A theoretical analysis
βIn this paper, we introduced a simple framework to study the implications of new generative Al technologies that promise to provide context-specific information and recommendations to human decision-makers. Our framework is based on three core ideas:
1. Good human decisions combine general knowledge with context-specific information.
2. Human effort directed at improving cognition generates both types of information, with the primary private return coming from context-specific information.
3. Individual contributions to general knowledge create externalities on others who build on this general knowledge.
These three observations together imply that the main motive for individual effort is often the acquisition of context-specific information, while the general knowledge an individual generates is primarily an externality. Consequently, better general knowledge in society is a complement to human learning effort, while better context-specific recommendations are substitutes. Because generative Al promises to provide this kind of context-specific information, it can be such a substitute, and reduce human effort. But then as lower human effort reduces general knowledge-building, generative AI (especially agentic AI) can dynamically push a society towards lower effective information and even lead to a knowledge-collapse steady state.β
https://www.nber.org/system/files/working_papers/w34910/w34910.pdf