The most complete public record of what happens when AI-invented law reaches a courtroom is the AI Hallucination Cases database, maintained by Damien Charlotin, a research fellow at HEC Paris. It tracks court and tribunal decisions worldwide in which a judge engaged — more than in passing — with AI-generated hallucinations in a filing: citations to cases that do not exist, quotations that appear in no opinion, and real citations attributed to the wrong case.
Two things about that number are easy to miss. First, it undercounts by construction: the database only records instances where a court caught and addressed the problem in a written decision. Fabricated citations that slipped through, were caught by opposing counsel and quietly withdrawn, or never drew a written order don't appear. Second, the consequences attach to names. These decisions identify the filing attorneys, and many impose sanctions, fee awards, or referrals to disciplinary bodies — all of it public, searchable, and permanent.
The obviously fake cite — Smith v. Jones, 999 U.S. 9999 — is actually the easy case; any lookup catches it. The dangerous one is the mismatch: a citation that resolves to a real reporter volume and page, attached to the wrong case name or the wrong proposition. It survives a glance because the citation format is perfect and the case at that address exists. It fails only when someone pulls the opinion — and in the sanctions decisions, that someone is usually the judge.
Steps 1 and 2 are mechanical, high-volume, and exactly where AI drafting fails most often — which makes them the natural place for automation. Steps 3–5 remain professional judgment, and any tool that claims otherwise is overselling.
LegalCite verifies that citations exist in public court records (CourtListener) and match the case names attributed to them. It does not assess good-law status or holdings, and nothing here is legal advice. Sources: AI Hallucination Cases database (count verified July 2, 2026); PlatinumIDS analysis (early-2026 count).