Private Equity Bets on AI Growth

EQT’s Eric Liu says AI is reshaping private equity by widening the gap between AI software winners and losers. He also highlights how EQT manages regulatory risk in technology and healthcare and notes that high-quality assets are still attracting bids despite a slower private equity exit market.

Analysis

The useful signal here is not the AI narrative itself, but what it does to underwriting dispersion. In private equity, AI should widen the gap between managers that can quantify operating leverage in portfolio companies and those still relying on financial engineering; that is a multi-quarter advantage for firms with strong value-creation teams, but it also raises the bar for entry multiples because everyone can claim AI upside. Net: near-term pricing power likely accrues to premium assets, while lower-quality software and services names face multiple compression as buyers discount them for disintermediation risk.

For EQT specifically, the cleaner read is resilience rather than a re-rating event. A slower exit market usually hurts transaction-related fee capture first, but the fact that high-quality assets still clear suggests sponsors with top-tier processes can keep deploying capital even when distributions are delayed; that supports fee-earning AUM and protects management fees over the next 1-3 quarters. The risk is that AI enthusiasm pushes more capital toward the same handful of assets, which can lower future IRRs if purchase prices outrun productivity gains.

Contrarian angle: the market may be overestimating how quickly AI translates into realizable PE returns. Most of the benefit is 12-18 months out and depends on portfolio execution, customer retention, and labor substitution, not just model adoption. What would falsify the bullish view is a continued exit freeze plus weaker realized gains in the next quarterly disclosures, which would show that AI-led operational improvements are not yet offsetting liquidity drag.

Key Decisions for Investors

• No immediate event-driven trade in EQT: wait for the next quarterly AUM/realization update before adding exposure; the thesis only works if fee-earning assets hold up despite slower exits.
• Long EQT / short a lower-quality listed asset manager or fee-sensitive private-markets proxy over 1-3 months if exit momentum remains weak; the relative winner should be the firm with the strongest ability to source and hold premium assets.
• Avoid chasing legacy enterprise software names with visible AI disruption risk until next earnings cycle; the second-order loser is the segment with sticky installed bases but weak product differentiation, where multiple compression can happen before fundamentals break.
• Watch for evidence of AI-driven margin expansion inside portfolio companies over the next 1-2 quarters; if realized, that supports a constructive add to EQT and peers with strong operational playbooks.
• Set a downside alert on EQT if realization rates or fundraising guidance deteriorate in the next report; that would indicate the exit market is impairing capital recycling more than AI is helping portfolio value creation.