Tech stocks suffered a selloff this week, and Goldman Sachs' latest warning says the surge in AI debt means pressure is quietly building in credit markets.

In the 2026 US midterm elections, Democrats won across the board in the Senate, House and multiple governor races, with many victorious candidates pledging to restrict data center construction and tighten industry regulation. Markets are now worried that AI infrastructure investment will face policy suppression.

On Thursday (November 5), the S&P 500 plunged 4.4% in a single day, and the Nasdaq 100 plummeted 6.4%, wiping out more than $3 trillion in market value and marking the worst single-day drop for tech stocks since the COVID-19 pandemic.

Yet the stock market shock may only be the prologue 鈥?Goldman Sachs credit strategist Amanda Lynam warned in a latest report that AI debt supply is flooding into the market far faster than anyone expected, and the risk to credit spreads over the coming weeks is "clearly biased toward further widening."

The report said that after the tech stock plunge, pressure in credit markets is quietly building: as of the close on November 4, USD investment-grade credit spreads had narrowed to just 4 basis points from their March high, while high-yield bond spreads were only 11 basis points from the March high 鈥?and that was before Thursday's stock market crash.

In the short week before the election results came out, the five major hyperscalers 鈥?Alphabet, Amazon, Microsoft, Meta and Oracle 鈥?completed five bond issuances totaling $67 billion, pushing their cumulative issuance for the year to $296 billion.

That figure has already exceeded the upper end of Goldman Sachs' full-year 2026 forecast 鈥?$25 billion. In other words, Goldman's forecast model is being shattered by reality every 48 hours.

$67 Billion in a Week, Full-Year Forecast Obsolete Ahead of Schedule

The speed at which Goldman's forecasts have failed is staggering. According to a report published by Amanda Lynam on Tuesday, since October 29, the five major hyperscalers had completed three bond deals totaling $41.7 billion, bringing cumulative issuance for the year to $271 billion. Just two days later, two more deals landed, adding another $25 billion 鈥?meaning Goldman was "slapped in the face" once every 48 hours.

The size of individual deals is equally stunning. Microsoft completed its first USD bond issuance since 2017, raising more than $25 billion, instantly overturning Goldman's earlier judgment that "Microsoft would not need to borrow at least this quarter" 鈥?a call that became obsolete in less than a day. Oracle, just five days after announcing its financing plan, exhausted its original four-year financing "ceiling" in a single morning.

Goldman offered two explanations for this above-expectation phenomenon: first, the five major hyperscalers collectively posted negative free cash flow in the third quarter of 2026, with capital expenditures exceeding operating cash inflows; second, some issuances were "early locks" on financing plans originally scheduled for 2027. In Goldman's words, "borrowers know full well that the issuance window may not stay open forever."

Bond issuance for the year already equals 36% of hyperscalers' 2026 capital expenditures, above Goldman's initial assumption of a 33% debt financing ratio. Looking ahead to 2027, Goldman expects direct hyperscaler bond issuance to reach $440 billion, plus about $300 billion in investment-grade and high-yield project financing, bringing the total AI-related debt supply pipeline to nearly $740 billion 鈥?and that does not include any emerging cloud computing companies, chipmakers or utilities.

Spread Pressure Builds, Credit Market "Saturation" Alarm Sounds

Goldman made clear that from now until early December, the risk to credit spreads is "clearly biased toward further widening." In the USD market, Goldman expects spreads to slightly exceed their March highs (93 basis points for investment grade, 335 basis points for high yield); in the euro market, it views April 2025 levels as the reference for a near-term downside scenario (128 basis points for investment grade, 446 basis points for high yield).

Goldman's year-end targets imply spreads slightly narrower than current levels, but Lynam admitted in the report that this mainly reflects the seasonal tightening pattern when supply dries up in December, "and supply technicals are especially critical this year."

In terms of excess returns, USD investment-grade bonds have returned just 0.1% relative to Treasuries this year, high-yield bonds 0.0%, and euro high-yield bonds even -1.0%. After ten months of record supply, credit investors have gotten almost nothing. This may also explain why Goldman recently quietly abandoned its preference for investment-grade bonds over high-yield bonds, shifting instead to favoring BBB-rated relative to A-rated and AA-rated allocations.

Pricing pressure on large deals also cannot be ignored. So far this year, the USD investment-grade market has seen 27 deals sized at $10 billion or more, totaling $501 billion 鈥?about 2.5 times the size of similar deals last year. Of these, 14 deals, accounting for about 66% of the total (roughly $330 billion), are AI-related. The new issue concession on AI-related deals has averaged about 18 basis points in November to date, far above the less than 7 basis points for non-AI deals, and significantly above the roughly 8 basis point average for AI deals since early 2025.

