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VERNON, CALIFORNIA \u2014 JULY 8: A 49.5-megawatt data center takes shape in Vernon, California. The AI infrastructure boom is driving record construction\u2014and pushing more of its financing into public bonds, private credit and off-balance-sheet vehicles. (Photo by Mario Tama\/Getty Images)<\/p>\n
Getty Images<\/p>\n
Bond demand is softening as AI\u2019s buildout leans more heavily on credit markets. Read the numbers carefully. This isn’t a looming crisis. The quieter question is who ends up holding the risk.<\/p>\n
The clearest AI signal right now isn’t a new model or chip. It’s in the bond market. Demand is softening as companies borrow more to finance the buildout. The numbers are easy to muddle, and much of the coverage does.<\/p>\n
Morgan Stanley forecasts<\/a> about $570 billion in global AI-related debt issuance in 2026. About $236 billion had priced by May 31, four times the year-earlier pace. The total reaches beyond hyperscalers to other AI-linked issuers and financing structures. It’s a broad wave, and it’s moving fast.<\/p>\n Apollo reports<\/a> that orders for hyperscaler bonds covered nearly five times the amount offered in February. By July, coverage had slipped below two times. That doesn’t mean the market is breaking. It suggests issuers may need to pay more. Deal mix could explain some of the drop, so treat it as directional. It’s still worth watching because the signal is early.<\/p>\n The $1.5 Trillion Number Is Easy to Misread<\/p>\n The $1.5 trillion figure is often described as debt hyperscalers must take on. It isn’t. In Morgan Stanley’s financing analysis<\/a>, the number is a funding gap, not a debt forecast. It covers the difference between about $2.9 trillion of global data-center investment through 2028, excluding power, and about $1.4 trillion funded by Big Tech cash flow. The gap could be filled with bonds, private credit, asset-backed securities, mortgages or equity. Morgan Stanley assigns about $200 billion to related corporate debt issuance through 2028.<\/p>\n That changes the story. Big Tech isn’t running out of cash. It’s choosing to spread the cost of the AI buildout across credit markets, which spreads the risk too.<\/p>\n Why the Giants Borrow When They Don’t Have To<\/p>\n “They\u2019ve run out of cash” doesn\u2019t hold up. Goldman Sachs analysts wrote<\/a> that consensus estimates put hyperscaler capex near 100% of operating cash flow. That’s heavy spending, but it isn’t the same as running dry. Morgan Stanley calls their starting position exceptional financial strength<\/a>.<\/p>\n Why borrow? It preserves options. Companies can keep building without giving up buybacks, acquisitions or liquidity. That\u2019s capital allocation, not distress.<\/p>\n Where the Risk Is Actually Moving<\/p>\n Public bond markets aren’t flashing red. J.P. Morgan notes<\/a> that spreads remain near cycle lows. Public bonds are also more transparent and easier to monitor and price.<\/p>\n More of the financing is expected to move outside public bond markets. Morgan Stanley projects an approximately $800 billion<\/a> private-credit opportunity in data-center financing through 2028. Much of that can sit in off-balance-sheet vehicles the Bank for International Settlements calls shadow borrowing<\/a>. Meta\u2019s $27 billion Hyperion joint venture<\/a> shows how this works. Funds managed by Blue Owl own 80%, while Meta holds 20%. Some of Blue Owl\u2019s funding comes from debt sold to PIMCO and other investors. The structure keeps much of the project debt off Meta’s balance sheet. Similar financing is spreading through securitized deals and project finance. Utilities are borrowing more<\/a> to serve data-center demand, with U.S. investment-grade utility issuance around $135 billion in 2025 and a projected $145 billion in 2026. Disclosure and creditor protections vary widely across these structures.<\/p>\n That makes losses harder to spot if use or pricing falls short. The public balance sheets investors watch most closely now tell only part of the story.<\/p>\n The Question Executives Should Actually Ask<\/p>\n Most of the debate asks whether AI will earn enough to justify the spending. Executives should ask a second question: who finances the wait, and who gets hurt if returns arrive late or fall short?<\/p>\n Companies should map these dependencies. They run through cloud vendors, utility financing and data-center developers. Most of the exposure is indirect. It may surface through higher prices, capacity limits or counterparty risk even if you never buy an AI-related security.<\/p>\n Falling cover ratios don’t signal a crisis. They suggest bond investors may demand wider spreads to absorb additional supply. Order books covered nearly five times the bonds offered in February, but less than two times in July. That means the price of patience is rising. More of the risk now sits outside the balance sheets everyone watches.<\/p>\n","protected":false},"excerpt":{"rendered":"VERNON, CALIFORNIA \u2014 JULY 8: A 49.5-megawatt data center takes shape in Vernon, California. 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