AI and the Problem of Knowledge Collapse

The article argues that widespread reliance on AI systems could cause "knowledge collapse," a process in which reduced access costs for certain knowledge paradoxically harm public understanding, innovation and culture. Andrew J. Peterson explains that large language models tend to generate output toward the center of their training data distribution, which is useful but can narrow the diversity of knowledge. The author's model of a community of learners choosing between traditional methods and a discounted AI-assisted process finds that a 20% discount on AI-generated content produces public beliefs 2.3 times further from the truth than no discount. The article also notes that humans, unlike AI models, may seek out diverse knowledge if they see it as worthwhile.