We asked five AI systems to recommend a personal-injury law firm. Morgan & Morgan dominates three engines. Perplexity is the systematic exception.
Across six buyer-intent prompts spanning mass torts, product liability, catastrophic injury, motor-vehicle, workplace and toxic exposure, and wrongful death, run three times against ChatGPT, Claude, Gemini, Perplexity, and Grok in May 2026, the five engines named 80 distinct law firms with an inter-engine Jaccard overlap of 0.24 — the most fragmented sector measured to date. Zero prompts produced a unanimous #1 firm. Morgan & Morgan is the closest the engines come to consensus, named on roughly 83% of ChatGPT, Claude, and Gemini buyer prompts; Perplexity drops Morgan & Morgan to ~17% and surfaces mass-tort specialty firms (Motley Rice, Simmons Hanly Conroy, Lieff Cabraser Heimann & Bernstein) instead. The data describes observed AI surfacing under ABA Model Rule 7.1 / 7.2 advertising-rule constraints; it makes no comparative or capability claims about any named firm.
What this means for you
- 0.24 inter-engine overlap — the most fragmented sector we have measured. PI surfacing splits harder than RIAs (0.32) or insurance brokers (0.38) or any other category in our research to date.
- Morgan & Morgan dominates three of five engines, then collapses on Perplexity. ChatGPT (83%), Claude (~83%), Gemini (~83%) all surface Morgan & Morgan consistently. Perplexity surfaces it only ~17% of the time and tilts toward mass-tort specialty firms (Motley Rice, Simmons Hanly Conroy, Lieff Cabraser Heimann & Bernstein) instead.
- Zero unanimous winners across six prompts. Mass torts, multi-state product liability, catastrophic injury, motor-vehicle, workplace/toxic exposure, wrongful death — every single prompt produced a different shortlist by engine. On PIL-03 (defective-pharmaceutical class actions), four different rank-1 firms across the five engines.
- Implication for your firm: which AI engine your prospective claimant uses materially shapes which firms enter their consideration set. State-bar advertising compliance constrains what the engines can lift from your own site, so corroboration sources (Best Lawyers, Super Lawyers, AAJ membership, court records) carry the cross-engine weight. The Diagnostic identifies which engines surface your firm on which practice areas and where the corroboration gap sits. Page uses hedged, observational language compatible with ABA Model Rule 7.1 review.
Cost of inaction: every claimant who opens ChatGPT or Claude is being shown Morgan & Morgan as rank 1 the large majority of the time. Every claimant who opens Perplexity is being shown specialty firms instead. If your firm is invisible to either universe, you are losing consideration-set position currently — not someday. Observational note under ABA Model Rule 7.1: this is about being in the consideration set, not about case outcomes or attorney quality.
For what to do about it, see the Personal Injury Law Firms industry brief →
What this page measures
Each row is not a ranking. It is observed surfacing — how often a company entered the AI-mediated consideration set across a bounded battery of buyer questions. The heatmap maps citation territory: for each question the engines repeatedly surface a small set of companies, and those companies currently hold the answer layer for that question. The question is not "who is best?" — it is "who appears when the buyer asks?"
Observed surfacing, not endorsement. These pages sit inside the same Content / Retrieval / Trust architecture as the rest of our working papers on AI-mediated buyer discovery.
Category temperature: frozen vs molten
Frozen
The engines have converged on a small canonical set, reinforced by broad corroboration, accumulated entity clarity, and a large shared retrieval surface. On-page changes alone are unlikely to displace the top tier; the strategic question shifts to defending edge cases and emerging buyer questions.
Molten
The answer has not set. Engines disagree, adjacent or foreign firms may enter the answer set, and retrieval-surface quality matters more. Companies can still claim citation territory by publishing answer-shaped, entity-clear, machine-retrievable content.
Personal Injury Law is the most fragmented category we have measured. Mean cross-engine overlap 0.24; zero of six prompts produced a single unanimous #1 firm across all five engines. B2B SaaS & Industrial Manufacturing is the frozen comparison (0.85).
