Meta Platforms Inc. is incurring approximately $6.5 billion (£4.82 billion) in additional financing costs to manage $27 billion of AI infrastructure borrowing off its balance sheet. This costly accounting decision reflects the prevailing atmosphere among major technology firms racing to enhance their AI capabilities without alarming investors. The arrangement employs a financing mechanism known as special purpose vehicle (SPV) financing, which allows an external entity to raise debt, construct data centers, and lease them back to the tech company.
While Meta records lease payments rather than conventional borrowing, the company has committed to decades of payments associated with extensive computing facilities. This financing structure was utilized for Meta’s $30 billion data center project in Louisiana, predominantly backed by notable private credit firms including Blue Owl Capital, Pimco, BlackRock, and Apollo.
Meta holds approximately 20 percent ownership in the SPV and has provided a residual value guarantee, meaning it may need to compensate investors if the project’s value falls below predetermined levels at the end of the lease. Similarly, Oracle has committed tens of billions of dollars in AI data center investments through comparable structures, including a $38 billion deal linked to its partnership with OpenAI. Elon Musk’s xAI has also tapped into this financing approach, securing $20 billion against Nvidia chips.
In some instances, Nvidia has even invested equity in clients, which they then use to purchase its hardware, creating a circular flow of capital that sustains revenue while the chip manufacturer’s liabilities remain elsewhere. Though the accounting methods are legal and disclosed, they unfold amidst staggering AI growth forecasts and an uptick in borrowing across the industry.
Morgan Stanley has projected that hyperscalers could raise $400 billion in corporate bonds in 2026 alone to support AI expansion. Meanwhile, JPMorgan estimates that AI and data center firms now comprise 14.5 percent of its $10 trillion investment-grade bond index, translating to approximately $1.5 trillion in debt exposure. UBS indicates that around $450 billion has been directed from private capital into tech infrastructure as of early 2025.
This scale and complexity are prompting market observers to recall prior tech bubbles. AJ Bell’s Russ Mould cites Richard Bookstaber’s analysis of previous market crises, which suggested that leverage, complexity, and opacity often fuel bubbles. “The use of special purpose vehicles and off-balance sheet structures to fund enormous AI capital investment will bring back bad memories for experienced investors,” he remarked. He also noted that while these arrangements comply with accounting regulations, “more debt and more complexity mean more risk,” especially if returns fail to meet spending.
Despite concerns, the current landscape does not appear to mirror the late-1990s telecom crash, as the largest U.S. tech companies maintain substantial cash reserves. Only Oracle and Apple among the hyperscalers currently carry more long-term debt than cash and short-term investments. Nvidia’s debt-to-capital ratio stands at 8.3 percent, Alphabet’s at 10.3 percent, and Meta’s at 27.9 percent, while Oracle’s is notably higher at 83.9 percent, still within investment-grade status but on negative watch.
Matt Britzman, a senior equity analyst at Hargreaves Lansdown, noted that among the four largest public market AI investors—Amazon, Alphabet, Meta, and Microsoft—total capital expenditures for the calendar year 2026 is forecasted to exceed $600 billion, indicating that these companies are not concealing their ambitions. He added that the combined operating cash flow for this group is anticipated to approach $700 billion in 2026. “Off-balance sheet arrangements also look modest in scale relative to the enormous cash flows that big tech are pulling in, which reduces concerns about hidden leverage,” Britzman explained. Demand for computing resources remains robust, as cloud giants continue to experience rental demand for six-year-old A100 chips.
The pressing inquiry, however, shifts from current solvency to future durability. Gartner has forecast that global AI spending will reach $2.52 trillion by 2026, reflecting a 44 percent year-on-year increase. By 2030, AI is expected to dominate IT budgets completely. However, credit agencies have cautioned that some of the sector’s largest clients, including OpenAI, may not achieve profitability until later in the decade. These data centers are financed based on long-term demand assumptions, which span two decades.
If anticipated demand translates into projected revenue growth, these financing structures may prove insightful. Conversely, should demand falter, the associated risks will be housed within private credit vehicles and long-term leases, raising questions about the sustainability of these investments as the AI landscape evolves.
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