Balancing Coordination and Creativity in Asian AI Applications: The Cost and Outcome of Open-source AI Innovation in China

Open-source AI has become integral to global AI applications. Yet success in open-source AI systems does not arise from openness alone; it rests heavily on management that aligns heterogeneous actors, addresses benefit concerns, and sustains orderly collaboration amid rapid technological evolution. Although previous research has explored community-based management in open-source ecosystems, little attention has been given to how formal open-source organizations manage the costs that arise as AI projects scale and institutionalize, especially in China contexts where collectivism and harmony influence collaborative dynamics. This study proposes a cost-outcome framework that captures three management costs and examines how these costs mediate the relationship between management arrangements and AI innovation outcomes. Drawing on a survey of more than 600 key informants from Chinese open-source AI organizations, it reveals how organization structures influence knowledge convergence in AI algorithms, market diffusion of AI applications, and industrial collaboration through the mechanism of incentive coordination, rule enforcement and value transformation. This study reveals a fundamental trade-off between openness and control that defines the management of open-source AI ecosystems: decentralized structures foster creativity but risk inefficiency and value leakage in ethical AI, whereas formalized systems secure coordination and diffusion but may suppress diversity and slow boundary spanning.