How to Build the Future of AI in the Free World (Carnegie Endowment)

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Paper

The Compute Coalition: How to Build the Future of AI in the Free World

AI infrastructure will shape the global balance of power. Democracies have a narrow window to pull ahead.

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By Alasdair Phillips-Robins, Teddy Tawil, Sam Winter-Levy

Published on Jun 8, 2026

Introduction

Across the world, one of the largest peacetime industrial mobilizations in history is under way. This year alone, America’s biggest technology companies will spend some $670 billion, or about 2 percent of U.S. GDP, building compute clusters. Worldwide, companies and governments will pour almost $1 trillion into data centers with a single goal: building transformative AI.

The United States currently dominates the buildout: As of May 2025, almost three-quarters of the world’s advanced AI computing clusters were on American soil. U.S. projects also move faster than those in most other countries. But that lead is fragile. Domestic constraints—grid capacity, permitting rules, political opposition—are tightening. Abroad, China is mobilizing to close the gap, while Gulf states are touting energy and capital to attract developers.

Many traditional U.S. allies, meanwhile, risk being left behind. Across Europe, every major publicly reported AI data center combined appears to contain less computing power than the Amazon-Anthropic mega-cluster at New Carlisle, Indiana. Some U.S. allies, including Australia, Italy, and South Korea, have no major publicly known operational AI chip concentrations at all. And future plans, such as Australia and France’s gigawatt-scale projects, will at best match what industry leaders built years earlier.

That failure is more than a question of economic returns. The countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used. If democracies are in the lead, they will have a shot at ensuring that transformative AI is safe, secure, and reflects liberal values. If authoritarian powers pull ahead, they will gain the tools to reshape the world order in their image, through repression at home and military dominance abroad. And because authoritarian regimes are more likely to suppress dissent and hide failures, an AI race they lead raises the risk of catastrophic accidents.

The free world still has the energy, capital, talent, and industrial might to win. This paper, built on a detailed new financial model of data center competitiveness, explains what governments can do. The model considers a wide range of factors, including construction, IT, and other capital costs; operating expenses; revenues; tax systems; and operational timelines. It helps explain what is driving billion-dollar company decisions, and how governments can change them.

The core finding: time to power is what matters most. In policy circles, the conventional wisdom often emphasizes energy costs and tax incentives. Our model suggests these factors are secondary. Countries that can get data centers online quickly produce dramatically better returns than those where projects languish in permitting and grid connection queues. We estimate that a one-year delay in operation would cost an illustrative 100-megawatt U.S. data center more than $500 million over its life cycle, or more than 5 percent of its total value (see figure 1). Put differently, companies should be willing to pay at least double U.S. power prices to operate a data center just one year sooner.

The United States and the United Arab Emirates (UAE) top our country competitiveness rankings, in large part because major projects move fastest in those countries (see figure 2). By contrast, the country where timelines are longest, Germany, fares worst. But these rankings can shift quickly: with a one-year delay, the United States drops to fifth, behind the UAE, Finland, Canada, and India. If India sped things up by a year, it would jump ahead of Finland, Canada, and the United States, rising from fifth to second place. If war in the Middle East slows the UAE by a year and a half, it drops to fourth.

Other factors matter, but none matches the significance of speed. Even doubling electricity prices would cost a U.S. facility less than a one-year delay. Moderate tariffs on GPU servers would cost a U.S. facility about one-third as much. And removing typical state tax incentives comes in at about 60 percent of the cost of such a delay. Because IT equipment—priced similarly worldwide—accounts for the large majority of capital expenditures, national differences in non-IT factors, such as labor and land costs, have a small effect on overall project economics, and subsidies must be very large to move the needle significantly.

Our financial model focuses on what individual countries can do to compete. But no single democracy—not even the United States—can build the world’s AI infrastructure alone. A broader coalition of democracies, one that pools its geography, power generation, capital markets, and supply chain strengths, would be stronger, more resilient, and better positioned to ensure that transformative AI is developed and governed on democratic terms.

Democracies thus need to both compete at home and cooperate abroad. This paper makes recommendations on both fronts. On the domestic level, it identifies the reforms that would most improve countries’ competitiveness in attracting AI infrastructure. On the international level, it makes the case for a broader democratic coalition that can collectively outcompete authoritarian alternatives across the AI supply chain. The need is not for an overall acceleration in the AI buildout, but for a shift in relative progress to keep the center of gravity in the democratic world.

