The race to build artificial intelligence infrastructure is increasingly being financed with debt, with Morgan Stanley projecting that AI-linked borrowing could approach $570 billion globally in 2026. By the end of May, issuance connected to AI spending had already reached about $236 billion, four times the level recorded a year earlier, according to the bank.
The surge reflects a growing mismatch between the scale of planned data-center construction and the cash generated by even the largest technology companies. Alphabet, Amazon, Microsoft and Meta are expected to spend roughly $700 billion on AI-related infrastructure in 2026, while hyperscaler capital expenditure could pass $1 trillion in 2027, Morgan Stanley estimated.
That funding shift is changing the economics of the AI buildout. Data centers require large upfront payments for land, construction, power equipment, networking and advanced chips, while the revenue from cloud capacity can take years to arrive. Companies that once financed most expansion through operating cash flow are turning to bond markets, leases and outside capital to keep projects moving.
Alphabet and Amazon deepen bond-market reliance
Alphabet reported $119.8 billion in quarterly revenue, up 24% from a year earlier, with Google Cloud revenue rising 82% to $24.8 billion. Yet its capital expenditure reached about $44.9 billion during the quarter, pushing free cash flow to negative $5.9 billion, according to the company’s results.
The company has lifted its 2026 capital-spending forecast to between $195 billion and $205 billion. Alphabet raised about $31.5 billion through a global bond offering in February that included a 100-year tranche, then issued another $25 billion of investment-grade U.S. dollar notes in August.
Amazon has outlined an even larger 2026 capital expenditure plan of $220 billion. Amazon Web Services recorded 37% year-on-year quarterly revenue growth, its fastest growth rate in more than four years, but the group’s free cash flow fell from positive $18.2 billion a year earlier to negative $7.6 billion over the past 12 months.
Amazon raised roughly $37 billion in the U.S. bond market in March and issued €14.5 billion of euro-denominated bonds the following day, bringing the combined deal size near $54 billion. It followed with a further $25 billion dollar-bond sale in July.
Andy Jassy, Amazon’s chief executive officer, has said data centers frequently incur construction costs about two years before they begin operating. That lag places pressure on cash flow: debt is raised and construction begins well before new cloud capacity can produce meaningful revenue.
Meta and Oracle show the pressure on free cash flow
Meta Platforms also increased borrowing as it scales its data-center footprint. The company completed a $25 billion bond sale in April after quarterly revenue climbed 28% to $60.8 billion.
Its free cash flow, though, dropped sharply to $784 million from $8.55 billion a year earlier, according to Meta’s financial results. The company raised its 2026 capital expenditure guidance to a range of $130 billion to $145 billion, a level that would make its infrastructure budget comparable with the annual spending of major industrial groups.
Oracle has shown one of the clearest examples of the financing gap created by AI infrastructure spending. In fiscal 2026, Oracle reported capital expenditure of about $55.66 billion, exceeding operating cash flow of roughly $32 billion. Its free cash flow was negative $23.69 billion.
Oracle completed around $43 billion in debt financing and $5 billion in equity financing during fiscal 2026. By the end of May, the company reported approximately $130.1 billion of future principal repayments on its borrowings. On July 9, S&P Global Ratings lowered Oracle’s long-term credit rating to BBB- from BBB, placing it at the lowest investment-grade rating tier.
The downgrade illustrates a constraint that could become more visible across the sector. Large cloud companies retain substantial revenue and highly profitable businesses, but their credit profiles may increasingly depend on whether AI-related capacity generates enough cash to justify the pace of construction.
Lease commitments add more than $1 trillion to the pipeline
Bond sales capture only part of the financial commitment. Company filings reviewed by Reuters showed that Microsoft, Meta, Oracle, Amazon and Alphabet had disclosed about $1.09 trillion in future lease-payment commitments that had not yet begun.
Microsoft listed approximately $329.1 billion in such commitments, followed by Meta with $279 billion, Oracle with $260 billion, Amazon with $137.2 billion and Alphabet with $85.2 billion, according to the filings cited by Reuters.
Meta subsequently signed around $68 billion in additional data-center lease agreements in July, lifting the known total for the five companies to about $1.16 trillion. Many of the contracts extend for more than a decade and have not yet been fully recognized as lease liabilities on company balance sheets.
These figures do not all measure identical assets. Amazon’s disclosed commitments, for example, include warehouses, offices, aircraft and vehicles alongside data-center facilities. Even so, the scale of future contractual obligations shows how AI expansion is moving beyond conventional capital expenditure into long-term infrastructure agreements.
Nvidia seeks outside capital for compute projects
Nvidia is pursuing a separate route: bringing private capital directly into AI infrastructure financing. On Aug. 10, the chipmaker said it was working with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR on an independent compute-financing platform intended to mobilize more than $500 billion over time.
Nvidia said it could backstop up to 25% of potential transactions, equivalent to roughly $125 billion if the platform reached its stated target. The company did not disclose partner allocations or a timeline for deployment.
The proposal frames AI computing capacity as a financeable long-duration asset, built around chips, data centers, power systems and contracted demand for computing services. It could give cloud providers and specialist operators another source of funding beyond their own balance sheets and the corporate bond market.
The approach also spreads AI infrastructure exposure across private-credit firms, asset managers and infrastructure funds. That may ease the immediate funding burden on technology companies, while connecting more of the financial system to assumptions about long-term demand for AI computing capacity.
Rising government bond yields would raise the cost of that financing across corporate debt, leases and private-credit structures. For cryptocurrency markets, the more relevant consequence is not a guaranteed price move but a tighter relationship with liquidity conditions: when financing costs rise and risk appetite weakens, highly volatile digital assets can face the same pressure affecting technology equities and other growth-sensitive markets.
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