Why Aswath Damodaran Fears the AI Crash Will Be Worse Than Dot-Com

Critically, a large portion of this spending is financed through debt, much of it coming from private credit markets rather than traditional banks . "The problem with the AI capex boom is that not only is it immense, but a big chunk of it is funded with debt," Damodaran has said . If AI revenues fail to justify the scale of investment, companies saddled with debt could face distress and default, and that trouble would not stay confined to the companies themselves. It would spill over to private credit funds, lenders, and the broader financial system .

Why the Risk Is Worse: Contagion Beyond Equities

Damodaran argues that the consequences of an AI correction would be more severe for three key reasons:

1. Contagion beyond equity markets. Debt defaults would pull down private credit funds, banks, and the broader credit system — not just stock prices . Unlike the dot-com bust, which was largely a stock market event, an AI correction could trigger a financial system stress event.

2. Macroeconomic scarring. "This is scaring the macroeconomy a lot more than the dot-com boom did," Damodaran has said . Data center construction, power companies, and water utilities are all tied into the AI investment cycle. A pullback would ripple through hiring, energy demand, and construction — hitting the real economy, not just portfolio values .

3. Unprecedented overspending. Damodaran notes that much of the economy is "more promise than actual delivery," and warns that "collectively, we're probably overspending" at a scale never seen before .

"When the correction comes," he said in a May 2026 interview, "it will be worse. It'll be more market-wide. It won't be just a tech company correction."

The Scale of AI Infrastructure Spending Is Staggering

• Global tech companies are spending trillions of dollars on AI infrastructure to gain a competitive lead .
• Major firms like Meta, Alphabet, and Oracle are tapping bond markets at a record pace to fund their capital expenditure . Meta alone used structured financing for a $27 billion data center in Louisiana .
• A November 2025 Bank of America survey of fund managers managing $550 billion in assets found that a record share believe companies are overinvesting in AI — a sentiment not observed since August 2005 .
• Analysts at Man Group, the world's largest publicly traded hedge fund, warned that an oversupply of tech debt could test bond market appetite .

Other Analysts and Officials Are Drawing Similar Parallels

Damodaran is not alone in sounding the alarm. Several major institutions and analysts have raised concerns:

• Bank of England (December 2025): Warned that the multi-trillion dollar, debt-fueled AI spending boom risks unraveling given "materially stretched" stock valuations. The central bank said a correction in AI stocks would spill over to wider debt markets and pointed to early warning signs in credit default swaps .
• CNBC (November 2025): Reported that investor fears over AI capital expenditure have spread to the bond market, as tech giants' pace of debt issuance tests market appetite .
• Fortune (November 2025): Noted that global fund managers are signaling a "historic reversal" in sentiment regarding corporate spending, driven explicitly by AI capex concerns .

What Damodaran Is Not Saying

It's important to understand what Damodaran is not arguing. He is not claiming that AI technology is worthless or that AI will fail to deliver long-term economic value. In fact, he has acknowledged that AI could be transformative . His concern is about the structure and financing of the investment cycle, not the technology itself. The danger, in his view, is that overconfidence and debt-fueled capital expenditure have created a setup where any disappointment will be amplified through the credit system and into the broader economy .

The Bottom Line

Damodaran's core warning is clear: the AI investment boom is different from the dot-com era in ways that make the next downturn potentially more severe. The debt-financed, physical nature of AI infrastructure spending means that when — not if, in his view — a correction comes, the pain will be "more market-wide." Investors, policymakers, and business leaders should be paying close attention to the balance sheets behind the AI buildout, not just the promise of the technology itself.