In August, Nvidia pulled together six of the biggest names in finance—Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR—to set up financing platforms that could mobilize over $500 billion for companies building AI infrastructure. It was the latest move by a company that has gone from making chips to orchestrating the wider playing field in AI—and a sign that growth for the world’s most valuable company depends as much on the next big engineering challenge as on whether customers can keep affording its products.
It also points to the wider direction of travel in AI. Even as the world obsesses about model releases, chip capabilities, and data center capacity, the variable that increasingly determines who gets to compete is simply money. The next phase of the AI race between companies and countries will depend in large part on who can pay for it; the action is shifting from frontier labs to credit desks. Whoever can raise capital cheaply and at scale will set the pace, and that race still favors the United States, whose capital markets are deeper than anyone else’s. For China and Europe, the road to closing the technological gap runs through the financial one.
Technological revolutions tend to be remembered for their technologies: steam engines, railways, the internet. Just as important is the financial machinery that made them possible: the joint-stock company dispersed the risk of trading voyages; early bond and equity markets financed railroads before they could produce a return; venture capital supplied high-risk money to companies too new to raise it from traditional investors.
AI is reaching its own version of this moment. For half a century, technology was asset-light: software could be written once and distributed to billions at negligible marginal cost. AI is turning technology into a heavy industry. The latest systems require factories, advanced chips, substations, and enormous supplies of electricity. Even the world’s richest companies are finding it hard to fund all of this from their income.
The five largest American hyperscalers alone are on track to spend $730 billion on capital expenditure in 2026. A shrinking proportion is coming from their own balance sheets; they are expected to finance up to a third of it with debt.
If the most profitable companies in history have to borrow to stay in the race, what hope is there for everyone else?
Nvidia’s deal is one answer. Project finance has long funded infrastructure with high up-front costs, paid down by steady returns over many years: power plants, toll roads, airports. What’s new is the attempt to turn compute into an asset class that pension funds and insurers are comfortable owning, the way they already own infrastructure.
The arrangement carries risks. The parallel with infrastructure is imperfect: Energy facilities are built to last decades, usually in response to foreseeable demand, and generate predictable returns. In contrast, graphics processing units (GPUs) are short-lived assets; AI demand is unpredictable; and it is too early to project returns.
Still, if the upsides from AI are speculative, so are the downsides—and if the model works, it could reshape the AI market much as venture capital changed the risk calculus for starting a business.
Nvidia’s announcement came with familiar worries about circular financing, in which the chipmaker’s money comes back to it as chip orders. But this arrangement is the opposite. Nvidia will not, for the most part, be lending its own money—nor will its six partners. The capital comes from third parties: pension funds, insurers, sovereign wealth funds, household savings. Rather than a closed loop, the buildout is being wired into the wider financial system: the deal does not artificially boost demand but restructures the cost of supply for a wider bench of competitors beyond the hyperscalers, who can themselves only partially afford it.
Financial structure will in part pick the winners of the technology race—not by who has more money, but by whether the wider financial architecture puts capital to optimal use.
Europe illustrates the problem. The continent has one of the world’s largest pools of savings; eurozone households alone hold about 10 trillion euros (about $11 trillion) in bank deposits. Yet European institutional investors would rather buy U.S. tech bonds than back their own startups. The money is there; what’s missing is the plumbing to turn savings into investment.
If AI comes down to access to capital, Europe’s AI strategy needs a capital-markets strategy—and progress on long-stalled capital-markets integration would do more for European competitiveness than another round of gigafactories.
China’s problem runs the other way. State money is good at pouring concrete; it is less good at matching capacity to demand: after a policy-driven building spree, Chinese media reported last year that as much as 80% of newly built computing capacity sat unused. The Stanford HAI AI Index 2026 estimates that last year, private investment in AI in the U.S. was more than 23 times that in China.
With finance picking the winners, the United States starts with an edge: No other country is as good at turning the savings of pensioners and households into risky, long-lived assets at scale. America’s bond markets, private credit funds, and asset managers are already looking for exactly this kind of asset—how else could a chipmaker line up a half-trillion-dollar financing target in a single announcement?
The first phase of the AI boom relied on technical breakthroughs: models, chips, applications. The next phase depends on the financial system’s ability to underwrite the industry’s expansion beyond a small set of companies. Nvidia has made a bold attempt to reconfigure the economics of AI; competitors will need equally imaginative financial machinery of their own.
Technological revolutions do not happen simply because someone invents the future. They happen when they also figure out how to pay for it.
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