Thanks - that makes sense. On that basis the article’s thesis is totally wrong - it would be like a computer program rating its ability based on how well it predicts moves played by grandmasters in the past. It’s not inventing new moves.
LLMs are not simple things like a Markov model, there's a lot going on in there, it's not deterministic, and it's completely capable of creating entire new styles of play based on complex interactions of internal states.
"Large Language Models as Markov Chains" https://www.alphaxiv.org/pdf/2410.02724
Anything that's sequential, like language is, will exhibit Markovian properties, and be somewhat a "Markov" model.
Markov chains are a different concept than a Markov model, but I do agree that, technically, an LLM is a Markov model, just with an internal state space that is nothing like what is usually meant when ML people refer to Markov models.