ABSTRACT
Commercial AI can generate liabilities that exceed the assets committed to paying them. This paper develops a deployment-based compensation mutual subject to two constraints: no public financial support and no admission of exposure whose credible losses exceed secured private resources. Commercial deployers remain accountable for operations; paid model suppliers contribute directly; genuinely free publication creates no financing duty. A model separates loss funding, incentives for precaution, and residual liability. It establishes a financing bound on deployment and an incidence benchmark under complete contracting. With precaution supplied by both parties, a budget-balanced division of expected losses generally fails to reproduce both social marginal incentives. Supplier participation therefore requires enforceable safety obligations as well as contributions. The analysis also distinguishes reductions in accident probability from containment of loss severity: only the latter necessarily relaxes a guarantee based on credible worst-case losses. Pro rata shareholder liability extends recovery beyond corporate assets, but receives no advance funding credit without timely, secured backing. A numerical illustration separates these mechanisms without claiming empirical calibration. Price-Anderson supplies a precedent for linking operation to financial responsibility, while its public financial mechanisms and liability ceilings are rejected. The resulting institution conditions commercial scale on privately supportable exposure and preserves claims when its forecasts or enforcement fail.
DeVadoss, John, AI Liability and Industry Funded Compensation: Deployment Responsibility and the Economics of paid Model Supply (January 8, 2026).
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