When Does Regulation by Insurance Work? The Case of Frontier AI
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911477828419584 |
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| author | Trout, Cristian |
| author_facet | Trout, Cristian |
| contents | No one doubts the utility of insurance for its ability to spread risk or streamline claims management; much debated is when and how insurance uptake can improve welfare by reducing harm, despite moral hazard. Proponents and dissenters of "regulation by insurance" have now documented a number of cases of insurers succeeding or failing to have such a net regulatory effect (in contrast with a net hazard effect). Collecting these examples together and drawing on an extensive economics literature, this Article develops a principled framework for evaluating insurance uptake's effect in a given context. The presence of certain distortions - including judgment-proofness, competitive dynamics, and behavioral biases - creates potential for a net regulatory effect. How much of that potential gets realized then depends on the type of policyholder, type of risk, type of insurer, and the structure of the insurance market. The analysis suggests regulation by insurance can be particularly effective for catastrophic non-product accidents where market mechanisms provide insufficient discipline and psychological biases are strongest. As a demonstration, the framework is applied to the frontier AI industry, revealing significant potential for a net regulatory effect but also the need for policy intervention to realize that potential. One option is a carefully designed mandate that encourages forming a specialized insurer or mutual, focuses on catastrophic rather than routine risks, and bars pure captives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06597 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Does Regulation by Insurance Work? The Case of Frontier AI Trout, Cristian Computers and Society No one doubts the utility of insurance for its ability to spread risk or streamline claims management; much debated is when and how insurance uptake can improve welfare by reducing harm, despite moral hazard. Proponents and dissenters of "regulation by insurance" have now documented a number of cases of insurers succeeding or failing to have such a net regulatory effect (in contrast with a net hazard effect). Collecting these examples together and drawing on an extensive economics literature, this Article develops a principled framework for evaluating insurance uptake's effect in a given context. The presence of certain distortions - including judgment-proofness, competitive dynamics, and behavioral biases - creates potential for a net regulatory effect. How much of that potential gets realized then depends on the type of policyholder, type of risk, type of insurer, and the structure of the insurance market. The analysis suggests regulation by insurance can be particularly effective for catastrophic non-product accidents where market mechanisms provide insufficient discipline and psychological biases are strongest. As a demonstration, the framework is applied to the frontier AI industry, revealing significant potential for a net regulatory effect but also the need for policy intervention to realize that potential. One option is a carefully designed mandate that encourages forming a specialized insurer or mutual, focuses on catastrophic rather than routine risks, and bars pure captives. |
| title | When Does Regulation by Insurance Work? The Case of Frontier AI |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2512.06597 |