Asset management giant: Market expectations for AI productivity are too optimistic.

One of the world’s largest insurance and asset management groups, Allianz, has warned that market expectations for AI-driven productivity gains show signs of irrational exuberance, and that the actual impact of AI on the real economy will be far more complex and uneven than what market pricing reflects.

On July 3, according to Bloomberg, Allianz Chief Economist Ludovic Subran said at an annual economic conference in France that day, “We don’t really know how much AI will be adopted and how it will affect the real economy, but the market is already very optimistic—especially about productivity gains—while reality will be a more mixed picture. For me, this is exactly where I see a certain degree of irrational exuberance.” He also expressed concern about the overall “market psychology” surrounding AI investment.

Subran’s comments echoed statements made earlier this week by officials from the International Monetary Fund (IMF), and also were in line with a warning issued by the Bank for International Settlements (BIS) last Sunday—BIS has listed AI as one of the four “pressure points” threatening global economic prosperity.

AI impact will be a “mixed picture,” not the all-around windfall the market expects

Subran acknowledged that the changes foreshadowed by AI will be revolutionary, calling it a “Renaissance moment,” and said AI will profoundly reshape the services-sector economy. However, he also stressed that this technology has given rise to some “weird phenomena” in the behavior of companies and investors.

His core view is that AI’s impact on different economies will not be distributed evenly. Current optimistic market expectations are built on the assumption that productivity will rise comprehensively and rapidly, but the real situation will be “a mixed picture”—the degree of benefit will differ significantly across industries and among companies, which is clearly at odds with the market’s current overall pricing logic.

Subran took aim at the sharp surge in U.S. AI sector capital expenditures. Citing a view similar to that of IMF officials, he specifically criticized some companies for getting caught in a “debt expansion cycle”—large-scale capital investment increases debt, while the timing and magnitude of investment returns are highly uncertain.

As a Wall Street China Insights report noted, Tobias Adrian, Director of the Monetary and Capital Markets Department at the International Monetary Fund (IMF), said that current valuations of AI-related stocks may not yet have formed a bubble, but what financial regulators should truly be wary of is that global large technology companies are funding ever more of their AI infrastructure investments through mid- and long-term debt, at a massive scale. This mismatch in the maturity of assets and liabilities is a potential source of future financial stability risks.

Before that, on June 29, Wall Street China Insights also wrote in an article that the Bank for International Settlements warned of three major threats: an AI bubble bursting, inflation, and sovereign debt. The report pointed out that the AI “circular financing” structure is opaque and involves risks of assets being pledged multiple times; when the tide turns, it could trigger a credit tsunami on the scale of 2008. At the same time, with rising risks of second-round effects from inflation and the fact that hedge funds’ high-leverage basis trades can easily trigger deleveraging and leading-to-fire-sale selling, overall vulnerability in the global financial system is increasing.

Subran specifically mentioned the polarization of corporate behavior: companies such as Apple and Microsoft are taking “not many” actions in the AI field, while other companies are “overinvesting.” In his view, this polarization itself is a signal that the market structure is imbalanced.

“If you issue debt to return money to shareholders, that doesn’t look like a good sign to me,” Subran said. He also expressed specific concerns about potential risks for data centers, including the risk of technological obsolescence facing some data centers, as well as the operational logic of turning capital expenditures into something monetizable.

Subran observed that the stock market and the bond market price AI risks very differently. In the bond market, he believes investors have remained relatively rational—“When you look at corporate credit spreads in that sector, especially for hyperscalers, they are more cautious than before,” he said—adding that there is no complacency in the bond market: “The bond market still has plenty of ‘debt vigilantes.’”

However, the situation is completely different in the stock market. “On the equity side, it seems the sky’s the limit—and of course that’s not the case,” Subran said directly. In his view, this stock-bond divergence is the most straightforward manifestation of irrational exuberance in the current AI investment boom—stock market pricing reflects the most optimistic scenario, while the bond market’s caution suggests that real constraints still exist.

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