat to its technological leadership.

"The score is changing, but the artificial intelligence race is not over," summarized an international relations expert. "However, the United States needs to take seriously the connection between energy security and technological development, and adopt a more proactive strategy to address this challenge."

For observers concerned about global technological competition, key indicators in the coming years will not only be the performance and application scope of AI models but also the development speed and scale of the energy infrastructure supporting these technologies. In this new race, energy may become a crucial factor in determining the final outcome.