  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.
