Then the missing diversity comes from the non-AI-generated stuff that’s included in the mix.

I’m not sure what the problem is here. The cause of model collapse when AIs are fed on the output of previous generations is that the rare “fringes” of the data are lost over time. The training data becomes increasingly monotonous. Adding that fringe data back in should cure that.