You seem to be on the "statistical next token predictor" side. I'm more.on the side of those who invented it (they should know) that think these machines can understand things
In 1964, Joe Weizenbaum created a chatbot called "Eliza" based on pattern matching and repeating back to users what they said. "He was surprised and shocked that some people, including Weizenbaum's secretary, attributed human-like feelings to the computer program." People are notorious for anthropomorphizing and attributing to things attributes (including human-like attributes) that they do not possess. [1,2] LLMs are a "statistical next token predictor" by their design. The discovery that coherent and interesting communications are relatively easily statistically modeled and reconstructed if given enough computing power and corpus of training data does not therefore imply that these programs have latent thinking and understanding capabilities.
Just the opposite: it calls into question if _we_ have thinking and understanding capabilities or if we are complicated stochastic parrots. [3] The best probing of these questions is done at the limits of comprehension and with unique and previously unseen information. I.e., how do you comprehend and process to previously unseen/unfelt/not-understood qualia? Not about how you deal with the mundanity of reactions between people (which are somewhat trivial to describe and model). [4]