Big Tech's Hidden AI Debt Reaches $400bn as Bubble Concerns Intensify
Major technology corporations are employing sophisticated accounting techniques to conceal billions in artificial intelligence infrastructure debt, sparking growing concerns about a potential sector-wide bubble. Meta's recent $6.5 billion financing arrangement exemplifies this trend, where the company pays substantial extra costs to keep $27 billion of AI borrowing off its official balance sheet.
The Special Purpose Vehicle Strategy
This financial approach utilizes special purpose vehicle financing, allowing external entities to raise debt, construct data centers, and lease them back to technology groups. While Meta technically records lease payments instead of traditional borrowing, the reality involves decades-long commitments tied to massive computing facilities.
Meta's Louisiana data center project, valued at $30 billion, was primarily financed through private credit giants including Blue Owl Capital, Pimco, BlackRock, and Apollo. The social media giant owns approximately 20 percent of the vehicle and has provided a residual value guarantee, potentially requiring compensation to investors if project values decline below agreed levels at lease termination.
Industry-Wide Adoption
Oracle has similarly moved tens of billions in AI data center investments through comparable structures, including a $38 billion package connected to its OpenAI partnership. Elon Musk's xAI secured $20 billion using a related framework, with debt collateralized against Nvidia chips.
In some instances, Nvidia has invested equity directly into customers who then purchase its hardware, creating a circular capital flow that maintains revenue streams while keeping liabilities separate from the chip manufacturer's balance sheet.
Mounting Sector Debt
Morgan Stanley projects hyperscalers could issue $400 billion in corporate bonds during 2026 alone to fund AI expansion. JPMorgan calculations reveal AI and data center firms now represent 14.5 percent of its $10 trillion investment-grade bond index, equating to approximately $1.5 trillion in debt exposure.
UBS reports indicate roughly $450 billion has flowed from private capital into technology infrastructure by early 2025. This scale and complexity are triggering alarm bells among market observers who recall previous technology bubbles.
Expert Warnings
AJ Bell investment director Russ Mould references Richard Bookstaber's research on market crises, noting: "He argued that leverage, complexity and opacity help to 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."
Mould emphasizes that while these arrangements comply with accounting regulations, "more debt and more complexity mean more risk," particularly if anticipated returns fail to materialize.
Strong Fundamentals vs. Future Uncertainty
The current situation differs from the late-1990s telecom collapse, with major U.S. technology firms maintaining substantial cash reserves. Among hyperscalers, only Oracle and Apple currently carry more long-term debt than combined cash and short-term investments.
Debt-to-capital ratios vary significantly:
- Nvidia: 8.3 percent
- Alphabet: 10.3 percent
- Meta: 27.9 percent
- Oracle: 83.9 percent (investment grade but on negative watch)
Hargreaves Lansdown senior equity analyst Matt Britzman observes: "Among the four largest public market AI investors – Amazon, Alphabet, Meta and Microsoft – total calendar-year 2026 capex is forecast at over $600 billion, so it's not like these companies are trying to hide their ambitions."
Combined operating cash flow across this group is projected to approach $700 billion in 2026, with off-balance sheet arrangements appearing modest relative to these enormous cash flows.
The Critical Question
The fundamental issue revolves less around current solvency and more concerning future sustainability. Gartner forecasts global AI spending will reach $2.52 trillion in 2026, representing 44 percent year-on-year growth. By 2030, artificial intelligence is expected to dominate information technology budgets completely.
However, credit agencies have noted that some major sector customers, including OpenAI, aren't anticipated to achieve profitability until later this decade. Data centers are financed based on 20-year demand projections. If these translate into expected revenue growth, current financial structures will appear visionary. Conversely, if demand falters, risks will concentrate within private credit vehicles and long-term lease obligations.
Britzman adds: "Demand for compute remains extremely strong, and cloud giants are still seeing rental demand for six-year-old A100 chips." This sustained demand provides some reassurance, but the unprecedented scale of hidden debt continues to generate apprehension among financial analysts monitoring the artificial intelligence investment boom.
