Big Tech Is Borrowing Hundreds of Billions to Build AI — But What If It Doesn’t Pay Off? Right now, some of the world’s largest technology companies are engaged in one of the largest capital spending cycles in corporate history. They are investing hundreds of billions of dollars annually to build the data centers, power infrastructure, and computing capacity needed to support artificial intelligence. Analysts project that the largest hyperscalers will collectively spend well over $600 billion in 2026 on AI-related infrastructure, with spending potentially approaching $1 trillion in 2027. A growing share of this spending is being financed through debt, often structured in ways that keep large portions of it off the companies’ main balance sheets. How Companies Are Financing the Buildout Instead of funding projects entirely through cash flow or traditional corporate debt, many companies are using Special Purpose Vehicles (SPVs) . These are separate legal entities created to own and finance specific data center projects. In a typical structure, the technology company takes a minority equity stake (often around 20%), while private credit firms and institutional investors provide the majority of the capital — frequently in the form of debt. The SPV borrows the money to build the facility and then leases it back to the technology company under a long-term agreement. Because the debt resides in the SPV rather than on the parent company’s balance sheet, it does not appear as direct corporate debt. The company instead records long-term lease obligations. Meta has been one of the most active users of this approach. In 2025, it structured a roughly $30 billion financing for its Hyperion data center in Louisiana through an SPV, with the majority of the debt remaining off its corporate balance sheet. Similar structures are being used or explored by other companies, including CoreWeave and xAI . Analysts estimate that between 2025 and 2028 , roughly $800 billion in private credit and other debt financing could be required to support AI data center development. The Scale of Financial Commitments While exact totals are difficult to quantify because much of the activity occurs through private entities, credit rating agencies have noted a significant rise in off-balance sheet commitments tied to data center projects. Moody’s has highlighted increasing leverage and off-balance sheet obligations among major technology companies as they scale AI infrastructure. This represents a shift from earlier stages of the AI buildout, when many projects were funded primarily through operating cash flow and equity. As capital requirements have grown, companies have increasingly turned to debt and structured financing arrangements. The Central Risk: Profitability All of this spending and borrowing rests on a critical assumption: that artificial intelligence will generate enough revenue and profit to justify the enormous investment. Many data centers are being built in anticipation of future demand rather than current proven need. Companies are taking on substantial fixed costs — including debt service and long-term lease obligations — with the expectation that AI products and services will become highly profitable in the coming years. If AI adoption or monetization falls short of expectations, companies could face a situation where they have significant ongoing financial commitments without sufficient revenue to cover them. This risk is amplified by the fact that much of the infrastructure being built has a long useful life, meaning companies will carry these costs for many years. Additional risk factors include: Interest rate sensitivity: Higher-for-longer interest rates increase the cost of servicing debt. Potential overbuilding: If too much capacity comes online at once, it could lead to pricing pressure and lower returns. Execution risk: The technology must continue to improve and find profitable commercial applications at scale. Why This Matters The outcome of this massive investment cycle will have broad implications. If AI delivers strong profits, the companies involved could see significant returns on their investments. However, if returns fall short, the combination of high fixed costs and complex financing structures could create financial strain. This is not a theoretical concern. History shows that periods of rapid infrastructure spending in new technologies (such as fiber optic networks in the late 1990s) have sometimes resulted in overcapacity and disappointing returns when demand did not materialize as quickly as expected. While today’s leading technology companies are financially much stronger than many of the companies that overbuilt during the dot-com era, the sheer scale of current spending means the stakes are very high. The Bottom Line Major technology companies are making one of the largest financial bets in corporate history on artificial intelligence. They are spending and borrowing at unprecedented levels, using increasingly complex financing structures to fund the buildout while attempting to manage reported leverage. If AI generates strong profits in the coming years, this strategy could succeed. But if the technology takes longer to deliver returns — or if it fails to generate profits at the scale currently anticipated — the weight of these financial commitments could become a significant challenge. Right now, markets appear confident that these investments will pay off. Whether that confidence is justified will depend heavily on how quickly and profitably artificial intelligence scales in the real world. Sources Moody’s Ratings analysis on hyperscaler leverage and off-balance sheet commitments (2026) Morgan Stanley and JPMorgan projections on AI infrastructure financing needs (2025–2026) Reporting from The Information and Bloomberg on Meta’s Hyperion SPV financing (~$30 billion deal) Industry estimates on private credit requirements for data center development This article is based on available reporting and financial analysis as of July 2026.