JPMorgan Says AI Branding Alone No Longer Impresses Lenders

JPMorgan says companies can no longer rely on an artificial intelligence label alone to secure financing, as lenders increasingly scrutinize their business models, financial strength, and ability to repay debt.

“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, wrote in a report published Oct. 23.

Elon Musk raised similar concerns on Sunday, warning that debt financing is becoming harder to obtain for AI neocloud companies, whose businesses depend heavily on data center infrastructure.

The shift could also affect cryptocurrency markets. AI has been a major driver of risk appetite across financial markets, including Bitcoin. Both Bitcoin and Nvidia reached market lows within weeks of ChatGPT’s launch in late 2022 and have recorded substantial gains since then. Any significant disruption in the AI sector could therefore spill over into crypto markets.

Investors are increasingly questioning whether the massive investments in AI will translate into sustainable earnings and cash flow. Since Oct. 6, the tech-heavy Nasdaq has fallen 6.7%, declining from 31,360 to 29,263. AI-related stocks, including Nvidia and Oracle, have dropped more sharply than the broader index.

AI Credit Risks Surface in Debt Markets

Concerns are also mounting in credit markets, where the cost of insuring against defaults on debt issued by several AI-linked companies has reached record levels.

This protection is provided through credit default swaps (CDS), financial contracts that pay out if a borrower defaults or fails to meet its debt obligations. Buyers pay an annual premium, known as a spread, measured in basis points. For example, a spread of 100 basis points would cost $100,000 annually to insure $10 million in debt. Rising spreads indicate that investors are demanding greater compensation for taking on credit risk.

Trading activity reflects the growing demand for protection. Morgan Stanley data showed that weekly CDS trading tied to bonds issued by Oracle, SpaceX, Alphabet, Amazon, Meta, Broadcom, and Nvidia averaged more than $9 billion in September. That was a sharp increase from less than $2 billion per week in July.

The pressure appears most acute among companies that rely heavily on borrowing to finance their operations and expansion.

Biemann described an unnamed AI infrastructure company that has accumulated tens of billions of dollars in debt and other financial obligations. Its five-year CDS spread surged to 1,100 basis points, meaning protection on $10 million of debt would cost more than $1 million annually.

“Pricing at these levels implies near or above a 50% probability of default over five years, up from below 30% a year ago,” Biemann wrote.

JPMorgan Compares CDS Signals With Lehman Brothers

Biemann cited Lehman Brothers as an example of how CDS markets can provide early warnings of financial stress. The investment bank’s CDS spreads averaged below 100 basis points in the third quarter of 2007 before climbing above 1,400 basis points by mid-September 2008, when Lehman filed for bankruptcy and triggered turmoil across global markets.

However, Biemann cautioned against treating current credit-market signals as a precise forecast of an impending collapse. Investors should interpret CDS movements as warning indicators rather than a countdown to default.

He also noted that trading in CDS linked to major AI companies appears more consistent with portfolio hedging than widespread bets on an imminent financial crisis.

Established AI leaders with strong cash generation currently pay between 50 and 120 basis points for five-year credit protection. Biemann said those levels still resemble the cost of portfolio insurance more than an indication of financial distress.

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

The main concern, Biemann said, centers on business models that depend on continued access to debt markets while requiring heavy investment and generating limited cash flow. For AI companies, demonstrating a credible path from capital spending to sustainable earnings may become increasingly important for securing financing.

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