No one ever made the claim it was magic, not even remotely. Regarding the rest of your commentary: a) The original claim was that LLM's were not understood and are a black box. b) Then someone claims that this is not true, and they know well how LLM's work, it is simply due to questions & answers being in close textual proximity in training data. c) I then claim this is a shallow explanation because you then need to invoke additionally a huge abstraction network - that is a black box, d) you seem to agree with this while at the same time saying I misrepresented "b" - which I don't think I did. They really claimed they understood it and only offered this textual proximity thing.

In general, every attempt at explanation of LLM's that appeal to "[just] predicting next token" is thought terminating and automatically invalid as explanation. Why? Because it is confusing the objective function with the result. It adds exactly zero over saying "I know how a chess engine works, it just predicts the next move and has been trained to predict the next move" or "A talking human just predicts the next word, as it was trained to do". It says zero about how this is done internally in the model. You could have a physical black box predicting the next token, and inside you could have simple frequentist tables or you could have a human brain or you could have an LLM. In all cases you could say the box is predicting the next token and if any training was involved you could say it was trained to predict the next token.