I don't think the question of are we doing the same is meaningful except on the surface, where we focus on the function that is performed, and ignore what we know about the mechanism performing the function.

On the surface, in the presence of in context learning, novel out of distribution contexts, and reactive coupling with a world context like a python repl, simulation, robot, or other source of empirical feedback, then yes, there is a sense in which LLMs do the same kinds of things, and can perform the same kinds of functions.

Given an experimental and out of distribution context that no human has seen, an LLM can generate novel hypotheses, experiment to test these hypotheses, and converge on the truth. It doesn't matter if this functionality attains from a corpus conditioned token generator, or a biological network of spiking neurons. It's important to point out that both systems support that function, without appealing to reductions, which in both cases would trivialize and obscure the higher order functions. If we're physically reductive with LLMs that throws away the functionalist view, and reduces our ability to actually expect, predict, and elicit higher order functionalities.