As AI Arrives, Law Firms More Profitable Than Ever – Survey

Thomson Reuters’ survey and LSEG data show a boom in BigLaw in 2025: AmLaw 100 firms’ profit per lawyer is up over 53% since 2019, tech spending rose more than 10% year‑over‑year, worldwide M&A activity increased 49%, mega deals >$10bn more than doubled, and US-related M&A hit a 27‑year high. The profit surge is attributed to high demand, rising charge-out rates and billable-hours leverage using large junior teams, with AI and legal tech adopted broadly but not yet deployed at workflow‑core scale or materially cannibalizing billable revenue. Implication for investors: legal services and legal‑tech vendors may see continued revenue tailwinds as firms invest in AI that currently appears to support — rather than displace — profitable billable work.

Analysis

Market structure: BigLaw, legal information vendors and deal-adjacent service providers are primary beneficiaries — AmLaw PPL +53% since 2019 and >10% tech spend y/y imply pricing power remains intact for top firms and recurring-revenue data providers. Vendors that bundle research, workflow and deal data (e.g., Thomson Reuters (TRI), RELX/Lexis, LSEG) capture both higher client spend and transaction-driven data fees; standalone commoditized legal-process vendors and low-margin staffing outfits are most exposed. Expect fee-per-deal and advisory fee pools to grow ~20–50% in high-activity quarters, concentrating margin gains at market leaders.

Risk assessment: Tail risks include regulatory limits on AI usage in legal work (adverse rulings or liability class actions) and a rapid client pushback on rates if macro weakens — each could reduce fees by 10–30% over 6–18 months. Short-term (weeks) volatility is tied to M&A prints and quarterly results; medium-term (3–12 months) risk is “workflow redesign” adoption that could compress hours if firms automate aggressively; long-term (2–5 years) risk is commoditization if NewMod AI-first entrants scale. Hidden dependency: data vendors’ margins rely on billable-hour economics persisting; if clients demand fixed-fee AI workflows, revenue mix shifts.

Trade implications: Prefer long, concentrated exposure to information-services/market-data providers with ~3–5% position sizes (TRI, LSEG, RELX) using 6–12 month horizons; use call spreads to cap cost. Short selective staffing/recruiting names (KFY, ASGN) on 1–2% notional as wage inflation and substitution reduce operating leverage. Hedge macro tail with buying protection in credit (IG CDS) if M&A volumes collapse >30% QoQ.

Contrarian view: Consensus assumes AI will either destroy hours or is benign; miss is a phased outcome — incremental legal-AI adoption will lift productivity but also create price competition in standard work first. Mispricing likely in data providers already priced for secular decline; instead, they are under-earning optionality from higher transaction volumes. Catalyst watch: M&A flow reports (monthly LSEG/Refinitiv prints), TRI/RELX earnings guidance, and any jurisdictional AI liability rulings in next 90–180 days.

Key Decisions for Investors

• Establish a 2.5% long position in Thomson Reuters (TRI) over 6–12 months; complement with buying a 12-month 5% OTM call spread (size 0.5% notional) to leverage upside from continued M&A-driven data demand, trim if TRI issues guidance <5% organic growth or M&A volume drops >30% QoQ.
• Initiate a 2% long position in LSEG (LSEG) or RELX (RELX) — whichever is more liquid in your venue — to capture recurring data and listing/clearing fee upside; exit/trim within 9–12 months if combined legal/transaction revenue growth underperforms sector by >200bps.
• Open a 1–1.5% short position in Korn Ferry (KFY) and a 1% short in generalist staffing/consulting (ASGN) to exploit margin pressure from rising lawyer wages and potential substitution by AI; cover if unemployment for legal sector rises >1ppt or corporate deal activity stays within ±5% of trailing 12-month average.
• Buy 6–12 month protection in IG credit (e.g., 3–5yr CDS or put spreads on financials exposure) sized 0.5–1% notional to hedge a tail-event where M&A collapses >30% and causes rapid fee revenue contraction across data and advisory businesses, monitor regulatory AI rulings over next 90 days as a trigger.