Forthcoming in Cornell Journal of Law and Public Policy

This Article examines the relationship between organizational form and the integrity of knowledge production in generative artificial intelligence (GenAI) firms. As large language models and related technologies increasingly function as public epistemic infrastructures, their institutional architecture becomes a matter of urgent legal and policy concern. Drawing on theories of institutional design and the public-good nature of knowledge, the Article argues that hybrid models, those who ostensibly combine for-profit incentives with public-interest commitments, such as the current form of OpenAI, are structurally flawed. While such entities claim to balance commercial efficiency and ethical governance, they in fact lack enforceable accountability mechanisms and fail to align fiduciary duties with epistemic integrity.

The Article analyzes different organizational forms: traditional for-profit corporations, nonprofit institutions, and hybrid entities, and finds that for-profit models optimize scalability but risk epistemic degradation; nonprofit models safeguard mission fidelity but struggle with capital mobilization; and hybrid models suffer from dual incoherence, inheriting the limitations of both without securing the strengths of either. The Article concludes that preserving the democratic legitimacy and epistemic efficiency of GenAI systems requires legal innovation in organizational form, before structural path dependencies become entrenched.