AI might be a sharper threat to private equity lawyers than to almost any other kind of lawyer.
First of all, what do private equity lawyers do? To put a complex process simply: a private equity fund buys companies with borrowed money, holds and manages those companies for a few years and sells them for a profit.
Each purchase and each sale is a transaction, each of which requires lawyers to investigate the target (looking at the financial, legal and structural health of a company), negotiate the purchase agreement, document the loans, and structure the fund itself. The firms that do this work (think Paul Weiss, Kirkland, Simpson Thacher, Weil and more) are among the most profitable businesses in the world, with partners earning several million pounds a year.
That profitability rests on a particular shape of the team. While partners sit at the top, the associates and trainees generate hours underneath them. While partners generally win the client and make the key decisions, associates are reviewing the data, marking up the contracts, running checklists and billing for every 6 minutes of it. The more junior hours a partner can bill on top of their own, the more money the firm makes.
AI goes straight to the base of this pyramid. Take due diligence as an example: a core process of any private equity or M&A deal. Thousands of documents are read to locate change-of-control clauses, find red-flags in their relevant contracts, and evaluate tax positions. These are the tasks that large language models are now increasingly getting good at. Harvey, for example, the best-funded legal AI company, reached a valuation of $15.5 billion in September 2026. Its pitch is precisely contract analysis, due diligence and drafting for large firms. It has been running in production at Allen & Overy (Now A&O Shearman) for years. These reviews that would have taken a week can now, in theory, take a day, and most of that day is a senior lawyer checking the machine’s work. So far, this is true of any firm. The bottom of the hierarchy is, in some way, being threatened by the rise of AI.
Why is the private equity space different?
Profitability models differ dramatically depending on whether a practice area relies on specialised expertise or sheer labour capacity. Law firms will often organize their business models around ‘leverage’ - this is the ratio of junior lawyers (associates) to senior lawyers (partners). In areas like litigation and tax, the core asset is specialised knowledge and strategic judgement, with the leverage ratio being law and there being relatively few junior lawyers per partner. Revenue is driven by premium hourly rates for that partner’s expertise. Capacity driven models are arguably more aligned with Private Equity and M&A, where the core asset is, beyond expertise, speed. M&A and private equity transactions require vast amounts of document execution: due diligence, contract review, disclosures etc. The leverage ratio is extremely high in these situations, with a small number of partners managing a larger army of associates. Revenue is instead driven by the massive multiplication of billable hours–for example, take Paul Weiss having 40 partners, in comparison to roughly 210+ non-partner lawyers. If AI is removing the junior hours, a practice that earns a lot of its profit from junior hours is losing out. The firms that look safest (the richest or most prestigious) are perhaps the ones whose income is most concentrated in exactly the work AI is now able to do.
AI therefore threatens this space more. Where diligence can now take a day with AI, every firm with that AI could do it. The thing an elite firm was charging for becomes a standard feature, which is a different threat to the one being faced by litigation or tax practices that, in some instances, charge a premium for judgement above all else. Where the premium is for expertise, AI can’t touch the practice as easily. Judgement is supported by AI, not supplied. In contrast, capacity is supplied by AI.
Where the opportunity now lies
One opportunity AI could bring is in making speed an even stronger competitive advantage. For private equity clients, legal work that keeps pace with the deal can be hugely valuable. Firms that use AI to turn around diligence, drafts and revisions faster, while maintaining the same quality of advice and service, could become more attractive to clients working to tight deadlines.
That requires firms to translate faster individual tasks into a faster transaction. That could be getting findings to the right people sooner, resolving issues earlier and keeping negotiations moving. The premium could increasingly depend on how quickly a firm can deliver advice the client trusts and can act on.
