# Generative AI and Copyright: A Dynamic Perspective

arXiv: https://arxiv.org/abs/2402.17801 · HTML: https://mlsanity.com/paper/2402.17801/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 69 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet](https://mlsanity.com/paper/2306.06130/) (2023)
- [The impact of generative artificial intelligence on socioeconomic inequalities and policy making](https://mlsanity.com/paper/2401.05377/) (2023)
- ["Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets](https://mlsanity.com/paper/2308.05201/) (2023)

## Built on by (most cited first, 7 of 7)

- [On the Fragility of AI Agent Collusion](https://mlsanity.com/paper/2603.20281/) (2026)
- [The Economics of AI Foundation Models: Openness, Competition, and Governance](https://mlsanity.com/paper/2510.15200/) (2025)
- [From Product Search to Preference Articulation: The Economics of Agentic Commerce](https://mlsanity.com/paper/2608.08395/) (2026)
- [AI and the Dynamic Supply of Training Data](https://mlsanity.com/paper/2404.18445/) (2024)
- [Strategic Response of News Publishers to Generative AI](https://mlsanity.com/paper/2512.24968/) (2025)
- [Creative Ownership in the Age of AI](https://mlsanity.com/paper/2602.12270/) (2026)
- [The Scaling Paradox in Human-AI Collaboration](https://mlsanity.com/paper/2608.00818/) (2026)

## Abstract (arXiv)

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.