How AI Is Reshaping Credit Markets

Most of the commentary on AI and markets is about equities. That's the wrong place to look. Since 2024, more than $600 billion of AI-related debt has been issued across the hyperscalers and the industries building their datacenters, and the pace is accelerating. The buildout is increasingly creditor-funded, which makes it a fixed income story now — one about supply, structure and who ends up holding the risk if the growth assumptions don't hold.

The end of asset-light

The premium ratings of US big tech were built on low capital intensity. That era is over. Hyperscaler capex has roughly tripled as a share of revenue versus the late 2010s, and in the most aggressive cases the change is startling: Oracle went from $21 billion of capex to $130 billion in two years, net leverage is above 3x, and free cash flow is deeply negative on the OpenAI buildout. Consensus estimates for 2026 capex have been revised up almost every month for a year. Nobody, including the companies, seems confident about the ultimate size of the spend.

Bond markets have started charging for this. Gross hyperscaler issuance is running near $170 billion so far this year and headed toward $200 billion — companies that visited the bond market occasionally as recently as 2023 are now a permanent supply overhang. Long-end hyperscaler curves have repriced against the IG long end, with the pressure concentrated where leverage is growing fastest: Oracle's 30-year basis has widened 88bp since the autumn to +117bp — the widest adjusted spread among the large US technology issuers. The market has effectively picked its proxy for leverage-funded AI ambition and is pricing it accordingly.

The debt is moving into new wrappers

The more interesting development is structural. Alongside conventional bonds, roughly $90 billion of datacenter joint-venture debt has come to the USD IG and HY markets since 2024 — bankruptcy-remote vehicles, secured by lease cash flows from specific data halls, that look much more like project finance than corporate credit. Meta's Beignet transaction is the template: about $27 billion of AI infrastructure moved off balance sheet into a JV with Blue Owl, with Meta as the anchor tenant through leases and guarantees rather than a guarantor of the notes. S&P rates the notes a notch below Meta itself, which tells you the credit risk didn't disappear; it moved.

This is where the historical comparison matters. The dot-com buildout was funded with equity, so when it failed, shareholders took the losses and credit markets were largely spared outside telecom. Today's borrowers are far stronger businesses, but the financing base is bigger and much more debt-heavy: bonds, leases, SPVs, private credit, GPU-backed ABS. A lower probability of failure can still leave credit markets with more exposure than last time. The practical discipline for investors is to ignore the label and work out the claim: who pays the rent, what secures the notes, whether the sponsor is actually on the hook, and what happens to coverage if AI demand disappoints.

So far the market hasn't done much of that work. The JV deals all yield somewhere between 6% and 8%, and their spreads are more correlated with each other than the rest of the IG and HY market is. In other words, they trade as one block, even though the deal terms differ a lot. That won't last.

Meanwhile the index itself is quietly filling with AI. Our analysis of the US IG non-financial universe puts AI-linked exposure — the hyperscaler platforms, the infrastructure enablers, datacenter project debt and the utilities carrying AI-driven load growth — at roughly 19% of the index today, up from around 3% a decade ago. Platform debt was already visible; the new part of the story is the spread into infrastructure, datacenters and power. On our funding-gap scenario, that share approaches 30% by 2031. Investors who think they can sit the theme out are, increasingly, just holding it unexamined.

Where the dispersion shows up

The first test is refinancing. Most of these structures were underwritten on construction risk — completion guarantees, contingency reserves, insurance against cost overruns. The harder question is what happens when the debt comes due. Only a fraction of the compute expected online by 2027 has been financed in public markets, and there's no obvious channel to refinance the rest: the market many participants assume will take the cash-flowing assets, datacenter ABS, is $44 billion — an order of magnitude too small. Deals with strong lease coverage and sensible amortisation will refinance fine; structures with heavy back-end risk will not, and the current uniform pricing gives no credit for the difference. The terms already vary far more than the yields do: call protection, leverage baskets, the right to layer additional pari passu debt at the project level.

The second is that the supply wave has left tech. Datacenter load is forcing the largest grid-investment cycle on record: US utility capex ran around $104 billion in 2015, reached an estimated $211 billion last year, and the current forecast has it at $277 billion by 2028. Utilities can't fund that from rates alone, so it lands in the bond market — a second structural supply theme in IG, this time from a regulated sector where balance sheets are already stretched. Their shock absorber of choice is the hybrid: US dollar corporate hybrid issuance hit $47 billion in 2025, an all-time high, and utilities were nearly half of it — equity-like capital raised to protect ratings while the capex goes out the door. For credit investors this is where the AI theme gets investable without touching a hyperscaler: regulated assets, real collateral, and a visible reason demand grows.

The third is disruption inside the borrower universe. AI is a threat to plenty of existing credits — most obviously leveraged software, much of it carrying private-equity debt loads into a winner-takes-all fight. That setup is bad for bondholders in a specific way: credit has no upside to pay you for backing winners, so a portfolio of software bonds just accumulates the losers. We'd rather own difficult-to-replace assets that AI makes more valuable — power, transmission, hard-asset suppliers — than software the next model release makes redundant.

Where this leaves credit investors

AI is not one trade. It's a funding cycle that has ended big tech's asset-light era, a migration of that funding into structures where the risk sits with creditors, and a source of dispersion that today's pricing mostly ignores. Supply pressure creates genuine opportunity where balance sheets are resilient. But each claim has to be priced on its own documents, not on the theme. Own the resilient balance sheets, get paid for structural complexity, and stay away from the credits whose business models are the thing AI is disrupting.

This article is for informational purposes only and does not constitute investment advice or an offer of any security. Figures cited from third-party research (Goldman Sachs Global Investment Research, BNP Paribas, CreditSights, Bloomberg, rating agencies) as of June–July 2026 and subject to revision. Forward views are scenarios, not forecasts.