Hedge funds using AI in their trading process posted the worst month on record in Feb, with AI index down 7.3%, compared to a 2.4% drop for the broader index
Dani Burger / Bloomberg :
Context & Ripple Effects
February 2018 was the stress test for a strategy that had been building since Man Group overcame its internal wariness about AI-centric funds: funds running AI in their trading process fell 7.3%, their worst month on record, while the broader hedge fund index lost just 2.4%. The gap is the point — the same models marketed as an edge amplified the drawdown instead of damping it.
That pattern did not stay contained to one bad month. It resurfaced when Nvidia's 10% single-day plunge erased over $200B and rattled AI bets broadly, and it ended careers: by mid-2026, AI-focused fund Situational Awareness had sold the bulk of its stock portfolio to Ken Griffin's Citadel after deep losses, while the AI frenzy split the VC market with top-to-bottom performer gaps more than doubling.
First-order effects
- Investors in AI-branded hedge funds absorbed triple the broader index's February loss, putting the 'AI edge' pitch under immediate scrutiny from allocators deciding whether the label signals skill or concentrated factor exposure.
Second-order effects
- Funds without AI branding gained a relative-marketing advantage in fundraising, while AI-labeled vehicles faced pressure to prove their models add value outside trending markets — a pressure that later forced Situational Awareness to hand its book to Citadel rather than ride out losses.
Third-order effects
- If AI-driven strategies systematically amplify drawdowns, the industry structure points toward consolidation around large incumbents like Citadel and Man Group that can absorb model failures, with 'AI fund' becoming a dispersion story — winners and losers diverging sharply, as already visible among VC funds backing the same theme.
The trend: AI-braded investment vehicles keep amplifying market drawdowns rather than hedging them, pushing capital and portfolios toward large incumbents and widening performance dispersion across everything labeled AI.