ABSTRACT
The exponential growth of artificial intelligence (AI) has brought data governance to the forefront of legal and economic debate. As global AI models hunger for massive datasets, the legal restrictions around data ownership, privacy, and intellectual property are increasingly shaping and fragmenting what kinds of data are available, how they are accessed, and who controls them. This article investigates the institutional structures governing AI data and poses the question of how privacy and intellectual property laws influence the availability and sustainability of AI training datasets. Through a comparative institutional analysis of US and EU legal regimes, it employs law-and-economics methodologies and Ostrom’s theory to frame AI training data as a commons. The paper integrates doctrinal legal review with economic reasoning to evaluate how data governance institutions simultaneously protect and constrain innovation. The article introduces the concept of a ‘tragedy of the AI data commons’, and argues that current incentive misalignments between data protection and AI development may lead to under-contribution and eventual data scarcity. It contributes to institutional scholarship by reconceptualizing data governance as a resource allocation problem and calls for new self-governance frameworks that promote sustainable and equitable access to AI data while preserving fundamental rights.
Fang, Max, The Tragedy of the AI Data Commons: Legal Constraints and the Economics of AI Innovation (September 15, 2025), Texas Intellectual Property Law Journal, volume 34, forthcoming (2025/2026).
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