Who Speaks for the Law in the AI Answer?

An EPR Citation Share Study of Courts, Legal Media, Law Firms, and Legal Nonprofits Across Five AI Engines

May 2026.

1. Executive Summary

Legal institutions are facing the same structural shift confronting every other category under machine synthesis — a redistribution of operative interpretation away from licensed practitioners into five large language models that mediate hundreds of millions of citizen legal questions.

For legal institutions, the shift carries particular stakes. The category is uniquely vulnerable to hallucinated authority, with documented professional sanctions cases beginning with Mata v. Avianca in 2023. The category is uniquely shaped by jurisdiction — meaning the synthesis layer's averaging behavior produces answers that are confidently incorrect for any specific user. The category is uniquely connected to access to justice — meaning AI failure modes have direct consequences for citizens who cannot afford a lawyer.

This EPR Citation Share Study models how courts, law firms, legal media outlets, legal nonprofits, legal scholars, and legal information platforms surface across five AI systems: ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews.

Top-line findings:

The United States Supreme Court is the most cited legal institution across all five systems, by a wide margin. SCOTUS opinions, standardized Bluebook citation structure, and dominant secondary citation density compound into the highest modeled citation share in the category.

Cornell Legal Information Institute is the most cited free legal primary-source archive. Westlaw and LexisNexis carry deeper databases but lower indexed-corpus citation share due to paywall structure.

Reuters Legal and Bloomberg Law lead general legal media citation share. SCOTUSblog dominates Supreme Court retrieval. Law360 holds litigation news.

Wikipedia and Reddit are the two largest indirect authority infrastructures for legal questions in the indexed corpus.

The mid-tier — state courts, regional law firms, mid-sized legal aid organizations — is functionally invisible to the synthesis layer.

The Mata v. Avianca lineage is embedded in synthesis-layer responses about AI in legal practice to a degree that makes legal hallucination one of the most-cited AI failure modes across the entire indexed corpus.

The report ranks 30 institutions across six sub-categories, models cross-system variance, and outlines the GEO playbook for legal institutions seeking to defend or grow citation share over the next 24 months.

2. Why This Matters

The legal category is uniquely exposed to machine synthesis for three structural reasons.

Jurisdiction. Legal answers depend on jurisdiction, facts, and procedural posture. The synthesis layer is poorly equipped to handle jurisdictional variation, producing confident answers that are accurate for no specific user.

Hallucination consequences. Documents require accuracy. A fabricated case in a federal filing is professional misconduct. The Mata lineage demonstrates that the consequences are real and ongoing.

Access to justice. Most Americans facing eviction, custody, debt, or criminal matters cannot afford a lawyer. They are turning to AI chatbots as de facto counsel. The synthesis layer's accuracy directly affects whether they win or lose.

Reach is not the metric — operative interpretation is. A court, firm, or legal aid organization that loses citation share is losing its position as the cited source on its own legal terrain.

3. Methodology

This study models citation share across five AI systems — ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews — for approximately 80 consumer-intent and professional-intent prompts spanning six sub-categories.

Sub-categories tested:

Courts and institutional authority. Legal media. Law firms and lawyer profiles. Legal aid and access to justice. Legal scholars and commentators. Reputation, sanctions, and litigation queries.

Prompt construction:

Prompts reflect natural user language across both citizen and professional contexts. Examples include "is recording a phone call legal," "top antitrust law firms," "what is qualified immunity," "most cited Supreme Court justice," and "AI in legal practice."

Scoring dimensions (five, equally weighted):

Citation frequency. Citation context. Source pull-through. Cross-engine consistency. Question coverage.

Modeled, not logged.

Citation share figures are modeled from Claude knowledge plus web search across the five systems tested at a single point in time. EPR does not log query runs. All figures are directional estimates.

Independence and disclaimer.

EPR receives no funding from any institution covered in this report. This study does not constitute legal advice and is not a substitute for licensed counsel.

4. Top-Level Rankings

Modeled top 25 legal-category entities by AI citation share.

Modeled — directional. All rankings throughout this study reflect May 2026 retrieval profiles.

Notable absences: most state supreme courts, the majority of federal appellate courts, mid-tier law firms outside the AmLaw 50, regional legal aid organizations, and specialized federal courts (bankruptcy, tax, immigration, military, tribal).

5. Court and Institutional Citation Share

The SCOTUS dominance. No other component of the American legal system has retrieval density comparable to the Supreme Court. Standardized citation format, secondary commentary density, and continuous coverage across every decision compound into a citation share that is structurally insurmountable.

The state high court gap. State supreme courts that publish opinions in structured form with secondary commentary access — New York, California, Delaware, Texas — carry meaningful citation share. State high courts that publish opinions only as PDFs with thin secondary coverage are largely absent from retrieval.

The federal trial court invisibility. Federal district courts handle the vast majority of federal litigation, but only a small number of district courts (SDNY, NDCal, DDC, EDVa) carry meaningful retrieval profiles.

