Nope. The main purpose of the whole endeavor is usually to predict the behavior of a complex system, because that's actually what we care about. If we can predict it, we can adapt to it, and eventually use it to our advantage.
Explaining why a complex system is the way it is, is merely nice-to-have. Models are opinions. All of them are wrong, but some are useful, and we rank them by how useful they are. The models and explanations are important because, beyond their elegance and convenience, it's also the case that more accurate models give you better predictions across larger domains, meaning we get better at getting something useful out of the complex system.
People get fixated on modern theoretical science, with bottom-up mathematical explanations traced through seas of empirical data, with whole magical rituals of peer review and double-blind studies and statistical significance around them. But they forget that the core of empirical science is literally throwing shit at a wall to see what sticks. That is the guiding principle, everything else is just making the process more efficient.
Understanding complex natural systems (or even engineered ones that got too complex) always starts with tests - tests on the real thing, then on approximate models that we poke and prod and bash into shape until they start acting similarly to the real thing. It's through the poking and bashing, and how they affect our proxy model, that we glean insights into nature of the simulated phenomena, and eventually formulate general theories - but more importantly, the models give us useful predictions from the start, before we have any theories explaining why.
I'm a scientist. Believe it or not, I believe in substantially more than prediction and I think its rather trivial to come up with examples where mere prediction is insufficient to meet a normal person's notion of an account of a thing (eg, pre-copernican planetary motion). I'm not saying you are wrong, per se, just that the idea that "it was prediction all along" is a very specific idea of what human beings are interested in and what we are up to.
> that we glean insights into nature of the simulated phenomena
That is right - most people believe that there is a simulated phenomenon "out there" that we learn about. I think there are strong reasons to believe this having to do with how models are related to predictions. The wrong ontology can make prediction very hard and the right one can make prediction substantially easier. Arguably, we are in that situation right now with language models - we just threw a lot of parameters at the problem and now we are able to predict but we still don't really understand. This is perhaps inevitable in the case of language, but I don't think we should look at models with tons of degrees of freedom and the ability to predict things as a death knell for the very idea of deeper understanding.
This is just your own idiosyncratic and biased belief. You're not describing anything objective about LLMs, you're describing your personal attitude to them. This colors your understanding in a way that can't really be reasoned with until you let go of the artificial constraints you're imposing on your own understanding.
If the LLM in their pocket has a more robust world model than they themselves and is e.g. able to refute their irrational convictions, it actually seems like a very good idea. (Big if, of course.)