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Abstract

Federal reinsurance for advanced artificial intelligence offers a credible foundation for managing risk at scale. Traditional legal tools such as regulation, litigation, and voluntary guidelines, lack the institutional capacity to address deep uncertainty, widespread spillover effects, and low-probability but catastrophic harms. A public financial infrastructure distributes risk, incentivizes responsible development, and enables earlier detection of emerging threats. Precedent exists in nuclear energy, agriculture, healthcare, and finance, where federal reinsurance enabled markets to function despite underlying volatility. The same institutional logic applies to frontier AI.

Part I explains how general-purpose and frontier AI models work, and why they have become a major policy concern. Part II reviews extant legal responses, including regulatory efforts in the European Union and California, recent developments in tort law, and the role of voluntary frameworks. Part III identifies a deeper structural gap: existing institutions are not equipped to govern fast-moving, high-stakes risks of this kind. Part IV draws lessons from historical cases where federal reinsurance helped manage similarly complex and uncertain domains. Part V develops a concrete proposal: a three-tiered system combining required private insurance, a shared industry risk pool, and a federal reinsurance backstop. The Conclusion shows how this structure limits financial fallout and creates both the incentives and information needed to govern advanced AI in a serious, adaptive, and forward-looking way.

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