AI for Scientific Discovery is a Social Problem

Artificial intelligence (AI) is increasingly applied to scientific research, but its benefits remain unevenly distributed across communities and disciplines. While technical challenges such as limited data, fragmented standards, and unequal access to computational resources exist, social and institutional factors are often the primary constraints. Narratives emphasizing autonomous "AI scientists," under-recognition of data and infrastructure work, misaligned incentives, and gaps between domain experts and machine learning researchers all limit the impact of AI on scientific discovery. This paper highlights four interconnected challenges: community coordination, misalignment of research priorities with upstream needs, data fragmentation, and infrastructure inequities. We argue that addressing these challenges requires not only technical innovation but also intentional efforts in community-building, cross-disciplinary education, shared benchmarks, and accessible infrastructure. We call for reframing AI for science as a collective social project, where sustainable collaboration and equitable participation are treated as prerequisites for technical progress