That's a bit of an overstatement.

The entire field of ML is aimed at problems where deterministic code would work just fine, but the amount of cases it would need to cover is too large to be practical (note, this has nothing to do with the impossibility of its design) AND there's a sufficient corpus of data that allows plausible enough models to be trained. So we accept the occasionally questionable precision of ML models over the huge time and money costs of engineering these kinds of systems the traditional way. LLMs are no different.

LLMs draw origins from, both n-gram language models (ca. 1990s) and neural networks and deep learning (ca. 2000). So we've only had really good ones maybe 6-8 years or so, but the roots of the study go back 30 years at least.

Psychiatry, psychology, and neurology on the other hand, are really only roughly 150 years old. Before that, there wasn't enough information about the human body to be able to study it, let alone the resources or biochemical knowledge necessary to be able to understand it or do much of anything with it.

So, sure, we've studied it longer. But only 5 times longer. And, I mean, we've studied language, geometry, and reasoning for literally thousands of years. Markov chains are like 120 years old, so older than computer science, and you need those to make an LLM.

And if you think we went down some dead-end directions with language models in the last 30 years, boy, have I got some bad news for you about how badly we botched psychiatry, psychology, and neurology!