AI for Web3 Ecosystems: A Socio-Technical Conceptual Framework

AI is increasingly used to improve Web3 infrastructure, smart contracts, and decentralized applications, but its effects are not only technical. This paper develops a socio-technical framework for examining AI in Web3, grounded in the Task, Technology, People, and Structure dimensions of Leavitt’s Diamond. Through a systematic review of 370 studies, we analyze how AI reshapes Web3 across the infrastructure, execution and logic, and application layers. We show that AI expands operational tasks, introduces new technical dependencies, redistributes expertise and decision-making, and alters governance structures such as incentives, decentralization, privacy, and accountability. Across the three layers, we identify six research agendas: the task complexity spiral, the decentralization-capability trade-off, role reconfiguration and capability inequality, the diagnostic-governance response gap, the accountability gap, and privacy erosion through asymmetric monitoring. The paper contributes a cross-layer socio-technical framework for evaluating AI for Web3 and outlines directions for building systems that are not only technically effective but also institutionally accountable.