I think the author is arguing against the idea of a next-token predictor as something that simply uses the weights in the neural net which record the probabilities of tokens following other tokens as a valid definition of what an LLM is. Essentially a massive extreme markov chain.

With reinforcement learning and probably attention and other tricks that affect the weights based on things that aren't strictly in the training data, so the argument goes, you can end up with tokens following strings of tokens that would not be possible to be output with the training data and original weights alone. So describing it as solely a next-token predictor is incorrect based on this framing of it.

But that's just my take on this, I'm still trying to wrap my head around it all.