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By
Peter Rudegeair
and
Peter Santilli
Aug. 16, 2026 9:00 pm ET
Each quarter, big tech companies disclose their massive capital expenditures on artificial-intelligence infrastructure, from data centers to chips.
But those figures don’t come close to expressing the full extent of future spending to which Google parent Alphabet, Meta Platforms, Oracle and many others have committed. That is because a huge swath of their coming financial obligations aren’t reflected on their balance sheets.
Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings. Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.
America’s blue-chip tech companies are placing these huge bets based on assumptions about what the demand for AI computing—and availability of AI hardware—will be in several years. Their hope is that they will easily meet all their obligations with future revenue as consumers and businesses adopt AI in every facet of American life.
If those assumptions about technology and demand prove wrong, these deals to clinch future capacity could become a monstrous burden for the tech companies and their investors.
Meta’s gigantic “Hyperion” data-center project in Louisiana, which is the size of about 1,700 football fields, helps explain how big obligations wind up off tech companies’ balance sheets.
A graphic showing the 80%-20% split between Blue Owl Capital and Meta Platforms in the special purpose vehicle that owns the Hyperion data center.
Though Meta is the builder, neither Hyperion nor the $27 billion in debt that’s financing its construction shows up on Meta’s balance sheet. Funds managed by the Wall Street firm Blue Owl Capital own the majority of a joint venture that, in turn, owns the campus.
Beignet Investor, a holding company that owns the Blue Owl stake, raised the construction financing in a bond sale.
Meanwhile, Meta is Hyperion’s minority partner and tenant. Its lease payments will provide the cash flows to help make the payments to bondholders.
Meta initially agreed to lease Hyperion for a four-year term starting in 2029, with options to renew for up to 20 years. It guaranteed that it would make bondholders whole if it doesn’t stay the entire two decades. The company doesn’t think payments under that guarantee are probable, so it hasn’t recorded any liability on its balance sheet.
In accordance with accounting rules, Meta’s Hyperion lease obligations will remain off balance sheet until it starts paying rent. It said its aggregate initial lease commitment is about $12.3 billion. Meta disclosed $347 billion in total obligations for leases that haven’t kicked in yet, including for Hyperion, as of June.
Across the companies the Journal analyzed, promises of payments under these uncommenced leases totaled $1.2 trillion in off-balance–sheet obligations, or about four times more than what was disclosed a year earlier. In addition to Meta, the Journal reviewed commitments for Alphabet, [Amazon.com](http://Amazon.com), Microsoft, Oracle, Nvidia, Broadcom, SpaceX and Advanced Micro Devices.
Data centers get stuffed with a lot of hardware, including the Nvidia chips that are used to train and run models and memory chips that store information. To buy all that, companies sign long-term contractual agreements well in advance to lock in production from their suppliers.
Those and other purchase obligations at the companies the Journal examined stand at a whopping $1.9 trillion. Under accounting rules, purchase commitments typically remain off balance sheet until a product or service is delivered.
Alphabet’s purchase commitments and contractual obligations have exploded and stood at $811 billion as of June 30. As with other companies, it is hard to tell from its disclosures what precisely it intends to buy. The company said the commitments primarily relate to “technical infrastructure and inventory” and “agreements to secure energy for data center usage.”
Alphabet also didn’t detail why those obligations increased so much from the $332 billion it reported three months earlier. The commitments span several years, with obligations under its energy agreements lasting as far out as 2054.
Off-balance-sheet exposures at some companies include agreements to buy other companies’ stock in the future or backstop leases for other tenants. Nvidia committed to make $27 billion in equity investments between April 26 and the end of its fiscal year in January 2027.
There are reasons to believe tech companies will make good on all their obligations. Optimists see the skyrocketing demand for AI tools—which has lifted the stock market and led to shortages of key hardware—as a proof point that demand is going to be strong for years, and the money to pay off all these bills will be rolling in.
For the more anxious set on Wall Street, it is a worrying sign that some tech companies that once seemed to have fortress balance sheets have needed to tap the capital markets frequently.
Alphabet and Amazon recently posted results showing negative free cash flow, meaning their capital spending exceeded the cash they brought in from operating their businesses.
And that is before considering the implications of trillions in off-balance–sheet commitments. Whether or not the revenues ever arrive, purchase commitments and signed leases can’t be canceled, for the most part.
If things go wrong, tech companies will be paying an expensive tab for infrastructure that they can’t profitably use. These obligations could also lead increasingly indebted companies to have to borrow even more.
“As these off-balance sheet commitments become more frequent, larger, and more complex, it is becoming increasingly difficult for investors to assess companies’ total potential leverage,” Morgan Stanley accounting analysts wrote in April.
Paywall link: [https://www.wsj.com/tech/ai/why-big-techs-ai-spending-is-3-trillion-higher-than-it-seems-e1067bb2](https://www.wsj.com/tech/ai/why-big-techs-ai-spending-is-3-trillion-higher-than-it-seems-e1067bb2)