The most significant concern surrounding the current AI boom may not be valuation, but rather the method of financing.

On June 30th, Tobias Adrian, Director of the Monetary and Capital Markets Department at the International Monetary Fund (IMF), stated that current AI-related stock valuations may not have formed a bubble. The real issue that financial regulators should be wary of is that global tech giants are making massive investments in rapidly evolving AI infrastructure by increasingly relying on medium- to long-term debt financing. This mismatch between asset and liability durations is a potential future source of financial stability risk.

Speaking at the European Central Bank's annual forum in Sintra, Portugal, Adrian said that as long as AI business profits continue to grow, this model can be sustained. However, if future AI profitability falls short of market expectations, corporate debt-servicing capacity could come under rapid pressure, and that is the real issue to watch.

This statement addresses recent market discussions about an "AI bubble." Previously, the Bank for International Settlements (BIS) had listed AI as one of the four major risks threatening global financial stability, and ECB Executive Board member Isabel Schnabel also raised the question at the start of the forum of whether the market is discussing a financially driven AI bubble.

IMF: Valuations Are Less Dangerous Than Imagined

Adrian believes there are still clear differences between current market performance and a traditional bubble.

He stated that the core measure of a stock bubble remains whether valuations have decoupled from earnings, and the recent market correction has already eased previous valuation pressures. On one hand, AI-themed stocks have experienced pullbacks, with share prices having retreated. On the other hand, corporate earnings have consistently exceeded market expectations, leading to a reduction in price-to-earnings ratio pressure.

Although investors previously held "extremely aggressive" expectations for AI's commercial returns, actual corporate profits have also continually surpassed market forecasts. This suggests the current market rise is not entirely detached from fundamentals.

Adrian also pointed out that the market has not seen the "rising tide lifts all boats" phenomenon typical of a bubble period.

Over the past year, the significant gains have been concentrated in foundational hardware companies like AI chipmakers, while the software sector has undergone a notable correction. If the market had entered a bubble phase, capital would typically chase all AI-related stocks indiscriminately, rather than the clear structural divergence seen today. In his view, this indicates the market is still pricing based on profitability.

The Real Problem: AI Investment is 'Borrowing Long to Buy Short-Lived Assets'

Compared to rising share prices, Adrian is more concerned about changes in the financing structure of tech giants.

He noted that major cloud computing companies are currently increasing leverage, raising funds by issuing medium- to long-term bonds to purchase AI chips and build data centers on a massive scale. The problem, however, is that the lifecycle of this AI infrastructure is far shorter than the duration of the debt. In particular, AI chips like GPUs have extremely rapid update cycles; a single generation of products can become obsolete or performance-deficient within a few years, while the bonds issued by companies often have maturities of several years or even a decade or more.

This means companies are essentially using long-term debt to invest in assets that depreciate very quickly, creating a classic duration mismatch.

Adrian believes that as long as AI models continue to generate profits and corporate clients and consumers are willing to keep paying for cutting-edge models, this financing model will not be problematic. However, if future AI commercialization falls short of expectations and profitability declines, corporate cash flow may struggle to cover the costs of long-term debt, potentially exposing financial stability risks quickly. He stated:

"The truly critical question is whether AI can ultimately generate sustained profits, not how high the stock price is today."