Language models are not databases and they are not markov bots (similar function but work directly using statistical word association maps). The big difference is that those things are algorithms someone wrote and can fully comprehend what they do, but machine learning models are large algorithms built by another algorithm processing training data. There is much more uncertainty about what is going on under the hood.

There is also great uncertainty about what concepts like understanding or thinking might mean in computer science terms. The main thing we can really know is that ultimately a human mind is a computer, which means that understanding and thinking have some yet unknown mathematical representation, and therefore a comparison can be made. We should eventually be able to quantify whether or to what extent a given algorithm thinks. But you said in another comment that you don’t believe minds can be represented mathematically; this should mean that such comparisons would be apples to oranges, but you’re making them anyway for some reason, and implying they have predictive power for the limitations of LLMs.

Certainly they do have limitations, at least individually and possibly as a technology. There are things given models are bad at, there are things they initially seem to be able to do well as humans but fail in different ways that suggest over-reliance on pattern matching. But these have been determined empirically through testing. The idea that they are “just statistical models” and this knowledge can be used to say what is impossible for them from philosophical first principles keeps getting repeated but has never worked in practice. The reality is that no one knows enough to say for sure where the line is.