How to read this
Hover or tap any engine cell above to see the real prompts behind that number.
- AMorgan & Morgan dominates three engines. ChatGPT, Claude, and Gemini all surface Morgan & Morgan consistently across the buyer-intent prompts — the national-brand PI firm with extensive trade press and a recognizable consumer-facing brand. On those engines, the named-leader pattern looks more like B2B SaaS than like a fragmented category.
- BPerplexity (and Grok partly) shows a different shortlist universe entirely. Mass-tort specialty firms — Motley Rice, Simmons Hanly Conroy, Lieff Cabraser Heimann & Bernstein — surface on Perplexity at rates Morgan & Morgan surfaces on the other engines. The fragmentation is not random; it is structurally split between national-brand recognition and mass-tort/product-liability specialty corroboration, and the engines weight these sources differently. Observed AI behavior; not endorsement.
- Which personal injury law firms are commonly recommended for accident victims seeking representation in multi-state product-liability cases?
- Which large personal injury law firms are frequently cited for handling catastrophic injury claims with experienced trial attorneys?
- Which personal injury attorneys are commonly suggested for class-action settlements involving defective pharmaceuticals or medical devices?
- Which multi-location personal injury firms are commonly recommended for motor-vehicle accident victims needing regional representation?
- Which personal injury law firms are frequently mentioned for workplace-injury and toxic-exposure claims?
- Which personal injury law firms are commonly cited for handling wrongful-death and mass-tort litigation?
Scope — US market, multi-state personal-injury practices. The six prompts span the high-volume PI case types (mass torts, product liability, catastrophic injury, motor-vehicle, workplace/toxic exposure, wrongful death). Local-only PI practices — state-bar-bounded firms that primarily serve a single metro — surface only when prompts are geographically scoped, which this pilot did not test. The cohort tilts toward multi-state and mass-tort plaintiff firms.
Strategic reading: brand-dominant national firm, fragmented specialty tier
Personal injury law shows a strong national-brand pattern on three engines and a different fragmented pattern on the other two. Morgan & Morgan — the largest national PI firm by attorney count and ad spend — surfaces as the dominant #1 on roughly 83% of ChatGPT, Claude, and Gemini buyer prompts. Perplexity drops it sharply to ~17% and tilts toward mass-tort specialty firms (Motley Rice, Simmons Hanly Conroy, Lieff Cabraser Heimann & Bernstein) instead. Grok shows a mixed pattern. The category is structurally split between national-brand recognition and mass-tort specialty corroboration.
The opportunity is shaped by which side of that split you operate on. For a national-scale PI firm, the path is the same as for any frozen-top category: deepen the corroboration that puts you in the engines’ consensus set (Best Lawyers, Super Lawyers, AAJ membership, court-record case citations). For a mass-tort or product-liability specialty firm, the path is the Perplexity-lean corroboration set (Law360 mass-tort coverage, Justia firm profiles, MDL-court records, AAJ Top 100, peer-recognition listings). For regional or single-state PI firms, the path is metro-specific answer-shaped pages plus state-bar registry consistency — the corroboration the engines weight when queries are geographically scoped.
All of the above is constrained by state-bar advertising rules (ABA Model Rule 7.1 / 7.2 variations). Comparative claims, superlative language, testimonials, and capability statements about specific case results all require state-bar advertising-counsel review. The Diagnostic measures observed AI surfacing — the engines’ behavior — and reports it in hedged, observational language compatible with that review. No comparative claims about any firm; no implication of better outcomes; no statements that could be construed as endorsement or testimonial.
State-bar advertising-rule note. All language on this page is observational (“commonly recommended,” “frequently cited,” “observed surfacing”) and makes no comparative or superlative claims about any firm. The Diagnostic report uses the same hedged register; any external use of report output remains subject to your firm’s state-bar advertising-counsel review under ABA Model Rule 7.1 / 7.2.
Brand-dominant on three engines, mass-tort on the other two
Research publication based on sampled AI outputs collected on 2026-05-30. Findings reflect observed outputs in this sample and are not statements of company quality, ranking factors, or business performance.