However, even getting truth answers will soon be beyond AI. It is of course making impacts in Math and in publishing in "data heavy" subjects where you can just formally verify, program, and/or munge data.

But in "real science" where we actually need to build a lab, buy supplies, hire personnel, get approval, form physical test harnesses, install sensors in proper locations, calibrate everything, then finally make one measurement, question the whole thing, then finally make a conclusion, AI is of limited benefit.

Yes, it can help speed up coming up with some formulas, spitballing, or checking existing research. It can even help write the jank code for sensor calibration, measurement taking, etc. That's not nothing, but we are _very far_ from meaningful contributions to real experimental hard sciences.

As for math, theoretical physics, computer science, and other "logic based" studies, the question becomes one of benefit to cost of true understanding versus simply knowing something is true.

I'd argue that it paints a starkly negative picture of most science. Once answers are easy to obtain, it seems most people are concluding that actually understanding why its true isn't worth the effort, which means that the question maybe wasn't worth asking.

Its the science equivalent to the recent realization in tech: most ideas suck, your product didn't need more bells and whistles. Actually the engineers pushing back on every shower thought from product was a good thing. The friction that stops you from putting every random-ass feature into your already bloated useless SaaS product was good, and now the sales guy is pushing features to prod for a customer demo. We thought "too many ideas, not enough engineers", we now know "not enough meaningful quality ideas".

In the same way we thought "too many questions, not enough scientists", we now know "not enough real study on meaningful topics".