For much of the past decade, the global race for artificial intelligence supremacy has been framed as a contest over algorithms, talent, and semiconductor chips. Governments and corporations alike have treated access to advanced processors as the decisive factor separating leaders from laggards. Export controls, industrial subsidies, and geopolitical maneuvering have all revolved around who designs, manufactures, or controls the most powerful chips. Yet this focus, while understandable, is increasingly incomplete. As the scale of AI systems explodes, a more basic input is emerging as the true strategic choke point: energy. In the coming years, reliable and affordable electricity will determine which countries can sustain large-scale AI development and deployment-and which will fall behind.

This shift is best understood through the lens of political economist Albert O. Hirschman, who argued in National Power and the Structure of Foreign Trade (1945) that national power derives from control over bottlenecks that others depend upon. In today’s AI ecosystem, the United States has exercised power by dominating chip design and restricting China’s access to cutting-edge semiconductors. China, for its part, has countered through leverage over rare-earth materials essential for chips, magnets, and advanced electronics. But as AI models grow ever larger and more computationally intensive, the bottleneck is moving downstream. Chips matter, but chips without electricity are inert. Data centers without abundant power are simply expensive warehouses.

The energy intensity of AI is staggering. Training large language models, running inference at scale, and supporting cloud-based services require vast and continuous flows of electricity. Data centers must operate 24/7, with minimal interruptions and tight tolerances for voltage fluctuations. According to the International Energy Agency, roughly 20 percent of planned global data-center capacity could be at risk by 2030 due to grid bottlenecks and delays in connecting new facilities to power networks. This is not a marginal issue. As grids strain to accommodate new demand, electricity prices rise, and those costs cascade through the economy, affecting households, manufacturers, and public services alike.

China appears to have grasped this reality more clearly than any other major power. Over the past decade, it has undertaken a massive build-out of energy supply and transmission infrastructure, much of it centered on renewables. Investments span solar, wind, hydropower, ultra-high-voltage transmission lines, and large-scale storage. Crucially, Beijing has focused not only on generation but also on moving electricity efficiently from inland production centers to coastal demand hubs. This integrated approach has improved reliability while driving down costs.

Manufacturing policy has reinforced this energy strategy. China’s dominance in clean-energy manufacturing has slashed costs globally, with the price of solar panels falling by roughly a factor of twenty over the past two decades. Today, China is capable of adding between 500 gigawatts and one terawatt of new power capacity per year-an astonishing figure by historical standards. This abundance of electricity gives Chinese policymakers room to maneuver in other areas. To offset the higher cost of domestically produced chips, local governments offer electricity subsidies to data centers. Facilities using Chinese chips can reportedly cut power bills by up to 50 percent, turning energy policy into an indirect but powerful tool of industrial support.

The United States, by contrast, looks increasingly complacent. While it remains the global leader in AI model development and chip design, its energy strategy is fragmented and slow-moving. According to the Financial Times, China added 429 gigawatts of new power generation capacity in 2024-more than six times the net capacity added in the US during the same period. This gap is particularly troubling given the surge in American data-center construction. Hyperscale facilities can be built in a few years, but the transmission lines needed to supply them often take close to a decade to plan, permit, and complete.

This mismatch between private investment timelines and public infrastructure delivery is already creating stress. OpenAI and its partners have announced plans for data centers requiring up to 10 gigawatts of capacity-roughly equivalent to New York City’s peak summer electricity demand. Yet US grids, planned largely at the local level, were never designed for such concentrated, round-the-clock loads. A Bloomberg analysis of tens of thousands of pricing nodes shows wholesale electricity prices near major data-center hubs rising sharply-up to 267 percent higher than five years ago in some regions. Without structural reform of energy policy, grid planning, and interconnections, the US risks turning its current AI advantage into a liability.

Europe occupies a more ambiguous position. On paper, the European Union has several strategic strengths that could make it a serious contender in the energy-intensive phase of the AI race. More than one-fifth of global clean and sustainable technologies are developed in the EU, and European firms possess deep expertise in grid equipment, power electronics, and energy storage. The continent’s electricity system is among the most interconnected in the world, offering resilience and flexibility that many other regions lack. European energy policy explicitly frames grids as a strategic asset for autonomy and security, and EU plans call for domestic factories to meet at least 40 percent of annual deployment needs for strategic net-zero technologies by 2030.

Yet these advantages risk being squandered by institutional inertia. Europe’s decarbonization drive has already collided with high energy costs, slowing growth and straining public support. Grid expansion, while recognized as essential, remains painfully slow. Planning may occur at the European level, but execution is still largely local, with complex permitting processes and fragmented authority. As a result, the average grid project takes more than ten years to complete, with roughly half that time consumed by permits alone. According to the European Parliamentary Research Service, current grid investment plans cover only 10–15 percent of what is actually required. More than 500 gigawatts of offshore wind projects-enough to power entire economies-remain stuck in connection queues, waiting for assessments that may take years.

The implications of these divergent trajectories are profound. The next phase of the AI race will not be won solely by whoever designs the most advanced chips or trains the largest models first. It will be won by those who can reliably supply vast amounts of low-cost electricity at scale. In this sense, China’s strategy is comprehensive. By pairing industrial policy with aggressive energy investment and local execution, it addresses every dimension that matters: chips, manufacturing, grids, and power prices. The US, meanwhile, risks being trapped by its own success-so focused on its current dominance that it fails to anticipate the infrastructural demands of sustaining it. Europe, uniquely positioned to offer cleaner and more secure energy, may simply move too slowly to seize the opportunity.

Ultimately, AI is not just a digital technology; it is an industrial one. Its foundations are physical: data centers, cables, cooling systems, and above all, electricity. As Hirschman might have predicted, power will accrue to those who control the choke points. In the decades ahead, energy will be that choke point. Countries that recognize this early and act decisively will shape the future of AI. Those that do not may discover, too late, that intelligence without power is no intelligence at all.

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Source: Weekly Blitz :: Writings