Meta, Oracle face bond market scrutiny amid AI spending surge

Meta and Oracle are both drawing heightened scrutiny from credit investors as their capital expenditures balloon to fund data center buildouts. Oracle’s fiscal 2026 capital expenditure hit $55.66 billion, overshooting its own $50 billion target. The company is eyeing up to $95 billion in capex for fiscal 2027, a figure that includes customer repayments. Meta, meanwhile, tapped the bond market for $30 billion in October 2025, its largest debt offering of the year, to bankroll its own AI infrastructure push.

Oracle’s credit profile takes a hit

S&P Global downgraded Oracle’s credit rating to BBB- in July 2026, citing elevated debt levels. That puts the company just one notch above junk status. Oracle’s 5-year credit default swap rates traded at multi-year highs in July 2026. To fund its ambitions, Oracle plans to raise nearly $40 billion through a combination of debt and equity in 2027. A $20 billion equity issuance has already been announced.

Meta doubles down with record bond sales

Meta’s $30 billion bond issuance in late 2025 was driven by strong investor demand, with proceeds flowing into data infrastructure designed to support the company’s AI projects. Meta is also securing additional financing for specific capital projects, including a $12 billion data center in Texas. Credit spreads on Meta’s bonds have been widening alongside those of Alphabet and Amazon, signaling investor demands for higher returns across the sector.

The hyperscaler debt problem

AI-related capital expenditures are on track to exceed free cash flow for several major hyperscalers by 2027. For Oracle specifically, the jump from $55.66 billion in fiscal 2026 capex to a potential $95 billion in fiscal 2027 represents a roughly 70% increase year over year, funded substantially through new debt and equity issuance rather than organic cash flow. Meta’s willingness to issue its largest-ever bond offering and pursue project-specific financing for individual data centers indicates that even cash-rich tech firms are finding that AI infrastructure demands more capital than internal resources can comfortably provide.

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