Agentic Learning DAOs for Sustainable Energy Transition

This chapter proposes an adaptive, AI-enhanced blockchain framework for incentivizing research on sustainable energy systems. Building on the multi-level perspective (MLP), institutional theory, and learning organization paradigm, the model couples blockchain-based digital autonomous organizations (for transparent, tokenized rewards and on-chain governance) with artificial intelligence (AI) agents (for data-driven evaluation, adaptive rule setting, and automated verification). Within the MLP, the AI–blockchain niche challenges incumbent research regimes by rapidly funding high-risk, high-impact projects and learning from outcomes in real time. Institutional theory clarifies how smart-contract rules and AI monitoring can realign academic norms—prioritizing open data, replication, and interdisciplinary collaboration—while providing the legitimacy and accountability needed for wider adoption. The learning organization lens frames the DAO community as a reflexive system, where blockchain supplies immutable feedback loops, and AI agents convert those loops into continuous improvements of incentive parameters and strategic direction. Together, these technologies create a decentralized yet intelligent incentive architecture that overcomes siloed structures, accelerates knowledge diffusion, and fosters collective problemsolving. The chapter argues that such AI–blockchain integration offers a viable pathway to transform research incentives, mobilizing diverse actors and speeding sociotechnical transitions toward global sustainable energy transitions