Insurance can discipline risky frontier AI activity for catastrophic harms, but market forces alone are unlikely to realize that benefit; targeted mandates to foster specialized insurers and exclude pure captives are needed to turn insurance into an effective regulatory tool.

Main Finding

Insurance can produce a meaningful net regulatory effect (i.e., reduce harm relative to a no-insurance baseline) but only under limited conditions. The paper develops a two-step framework: (1) identify the background distortions insurance can correct (this determines the potential for a regulatory effect); (2) assess whether insurers can realistically realize that potential by mitigating moral hazard. Applying the framework to frontier AI, the author finds significant potential for insurance to improve safety—especially for catastrophic, non-product accidents—but that policy intervention (a carefully designed insurance mandate that encourages specialized insurers or a mutual, focuses on catastrophic risks, and bars pure captives) will likely be required to realize that potential.

Key Points

• Definition and core question

  • “Regulation by insurance” = insurers’ capacity to reduce activity/care-related harm net of the moral hazard insurance creates.
  • Net regulatory effect exists when insurers’ preventive efforts more than offset moral hazard.

• Two-step evaluative framework

• Catalog distortions and biases that create potential for insurance to improve outcomes (the “potential”).

• Analyze insurer incentives, tools, and market structure to see if insurers will realize that potential (the “realization”).

• Catalog of background distortions (sources of potential)

  • Judgment-proofness (firms lack assets to internalize full liability).
  • Competition dynamics: races for market share, winner-take-all effects, fixed/upfront costs, R&D spillovers.
  • Information asymmetries (buyers less informed than sellers).
  • Collective-action problems and the “unilateralist curse” (industry-level reputation externalities).
  • Financial distress and firm survival motives.
  • Charity hazard (expectation of government bailouts).
  • Behavioral biases (overconfidence, availability bias, optimism in young/venture-funded firms).

• Accident typology matters

  • Product vs non-product accidents; catastrophic vs routine/frequent losses.
  • The author argues insurance has less scope to improve outcomes for routine/product accidents (market and liability often already provide discipline), and more scope for catastrophic, non-product accidents where market discipline is weak and psychological biases are strong.

• Moral hazard and its scale

  • Moral hazard increases with coverage limits and is a central countervailing force.
  • Realization of regulatory potential depends on insurer ability to monitor, price, condition coverage, and exert credible discipline.

• Determinants of insurer effectiveness (factors that influence realization)

  • Policyholder type (startups vs incumbents, public vs private, governance structures).
  • Risk type details:
    • Liability vs third-party moral hazard vs dynamic (learning) risks.
    • Magnitude/concentration vs frequency of losses.
    • Predictability and novelty of losses.
  • Insurer type: specialized insurers, mutuals, captives, reinsurers differ in incentives and capacity to exert discipline.
  • Market structure: competition among insurers can either strengthen discipline (through underwriting expertise) or weaken it (underpricing to capture share, coordination failures).

• Application to frontier AI (diagnosis)

  • Frontier AI exhibits many of the distortions insurance might fix:
    • Potential judgment-proofness for some actors.
    • Strong race dynamics and first-mover incentives (existential race for market share).
    • Large positive spillovers and high fixed upfront costs for safety R&D.
    • Huge industry-wide reputation externalities from a catastrophic incident (AI “Three Mile Island”) and a unilateralist curse.
    • “Too big to fail” concerns for major developers.
    • Behavioral immaturity: overconfidence and optimism among founders and VCs.
  • These features create substantial potential for a net regulatory effect—particularly for rare, catastrophic, non-product harms that private markets and liability will not discipline well.

• Policy prescription (how to realize the potential)

  • A narrowly targeted, deep, and restrictive insurance mandate is preferable to a broad, shallow, permissive one.
  • Optimal elements include:
    • Mandate focusing on catastrophic risks that are otherwise excluded.
    • Encouragement or requirement to form a specialized insurer or mutual (to align long-run incentives and capacity to underwrite novel catastrophic risks).
    • Barring pure captives (to avoid evasion of discipline).
    • Synergy with no-fault or exclusive liability channels may improve feasibility.
  • Without intervention, the market is unlikely to produce the specialized insurance structure needed.

