What they're getting towards (one thing, anyways) is that "indistinguishable to the model" and "the same" are two very different things.

IIRC, one possibility is that LLMs which learn from one another will make such incremental changes to what's considered "acceptable" or "normal" language structuring that, over time, more noticeable linguistic changes begin to emerge that go unnoticed by the models.

As it continues, this phenomena creates a "positive feedback loop" in which the gap progressively widens -- still undetected, because the quality of training data is going down -- to the point where models basically "collapse" in their effectiveness.

So even if their output is indistinguishable now, how the tech is used (I guess?) will determine whether or not a self-destructive LLM echo chamber is produced.