AI Companies’ Debt Now Equals 68% of New Long-Term U.S. Treasury Borrowing This Year, JPMorgan Finds
JPMorgan and Goldman Sachs have both run the numbers on how much debt the biggest AI companies are piling into bond markets this year, and the figure they landed on threatens to reshape who actually sets long-term interest rates in…
The AI infrastructure boom has a scale problem the bond market cannot ignore. Michael Cembalest, chairman of market and investment strategy at JPMorgan, estimates the five major hyperscalers plus Nvidia have issued roughly $320 billion of debt so far in 2026, including special purpose vehicles where the companies ultimately stand behind data center lease obligations. In 10-year-equivalent terms, Cembalest puts the long-duration component at roughly $303 billion. That is equivalent to around 68% of new long-duration Treasury borrowing this year.
Six companies, Oracle (NYSE:ORCL | ORCL Price Prediction), Microsoft (NASDAQ:MSFT), Amazon (NASDAQ:AMZN), Alphabet (NASDAQ:GOOGL), Meta Platforms (NASDAQ:META), and NVIDIA, now compete with the U.S. Treasury for the same pool of long-duration bond demand.
Two Banks, One Number
The figure gains credibility from a second bank’s independent analysis. Goldman Sachs sees roughly $300 billion of AI-related issuance already completed this year, with senior hyperscaler and chip supply slowing sharply into year-end. Two independent bank estimates converge on roughly the same figure.
Forward supply is where the stress emerges. Goldman credit trader Jeffrey Papai calls 2027 the real stress test. Papai’s scenario points toward roughly $340 billion of senior hyperscaler and chip issuance in the coming year, potentially before another large layer of data center and structured chip financing is added on top.
Why Structure Matters More Than Size
The structural composition of the pipeline drives the concern more than its headline size. Limited hyperscaler maturities mean there is much less natural recycling of investor capital than in sectors such as banks, leaving the market dependent on fresh demand for fresh supply. A market financed by reinvested proceeds absorbs shocks; a market financed by net new inflows depends on appetite that can turn. If AI issuers absorb a meaningful share of long-duration capacity, the marginal buyer of a 30-year corporate bond and the marginal buyer of a long Treasury become increasingly the same investor, determining the clearing price of mortgages and long yields alongside AI capex.
Bull Case Counterargument
The counterargument rests on the enormous cash flow behind these balance sheets. Bank of America argued the big five hyperscalers’ debt-to-cash ratio dipped from 0.94 to 0.75. By 2029, Bank of America projected operating cash flow jumping to By 2029, Bank of America projected operating cash flow jumping to $1.1 trillion,.1 trillion, a 95% increase. For most of these issuers, this represents balance-sheet optimization by cash-rich businesses.
Where the Risk Concentrates
Oracle is the exception. Bank of America projects Oracle running negative free cash flow until 2029, meaning capex will exceed cash from operations, leaving little capacity for more debt. Oracle’s fiscal 2026 capital expenditures reached $55.7 billion against operating cash flow of roughly $32 billion, and the company plans to raise around $40 billion in debt and equity during fiscal 2027. According to Goldman Sachs credit trader Jeffrey Papai, 2027 is the real stress test for this issuance wave. Oracle shares are down 17.91% year to date as of Wednesday’s close.
Law360 reported on January 14, 2026 that Oracle was sued by a pension plan over AI-linked debt disclosures. The complaint alleges disclosure deficiencies tied to the AI buildout.
The aggregate picture is manageable, though dispersion across issuers is wide. Oracle is where the risk concentrates, and hyperscaler spreads versus Treasuries are the metric to watch as 2027 supply comes into view, according to Goldman Sachs.
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AJ has spent the past 10 years writing about financial markets at The Motley Fool. His coverage centers on technology stocks and the broader macroeconomic trends, from interest rates to geopolitics, that shape where markets are headed next. AJ is drawn to the stories where big-picture economics and individual companies collide.
