Executive Summary

As content producers increasingly gate material in response to AI-driven substitution—despite no changes to fair use law—there is growing risk that socially valuable inputs may disappear from the generative AI training ecosystem. This paper proposes a narrowly tailored, contingent licensing scheme to preserve access to high-value content when market failures prevent voluntary licensing. The scheme activates only when three conditions are met: (1) the content is demonstrably valuable for training; (2) the producer is economically marginal—that is, likely to restrict or withdraw access absent compensation; and (3) voluntary licensing has failed due to high transaction costs or bargaining asymmetries. While the proposal is focused on economically marginal creators at risk of exit, it allows for future extension to inframarginal producers if systemic gating emerges (defined here as a sustained, measurable reduction in access to critical content, whether by a majority of producers or by a small set whose gating materially degrades model performance). Drawing on the model of compulsory music licensing, the fallback mechanism operates only when necessary and always includes an opt-out, offering a light-touch intervention to sustain open access without undermining innovation or core publication incentives. In this way, the proposal aims to preserve innovation conditions when asymmetric withdrawal risks distorting competition and locking in advantages for firms with early licensing deals or deep proprietary libraries. Stronger measures that compel content creators to license their works, and without an opt-out, are considered but tentatively rejected as inefficient and likely to distort functioning markets.