How should hedge funds manage what AI says about their performance?

Hedge funds face a particular AI reputation layer because the audience asking the engines is sophisticated and consequential. Allocators prompting the engines about manager track records, fund performance, key personnel, prior controversies, and peer comparisons read the responses as a starting input to formal diligence. The AIQ setup for a fund typically includes prompts in each of those categories run across the eight engines, with peer benchmarking against the named comparable funds. The source-quality assessment matters as much as the sentiment: when the engines are citing dated trade press or contested commentary, even neutral responses carry less weight than when they are citing current authoritative coverage. The entity layer is where most funds find the largest gaps: a Wikipedia article that exists but is thin, a Wikidata entry missing key relationships, schema markup (structured tags that tell search engines what a page is about) absent or wrong on the firm’s owned properties. Source-layer work (changing the sources the engines read, not the engines themselves) on those gaps over the months before fundraising activity produces materially different AI responses by the time LPs start asking.