Legendary investor Jeremy Grantham has issued a stark warning about the artificial intelligence boom, comparing the current market euphoria surrounding AI stocks to historic bubbles that ended in spectacular crashes. In a recent interview with Morningstar, Grantham argued that the AI hype represents a classic bubble pattern that has repeated throughout market history.
Grantham, known for his prescient market predictions, drew parallels between today’s AI mania and previous technological revolutions that temporarily inflated stock valuations. He specifically compared the AI bubble to the dot-com era of 1998-1999 when internet stocks soared to unsustainable levels before crashing, as well as the 1920s bubble driven by railroads and electrification.
“The bigger the new idea, the bigger the new invention, the more the market becomes overpriced, the more it attracts euphoria,” Grantham explained, suggesting that transformative technologies inevitably create excessive market speculation. According to the veteran investor, this pattern is not accidental but rather a predictable human response to revolutionary innovations.
Grantham’s analysis follows a familiar three-phase pattern: short-term overdoing, intermediate-term crash, and long-term world-changing impact. “Really great things happen in the internet phase, ‘98-‘99. But they overdo it,” he noted. “When you have these great developments, they overdo themselves in the short term, they crash in the intermediate term, and then they come out of the wreckage and change the world in the long term.”
While Grantham didn’t provide specific predictions about the magnitude or timing of a potential crash, his warnings align with other prominent market bears. David Rosenberg has characterized the current environment as a “mega-bubble” headed for a spectacular correction, citing historically high price-to-earnings ratios and elevated household stock ownership. Similarly, John Hussman has warned that stocks appear more overvalued than at any time since 1929.
Grantham’s track record on market predictions has been mixed. In 2022, he warned of a multi-asset “superbubble” and predicted the S&P 500 could drop 43%. The index fell approximately 25% that year but recovered all losses in 2023 and has gained 20% in 2024, demonstrating the difficulty of timing market corrections even when identifying genuine bubbles.
The concerns about AI valuations center on massive corporate spending with unclear returns and lofty tech sector valuations that may not be justified by current fundamentals.
Key Quotes
The bigger the new idea, the bigger the new invention, the more the market becomes overpriced, the more it attracts euphoria. It’s not accidental.
Jeremy Grantham explained the psychological mechanism behind technology bubbles, suggesting that truly revolutionary innovations inevitably create excessive market speculation and unsustainable valuations.
When you have these great developments, they overdo themselves in the short term, they crash in the intermediate term, and then they come out of the wreckage and change the world in the long term. And that’s what I expect will happen this time.
Grantham outlined his three-phase framework for understanding technology bubbles, acknowledging AI’s transformative potential while warning that a painful correction likely lies ahead before the technology reaches its full impact.
Really great things happen in the internet phase, ‘98-‘99. But they overdo it.
Drawing on the dot-com bubble as a historical precedent, Grantham emphasized that even genuinely revolutionary technologies can become vehicles for excessive speculation and market mania.
Our Take
Grantham’s warning deserves serious consideration despite his mixed prediction record because it addresses a fundamental tension in AI investing: the gap between transformative long-term potential and near-term financial returns. The comparison to previous technology bubbles is apt—the internet, railroads, and electrification all changed civilization, yet investors who bought at peak euphoria suffered devastating losses.
What makes this analysis particularly relevant is the unprecedented scale of AI infrastructure spending by major tech companies with limited visibility into monetization timelines. The pattern of “build it and they will come” worked eventually for the internet, but not before a brutal shakeout. The critical question isn’t whether AI will transform the world—it almost certainly will—but whether current valuations properly discount the risks, timeline uncertainties, and competitive dynamics that will determine which companies actually capture value from this revolution. Investors should heed the lesson that being right about technology but wrong about timing and valuation can still result in permanent capital loss.
Why This Matters
This warning from a respected market veteran highlights growing concerns about AI investment sustainability and raises critical questions about whether current valuations reflect realistic expectations for AI technology returns. Grantham’s analysis matters because it challenges the prevailing narrative of unlimited AI growth and suggests investors may be repeating historical mistakes.
The comparison to the dot-com bubble is particularly significant because while the internet ultimately transformed the world, countless investors lost fortunes during the crash, and many promising companies went bankrupt. If AI follows a similar pattern, we could see a painful correction that wipes out billions in market value before the technology reaches its full potential.
For businesses investing heavily in AI infrastructure and capabilities, this warning suggests the need for more disciplined capital allocation and realistic ROI expectations. The implications extend beyond stock prices to affect hiring decisions, research funding, and strategic planning across the technology sector. Understanding that transformative technologies often experience boom-bust cycles before achieving lasting impact could help companies and investors navigate the current AI revolution more successfully, avoiding the euphoria-driven mistakes that characterized previous tech bubbles.
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