LLMs are nothing like Markov chains. Deep learning models / neural networks are universal approximators. That is a technical term for models that are capable o

LLMs are nothing like Markov chains. Deep learning models / neural networks are universal approximators. That is a technical term for models that are capable of learning to model ANY finite relationship. The mathematical requirement is just to have two layers, with enough units in the first layer. And the term finite relationship means any mapping that involves a finite number of discontinuities. (Not a practical limitation, since there is no alternative way to model relationships that cannot be characterized without an infinite amount information anyway.) So yes, they learn algorithms. With more layers and recurrent connections, they are vastly more efficient as well. Deep learning models don't just do associations, or correlations, or statistical things like conditional probability. They learn functional relationships between concepts. Without limit in terms of the complexity of those relationships, given enough data, parameters and computing time. So no, nothing remotely as limited as Markov chains. Your pessimism about them has some merit. Just not relevant here.