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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, makes a narrower point: AI-related investment and financing now reach across asset classes that investors may otherwise treat as separate sources of diversification. That breadth makes exposure worth examining; it does not prove the buildout will earn adequate returns or rule out overinvestment.
What did Jared Gross say about the AI buildout?
In a September 15, 2026 report, Institutional Investor identified Gross as J.P. Morgan Asset Management’s head of institutional portfolio strategy and reported his concern that AI exposure has spread beyond the public-equity holdings investors might first associate with the boom. The report discusses private credit financing for data centers and debt issuance by large technology companies alongside equity exposure.
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The headline’s “boom, not bubble” framing should not be read as a verified verbatim quote or as proof that Gross ruled out bubble risk. No complete interview transcript or recording is available in the cited reporting. The defensible takeaway is about portfolio exposure: financing the AI buildout can connect risks across assets that may look diversified on paper.
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Institutional Investor reported that more than $800 billion had been invested in AI through July 2026, on top of almost $500 billion in 2025. Those are the publication’s reported totals, not a standardized official series established by the available article excerpt. The excerpt does not specify the underlying dataset, precise definition of “invested,” or methodology, so the figures should be attributed to the publication rather than treated as directly comparable market statistics.
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How should investors think about AI exposure?
Start by looking through labels such as “equity,” “credit,” or “real estate” to the economic driver behind an investment. Several different holdings can depend on the same underlying outcome: sustained demand for AI computing and data-center capacity, together with the ability of companies and borrowers to turn that demand into returns.
- Public equities: listed companies may be exposed through AI-related products, infrastructure spending, or expectations for future growth.
- Private credit and debt: data-center financing and borrowing by large technology companies can make investors exposed through lending as well as shares.
- Private equity, infrastructure, real estate, and venture capital: these are additional channels identified in the report’s discussion of the wider investment landscape. The cited material does not provide a complete breakdown of their relative size.
This is a way to identify possible concentration, not a claim that all such investments have identical risks. Their borrowers, valuations, cash flows, and protections can differ. The practical question is whether apparently separate positions would be vulnerable to the same change in demand, financing conditions, or expected returns.
What do public data show about the buildout?
Public economic indicators capture different layers of data-center investment. In a July 17, 2026 FEDS Notes analysis, the Federal Reserve treats data-center construction and equipment investment as complementary measures. Construction reflects physical facilities, while equipment investment captures another important part of the buildout. Construction alone is therefore an incomplete measure of data-center value.
A slowdown in either indicator would not, by itself, establish that AI demand had collapsed or that the cycle had ended. It could reflect infrastructure demand being met, lower expected returns, financing conditions, or other factors. Distinguishing among these explanations requires more than reading one spending series in isolation.
Does comparison with the late-1990s internet boom settle the bubble question?
No. Institutional Investor reports that the current buildout is being compared with the late-1990s internet infrastructure expansion, but the available material does not provide a like-for-like numerical comparison. The analogy can prompt useful questions about capacity, financing, and eventual returns; it cannot establish that the two cycles are equivalent or predict how the current one will end.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What would help distinguish a durable boom from a bubble?
The reported investment scale and the spread of financing channels establish that AI is a significant portfolio-risk topic. They do not establish whether valuations are justified, whether future revenues will support the investment, or whether data-center capacity will be overbuilt. A firmer judgment would require evidence on those questions, along with a clear account of what the investment totals include.
Quick Recap
Best Value
- Look for evidence that demand for computing capacity translates into sustained use and revenue, not just announced construction or equipment purchases.
- Consider who is funding each part of the buildout and how exposed those lenders and investors are to a change in expected returns.
- Read construction and equipment indicators together rather than treating either as a complete measure.
- Keep historical comparisons in perspective unless the underlying measures and time periods are genuinely comparable.
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