Commons-Governed Artificial Intelligence: A Taxonomy of Collective Governance

Abstract: The governance of artificial intelligence is overwhelmingly theorized through two institutional frames. In the market frame, the data, models, and compute that constitute the AI stack are private goods exchanged under property and contract; in the state frame, a regulator imposes rules from above. A third possibility, the collective and self-organized stewardship of AI-relevant resources by the communities that produce and depend on them, remains comparatively under-theorized, even as it proliferates in practice through data trusts and cooperatives, federated learning consortia, public compute initiatives, open-weight model collaborations, and community data sovereignty regimes. This article argues that these arrangements form a coherent institutional family, which we call commons-governed artificial intelligence, and that the analytic vocabulary developed by Elinor Ostrom and her successors for common-pool and knowledge commons is the right backbone for classifying them. We contribute a two-dimensional taxonomy whose first axis is the resource layer of the AI stack held in common, distinguishing data, compute, models, knowledge and evaluation, and energy, and whose second axis is the governance function performed, derived from Ostrom design principles. We populate the taxonomy by examining the published evidence layer by layer, locate ten recurrent institutional archetypes within it, synthesize their positions through a maturity matrix and a comparative reading against the eight design principles, and treat the energy and sustainability of computation as a first-class commons-governance problem rather than an externality. We close with the tensions that constrain the project, openwashing, the compute bottleneck, free-riding, and the tension between scale and sustainability, and with a research agenda for a polycentric AI commons.