Generative AI and Copyright: A Dynamic Perspective
S. Alex Yang, Angela Huyue Zhang
TL;DR
This paper develops a dynamic, two-period model to analyze how the fair-use standard and AI-copyrightability jointly shape incentives for AI development, creator income, and consumer welfare in the generative-AI era. By embedding endogenous data acquisition and model-improvement decisions, it reveals nuanced intertemporal tradeoffs that depend on data availability, model quality, and market growth. The analysis shows that in data-abundant regimes generous fair use generally boosts welfare and development, while AI-copyrightability has complex, regime-dependent effects; in data-scarce regimes, the policies interact more tightly and must be jointly calibrated. The work offers a dynamic, context-sensitive framework for policymakers and strategic guidance for AI firms and content creators navigating a heterogeneous regulatory landscape.
Abstract
The rapid advancement of generative AI is poised to disrupt the creative industry. Amidst the immense excitement for this new technology, its future development and applications in the creative industry hinge crucially upon two copyright issues: 1) the compensation to creators whose content has been used to train generative AI models (the fair use standard); and 2) the eligibility of AI-generated content for copyright protection (AI-copyrightability). While both issues have ignited heated debates among academics and practitioners, most analysis has focused on their challenges posed to existing copyright doctrines. In this paper, we aim to better understand the economic implications of these two regulatory issues and their interactions. By constructing a dynamic model with endogenous content creation and AI model development, we unravel the impacts of the fair use standard and AI-copyrightability on AI development, AI company profit, creators income, and consumer welfare, and how these impacts are influenced by various economic and operational factors. For example, while generous fair use (use data for AI training without compensating the creator) benefits all parties when abundant training data exists, it can hurt creators and consumers when such data is scarce. Similarly, stronger AI-copyrightability (AI content enjoys more copyright protection) could hinder AI development and reduce social welfare. Our analysis also highlights the complex interplay between these two copyright issues. For instance, when existing training data is scarce, generous fair use may be preferred only when AI-copyrightability is weak. Our findings underscore the need for policymakers to embrace a dynamic, context-specific approach in making regulatory decisions and provide insights for business leaders navigating the complexities of the global regulatory environment.