Let's agree to disagree then. An LLM has no notion of semantics, it's just outputting the most likely word to follow up to what it's already written and the user's input.

On the contrary, expert systems from back in the 90s for, say, predicting the atomic structure of an element, work like a human brain on steroids. It features an arbitrary large search tree that the software knows how to iterarively prune according to a well known set of chemical rules. We do the same when analyzing a set of options.

Debugging "current" AI models, on the other hand, is impossible because all we're doing is prescripting a composition of functions and forcing it to minimize a loss function. That's all we're doing. How can you currently tell that a certain model is going to work? Unless the mathematical theory ever catches up with the technology, we'll never know until we execute the code.