Transformation of open-source innovation governance of large models

Artificial general intelligence (AGI), represented by large-scale models, is a transformative driver of new-quality productive forces, making the effective construction and governance of open-source innovation commons increasingly critical. Yet traditional open-source models are under strain, and existing commons theories require further development. Model training costs rise exponentially with each iteration, while a sustainable profit cycle from innovation to industrial application has not been established, rendering long-term cost-bearing infeasible for individual organizations. The traditional community-enterprise collaboration model, once successful in generating ecosystem value, now faces escalating computing and data costs, uncertain commercial returns, and governance risks related to social and national security. Through comparative case analysis of OpenAI and the National AI Research Resource Pilot in the United States, this study identifies emerging dilemmas confronting social and commercial actors in supplying large-model innovation commons. It finds that government actors are increasingly central in governance. Accordingly, the study proposes a governance framework structured around diversified actor roles, governance dimensions, and phased processes, extending commons governance theory under conditions of new-quality productive forces. It further argues that China, as a major global market and innovation hub for large-scale models, should explore a tripartite society-market-government governance model to move beyond traditional society–market collaboration and capture the transformative potential of large-scale models.