Prof Gabriel Weil

Liability law could price frontier-AI risk without requiring government to predict which technical safeguards will work. Gabriel Weil frames dangerous AI development as a third-party externality: firms capture the upside while non-users inherit risks they never accepted. Prescriptive rules demand an upfront consensus that does not exist; liability instead “mechanically scales with those risks” and puts private-sector expertise to work finding cost-effective mitigations.

Existing negligence and products-liability doctrines may miss the decision that matters most: whether deploying a poorly understood frontier system was reasonable at all. Negligence typically asks whether an available precaution would have prevented the injury, not whether the activity’s total risk justified its benefits; design-defect law applies a similar alternative-design test. Pure software is also usually treated as a service, which may make products liability unavailable in many AI cases.

Weil’s strongest strict-liability case is model-level misalignment, not every AI error or malicious use. If an agent commits what would be a tort for a human, while neither the user nor an intermediary intended or could reasonably foresee it, “the buck should stop with the original developer and provider of the model.” He resists holding AI doctors or autonomous vehicles to a stricter standard than competing humans while that would slow technologies already reducing injuries and deaths.

Punitive damages are Weil’s mechanism for making otherwise uninsurable catastrophe risk financially real. When a model causes a compensable injury but the same failure “easily could have gone a lot worse,” a court could charge for the risk irresponsibly run, not merely the realized harm. If the maximum insurable loss is $1 trillion, warning shots must be roughly 10 times as likely to internalize a $10 trillion catastrophe; a 1-in-1,000 catastrophe risk would therefore need about a 1% warning-shot probability.

Insurance could become the adaptive regulator that rulebooks struggle to be. Insurers can refuse coverage, demand safeguards, or lower premiums when a lab demonstrates real risk reduction—turning safety investments into an immediate bottom-line variable. Yet a regulator would still be needed where warning shots are too rare, losses too large, or a system presents something like a 5% extinction risk: “You can’t train a model like this; you can’t deploy it.”

Proposed Rhode Island and New York bills narrowly make developers the backstop for unintended model conduct. The bills exclude new liability for misuse and malicious modification, preserve ordinary negligence and products law, and offer a human-standard defense when AI substitutes for driving, medicine, or another human function. That narrow design has generated less backlash than SB 1047’s misuse-centered politics, although neither state bill appeared likely to advance that year.

Open weights and layered AI applications make responsibility allocation as important as the liability standard itself. Closed providers, scaffolders, and customers could allocate losses through contracts, joint-and-several liability, and contribution; open-weight releases lack that contractual chain, forcing courts to identify which step “made the world riskier.” For voice cloning, deepfakes, and calling agents, Weil would distinguish ordinary negligence from strict liability by weighing avoidable misuse risk against tightly coupled positive externalities.

Private regulatory markets can complement liability, but not if certification erases claims belonging to exposed third parties. Weil’s objection to California’s SB 813 model is that users may knowingly trade their right to sue for certification, while pedestrians and the broader public never consented to the risk. His synthesis: limit certification shields to user harms, preserve third-party liability, and potentially impose strict liability on firms that decline certification.

🔗 Original source & video: Liability for AI Harms: How Ancient Law Can Govern Frontier Technology Risk, with Prof Gabriel Weil