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.

翻译：人工智能的治理主要通过两种制度框架进行理论化。在市场框架中，构成AI堆栈的数据、模型和算力是在财产与契约框架下交换的私人物品；在国家框架中，监管机构自上而下施加规则。第三种可能性——由生产并依赖AI相关资源的社群对其进行的集体式、自组织的管理——在理论上仍相对薄弱，尽管在实践中它通过数据信托与合作社、联邦学习联盟、公共算力计划、开放权重模型协作以及社群数据主权制度等形式日益普及。本文认为，这些安排形成了一个连贯的制度家族，我们称之为“公共治理的人工智能”，而埃莉诺·奥斯特罗姆及其后继者针对公共池塘与知识公共资源所发展的分析词汇，正是对其分类的恰当基础。我们提出一个二维分类法：其第一轴是AI堆栈中作为公共资源持有的资源层，区分为数据、算力、模型、知识及评估，以及能源；其第二轴是所执行的治理功能，源自奥斯特罗姆的设计原则。我们通过逐层检视已发表证据来填充该分类法，在其中定位十个反复出现的制度原型，通过成熟度矩阵和与八项设计原则的比较性解读综合其定位，并将计算的能源与可持续性视为一等公共资源治理问题而非外部性。最后，我们探讨制约该项目的张力——绿色洗涤、算力瓶颈、搭便车问题，以及规模与可持续性之间的张力——并展望一个多中心AI公共资源的研究议程。