Something tells me that the author [0] is probably well aware of how these work under the hood, and the math behind it - When writing scientific articles with a laymen audience in mind, you'll often have to use laymen-specific terms. But feel free to enlighten us further!
Whatever his credentials, what he says is plain wrong. GPTs don't follow "the grass is" with "green" because it's the most probable continuation- this idea is incredibly naive and breaks down with sentences longer than a few words. And GPTs don't crowdsource the answers to questions, their answers are not necessarily the most common, and neither "the consensus view is determined by the probabilities of the co-occurrence of the terms"- there is no such algorithm implemented anywhere.
What LLMs crowdsource is a world model, and they need an incredible amount of language to squeeze one out from it, second hand. We do train them for the ability to predict the next word, which is a task that can only be performed satisfactorily by working at the level of concepts and their relationships, not at the level of words.
> We train them for the ability to predict thr next word, which is a task that can only be performed satisfactorily by working at the level of concepts and their relationships, not at the level of words.
I think what they mean (not OP here so just chiming in to to try interpret and answer your question) is that you don't know what you are talking about.
[0] - https://en.wikipedia.org/wiki/Jim_Waldo