Michael Burry Expands AI Short Bets Despite Nvidia’s Record Quarter

1 hour ago 3 sources negative

Key takeaways:

  • Burry's call hedge signals timing skepticism, not disbelief in Nvidia's core fundamentals.
  • Concentrated AI infrastructure spending is the vulnerability underpinning this short thesis.
  • Watch hyperscaler capex disclosures as the key catalyst to validate or refute this stance.

Michael Burry, the investor famous for “The Big Short,” is doubling down on his bearish stance against the artificial intelligence trade, increasing short positions in Nvidia, Oracle, Palantir, Nebius and other AI-linked companies.

In an August 26 Substack post titled “Nvidia, Friends, and the Rebel Alliance,” Burry revealed that his short stock holdings now account for more than 21% of his portfolio excluding put options. He also disclosed new bearish bets against Caterpillar, while adding December Nvidia call options with strike prices in the mid-to-high $200 range. He described those calls as a hedge rather than a bullish trade, representing about 3.5% to 4% of his portfolio.

Burry is not disputing Nvidia’s operational performance. The chipmaker reported fiscal second-quarter 2027 revenue of $96.2 billion, up 106% year over year, with Data Center revenue surging 117% to $89 billion. Nvidia guided for roughly $108 billion in revenue for the following quarter, above its prior $91 billion guidance.

Instead, Burry questions whether Nvidia’s exceptional pricing power and dominant market position can last long enough to justify its valuation. He argues that while Nvidia appears “wildly undervalued” on conventional near-term earnings measures, the market may be overpaying for profits that depend on unusually strong AI infrastructure spending and concentrated customer demand.

The broader debate extends beyond Nvidia: aggressive capital expenditure across the semiconductor and cloud sectors could weaken future earnings if infrastructure demand slows. Burry’s trade is therefore a wager that markets are overvaluing the durability of current AI economics.

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