Alphabet, Amazon, Meta, Microsoft and Oracle are changing how they pay for the AI boom, and the shift is sending ripples through credit markets. What started as a capital-spending race funded mostly by operating cash flow is now increasingly a bond-market story, with investors pricing in more supply, more complexity and more risk. By September 2026, AI-related issuer credit spreads were around 115 basis points, wider than about 78 basis points for the broader investment-grade market, according to Reuters, Goldman Sachs and ICE BofA data.
The change is no longer subtle. Vanguard says the five hyperscalers have moved from funding AI capex through operating cash flow to heavy bond-market borrowing. J.P. Morgan Asset Management puts hyperscaler gross bond issuance at about $17 billion in 2024, $109 billion in 2025 and $194 billion in the first half of 2026 alone. That pace has pushed the sector into the center of credit-market debate, where the question is no longer just how fast these companies can build data centers, but how they will finance the buildout without straining balance sheets.
The backdrop is an AI infrastructure spending spree that keeps expanding. Goldman Sachs now expects hyperscaler gross debt issuance to reach a record about $420 billion in 2027, up 60% from 2026, according to Reuters. That would extend a trend already visible in the market today: more paper, more leverage and a heavier flow of deal supply for fixed-income buyers to absorb. For investors, the issue is not simply the names involved. These are some of the largest and best-capitalized companies in the world. The concern is how much of their AI race is being shifted from equity-like flexibility into debt-like obligation.
That shift is showing up in market pricing. Reuters reported that hyperscaler bond cover ratios fell from about 5 times in February to about 2 times in July 2026, based on Apollo Global Management data. Lower cover ratios suggest the cushion behind new borrowing is thinning as issuance rises. At the same time, the market is being asked to finance a capex cycle that has few obvious off-ramps, because the spending is tied to the battle for compute capacity rather than to a normal product launch or one-time expansion.
The headline debt numbers may understate the real exposure. Financial Times reported that Big Tech has issued up to $300 billion in residual value guarantees in under a year, leaving most of that exposure off balance sheets. The same reporting said Morgan Stanley tallied more than $3.1 trillion in off-balance-sheet commitments and credit support across seven hyperscalers and chipmakers. The Bank for International Settlements describes these structures as shadow borrowing, meaning debt-like obligations that sit largely outside corporate balance sheets.
That matters because the liability profile is becoming harder to read just as the capex cycle is getting larger. Doug Colandrea, senior director at KBRA, told the Financial Times: “There has been a significant expansion in off-balance-sheet exposure over the past year. That adds a very high degree of complexity to their credit risk profiles.” For bondholders, the risk is not only the size of what is being borrowed, but the growing web of guarantees, leases and contingent claims that can surface later.
The market is already reacting to that complexity. Meta’s pioneering Beignet data-center bond traded at about 6.95% yield, up from a 5.65% low at issuance, according to FT Alphaville reporting cited by the Financial Times. That kind of move does not mean the credit story has broken. It does show that investors want more compensation for a structure tied to AI infrastructure, especially when the future cash flow from those assets may be harder to judge than a traditional corporate bond backed by a mature business line.
The core issue is timing. The money is being spent now, while much of the payoff is expected later, and not every contract or lease has the same visibility. Tim Musial, head of fixed income at CIBC Private Wealth US, told the Financial Times: “There is concern about all these future commitments because everyone is in a race to get the compute.” That race is what makes the sector hard to value. The buildout is not a short-term cyclical upgrade; it is a multi-year contest for infrastructure that could become either the backbone of future earnings or an expensive overbuild.
That uncertainty is why investors are watching return on invested capital so closely. Colby Stilson, head of fixed income at Brown Advisory, told Reuters: “We’re being very selective in terms of how we invest within hyperscaler debt… because of the coming supply and because of the lack of visibility into that return on invested capital.” His warning captures the central tension in the market: even for companies with formidable balance sheets, rapidly rising issuance can pressure spreads if buyers worry the assets being financed will not earn back their cost fast enough.
The broader bond market is also sending a message. AI-related issuer credit spreads at about 115 basis points, compared with about 78 basis points for the broader investment-grade market, suggest investors are demanding more yield for the sector’s unusual mix of growth, capex intensity and off-balance-sheet exposure. That premium is not huge by distressed-credit standards, but it is meaningful for companies that have historically enjoyed low funding costs and deep demand for their paper. For a market built on scale and liquidity, any widening becomes a signal.
The next stress test may not arrive all at once. Goldman Sachs’ 2027 issuance estimate points to another wave of supply just as some contracts signed in 2025 and 2026 begin billing in 2027 and 2028, according to UBP. That timing could matter for the private AI economy, including OpenAI and Anthropic, if the economics of those contracts need to support the infrastructure built to serve them. The question is whether the billing cycle catches up fast enough to justify the borrowing cycle already underway.
There is also a second layer of delayed liability. Financial Times reported that Nvidia’s OpenAI lease guarantees tied to the SB Energy Ohio campus begin recording as a balance-sheet liability in 2028. That kind of future accounting event is important because it shows how today’s AI funding structures can remain partially hidden until later reporting periods. The current borrowing wave may look manageable in the near term, but the real cost can emerge as leases, guarantees and data-center commitments roll into formal financial statements.
The big picture is simple: the AI boom is increasingly being financed like a credit story, not just an innovation story. The market is pricing more bonds, more guarantees and more deferred obligations into the same sector that investors once treated mainly as a cash-rich equity engine. For now, the hyperscalers still have scale, access and strong operating businesses. But the debt market is no longer taking it on faith that AI capex will pay for itself. It is asking for a spread.
