Collectively governed AI is the institutional model in which AI models are owned, governed, and directed by the communities whose knowledge constitutes their training data, rather than by private corporations that extracted the training data as a business input. The model treats the intelligence commons — the accumulated cognitive output of a profession, a community, or humanity at large — as a genuinely shared resource whose monetization should return value to its producers. Such structures are technically feasible and politically difficult, which is precisely why Noble's framework identifies them as a suppressed alternative worth naming.
The institutional template draws on multiple precedents. Cooperative ownership structures, well-established in agriculture and credit unions, provide one model. Data trusts, as theorized by Jaron Lanier and E. Glen Weyl, provide another. Open-source software foundations like the Apache Foundation or the Linux Foundation demonstrate that significant technological development can occur under collective governance structures. The question is not whether such structures can work but