In other words, the world's highest-rated borrowers are paying premiums close to junk bond levels to complete issuance, and every basis point of premium pushes the secondary market curve wider.

Election Shock: Data Center Moratoriums and Private Credit's Last Line of Defense

On the policy front, Goldman economists believe the election results are unlikely to change the data center and AI regulatory framework at the federal level 鈥?relevant legislation would still need bipartisan Senate support and must survive a veto override. Near-term risks mainly come from the local level: Democratic victories in state and local elections could trigger data center construction moratoriums in the short term, potentially affecting about 45% of planned US data center capacity, with Texas alone accounting for 25% of that.

Goldman economists expect these moratoriums to be short-lived, and that the issues will likely fade over the long term. But credit markets do not intend to wait for the outcome. Lynam said the high-yield primary market may be in a "price discovery phase" recently, and the market will pay close attention to the structural design of project financing deals. "Recent price action has fully demonstrated the importance of offtaker quality."

When the syndicated loan market becomes congested, Goldman views private capital as a backup exit. The private market currently manages $17 trillion in assets (excluding secondaries and funds of funds), has $4.6 trillion in dry powder, and has completed more than $225 billion in data center financing since early 2025 (excluding GPU and chip collateralized deals). But Goldman also acknowledges that private credit chose a rather delicate time to become AI financing's "lender of last resort" in a year when its own default rate just hit a record high.

The Race Between the Monetization Clock and the Saturation Clock

Goldman's analytical framework ultimately comes down to a race between two variables: faster AI monetization means less debt; delayed monetization and growing procurement commitments mean more debt 鈥?depending on market conditions." The former is at best a 2028 story, while the latter advances every 48 hours: $296 billion in hyperscaler bonds have already landed, total global AI-related debt stands at $687 billion, and next year another $440 billion in direct hyperscaler issuance plus $300 billion in project financing are waiting, with trillions more in off-balance-sheet commitments not yet included in debt statistics.

Goldman's base case is that the usual December supply lull will provide breathing room for its year-end spread targets. But as Lynam put it, from now until early December, the risk balance has clearly tilted toward wider spreads. Credit markets were once the last asset class in the AI trade still giving the benefit of the doubt; as excess returns go to zero and issuers publicly race to lock in financing windows, that benefit of the doubt is rapidly being exhausted.

Off-Balance-Sheet Liabilities: The Trillion-Dollar Hidden Worry Beneath the Iceberg

What bond issuance data reveals is only the tip of the iceberg. In Tuesday's report, Lynam for the first time systematically quantified hyperscalers' off-balance-sheet obligations: $1.7 trillion in operating and financing lease commitments, of which more than 75% relate to leases that have not yet begun, and under lease accounting standards these obligations are not yet reflected on either party's balance sheet; $1.9 trillion in other unconditional purchase commitments (covering third-party cloud capacity, servers, networking, power contracts and construction projects), of which $685 billion will mature before the end of 2027, and excluding contingent "backstop" obligations.

Goldman acknowledges that some of these commitments are already included in consensus capital expenditure expectations, but still views them as additional cash usage 鈥?every commitment that cannot be covered by operating cash flow will ultimately convert into bonds, loans or equity financing. In Goldman's words: "Delayed monetization and growing procurement commitments ... are key risks driving more debt issuance."

The world's top rating agencies have begun to sound the alarm. Moody's noted on September 22 that hyperscalers' overall financial commitments have grown eightfold since 2023, reaching $2.8 trillion. S&P warned on October 15 that the AI financing ecosystem's "margin for error is narrowing," and that contingent guarantees and new off-balance-sheet financing tools could expand further and pressure ratings. In late October, S&P downgraded the rating outlooks for Broadcom and Meta, both citing off-balance-sheet obligations.

Goldman's conclusion is: "Evidence of monetization before 2028 is critical," otherwise ratings will face downward pressure. However, Goldman also noted that AI-related companies are generally in the higher tiers of investment-grade ratings, and even with multiple-notch downgrades, they are unlikely to be cut off from market financing channels, likening this to the slow downward migration of pharmaceutical industry ratings a decade ago.

It is worth noting that Meta explicitly stated in its SEC filing that the company "recognizes that timing is critical and needs to lock in capital before competitors exhaust various financing channels." When issuers themselves are publicly racing to tap the bond market's balance sheet, that balance sheet itself becomes a scarce resource.

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