According to data from Goldman Sachs' prime brokerage division, hedge funds are exiting technology stocks at the fastest pace in at least a decade.
Over the five trading days ending October 15, the proportion of technology holdings cut by hedge funds set the largest record since Goldman began tracking the data in 2016; total trading activity also fell at the fastest pace in nearly three months during the same period.
This selling occurred as global technology stocks came under pressure. As the artificial intelligence trade unwound globally, the Nasdaq 100 fell 5.5% last week, its worst performance since April 2025; over the five trading days covered by Goldman's data, the technology sector was also the worst performer in the MSCI World Index.
Goldman data showed that global equities overall saw net selling at a scale reaching two standard deviations of fund flows over the past year. Within that, the information technology sector led declines by a wide margin, with total positioning reductions the largest in two months; semiconductor stocks suffered the biggest losses in dollar terms.
Still, hedge funds continue to hold enormous technology positions. Goldman's report noted that net allocation to the technology sector accounted for 23.5% of its global prime brokerage book, still at the 90th percentile of the past five years.
But fund attitudes have become more cautious, with net leverage 鈥?a measure of how much long positions exceed short positions 鈥?falling to 48.6%, a one-year low.
The Real Trigger: Credit Cracks in the AI Financing Chain, Not Valuations Themselves
The global unwinding of the AI trade has a deeper catalyst: the financing structure of AI infrastructure is exposing credit risk. Taking CoreWeave as a sample: its 2032 bond spread breached 1,000 basis points, its five-year CDS hit a record high of 1,117 basis points, implying roughly a 63% probability of default within five years; its stock plunged 15% in a single day, and its bond price fell below 80 cents on the dollar. Nscale's IPO also met with price pressure, with some investors arguing for a valuation 37% to 47% below the $30 billion target.
This points to a more intractable problem than "valuations are too high": a structural mismatch between the pace of AI infrastructure spending and the pace of revenue. Expensive outlays on GPUs, data centers and power occur first, while revenue must wait for power-up, acceptance testing and actual customer activation; the higher the financing cost, the weaker a project's ability to withstand delays and underutilization.
When the core of market pricing shifts from "how big is compute demand" to "who pays for the high cost of capital," the first assets to be sold are inevitably those with the most fragile balance sheets and the greatest sensitivity to financing conditions 鈥?semiconductors, new cloud service providers and highly leveraged AI names 鈥?rather than mega-cap technology companies with ample cash flow.
The Nature of the Disagreement: How Far Has Deleveraging Gone?
The core disagreement on Wall Street today is not whether long-term AI demand exists, but whether deleveraging has already run most of its course. On the more cautious side, some Goldman teams note that on a one-year view AI positioning appears relatively cleared out; but on a three-year view, global net allocation to AI stocks remains at the 83rd percentile, and the AI portfolio long-short ratio of 3.27 is still at the 64th percentile. JPMorgan's TPM positioning indicator sits in a neutral range around the 46th percentile, far above the 19th percentile low at the end of July, meaning there is still room for "further deleveraging." Trading feedback from Goldman's Asia team shows that the "capitulation" in Taiwan's market this time was less severe than in July, suggesting there may be more selling pressure ahead.
On the more constructive side, Morgan Stanley and Bank of America hold a different view. Morgan Stanley believes the months-long valuation adjustment in most stocks is nearing an end, and after sharp corrections, mega-cap technology valuations are close to historical cyclical lows, while fundamental indicators for core AI infrastructure chips 鈥?prices, orders and earnings trends 鈥?have not deteriorated to a corresponding degree; a buy-the-dip opportunity is taking shape after the pullback. Bank of America's bullish logic is similar 鈥?the market is already discounting future financing pressure in advance, but spot price resilience and inference demand can still support earnings expansion for some time.
The disagreement between the two camps boils down to one judgment: is the credit contraction on the financing side a substantive blow to the long-term AI narrative, or a forced cleanup of excessive leverage, with demand itself not deteriorating?
There is a similar balanced formulation inside Goldman 鈥?its analyst Mark Wilson acknowledges that the "second derivative" of capital expenditure and earnings growth will slow from current levels, but stresses that prices have already reflected this expectation to a considerable extent, and cites the OpenAI CFO's statement that there is no evidence of slowing on the compute demand side.
In short, the current exit is not smart money systematically abandoning technology stocks, but rather a forced compression of positions from "high-leverage AI exposure" to "low-leverage certainty holdings" under tighter financing conditions. The intensity of the selling reflects the fragility of leverage, not a final verdict on the AI narrative.
Therefore, an AI stock rebound requires attention to whether junk-rated AI assets stop falling and whether net leverage stabilizes and rises. If the one-year low of 48.6% continues to decline, it indicates deleveraging is still underway; if subsequent data show consecutive stabilization, it points to a marginal fading of selling pressure.
Whether the divergence between the bond market and the stock market converges is also a signal to watch. In this decline, the 10-year Treasury yield actually fell to a multi-week low even as inflation exceeded expectations; bonds are completing tightening on behalf of the Federal Reserve while also pricing growth risk. If credit spreads in the bond market stop widening and junk-rated AI bond yields peak and turn lower, that will be a more reliable turning signal than a stock price rebound.
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