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Is AI a bubble? The available reporting does not settle that question. Jared Gross, J.P. Morgan Asset Management’s head of institutional portfolio strategy, argues that AI exposure has spread across asset classes that investors may otherwise treat as diversifying. His point is about portfolio risk—not proof that today’s infrastructure spending will pay off or that the cycle cannot be a bubble.
What did Jared Gross say about AI investment?
In a September 15, 2026 report, Institutional Investor described Gross’s concern that AI-related financing and investment reach beyond public technology stocks. Investors may also have exposure through private credit financing for data centers and debt issued by large technology companies building AI capacity.
The report says more than $800 billion had been invested in AI through July 2026, in addition to almost $500 billion in 2025. Those figures are the publication’s reported totals; the available article material does not establish their precise definition, underlying dataset, or methodology. They should not be treated as a standardized official spending series.
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How should investors think about AI exposure?
Gross’s warning is that familiar asset-class labels may obscure common exposure to the same investment cycle. A portfolio can hold different kinds of securities and still be vulnerable to overlapping AI-related risks if their returns depend on continued demand for data-center capacity, financing availability, or expectations about future AI revenue.
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- Public equities: Shares of companies developing AI systems or selling the computing capacity and equipment used to run them.
- Private credit: Loans financing data-center construction and related infrastructure.
- Corporate debt: Debt issued by large technology companies investing in AI capacity.
This is not a complete map of the market or a full financing breakdown. It is a reminder to examine what drives an investment’s returns, rather than assuming that different asset-class labels automatically mean independent risks.
Does the scale of investment prove AI is a bubble?
No. Large investment totals show that substantial capital is flowing into AI, but they do not establish whether asset prices are excessive, whether infrastructure will earn adequate returns, or whether capacity will be overbuilt. The reporting identifies the scale and breadth of exposure; it does not resolve the bubble question.
There are competing interpretations of a buildout. Persistent demand could support continued investment. A slowdown could mean infrastructure needs have been met, but it could also reflect weaker expected returns or tighter financing conditions. The Federal Reserve’s July 17, 2026 analysis explains why investment indicators need to be read together rather than as a single verdict on demand or profitability.
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Construction spending and equipment investment capture complementary parts of the AI buildout. Data-center construction measures physical structures, while equipment investment reflects another substantial layer of the infrastructure. The Federal Reserve cautions that construction alone does not represent the dominant source of data-center value, so it is an incomplete proxy for the scale of the buildout.
Even a combined view of these indicators cannot by itself show whether companies will earn enough from the capacity they install. Investment data describe activity; they do not directly establish future returns or whether current valuations are justified.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How useful is the comparison with the late-1990s internet buildout?
The Institutional Investor report notes comparisons with the late-1990s internet buildout, but the available material does not provide a like-for-like numerical comparison. The analogy can frame a debate about rapid infrastructure investment and uncertain payoffs; it cannot establish that the two cycles are equivalent or predict how the current one will end.
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