How AI training on AI-generated data triggers irreversible decline

Training new AI models on synthetic data from older models recursively degrades quality—a phenomenon called model collapse. Researchers developed the first economic theory of this problem, showing that targeted subsidies for human-created data (worth roughly the divergence between real and synthetic distributions) can improve final model quality by 23% while reducing drift in diversity metrics.