Model Collapse

Model collapse is a theoretical failure mode in which AI models trained on AI-generated data gradually lose the diversity and quality of their outputs — because they're learning from synthetic data rather than the rich variety of human-generated content. As AI generates more of the text on the internet, future training data increasingly contains AI output, potentially degrading model quality over time. Model collapse is more of a concern for AI developers than for business users, but it's relevant context for understanding why data provenance and quality matter in AI development.