Why Big Tech’s AI Spending Is $3 Trillion Higher Than It Seems
Massive spending commitments for data-center leases and chips aren’t shown on companies’ balance sheets
By Peter Rudegeair and Peter Santilli, The Wall Street Journal, Aug. 16, 2026
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. …
[snip complicated lease obligations held by a third company and financed by bondholders]
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…
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. …[end quote]
There are some pretty scary charts in this article. I can’t copy the whole thing due to copyright rules.
Bottom line: There are breathtakingly huge obligations not on balance sheets. If AI doesn’t produce massive profits from end-users (not the circular profits within the AI ecosystem) there will be a lot of surprised losers.
Timeline of risk playing out if end-users don’t pay enough to justify the huge investments, according to Gemini.
Summary Table: Timeline of Risk Transmission
| Timeframe | Trigger Event | Market Transmission Channel | Most Vulnerable Assets |
|---|---|---|---|
| 0–12 Months | Free cash flow compression; widening CDS spreads on leveraged tech. | Equity multiple compression; credit downgrades for Tier-2 players. | Neoclouds, high-yield AI bond issuers, BBB-rated tech debt. |
| 12–24 Months | Off-balance-sheet commitments convert to active balance-sheet liabilities. | Accelerated depreciation charges; margin compression in quarterly earnings. | Hardware suppliers, data center REITs, mid-tier tech equities. |
| 24–36 Months | Full realization of ROI gap (if end-user software revenues lag behind). | Capex pullbacks, structural re-rating of tech multiples, debt restructurings in SPVs. | S&P 500 index concentration (top tech holdings), private credit funds. |
| Wendy |