Earlier quoted context omitted.
In the same way we can train models that can identify people from their choice of words, phrasing, grammar, etc, we can train models that identify other models.
That's anthropomorphizing them - a large language model doesn't have a bottleneck the same way a human does (in terms of being able to express things), it can get on a path where it just outputs memorized text directly and it won't be consistent with what it usually seems to know at all. Also, you could break a discriminator model by running a filter over the output that changes a few words around or misspells things…
But yes, you could break the discriminator model, in the same way people disguise their own writing patterns by using synonyms, making different grammar/syntax choices, etc. Building a better evader and building a better detector is an eternal cat and mouse game, but it doesn't reduce the need to participate in this game.