Modeling the Economic Impacts of AI Openness Regulation

TL;DR

This paper develops a theoretical model to analyze how different AI openness regulations influence the strategic decisions of AI developers and the overall market, providing insights for effective AI governance policies.

Contribution

It introduces a stylized game-theoretic model to evaluate the impact of openness standards and penalties on AI model release and fine-tuning strategies.

Findings

Market equilibria depend on baseline model performance.

Regulatory penalties and open-source thresholds influence release strategies.

The model offers a foundation for refining AI openness policies.

Abstract

Regulatory frameworks, such as the EU AI Act, encourage openness of general-purpose AI models by offering legal exemptions for "open-source" models. Despite this legislative attention on openness, the definition of open-source foundation models remains ambiguous. This paper models the strategic interactions among the creator of a general-purpose model (the generalist) and the entity that fine-tunes the general-purpose model to a specialized domain or task (the specialist), in response to regulatory requirements on model openness. We present a stylized model of the regulator's choice of an open-source definition to evaluate which AI openness standards will establish appropriate economic incentives for developers. Our results characterize market equilibria -- specifically, upstream model release decisions and downstream fine-tuning efforts -- under various openness regulations and present a range of effective regulatory penalties and open-source thresholds. Overall, we find the model's baseline performance determines when increasing the regulatory penalty vs. the open-source threshold will significantly alter the generalist's release strategy. Our model provides a theoretical foundation for AI governance decisions around openness and enables evaluation and refinement of practical open-source policies.