The Costs and Outcomes of Organizing Open-source AI Innovation: Survey Evidence from China

Open-source AI innovation has become integral to global AI development, particularly in China, where it enables distributed knowledge creation, collaborative problem-solving, and rapid advancement in technologies like machine learning models, large language models, and ethical AI frameworks. Yet success in open-source AI systems does not arise from openness alone; it rests heavily on governance processes that align heterogeneous actors, address ethical concerns such as data privacy and algorithmic bias, and sustain orderly collaboration amid rapid technological evolution. Although previous research has explored community-based governance in open-source ecosystems, relatively little attention has been given to how formal open-source organizations manage the governance costs that arise as AI projects scale and institutionalize, especially in Asian contexts where cultural values like collectivism and harmony influence collaborative dynamics. This study proposes a governance-cost framework that captures three categories of governance costs - incentive coordination, rule enforcement and value transformation - and examines how these costs mediate the relationship between governance arrangements and AI innovation outcomes. Drawing on a survey of more than 600 key informants from Chinese open-source AI organizations, the study analyzes how organizational structures influence knowledge convergence in AI algorithms, market diffusion of AI applications, and industrial collaboration for ethical AI deployment through the mechanism of governance costs. The findings show that decentralized organizations enhance knowledge convergence by reducing coordination frictions in AI model development, while firm-led and public institution-based organizations demonstrate stronger market diffusion and collaboration performance due to more stable enforcement systems and clearer value-distribution mechanisms that align with Chinese values of collective benefit and societal harmony. Governance costs significantly mediate these relationships, indicating their central role in shaping AI innovation outcomes. By identifying governance costs as foundational mechanisms, this study advances theoretical understanding of open-source AI governance and provides actionable policy guidance for designing cost-efficient, ethically grounded governance in rapidly evolving Asian AI ecosystems, emphasizing the need for policies that integrate Asian policy tendency and cultural values to counterbalance Western-dominated AI frameworks.