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With Burry as Adviser, a New Short-Focused Fund Takes Aim at Private Credit Risks
- Laks Ganapathi launched the short-focused hedge fund Minerva Investment Management, hiring famed investor Michael Burry as senior adviser to scan healthcare, retail, and smaller banks for short targets.
- Burry, famous for predicting the 2008 housing crash, argues AI companies mask massive financial liabilities by keeping over $1.2 trillion in non-cancellable data center leases hidden off balance sheets.
- Big Tech companies accumulated over $3 trillion in off-balance-sheet obligations, with Microsoft shifting data center leases to operating expenses to lower depreciation rates without changing actual spending.
- U.S. private credit default rates reached an all-time high of 6.3% in August, with bankruptcies at First Brands and Tricolor revealing how private credit opacity masks financial strain on borrowers.
- Ganapathi warned that current conditions differ significantly from past crises, stating, "This time around, it is not going to be like 2008. It's going to be a lot worse," if AI revenue disappoints.
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Big Short's Michael Burry Switches His AI Trade as He Positions for a Bubble That 'May Burst' Sooner
Michael Burry has shifted his high‑profile bet against the artificial intelligence boom into long‑dated options, warning the 'bubble in AI may burst sooner than later' after new research prompted him to bring forward his timeline. In a 28 September post on his Cassandra Unchained Substack, the investor, who became known for betting against the US housing market before the 2008 financial crisis, said he had replaced a series of outright short pos…
·United Kingdom
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Sydney Morning Herald+3 Reposted by 3 other sources
AI’s hidden $4.3 trillion bill could wreck world economy, warns Big Short investor
Michael Burry, the hedge-fund manager famous for his bet against the US subprime mortgage bubble that sparked the GFC, has targeted the gargantuan data centre bill he says AI companies are hiding.
·North Sydney, Australia
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Total News Sources23
Leaning Left7Leaning Right3Center5Last UpdatedBias Distribution47% Left
Bias Distribution
- 47% of the sources lean Left
47% Left
L 47%
C 33%
R 20%
Factuality
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