Stocks across the world are selling off again on Thursday in an already brutal week for AI stocks, which has been marked by investor jitters over the sustainability of AI spending, the prospect of capital constraints and intensifying competition from China.
A hotter-than-expected US inflation report on Wednesday added to the pressure, reviving fears of further interest rate hikes even as Treasury yields remained elevated at multiyear highs.
With inflation rising faster than expected in September, investors are bracing for the possibility of additional rate increases.
A high-rate environment could create difficult conditions for the capital-intensive AI boom, which has become increasingly dependent on borrowed money.
South Korea’s Kospi closed 7.5% lower, while Japan’s Nikkei 225 fell about 5%. The S&P 500 was down 0.4%, while the Nasdaq Composite dropped more than 1%.
Among the most closely watched chip stocks, Nvidia, Intel, Advanced Micro Devices and Micron fell around 2.5%, 4.6%, 4.3% and 4.4%, respectively, in trading on Thursday, struggling to recover after three bruising sessions.
The iShares Semiconductor ETF has fallen about 11% over the past five trading sessions.
Twenty of the world’s most valuable AI and semiconductor stocks have shed more than $1 trillion in market value so far this week, CNBC said.
The declines reflect growing investor concern that higher borrowing costs could undermine the enormous capital spending required to build AI infrastructure, putting pressure on technology companies that have benefited from the investment boom.
The central question facing investors, then, is whether higher interest rates will eventually slow the flow of capital into AI infrastructure, even if demand for computing power remains strong?
AI executives warn that access to capital could become a constraint
Earlier this week, the chief executives of Intel and Broadcom warned that access to capital could emerge as a major constraint on the AI boom, even as demand for computing infrastructure continues to outstrip supply.
″What I think might be the biggest constraint is capital,” Hock Tan said at the DBS Asia Future Forward Conference in Singapore on Monday.
Intel chief executive Lip-Bu Tan echoed those concerns, pointing to the rising cost of capital and questions about whether companies investing heavily in AI will eventually generate sufficient returns.
“If there’s any challenge in the front, it’s capital and the cost of capital, and then the other part is in the longer run, people want to see the ROI,” he said.
IMF warns of economic risks from an AI investment slowdown
The implications of a significant slowdown in AI investment could extend beyond the technology sector.
The International Monetary Fund forecast global economic growth of 3.1% for 2026 earlier this week, a slight increase from its July forecast of 3.0%, as still-strong AI infrastructure investment helped offset higher oil prices caused by the war in the Middle East.
However, the IMF warned that a steep decline in AI investment could trigger a sharp fall in asset prices and ultimately cut global GDP by 1.1% by 2028, including a 1.8% decline in US output.
“We now have an economy that is standing on one leg, the AI leg,” IMF Managing Director Kristalina Georgieva told a Bretton Woods Committee event.
“What if it wobbles? What if there is disappointment and that leads to potentially a more difficult environment?”
The IMF highlighted similar risks in its Global Financial Stability Report, warning policymakers against “complacency” in guarding against a potential pullback in AI investment and stock valuations.
“If there are questions about the monetization of AI, the equity investments will deplete in value, the debt may be difficult to service, and the revenue commitments in the future may not materialize,” Jason Wu, assistant director of the IMF’s Global Markets Analysis Division, told a press conference at the Bangkok meetings.
A downturn could create a chain reaction across the industry.
Falling valuations could make it harder for companies to raise equity, while weaker cash flows could undermine their ability to service debt.
If infrastructure providers then reduce investment, the consequences could spread to chipmakers, equipment suppliers and other businesses that depend on continued expansion.
Neocloud companies face pressure as borrowing costs rise
The pressure is already visible among neocloud providers, or companies that offer AI infrastructure as a service.
CoreWeave shares fell 15% on Wednesday, Nebius dropped 10%, and IREN closed down 12%.
These companies rely on debt funding to finance expansion and purchase AI servers, leaving their business models particularly vulnerable to rising interest rates.
Yields on CoreWeave’s bonds approached 15% on Wednesday, highlighting the growing cost of borrowing and drawing a “yikes” from Tesla and SpaceX chief executive Elon Musk on X.
“Investors are separating AI companies with deep cash flows from those that need debt to build,” said Kenneth Goh, UOB Kay Hian’s director of private wealth management in a Reuters report.
“Private credit is where it could spread next,” Goh said.
“Listed lenders to tech and venture companies already trade at steep discounts to their asset values, and more cautious lenders can slow the AI build-out well before any defaults appear.”
Anthropic and OpenAI’s revenue figures raise fears of slowing growth
Anthropic’s revenue data was also seen by a section of investors as evidence of AI growth starting to slow already, in what may eventually hit spending down the line.
The artificial intelligence company behind Claude reportedly generated $16.8 billion in revenue in the quarter ended September 30, up 50% from the previous quarter, according to a Financial Times report citing people familiar with the matter.
While the growth remains substantial, it represented a slowdown from the roughly 140% increase between the first and second quarters.
The deceleration matters because investors have priced in exceptionally high expectations for the AI industry.
Strong revenue growth may not be enough to sustain valuations if it falls short of what the market anticipates.
Anthropic’s reported growth deceleration comes as the company plans to spend over $500 billion on chips and infrastructure over the next decade, according to a Reuters story from last month.
