JPMorgan has cautioned that an artificial intelligence label is no longer enough for companies seeking loans, as lenders increasingly demand evidence of financial stability, manageable liabilities, and a clear path toward generating cash flow.

“The AI label alone is no longer enough to reassure lenders. Companies must show that they can finance investment, service their debt and eventually turn spending into cash flow,” Justin Biemann, global investment strategist at J.P. Morgan Private Bank, said in a report published Oct. 23.

Elon Musk raised similar concerns on Sunday, warning that debt financing is becoming harder to secure for AI neocloud companies, which rely heavily on data centers to power their businesses.

The tightening financing environment could also affect cryptocurrency markets. AI has been a major catalyst for risk-taking across financial assets, including Bitcoin. Both Bitcoin and Nvidia reached lows within weeks of ChatGPT’s launch in late 2022 and subsequently recorded substantial rallies. If confidence in AI weakens, the resulting market pressure could spread to digital assets.

Investors are increasingly questioning whether massive AI investments will generate enough revenue and profits to justify their costs. Since Oct. 6, the Nasdaq has fallen 6.7%, declining from 31,360 to 29,263. AI-related stocks, including Nvidia and Oracle, have suffered steeper losses than the broader index.

AI Companies Face Growing Credit Market Pressure

Concerns over AI financing are becoming more pronounced in credit markets, where the cost of insuring against defaults on debt issued by several AI-linked companies has reached record highs.

Credit default swaps (CDS) allow investors to hedge against a borrower failing to meet its debt obligations. Buyers pay an annual premium, measured in basis points, for protection against specified credit events. At a spread of 100 basis points, insuring $10 million in debt costs $100,000 annually. Rising spreads indicate that investors are demanding greater compensation for taking on credit risk.

Morgan Stanley data showed that weekly trading in CDS tied to bonds issued by Oracle, SpaceX, Alphabet, Amazon, Meta, Broadcom, and Nvidia averaged more than $9 billion in September. In July, the weekly average was below $2 billion.

The risks appear especially pronounced for companies that depend on borrowing to finance expensive AI infrastructure projects.

Biemann pointed to an unnamed AI infrastructure company with tens of billions of dollars in debt and other financial obligations. Its five-year CDS spread has climbed to 1,100 basis points, meaning investors would pay more than $1 million annually to insure $10 million of its debt.

Biemann said spreads at that level suggest an implied five-year default probability near or above 50%, compared with less than 30% a year earlier.

JPMorgan Warns Against Misreading Default Signals

Biemann cited Lehman Brothers as an example of how CDS spreads can expose financial vulnerabilities before a collapse. The investment bank’s spreads averaged below 100 basis points in the third quarter of 2007 before rising above 1,400 basis points by mid-September 2008, when Lehman filed for bankruptcy and triggered widespread market turmoil.

However, Biemann emphasized that rising CDS spreads should be viewed as warning indicators rather than a precise timeline for a potential default.

He said activity in CDS linked to major AI companies currently appears more consistent with investors hedging their portfolios than positioning for an imminent financial crisis.

Large AI companies with strong cash flows pay between 50 and 120 basis points for five-year credit protection. Biemann said these levels still resemble the cost of portfolio insurance rather than a clear signal of financial distress.

Some established infrastructure borrowers have seen their spreads approach 300 basis points, while credit protection across the broader investment-grade market remains relatively stable.

The greatest concern lies with companies that must maintain heavy spending while relying on continued access to debt markets and producing limited cash flow. As lenders tighten their standards, AI businesses will increasingly need to demonstrate that their investments can generate sufficient earnings to support their debt obligations.