Tech stocks suffered a selloff this week, and Goldman Sachs has issued a fresh warning that the surge in AI-related debt is quietly building pressure in credit markets.

In the 2026 US congressional midterm elections, Democrats swept the House, Senate, and multiple governorships, with many winning candidates pledging to restrict data center construction and tighten industry regulation, fueling market concerns that AI infrastructure investment will face policy suppression. On Thursday (November 5), the S&P 500 plunged 4.4% in a single day, the Nasdaq 100 plummeted 6.4%, and more than $3 trillion in market value was wiped out, marking the worst single-day drop for tech stocks since the COVID-19 pandemic.

However, the stock market turmoil may only be a prelude — Goldman Sachs credit strategist Amanda Lynam warned in a latest report that AI debt supply is flooding into the market at a pace far exceeding anyone's expectations, and the risk for credit spreads in the coming weeks is "clearly skewed toward further widening."

The report stated 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, and high-yield bond spreads were only 11 basis points from their March high — and this was data before Thursday's stock market crash.

In the short week before the election results were announced, the five major hyperscalers — Alphabet, Amazon, Microsoft, Meta, and Oracle — completed five bond issuances totaling $67 billion, pushing their cumulative bond issuance for the year to $296 billion. This figure has already surpassed the upper limit of Goldman Sachs' full-year forecast for 2026 — $25 billion. In other words, Goldman Sachs' forecast model is being shattered by reality every 48 hours.

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

The speed at which Goldman Sachs' forecasts have failed is staggering. According to a report published by Amanda Lynam on Tuesday, as of 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 — equivalent to Goldman Sachs being "slapped in the face" once every 48 hours.

The sheer size of individual deals is equally stunning to the market. Microsoft completed its first USD bond issuance since 2017, raising over $25 billion, single-handedly overturning Goldman Sachs' earlier judgment that "Microsoft would not need to borrow at least this quarter" — a judgment that became obsolete in less than a day. Oracle, just five days after announcing its financing plan, exhausted its originally planned four-year financing "ceiling" in a single morning.

Goldman Sachs offered two explanations for this better-than-expected phenomenon: First, the five major hyperscalers collectively experienced negative free cash flow in Q3 2026, with capital expenditures exceeding operating cash inflows; second, some issuances were "early lock-ins" of financing plans originally scheduled for 2027. In Goldman Sachs' words, "borrowers know full well that the issuance window may not remain open forever."

Cumulative bond issuance for the year now equals 36% of hyperscalers' 2026 capital expenditures, higher than Goldman Sachs' initial assumption of a 33% debt financing ratio. Looking ahead to 2027, Goldman Sachs expects hyperscalers' direct bond issuance to reach $440 billion, plus approximately $300 billion in investment-grade and high-yield project financing, bringing the total AI-related debt supply pipeline to nearly $740 billion — and this does not include any emerging cloud providers, chip manufacturers, or utility companies.

Spread Pressure Mounts, Credit Market "Saturation" Alarm Sounds

Goldman Sachs explicitly stated that between now and early December, credit spread risk is "clearly skewed toward further widening." In the USD market, Goldman Sachs 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 near-term downside scenarios (128 basis points for investment grade, 446 basis points for high yield).

Goldman Sachs' year-end targets imply slightly tighter spreads from current levels, but Lynam acknowledged in the report that this mainly reflects the seasonal tightening pattern when supply dries up in December, "and supply technicals are particularly critical this year."

In terms of excess returns, USD investment-grade bonds have delivered only 0.1% excess return 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 gained almost nothing. This may also explain why Goldman Sachs 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, 27 deals of $10 billion or more have appeared in the USD investment-grade market, totaling $501 billion, roughly 2.5 times the size of similar deals last year. Of these, 14 deals — accounting for approximately 66% of total volume (about $330 billion) — are AI-related. The new issue concession for AI-related deals has averaged about 18 basis points in November to date, far higher than the less than 7 basis points for non-AI deals, and significantly above the approximately 8 basis point average for AI deals since early 2025. In other words, the world's highest-rated borrowers are paying premiums approaching junk-bond levels to complete issuance, and each premium further widens the secondary market curve.

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

On the policy front, Goldman Sachs economists believe the election results are unlikely to change the data center and AI regulatory framework at the federal level — related legislation still requires 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 approximately 45% of US planned data center capacity, with Texas alone accounting for 25% of that. Goldman Sachs economists expect these moratoriums to be short-lived, and in the long run, related issues are expected to fade.

However, credit markets are not planning to wait for the outcome. Lynam said the high-yield primary market may currently be in a "price discovery phase," with the market paying close attention to the structural design of project financing deals, noting that "recent price action has fully demonstrated the importance of offtaker quality."

When the syndicated loan market becomes congested, Goldman Sachs views private capital as a backup exit. Private markets currently manage $17 trillion in assets (excluding secondary markets and fund-of-funds), with $4.6 trillion in dry powder, and have completed over $225 billion in data center financing since early 2025 (excluding GPU and chip collateralized transactions). But Goldman Sachs also acknowledges that private credit choosing to become AI financing's "lender of last resort" in a year when its own default rate has just hit a record high is a rather delicate timing.

The Race Between the Monetization Clock and the Saturation Clock

Goldman Sachs' analytical framework ultimately boils down to a race between two variables: accelerated AI monetization means less debt; delayed monetization and growing procurement commitments mean more debt — "depending on market conditions."

The former is at best a 2028 story, while the latter is advancing 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 hyperscaler direct issuance plus $300 billion in project financing is waiting, with trillions more in off-balance-sheet commitments not yet entering debt statistics.

Goldman Sachs' base case is that the customary December supply lull will provide breathing room for its year-end spread targets. But as Lynam put it, between now and early December, the risk balance has clearly tipped toward spread widening. Credit markets were once the last asset class in the AI trade still granting the "benefit of the doubt"; with excess returns at zero and issuers openly racing to lock in financing windows, that "benefit of the doubt" is rapidly being exhausted.

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

What bond issuance data reveals is merely the tip of the iceberg. In her Tuesday report, Lynam for the first time systematically quantified hyperscalers' off-balance-sheet obligations:

1. $1.7 trillion in operating and finance lease commitments, of which more than 75% involve leases that have not yet commenced and, under lease accounting standards, are currently not reflected on either party's balance sheet;

2. $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 come due before the end of 2027, and excluding contingent "backstop" obligations.

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

Top global rating agencies have begun issuing warnings. Moody's noted on September 22 that hyperscalers' total 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 may further expand and pressure ratings. In late October, S&P downgraded the rating outlooks for Broadcom and Meta, both citing off-balance-sheet obligations.

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

Notably, Meta explicitly stated in its SEC filing that the company "recognizes that timing is critical and needs to lock in capital before competitors exhaust all types of financing channels." When issuers themselves are publicly racing to front-run the bond market's balance sheet, that balance sheet itself becomes a scarce resource.