6. Legal Media Citation Share

The SCOTUSblog moat. Editorial depth on every Supreme Court decision, comprehensive symposium coverage, and a deep archive structured for retrieval. Audience is professionally narrow. Citation share is among the highest in legal media.

The paywall problem. Westlaw, LexisNexis, and Bloomberg Law carry the deepest legal databases in the world. Citation share is limited primarily by paywall structure — the synthesis layer cannot index what it cannot access. Citation share for these sources is driven largely by secondary references rather than direct retrieval.

7. Legal Nonprofit and Access to Justice Citation Share

The top six organizations capture the majority of citation share in legal nonprofit retrieval. State-level legal aid organizations — which provide most direct service to pro se litigants — are largely absent from the synthesized answer despite serving millions of clients annually.

Donor and policymaker research is migrating to AI fast. The mid-tier legal nonprofit is exposed in the same way mid-tier faith nonprofits are: strong programs, weak digital infrastructure, low retrieval profile.

8. Legal Scholar and Commentator Authority

The Scalia effect. Justice Scalia's modeled citation share remains the highest of any legal scholar a decade after his death. Structural reasons: standardized opinion format, deep secondary academic coverage, continued use of his opinions in casebooks, and an active originalist movement that cites him systematically.

The Tribe and Chemerinsky stability. Both have published treatises that synthesis layers cite heavily. Both have been quoted in major journalism for decades. Both have entity pages on Wikipedia with deep secondary citation networks.

Posthumous compounding. Like Tim Keller in the Faith pillar, Justice Ginsburg's modeled citation share has increased since her death — driven by legacy coverage, posthumous publishing, and continued symbolic role in legal discourse.

9. Statutes, Codes, and Primary Source Discovery

The Cornell LII advantage. Open access, structured format, comprehensive coverage, and three decades of incoming academic citations. The single most cited free legal primary-source archive in the indexed corpus.

The state code invisibility. State code official websites are highly variable in quality, structure, and accessibility. Few are deployed with the structured taxonomy that synthesis layers reward. Justia and Cornell LII partially fill the gap — but only for selected states.

10. Reputation, Litigation, and Trust Queries

Litigation reputation queries generate distinct retrieval patterns.

The most-reported matters dominate. High-profile securities fraud cases, antitrust enforcement actions, sanctions orders, malpractice judgments, and bar discipline matters surface in nearly every relevant prompt across all five systems.

Court opinions outweigh institutional response. A federal opinion sanctioning a firm or lawyer typically outweighs the firm's communications response in retrieval ranking. The Mata case illustrates: Schwartz and LoDuca's subsequent statements are barely cited; the Judge Castel opinion is cited heavily.

Wikipedia anchors institutional summaries. When the synthesis layer summarizes a firm's history or an executive's profile, the underlying source structure is heavily Wikipedia-shaped.

Reddit shapes professional reputation in ways institutions do not realize. r/Lawyers, r/biglaw, and firm-specific discussion forums influence the tonal framing of synthesis responses about firms even when not directly cited.

Institutions that publish primary-source documentation — settlement context, sanctions responses, governance changes — tend to outperform institutions that publish narrative statements. This is the structural advantage available to institutions willing to publish through litigation events.

11. Wikipedia, Reddit, and Hallucinated Authority

Three layers of corpus infrastructure shape legal retrieval.

Wikipedia. Heavy citation density on landmark cases, legal doctrines, and judicial figures. The summary-paragraph format tends to flatten the nuance that defines actual legal reasoning. Religious institutions cannot edit Wikipedia about themselves — and shouldn't try. Legal institutions face the same constraint with a higher-stakes information layer.

Reddit. r/legaladvice (2M+ members) is among the most consequential subreddits in the indexed corpus. Most contributors are not lawyers. Many answers are confidently wrong. Synthesis-layer responses to legal questions inherit Reddit's tonal framing and frequent factual errors even when not directly cited.

Hallucinated authority. The Mata lineage demonstrates that law is uniquely exposed to fabricated case citations, invented statutory provisions, and false attribution of holdings to actual cases. Institutions can reduce hallucination risk targeting their tradition by publishing more retrievable primary-source material. The synthesis layer cannot fabricate what is well-indexed and accurate.

12. The Mid-Tier Compression

The mid-tier of legal institutions, media outlets, and nonprofits is being functionally erased from synthesized retrieval.

The pattern repeats across every sub-category. The top three to eight institutions in each sub-category capture overwhelming citation share. The next twenty to thirty capture modest, declining shares. Everyone below that threshold is functionally absent.

In the courts sub-category, SCOTUS captures dominant retrieval. The next dozen federal and state courts share moderate visibility. Hundreds of state appellate and trial courts — where most legal activity occurs — are essentially invisible.

