Former Fidelity fund manager George Noble has warned that an AI bubble crash could cause 17 times more damage than the dot-com collapse, which erased about $5 trillion from the Nasdaq. Summary George Noble warns an AI crash could cause 17 times more damage than the dot-com collapse. Polymarket traders have raised the odds of an AI bubble bursting in 2026 above 17%. Dalio and the U.S. Treasury identify liquidity and economic links as key risks. According to Polymarket, the probability of an AI bubble bursting in 2026 has climbed above 17% after recently falling from 30% to 14%. Contracts using different resolution criteria placed the likelihood between 16% and 24% as traders weighed falling technology shares, revenue concerns, and weakness across global markets. Noble tied his forecast to the large sums flowing into AI infrastructure, arguing that the financial fallout could extend far beyond technology companies if expected returns fail to arrive. “The fallout from this could really be much more significant,” Noble said while discussing the rise in AI capital spending. AI bubble odds have rebounded above 17% Fresh pressure on semiconductor and technology shares has added to those concerns. The Wall Street Journal reported that U.S. stock futures fell on Thursday as AI-related anxiety spread from Asian markets, where SK Hynix and Samsung Electronics dropped almost 9%. Both South Korean chipmakers plan to spend billions of dollars on semiconductor plants and AI capacity. Their declines came as investors questioned whether the revenue generated by AI services would justify the industry’s expanding infrastructure bill, according to the report. IBM has added to the unease after its shares suffered their steepest daily fall since 1968, dropping almost 25% earlier this week. Market data cited in the report showed IBM closing another 2.7% lower at $211.20 on Wednesday, taking its decline over several sessions past 26%. In its warning, IBM said spending on AI infrastructure was pulling corporate budgets away from software, contributing to weaker-than-expected revenue growth. The selloff erased tens of billions of dollars from IBM’s market value and weighed on other software and information technology stocks, according to the report. A draft U.S. Treasury Department report has also examined how an AI downturn could move through the economy. Drawing on research from the University of Texas at Austin cited by NOTUS, the report found that AI companies have become more closely linked to the U.S. economy than internet companies were during the dot-com period. Under the report’s downside scenario, disappointing productivity or profits could hurt private credit, chipmakers, cloud providers, electric utilities, and companies financing data centers. The Treasury did not predict an imminent crash, but it listed electricity shortages, financing limits, supply chain disruptions, and geopolitical tensions among the risks facing the sector. Cash demands could expose inflated AI valuations Ray Dalio has separately argued that liquidity, rather than weak technology, could break the AI boom. During a television interview reported by Bloomberg, the Bridgewater Associates founder explained that investors often mistake rising asset values for money they can readily spend. Dalio used private companies to illustrate the risk: a business can receive a billion-dollar valuation after raising far less in actual capital, but shareholders cannot use that paper wealth without selling. In his assessment, stress would emerge if many investors attempted to turn those valuations into cash at the same time. Bernstein and Cummings have pointed to another pressure building beneath the boom. In a recent Substack post, the economists wrote that the AI bubble was “still inflating,” while technology investment had reached nearly 5% of U.S. GDP, above levels recorded during the dot-com era. Their analysis also found that large technology companies were committing enough capital to AI projects to reduce their cash reserves. Combined with Noble’s warning and Dalio’s liquidity concerns, those figures leave investors focused on whether AI earnings can catch up with the money already committed to the sector.
