AI Boom Faces Rising Credit Risk

The warning lands in a market where credit conditions are already tighter than the near-zero-rate era that helped fuel previous tech investment cycles. The U.S. 10-year Treasury yield is around 4.7%, while the federal funds rate sits near 3.6%, keeping financing costs materially above the levels that supported years of cheap leverage. High-yield credit spreads, at roughly 2.7 percentage points, suggest investors are still demanding a premium for risk even as overall spread levels remain contained. For AI-heavy corporates, that is a reminder that large-scale infrastructure spending now carries a real funding cost.

Microsoft and Meta illustrate the tension. Microsoft’s latest filings showed cash used in investing rose sharply, driven by additions to property and equipment as it scales AI infrastructure. The stock has also shown how sensitive investors are to capital intensity: after a strong run, Microsoft recently slipped back below its 200-day moving average, and its RSI has cooled into neutral territory, signaling the market is reassessing the durability of that capex cycle. Meta has disclosed heavy investment in AI initiatives and warned that those bets may not succeed, even as its shares have held up better technically than some peers. Alphabet, meanwhile, saw a sharp drop that left the stock near the lower end of recent trading ranges, underscoring how quickly investors can punish names when AI spending collides with concerns about returns.

The economic significance is broader than the technology sector. If the AI buildout increasingly depends on debt, the industry’s expansion will feed through to bond supply, bank lending and capital allocation across the economy. That can support equipment makers, data-center operators, utilities and construction firms in the near term, but it also raises the risk that some of today’s spending is being funded ahead of revenue visibility. In other words, the market is being asked to underwrite a future productivity boom before the cash flows are proven.

That is where the bull and bear cases diverge. Bulls argue hyperscalers remain among the most cash-generative companies in the world and can absorb borrowing without jeopardizing credit quality. They also say AI spending is still in an early-cycle phase, with demand for compute, cloud and enterprise software likely to justify the buildout over time. Bears counter that the faster the industry turns to debt, the more the AI narrative starts to resemble a capital-intensive utility project with uncertain payback, not a software-style compounder with high margins and low fixed costs.

For investors, the key issue is not simply whether AI demand is real, but whether the financing structure behind it stays sustainable. A prolonged period of elevated rates would squeeze returns on marginal projects and could force a more disciplined phase of spending, even at the biggest platforms. That would likely be positive for credit investors watching underwriting standards, but it could pressure equity valuations if AI growth slows before profits catch up.

The next catalyst is whether hyperscalers keep funding AI through operating cash or lean more aggressively on debt markets. If leverage keeps climbing, credit spreads, rating agency commentary and capex guidance will matter as much as user growth and model launches. The AI boom is still intact, but Jefferies’ warning suggests the market is moving into a phase where balance-sheet risk may matter as much as technological ambition.