AI provider compensation weakens defense; zero liability optimal in some monopoly cases
Gans's theory holds that compensation weakens defense and raises attacker profits, and zero liability is uniquely optimal under monopoly with many productive users.
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Holding AI providers liable may backfire: compensation weakens defense and raises attacker profits, and under monopoly with sufficiently many productive users, zero liability is uniquely optimal for a range of parameters.
Policy discussion has assumed provider liability improves safety, but this theory shows liability can improve welfare while increasing harm: a higher common price reduces effort on both sides without changing attack success, while compensation weakens defense.
This is a theoretical result from an SSRN preprint by Joshua Gans, economist at the University of Toronto, not empirically tested, per Tyler Cowen's blog summary.
The result is purely theoretical, with no empirical support and no independent replication; the preprint was posted to SSRN around 2026-09-23.