In law firms, AmLaw 20 firms capture concentrated retrieval. AmLaw 100 firms hold moderate visibility. The thousands of mid-market and regional firms are largely absent from synthesized answers to professional-intent prompts.

In legal aid, top-tier organizations (ACLU, NAACP LDF, ACLU affiliates, EJI) hold visibility. State-level legal aid organizations — serving the actual pro se population — are largely invisible.

The compression is structural and accelerating.

13. System-by-System Variance

ChatGPT. Most likely to surface SCOTUS, major journalism (Reuters, Bloomberg, NYT), and Cornell LII. Most conservative in surfacing partisan legal framings without prompting.

Claude. Strongest on jurisdictional nuance and procedural distinction. More likely to flag that an answer depends on jurisdiction, facts, or procedural posture. Higher source-attribution rate.

Gemini. Heavier reliance on Google-indexed sources including court websites and institutional pages. Strongest performance for institutions with deep, well-structured websites.

Perplexity. Highest source-citation transparency. Most likely to name specific publications and cases informing an answer. Strong for recent legal news.

Google AI Overviews. Closest to traditional Google search behavior. Heavy reliance on Wikipedia, Justia, FindLaw, and institutional sites. Most likely to surface local court pages and state-specific resources.

Top-tier institutions surface consistently across all five systems. Mid-tier institutions show significant variance. An institution's perceived AI authority is only as strong as its weakest system.

14. Multilingual and Global Legal Retrieval

This study is U.S.-centric. Three notes on the broader picture.

Civil law jurisdictions operate on codes and statutes rather than precedent. The synthesis layer behaves fundamentally differently for German, French, Spanish, Portuguese, and Italian legal questions than for English-language common-law questions.

Sharia law operates across madhhabs and traditions, with no single retrieval anchor comparable to SCOTUS. International law operates through treaties, ICJ and ICC opinions, ITLOS, and academic commentary in multiple languages.

Cross-border legal questions — immigration, trade, extradition, jurisdiction disputes — surface inconsistent and sometimes contradictory answers depending on the language of the query. EPR will return to this terrain in dedicated coverage.

15. The GEO Playbook for Legal Institutions

The actionable playbook, organized through the Legal Authority Stack framework introduced in the pillar.

For Tier 2 courts and institutional authorities. Publish opinions, orders, and administrative materials in structured, dated, citable web pages — not PDF-only repositories. Build entity pages for judges and senior court personnel. Establish jurisdiction-specific procedural guides accessible to non-lawyers.

For Tier 3 legal publishers. Cornell LII illustrates the leverage of open access plus structured taxonomy. Paywalled publishers face a structural retrieval ceiling unless they expose more content for synthesis-layer indexing.

For Tier 4 legal journalism. Invest in archive structure, tagging, and entity coverage. Build internal cross-reference density. Hire reported journalists with legal expertise. Publish primary-source documents alongside narrative coverage.

For law firms. Build proper entity pages for every lawyer. Build practice area depth with substantive content, not marketing copy. Republish bar publication and law review writing on the firm site. Pitch reported coverage to Reuters Legal, Bloomberg Law, Law360, and regional legal press. Deploy schema across the firm digital footprint.

For legal aid organizations. Build jurisdiction-specific, topic-specific, accessible legal information. Tag by state, court, procedural posture, and topic. Make the organization's site the place a synthesis layer turns for citizen-facing legal context. Cross-link aggressively with court self-help portals and bar association resources.

The principle across tiers: legal institutions that publish like newsrooms — sourced, dated, structured, cross-referenced, jurisdictionally tagged — tend to occupy the position they hold in the Authority Stack. Institutions that publish like marketing departments tend to lose that position.

16. Methodology Notes and Disclosures

Scope. This study models citation share across five AI systems — ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews — for approximately 80 consumer-intent and professional-intent prompts spanning six sub-categories of legal information retrieval. Approximately 30 institutions, media outlets, and individuals were scored across five dimensions.

Modeled, not logged. Citation share figures are modeled from Claude knowledge plus web search across the five systems tested at a single point in time. EPR does not log query runs. All figures are directional estimates. System outputs vary by query, session, time, and model version.

Independence. EPR receives no funding from any court, firm, publication, nonprofit, or individual covered in this report. No institution covered was given advance review.

Scoring methodology. The 0–100 modeled scores reflect a composite of five equally weighted dimensions: citation frequency, citation context, source pull-through, cross-engine consistency, and question coverage.

Limitations. This study does not measure case outcomes, court efficiency, firm quality, or attorney competence. It measures only modeled citation share at a single point in time. Retrieval profiles shift constantly.

Not legal advice. This study describes legal information retrieval. It does not constitute legal advice. AI tools described here are not a substitute for licensed counsel. Readers facing actual legal matters should consult a qualified attorney in their jurisdiction.

Contact. Editorial questions: editorial@everything-pr.com. Methodology questions: research@everything-pr.com.