Ioannis Tarnanas

MIT's Nobel Prize-winning economist proved that AI is mathematically guaranteed to destroy human knowledge. They published a massive NBER paper modeling the long-term impact of AI on human cognition. And they found the most alarming conclusion in the AI literature so far. It’s called "Knowledge Collapse." Here is how human progress actually works. When you struggle to solve a complex problem, you generate two things: General knowledge about how the world works, and context-specific knowledge about your exact problem. Normally, humans acquire both at the same time. You do the hard work to solve your specific problem, and in the process, you learn a general principle. You share that principle. That is how human knowledge grows. Then comes Agentic AI. AI is incredibly good at giving you the exact, context-specific answer you need right now. It hands the solution to you on a silver platter. So you stop doing the hard work. And because you stop doing the work, you stop generating the "general knowledge" that society relies on. Acemoglu calls it the "knowledge-collapse equilibrium." When AI reaches a certain accuracy threshold, the incentive for humans to learn drops to zero. Nobody verifies. Nobody explores. Nobody discovers new fundamental truths. Society gets increasingly sophisticated automated outputs, while our actual capacity to generate new knowledge quietly erodes. But here is the most terrifying finding in the paper. Welfare is "non-monotone" to AI accuracy. That means as AI gets more accurate, society actually gets worse off.