Anthropic’s case is reminiscent of how investors reacted earlier this month to OpenAI’s revenue figures too.
Earlier this month, several AI-related stocks, including Oracle, CoreWeave and Nvidia, sold off after reports that OpenAI had told investors it reached approximately $50 billion in annualised revenue at the end of September, below the $68 billion figure widely reported late last month.
The developments have added to concerns about whether AI companies can translate rapid adoption into sustainable profits and returns on investment.
Higher interest rates could make this challenge more difficult by increasing the minimum return required to justify new projects.
Why has AI spending remained insensitive to higher costs so far?
In a recent paper, Stijn Van Nieuwerburgh, an economist at Columbia University, estimated that spending on AI chips, data centres and the electrical systems needed to power them would exceed $10 trillion between 2025 and 2032.
That represents more than 3.6% of total US economic output each year.
A growing share of this investment is being financed through borrowing, which should, in theory, make spending more sensitive to interest rates.
So far, however, there is little sign that higher borrowing costs have done much to slow the boom.
The AI investment cycle has so far proved resilient despite elevated borrowing costs, rising electricity prices and increasingly expensive computing components.
The scale of expected demand and the strategic importance of AI have encouraged companies to keep spending.
The impact depends on whether the expected returns from AI investments are sufficient to justify the cost of financing them.
“If it’s a real transformational technology that potentially is going to define the tech space for years to come; if you have the balance sheet capacity to borrow to make these investments; and if you think the investments are going to be profitable and generate double-digit returns on invested capital, then it doesn’t actually make a huge difference if you’re borrowing at 5.5 or 6 percent,” said Andrew Sheets, global head of fixed income research at Morgan Stanley in a NYT report.
“That 50 basis points is not the limiting factor in whether or not the math works.”
Banks say AI spend continues but “skepticism” there
Executives at major financial institutions have also indicated that investment in AI infrastructure remains strong.
Citigroup chief executive Jane Fraser said earlier this week that she did not believe higher interest rates would slow investment in AI infrastructure.
“We’re not seeing a slowdown in investment in the broader AI ecosystem. There’s simply not enough compute,” she said, referring to the processing power required to develop and run AI models.
JPMorgan Chase chief financial officer Jeremy Barnum similarly said the momentum behind large AI projects remained intact.
“The capex is still getting spent,” Barnum told a media conference call. “The deals still need to get done. People still want access to the market.”
However, Michael Roberts, head of corporate and institutional banking at HSBC, said the speed at which AI companies are raising capital has prompted banks and investors to become more selective and demand higher returns to finance the technology rollout.
“I think there’s a healthy dose of skepticism,” Roberts said.
Higher rates may not stop the boom, but financing is under scrutiny
Despite a lack of consensus on the subject, the fact remains that the financing model behind the boom is facing greater scrutiny as investors demand clearer evidence of future returns and lenders become more selective.
“There is insufficient evidence of a systemic AI credit crisis. But the financing model behind the infrastructure boom is facing greater scrutiny,” Charu Chanana, chief investment strategist at Saxo, said.
According to Saxo, large technology companies generally have stronger balance sheets, more diversified revenue streams, and a greater ability to fund investments internally.
Some smaller infrastructure providers also have substantial cash reserves and committed financing.
However, the AI ecosystem is becoming increasingly interconnected, with infrastructure providers relying on access to financing and contracts from major customers, while equipment suppliers depend on continued investment in capacity by those same customers.
“Investors should increasingly distinguish between strong demand, sustainable financing and attractive valuations. They are not necessarily the same thing,” she said.
How should investors position themselves?
Chanana said the concentration of major US and global equity indices in a relatively small group of technology companies means weakness in AI-related stocks can weigh on broader markets, even when earnings elsewhere remain resilient.
There are three potential channels through which an AI stock correction could spill over into the wider market.
First, declines in technology heavyweights can have an outsized impact on the S&P 500 and global equity benchmarks because of their significant index weightings.
Second, investors facing losses in crowded technology positions may reduce exposure to other assets and sectors, creating selling pressure even in companies with sound fundamentals.
Third, if higher financing costs eventually slow investment in AI infrastructure, the effects could spread to semiconductor suppliers, industrial equipment manufacturers, power infrastructure, and construction.
However, Chanana’s assessment suggests that a correction in AI stocks does not necessarily have to trigger a broader equity bear market.
If corporate earnings remain resilient and economic growth holds up, investors could rotate towards other sectors, including healthcare, selected financials, industrials, and businesses with more reasonable valuations.
“The key distinction is between contagion and rotation. A market where AI stocks correct while other sectors gain leadership may ultimately be healthier than one where the entire market remains dependent on a handful of companies,” she said.
“What investors should watch is whether market breadth improves as technology weakens, or whether selling pressure begins spreading across sectors,” she said.
Dan Ives, technology analyst at Yorkville Ives, said the latest pullback presented a buying opportunity, arguing that the AI infrastructure boom remained in its early stages despite mounting concerns over funding costs.
“What would change our view is a demand signal, not a rate signal,” he said, pointing to potential cuts in hyperscaler capital expenditure plans, cancelled contracts or falling GPU rental prices as warning signs.
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