LLMs are modelled to predict the next token, and are indeed trained to do so on enormous bodies of text. But to be really good at predicting the next token (word) at the end of a long string of text, you must understand what the text means. If I give you the entire text of a long novel and at the end ask you a single "yes/ no" question about the plot, you only need to emit a single token, but emitting the correct one implies having understood the plot of the novel. This is what LLMs do. They're generating meaningful, coherent text, which implies understanding and cognition at a level that is much deeper than that of the single token they generate at each forward pass. Internally, the LLM has learned to represent the meaning of the entire prompt text, the concepts it implies and its possible continuations far beyond the horizon of simply outputting the next token.