Saying that AI is "matrices" is like saying human cognition is "neurons." Maybe true at some level, but it's a low-level implementation detail. The important part of a language model is the function that maps tokens to contextual embeddings. You could compute this function using analog computing, biological neurons, or any other substrate.
What's the argument here? It's not about the implementation method, it is about the behavior that emerges from it. You could calculate the next token by hand on paper if you had enough time.
It's incredible how the “Chinese Room” argument, as well as its counterarguments, is still incredibly pertinent despite being now more than 40 years old. Scientific American published many articles on this topic in the Eighties; they seem as fresh as ever.
