Goldman Sachs is warning that the U.S. equity rally could face its largest net supply shock since the dot-com era in 2027, as a potential wave of large initial public offerings collides with slower corporate buybacks, softer overseas demand and heavy capital spending on artificial intelligence. The bank's hedge fund business chief, Pasquariello, said the market may yet stage another advance, but argued that several sources of support for stocks are weakening simultaneously.
The warning puts fresh pressure on risk assets that have traded closely with technology shares, including major cryptocurrencies. A retreat in AI-linked equities would not automatically dictate digital-asset prices, but it could reduce the liquidity and risk appetite that have supported speculative markets during the technology-led rally.
Goldman's outlook is built around a supply-and-demand imbalance rather than a single prediction of an immediate market collapse. Corporate earnings remain strong and share repurchases have recently recovered, according to the bank. Yet Goldman expects the balance to shift as companies raise money publicly and direct more cash toward data centers, chips and other AI infrastructure rather than buying back stock.
Goldman sees 2027 as a potential supply inflection point
Pasquariello wrote that net U.S. equity supply could reach its highest level since 2000 in 2027. The estimate reflects expectations for major IPO candidates, including SpaceX and Anthropic, as well as a change in corporate cash allocation.
A stock market can absorb new shares when demand from funds, companies and overseas buyers remains strong. Goldman's concern is that several of those buyers may become less reliable just as more equity becomes available. Softer buybacks, reduced foreign purchases and systematic selling by commodity trading advisers, or CTAs, could leave public markets more exposed to large new listings.
The bank noted that corporate repurchases had rebounded to roughly $5 billion per day, providing near-term demand. That pace may cushion periods of market weakness, though it does not remove the longer-term issue created if companies devote a growing share of cash flow to AI-related capital expenditures.
Large technology groups have spent aggressively on computing capacity to train and run AI models. Goldman cited consensus expectations for Big Tech capital-expenditure growth to slow from 96% this year to 44% next year. Hyperscale cloud providers also recorded net free-cash-flow consumption of $31 billion in the third quarter, according to the bank's analysis.
Those figures place greater attention on whether AI revenue can grow quickly enough to justify the infrastructure buildout. If companies lower spending plans, semiconductor and cloud-related shares could face another valuation reset. If spending remains high without matching cash generation, funding pressure could move from equity valuations into credit markets.
Hedge funds have already cut AI exposure
Goldman's prime brokerage data showed hedge funds selling global AI stocks at their fastest pace in more than five years over the previous 20 trading days. The selling ran more than three standard deviations from the historical average, an indication that positioning had shifted sharply rather than gradually.
The Nasdaq 100 experienced an intraday one-day drop of 6.36%, its largest since the Covid-19 period, while hedge funds gave back nearly all of their gains for the year, according to the material. Goldman strategist Privorotsky framed the market debate in a note titled "1998 or 2000?", comparing present conditions with the late-stage dot-com boom.
The comparison captures two very different potential paths. In 1998, technology stocks endured sharp volatility before extending their gains. In 2000, valuations, new supply and deteriorating confidence converged into a prolonged downturn. Goldman's research does not present the current market as a replay of either episode, but it places the same variables earnings durability, funding conditions, valuations and equity supply at the center of the debate.
Earnings have so far offered a substantial offset. The S&P 500 posted 33% profit growth in the second quarter, while third-quarter growth was forecast at 35%, Goldman said. Pasquariello described that as the fastest growth rate in three decades outside post-recession recoveries.
Goldman still projects approximately 18% S&P 500 earnings-per-share growth next year. The bank's more cautious view rests on the likelihood that the current pace of profit expansion slows and that AI capital expenditure becomes more sensitive to funding costs and returns on investment.
Valuations have fallen, but credit conditions remain a concern
Some of the excess valuation associated with the AI trade has already been removed. Goldman said the Nasdaq 100's forward price-to-earnings ratio had declined to 18.4 times, its lowest level since 2020 and about four turns below the level seen before ChatGPT's November 2022 launch. The bank cited comparable pressure in the Philadelphia Semiconductor Index.
Lower valuation multiples can make stocks less vulnerable to disappointment, but they do not solve a funding problem. Pasquariello wrote that credit markets had begun reacting sooner to the costs associated with the AI buildout. December interest-rate concerns had resurfaced, and AI-related high-yield debt had come under pressure, according to the note.
Two experienced market participants told Pasquariello that equities appeared "too complacent" relative to credit. That gap matters because companies financing heavy infrastructure spending may eventually need debt markets, equity markets or both to remain receptive. A sustained rise in borrowing costs could force cuts to capital-expenditure plans and reshape the growth assumptions embedded in AI-linked assets.
Crypto markets remain exposed to technology-led risk moves
For cryptocurrency traders, the immediate lesson is less about a mechanical link between stocks and tokens than about the market environment that has favored both. Highly liquid cryptocurrencies often respond to changes in leverage, volatility expectations and appetite for growth-oriented assets. A rapid de-risking in technology shares can therefore spill into digital assets through fund positioning and derivatives markets.
Claims that crypto reliably acts as a shelter during a technology selloff are difficult to reconcile with periods when both markets are being repriced by the same forces higher funding costs, falling leverage and weaker demand for high-beta exposure. Bitcoin and other major tokens have at times diverged sharply from equities, but correlation can rise during broad liquidity shocks.
Goldman's 2027 supply forecast does not provide a timetable for cryptocurrency weakness, nor does it establish that a stock-market correction is imminent. It does, though, identify a potential transition from a market supported by unusually rapid earnings growth and buybacks toward one facing more public equity issuance and heavier demands on corporate cash.
That transition would make credit spreads, corporate spending plans, buyback announcements and the IPO calendar more relevant beyond traditional equity markets. For crypto traders, those signals may offer a clearer read on risk appetite than narratives tied solely to the next stage of the AI cycle.
For more on AI’s impact on markets, explore our guide to AI copy trading and data-driven investing strategies.
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