I don't know if this means something is truly wrong. AI is a mix of engineering and scientific research, just like most CS subfields. Recently, the emphasis has shifted towards engineering, as the applications of neural nets have skyrocketed after a few breakthroughs in performance.

It's similar to computer systems research. For example, a research paper on filesystems might tell us a simple trick which leads to better performance on NVMM. The paper may go into why the trick works, but it doesn't (and shouldn't need to) generalize and try to improve our general understanding of how to design filesystems on different hardware. We've been designing filesystems to this day and well, we are always still guessing about which approaches to use and hoping for the best. In the same vein, we don't even have a widely-accepted theory of how to use data structures yet.

So, I don't think that neural nets aren't scientific enough means that it's all BS. We have gaps in understanding, but the power of the models warrants a lot of continued work on finding useful applications.

Doesn't mean I don't think AI is over-hyped/overfunded though...