Investor Michael Burry has dramatically increased his bearish bets on the artificial intelligence sector, reportedly shorting NVIDIA, Tesla, Caterpillar, the iShares Semiconductor ETF, and other AI-related stocks as he warns that the market is experiencing what he believes could become the largest investment bubble in history. According to reports, Burry expects U.S. equities to decline by 30%–40% by March 2027 as AI-related valuations come under pressure.
At the center of Burry’s thesis is the growing disconnect between AI spending and financial returns. While companies building AI infrastructure have seen their valuations soar, many of the technology giants funding the AI boom have delivered relatively modest stock performance this year. Microsoft, Amazon, Alphabet, and Meta have collectively invested hundreds of billions of dollars into AI data centers, chips, and cloud infrastructure, yet their share prices have significantly lagged those of semiconductor companies.
Meanwhile, chipmakers have been among the biggest winners of the AI rally. Shares of leading semiconductor companies have surged roughly 200% from earlier lows as investors poured money into businesses supplying GPUs, memory, networking equipment, and chip manufacturing tools. Burry argues that this divergence resembles previous technology bubbles, where infrastructure providers dramatically outperformed the companies ultimately expected to generate profits from the new technology.
Technical indicators have also added to concerns about elevated valuations. The Philadelphia Semiconductor Index (SOX) has climbed approximately 65% above its 200-day moving average, a level that some market observers note was last seen during the peak of the dot-com boom. At the same time, the sector’s forward price-to-earnings ratio has reportedly reached its highest level in roughly 15 years, suggesting investors are paying increasingly aggressive premiums for future earnings growth.
Burry has also questioned whether companies will ultimately earn adequate returns on their massive AI investments. According to his analysis, demand for AI infrastructure continues to accelerate, but evidence that enterprises can consistently monetize generative AI remains limited. Some AI-related market indicators have already weakened, reinforcing concerns that investor expectations may have outpaced commercial reality.
Another key part of Burry’s argument centers on accounting. He believes large technology companies are understating the long-term cost of their AI infrastructure by spreading depreciation expenses over extended periods. As billions of dollars of AI servers and specialized chips age, higher depreciation charges could eventually pressure earnings at companies including Microsoft, Amazon, and Alphabet, reducing the profitability of their AI businesses.
While many analysts continue to believe artificial intelligence represents a transformational technology capable of driving decades of economic growth, Burry argues that even revolutionary innovations can experience speculative bubbles before sustainable long-term value emerges. His latest positions suggest he believes today’s AI rally may follow a similar path to previous technology manias, with valuations eventually correcting as markets demand stronger evidence of monetization.
