Michael Burry’s latest argument against hyperscalers like Meta and Oracle echoes one made by an investor who shorted Enron

• Michael Burry issued a new warning about AI stocks similar to what Jim Chanos has said.

• “The Big Short” investor said he believed hyperscalers spending big on AI could “overstate” earnings.

• Chanos has also warned about AI spending among hyperscalers.

Michael Burry is sounding an awful lot like the investor who shorted Enron.

The famed short-seller of “The Big Short” fame issued a new warning about the biggest AI stocks in a post on his X account on Monday.

Burry — who recently came out of a social media hiatus and is known for writing cryptic and frequently bearish posts about markets and the economy — said he was concerned about hyperscalers, the mega-cap tech firms like Meta, Oracle, and Microsoft that are shelling out big on AI infrastructure.

Those investment plans could result in painful losses for those companies, Burry suggested, given that semiconductor chips, like Nvidia’s, have a relatively short lifespan.

“Massively ramping capex through purchase of Nvidia chips/servers on a 2-3 yr product cycle should not result in the extension of useful lives of compute equipment. Yet this is exactly what all the hyperscalers have done,” Burry wrote.

He estimated that Meta, Google, Oracle, Microsoft, and Amazon — five hyperscalers and some of the biggest names in the AI trade — would “understate depreciation” by around $176 billion between 2026 and 2028.

By 2028, Oracle will likely “overstate” its earnings by around 26%, and Meta could overstate earnings by around 20%, Burry speculated, without elaborating on his calculations.

“But it gets worse. More detail coming November 25th,” he added.

His warning sounds similar to what Jim Chanos, the famed Enron short-seller, has said about the AI trade.

The Kynikos Associates founder recently said he was concerned about the billions mega-cap tech firms were shelling out on AI hardware.

Amazon, Meta, Microsoft, Alphabet, and Apple are on track to spend a collective $349 billion on capex this year, much of which is directed towards AI infrastructure.

“I’m starting to worry there’s so much spending right now on the AI physical boom — the buildout of data centers, chips, and so on — that if anyone decides to pause and ask, ‘What’s our real economic return here?’ it could be a big problem,” Chanos said, later adding that AI spending was growing faster than income or revenue.

“So we’re getting to the point now where within a year or two, some of these large companies are going to start having to make some very uncomfortable decisions as to when and how they will monetize AI, and what the returns will ultimately be on this massive spending, because the chips basically depreciate over two years,” Chanos added.