The sell-off in the Nasdaq 100 is no longer a simple verdict on artificial intelligence. It is becoming a test of how the buildout gets paid for. That is the read from Saxo, where chief strategist Charu Chanana argues investors are increasingly demanding profitability and capital discipline rather than rewarding growth promises at any price.
The numbers bear her out. The index has shed 5.5% over the past seven sessions — its weakest week since April 2025, according to Bloomberg — and closed Friday down 2.2% as the reassessment of AI risk ran into the weekend. What matters for investors is the distinction now emerging inside a single theme: solid operating results and rising credit risk can coexist under the same AI umbrella.
Suppliers are delivering; borrowers are sweating
Saxo points to recent strength at TSMC, Samsung and ASML as evidence that demand for chips and equipment remains intact. Bloomberg reported today that there are still few visible signs of weaker semiconductor orders, which means the recent selling cannot simply be read as a demand collapse that has already happened.
The pressure sits elsewhere — on companies carrying heavy debt. CoreWeave, Nebius and Oracle are all under scrutiny for their leverage, and Saxo singles out CoreWeave as a warning signal for how the AI buildout is financed. Funding costs have climbed sharply, and some bonds issued this year have hit record lows in secondary trading.
Jefferies puts hard numbers on the strain through the cost of insuring against default. Credit default swap premiums on CoreWeave have jumped from 749 to 1,118 basis points since September 11.
The problem extends beyond any single borrower. Rising financing costs could weigh on AI investment itself and, by extension, on the earnings of Asian suppliers, Bloomberg reports. Delayed infrastructure projects would then feed straight back into supply-chain expectations — a second dimension to the tech sell-off that has little to do with chip demand.
Valuation leaves little room for error
Jefferies calculates that global AI stocks trade at 23 times expected earnings, against 13 times for equities outside the sector. The broker also expects earnings growth at AI companies to slow markedly in the years ahead.
That combination is why Jefferies strategist Chris Wood recommends diversifying away from AI. He is not abandoning the theme outright: Wood is keeping exposure to Asian semiconductor names while avoiding companies with large AI investment programs. His positioning reflects a more selective approach rather than a full retreat.
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Gartner’s trillion-dollar question
How demanding the arithmetic could get is laid out in a Gartner forecast. To cover investment and financing costs plus a 12% return on capital, AI data centers would need to generate a cumulative $8.5 trillion in revenue by 2030.
Gartner expects at most $3.6 trillion in addressable AI revenue from businesses and consumers by that date. On the firm’s estimates, more than $3.5 trillion of infrastructure investment could be at risk of impairment.
Those are projections and risk scenarios, not losses already booked. They do, however, explain why investors are now watching financing costs and future utilization rates alongside growth rates.
Earnings season becomes a funding exam
The next concrete test arrives with quarterly reports. Netflix reports after the close on October 20, with Tesla and Meta following after the U.S. close on October 21.
Meta’s capital-spending outlook is seen as a key referendum on AI stocks. For the Nasdaq 100, it is not enough for companies to post higher profits — credible answers on how they will fund large investments and convert them into future earnings matter just as much. That is where the line currently runs between AI growth and a business model that can sustain it.
For any relief rally, a fresh promise of strong growth will not suffice. What counts now is demonstrable returns on the money being spent — and a financing structure that holds up.
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