• Anticipated objections and responses

  • Pricing difficulties: insurer information-gathering plus staged/conditional coverage can help price novel risks, but uncertainty is real and must be managed via market structure and mandate design.
  • Politics and lobbying: the insurance industry may push for permissive mandates; careful policy design and narrow scope can reduce capture.
  • Crowding out ex ante regulation: a well-designed insurance regime should complement, not replace, other regulatory tools; narrow catastrophic focus reduces substitution concerns.

Data & Methods

• Approach: theoretical and applied economic framework built from first principles plus a synthesis of an extensive economics and law-literature on insurance, moral hazard, and regulation-by-insurance.
• Tools:
  • Formal conceptual model: characterization of efficient care, introduction of background distortions, and analysis of moral hazard vs insurer corrective forces.
  • “Accident matrix” taxonomy (product vs non-product; catastrophic vs routine) to structure where insurance is likely/unlikely to help.
  • Cataloging of distortions (drawing on economic theory and prior empirical/case-study literature).
  • Comparative case studies (Appendix) examining historical insurer behavior in catastrophic-risk domains: cyber, pandemics, nuclear, terror—used to extract lessons about insurer capacity to price and discipline catastrophic risks.
  • Application of framework to frontier AI using industry-specific diagnostics: assessment of judgment-proofness likelihood, competitive dynamics, R&D spillovers, reputation/externality risks, TBTF considerations, and behavioral traits of actors.
• Not empirical estimation: the paper is primarily conceptual and prescriptive, using illustrative case studies and economic reasoning rather than new statistical estimation.

Implications for AI Economics

• Relevance of insurance as a governance instrument

  • Insurance can be a lever to internalize some of frontier AI’s externalities where private markets or liability alone fail—especially for catastrophic, systemic harms.
  • Economists modeling AI governance should treat insurance as a non-trivial policy lever with structured impacts on incentives, investment in safety R&D, and firm behavior.

• Market design matters

  • Standard, competitive, broad liability insurance markets are unlikely to produce optimal regulatory outcomes for frontier AI. Market structure (specialized insurers, mutuals, reinsurers) and regulatory design (mandates that shape insurer entry and permitted product design) will crucially affect outcomes.
  • Models of firm behavior and social welfare need to incorporate insurer heterogeneity (e.g., specialized mutual vs captive vs diversified insurer) and strategic interactions between firms and insurers.

• Interaction with competition, innovation, and R&D

  • Insurance-based regulation could mitigate the “race to the bottom” in safety investments created by winner-take-all competition, by shifting marginal incentives and funding safety R&D through insurers’ underwriting and loss-control provisioning.
  • However, pricing and coverage design must be attentive to R&D spillovers and to not stifle beneficial innovation—suggesting targeted coverage (catastrophic, tail risks) rather than broad coverage of routine operational errors.

• Macro- and systemic risk considerations

  • For systemic catastrophic AI risks, private insurance markets will face severe aggregation and correlation problems. Policy should consider backstops, reinsurance structures, and mechanisms to avoid insurer insolvency or perverse bailout incentives.
  • Insurer role in information aggregation and monitoring can help resolve some informational frictions in AI risk assessment; this should be modeled explicitly in policy and economic analyses.

• Research agenda and modeling implications

  • Empirical work: estimate likely distributions of catastrophic AI harms, firm balance-sheet exposures, and the extent of judgment-proofness across different firm types.
  • Market-formation models: analyze how mandates, mutual formation, and restrictions on captives affect insurer entry, pricing, and incentive provision.
  • Game-theoretic models: capture dynamic races, R&D spillovers, and insurer–firm strategic interactions (including signaling, monitoring, and underwriting tools).
  • Policy-welfare simulations: quantify tradeoffs between moral hazard and insurer-induced risk reduction under alternative mandate designs.

Overall, the paper argues that treating insurance as a carefully structured governance instrument—targeted at catastrophic, non-product AI harms and supported by market design and limited mandates—could materially improve safety incentives in frontier AI. But realizing that promise requires deliberate policy to shape insurer types, coverage scope, and market structure so that insurers can credibly and effectively discipline risky behavior without being undermined by moral hazard or competitive undercutting.