The environment for the model is its dataset and training algorithms. It's literally a model of it, in the same sense we are models of our physical (and social) environment. Human-like behavior is of course too specific, but highest level things like staged learning (pretraining/posttraining/in-context learning) and evolutionary/algorithmic pressure are similar enough to draw certain parallels, especially when LLM's data is proxying our environment to an extent. In this sense the GP is right.