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The Tragedy of the AI Data Commons
Max Fang's February 2025 Stanford working paper applying Ostrom's institutional framework to AI training data — the most rigorous contemporary analysis framing AI training data as a commons subject to regime change without community participation.
Max Fang's February 2025 Stanford working paper, titled "The Tragedy of the AI Data Commons," applies law-and-economics methodologies alongside Ostrom's design principles to frame AI training data as a commons undergoing unilateral regime change. The paper's central argument: the training data from which large language models learn was contributed by millions of individuals under governance arrangements designed for human consumption — the norms of the open internet, terms of service of social platforms, licensing frameworks of academic publishing. The appropriation of this data for AI training represents a fundamental shift in the terms under which the resource is used, undertaken without the participation of the community whose contributions constitute the resource.
In The You On AI Field Guide
The paper is significant because it brings Ostrom's framework to bear on the training data question with the analytical rigor both the economic and institutional dimensions require. Prior debates had typically