AI, Human Cognition and Knowledge Collapse

We study how generative AI, and in particular agentic AI, shapes human learning incentives and the long-run evolution of society's information ecosystem.We build a dynamic model of learning and decision-making in which successful decisions require combining shared, community-level general knowledge with individual-level, context-specific knowledge; these two inputs are complements.Learning exhibits economies of scope: costly human effort jointly produces a private signal about their own context and a "thin" public signal that accumulates into the community's stock of general knowledge, generating a learning externality.Agentic AI delivers context-specific recommendations that substitute for human effort.By contrast, a richer stock of general knowledge complements human effort by raising its marginal return.The model highlights a sharp dynamic tension: while agentic AI can improve contemporaneous decision quality, it can also erode learning incentives that sustain long-run collective knowledge.When human effort is sufficiently elastic and agentic recommendations exceed an accuracy threshold, the economy can tip into a knowledgecollapse steady state in which general knowledge vanishes ultimately, despite high-quality personalized advice.Welfare is generally non-monotone in agentic accuracy, implying an interior, welfare-maximizing level of agentic precision and motivating information-design regulations.In contrast, greater aggregation capacity for general knowledge-meaning more effective sharing and pooling of human-generated general knowledge-unambiguously raises welfare and increases resilience to knowledge collapse.