Going one level deeper, even AI-generated training data which doesn't contain hallucinations or other obvious errors could still negatively affect the performan

Going one level deeper, even AI-generated training data which doesn't contain hallucinations or other obvious errors could still negatively affect the performance of a new model. GenAI models have a tendency to repeat the same cliches, in sentence structure or the composition of images or whatever else, so even if their output is "fine" in the sense that it's factually correct and everyone has the right number of fingers, a model trained on it will still internalize those cliches and make them even more likely to show up again. That's even harder to audit out because it would only become apparent in aggregate when you realise your new model has learned to interpret "fantasy artwork" as "fantasy artwork in the generic Midjourney RLHF style" since it was trained on so many examples of that.