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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.
A well-known example is Uber. The ridehailing company burned through its entire 2026 AI budget in four months after rolling out Anthropic’s Claude Code to thousands of engineers in December, as agentic-coding adoption jumped from 32% in February to 84% by March, with 95% of engineers using some form of AI tool monthly by spring, according to Forbes.
Connor Kohlenberg, partner and London office lead for M&A at West Monroe, expects the same trajectory to hit the investment industry.
“Once you get hooked, it’s like a drug,” he said. “And then the drug prices always go up.”
Anthropic and OpenAI have now confidentially filed for IPOs—a shift that could push both companies away from the subsidized pricing that has defined the market so far. |
