A Formal Model of the Economic Impacts of AI Openness Regulation

2025-07-14

AI Summary

Ambiguity in defining “open-source foundation models” and the absence of standardized openness criteria hinder effective AI governance. Method: We formulate a two-tiered game-theoretic model between generalist model creators and specialist fine-tuners to analyze how alternative open-model regulatory policies affect upstream release decisions and downstream fine-tuning effort. Contribution/Results: We propose a novel performance-threshold-based framework for defining open-source models and identify asymmetric incentive effects of regulatory penalties and openness thresholds on both stakeholders. Through formal modeling, incentive-compatibility analysis, and policy simulation, we quantify market responses under multiple openness standards and characterize an effective regulatory regime: moderate penalties combined with calibrated performance thresholds jointly promote innovation incentives and risk mitigation. Our work provides a practically implementable paradigm for defining open-source AI models and informs differentiated, evidence-based regulatory design.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Technology governance, policy, and regulations

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.

Problem

• Modeling economic impacts of AI openness regulation
• Defining ambiguous open-source foundation models
• Evaluating incentives for AI developers under regulation

Innovation

• Modeling strategic interactions under openness regulations
• Evaluating economic incentives via open-source definitions
• Characterizing market equilibria with regulatory penalties