Domestically, the priority for countries that struggle to attract investment today should be removing the obstacles that slow projects down, including permitting backlogs, grid connection queues, transmission bottlenecks, and equipment shortages. Governments should not spend scarce public funds on a lucrative industry through energy subsidies, tax cuts, or other fiscal outlays. Rather, they should consider:

Creating fast-track review processes for data center projects that meet certain criteria, such as the use of clean energy, investment in grid infrastructure, or tax commitments. As long as individual governments can actually shorten approval timelines, they will have significant flexibility to shape the bargains they strike with data center developers.

Promoting grid flexibility and resilience by reforming interconnection queue processes, allowing operators to plan for load flexibility under the right circumstances, and encouraging the production of bottlenecked equipment.

Supporting clean behind-the-meter power, including solar microgrids and wind farm offtake, as a bridge to grid connections.

Internationally, the democratic world needs to start building the partnerships that will let it collectively dominate the AI value chain from natural resources to last-mile deployment. Fruitful collaboration could include mutual fast tracks for allied infrastructure investment; common industrial policy in sections of the supply chain, such as critical minerals, where chokepoints lie outside the democratic world; joint safety and security research on frontier AI capabilities and safeguards; and common transparency and incident reporting standards. Together, democracies can set the pace in the development and security of powerful AI systems.

This paper proceeds as follows. Part I explains the economics of data centers: it lays out the findings from our model, shows that time to power is a key driver of companies’ decisions about where to site data centers, and examines what drives differences in competitiveness between countries. Part II explains the geopolitical stakes. Those siting decisions will help determine the future of AI and the balance of global power. The paper thus makes the case for a democratic compute coalition, arguing that the United States and its partners each have strong incentives to cooperate. Part III outlines the policy agenda: domestic reforms to reduce time to power and international coordination to secure the AI supply chain.

I. The Economics of Compute

Companies are scouring the earth for land to build AI chip clusters and energy to run them. Data center economics show that the places getting chips online the fastest have a decisive advantage.

The Model

In June 2025, two major AI infrastructure projects hit milestones. In Abilene, Texas, OpenAI’s Stargate data center took delivery of its first Nvidia chips. On the other side of the Atlantic, in Brussels, the European Commission received seventy-six nonbinding “expressions of interest” for its AI Gigafactories initiative—an early step in its plan to support large computing clusters across the bloc.

By December, Stargate Abilene was running an estimated 100,000 Nvidia GB200 chips, or more than 200 MW of AI computing capacity. The commission, meanwhile, was still preparing to issue its formal call for proposals—a step that has since been delayed. More than a year after the program was announced, no construction has begun.1

OpenAI is not the only American firm forging ahead while European leaders issue paperwork. A single American company, Google, holds roughly one-quarter of global AI computing power. In 2025, the United States hosted around 75 percent of the world’s high performance AI compute. The European Union has less than 5 percent.

To understand how governments might improve their lot, we built a financial model to explain the major drivers of investment decisions. The model projects the life cycle value of a hypothetical 100 MW AI data center—large, but no longer frontier-training scale2—in ten countries, representing a range of players in the infrastructure race: Australia, Canada, Finland, France, Germany, India, South Korea, the United Arab Emirates, the United Kingdom, and the United States.

The model considers capital expenditures (IT equipment, construction, electrical systems, liquid cooling, on-site power generation), operating expenses (electricity, maintenance, staffing), tax treatment (corporate taxes, depreciation schedules, state and local incentives), time to operation, and projected revenues over a twelve-year life cycle, drawing on public data, equity research, and industry interviews.

The model is designed to be realistic, but its conclusions are necessarily tentative. Projecting the returns to investments of this complexity requires making many contestable assumptions, and any given real-world project will differ in multiple ways. The model is built primarily on public data, while much of the relevant information is proprietary. The model’s value lies more in the trends it reveals than any specific figure. A more complete explanation of our methods is in the appendices.

Three primary findings are relevant to policymakers.

Finding 1: Time to power is the most important driver of economic returns.

Delays in getting a data center up and running are extraordinarily costly. Our model shows that for a typical 100 MW U.S. data center, each additional year of delay costs roughly $550 million in life cycle value, about 5.5 percent of the data center’s roughly $10 billion life cycle value (see figure 3).3

Of all the external variables we considered—construction and labor costs, tariffs, local taxes, power costs, efficiency losses from the climate—delays are the number one driver of lost returns in our model, by a large margin.

In the United States, our model suggests that a one-year delay costs about 25 percent more than a doubling in electricity prices from their current level ($441 million), about 60 percent more than losing typical state tax incentives ($338 million), and three times as much as moderate tariffs on servers ($172 million). Even the cost of a three-month delay—about $178 million—greatly exceeds the impact o

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