NYU finance professor Damodaran warns an AI crash could hit harder than the dot-com bust
Source published ·Modelwire updated
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The development
Aswath Damodaran, a prominent finance scholar, argues that an AI sector downturn poses systemic risks exceeding the dot-com collapse because current infrastructure buildout relies on massive debt-financed physical assets rather than software-only models. Beyond crash risk, he identifies a structural tension: if AI succeeds at its core mission of labor displacement, the societal and economic fallout remains largely unpriced into current valuations. This framing shifts the conversation from hype cycles to balance-sheet vulnerability and the unresolved externalities of widespread automation.
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The sharper point buried in Damodaran's thesis is directional asymmetry: debt-financed physical infrastructure (data centers, power capacity, networking buildout) cannot be written down quietly the way overvalued software equity could in 2001. When hyperscaler capex is collateralized against real assets, a sentiment reversal triggers creditor exposure across the broader financial system, not just equity holders.
This is largely disconnected from recent activity in our archive, as we have no prior coverage to anchor it to. It belongs, however, to a thread running through public market commentary over the past 18 months: the question of whether AI infrastructure spending is a durable capital cycle or a coordinated bet that requires perpetual demand validation to stay solvent. Damodaran is essentially asking whether the demand side (enterprise adoption, productivity gains, monetizable output) can grow fast enough to service the supply side before refinancing windows close. That is a question no vendor roadmap answers.
Watch whether any major hyperscaler revises capex guidance downward in Q3 2026 earnings calls. A single credible pullback would confirm that internal demand modeling is softening, which is the first concrete signal Damodaran's timeline is compressing.
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MentionsAswath Damodaran · NYU · The Decoder
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