Jenna O’Malley/PitchBook News AI is getting more expensive, and most PE firms lack a framework for tracking their spending.
But the costs aren’t only financial: overreliance on AI is also dulling deal teams’ judgment and producing diligence work that reads as clearly AI-generated, testing the trust investors place in a firm
PE firms are pushing AI use from pilot projects into daily deal work. Budgeting hasn’t caught up. Siva Ilango, a London-based partner at JMAN Group, which advises PE funds on data and AI strategy, says most clients still do not understand the mechanics of token pricing, let alone budget for where it is headed.
AI models are priced per token, which correlates with the length and complexity of the text, with separate rates for what users send in and what the model generates. Output pricing is typically several times higher than input pricing, and running the same model on more complex, longer tasks compounds the difference quickly. Ilango says most of his clients do not understand the mechanism and do not know how many tokens a typical week of use is actually costing them.