<p>Generative AI systems have revolutionized content creation, data analysis, and decision-making. Yet, they remain constrained by their inability to capture the dispersed, context-specific knowledge emphasized by Friedrich Hayek. This paper examines the “knowledge problem” in the realm of AI, explaining how large language models and other generative technologies operate on aggregated datasets that overlook tacit insights crucial for dynamic, real-world adaptation. Drawing on Elinor Ostrom’s concept of polycentric governance, we propose a framework in which decentralized decision centers manage AI development and oversight collaboratively. Such an approach preserves local autonomy while facilitating cross-community learning, helping to mitigate risks related to bias amplification, single points of failure, and systemic fragility. We use real-world examples to show how polycentric structures can foster innovation, resilience, and ethical deployment of AI. By coupling the computational power of AI with decentralized, context-rich governance, society can harness emerging technologies without sacrificing the local knowledge and human creativity that underpin genuine progress.</p>