Let's say that the messy problems that don't have a nice analytic solution (a beautiful all encompassing model) are more and more leaning on 'very complex systems of equations or inequations, linear or non linear' and we can only approach or simplify them to heuristics (gradient descent, optimization of all sorts) because exact solution is too compute-expensive.
A tenfold difference in power might help a bit (being able to take 10x more sensors in or data with 10x better resolution might already help).
But a 10000x increase is a game changer, even if you waste so much of it. Though it all depends how it increases... Some processes I know would be immensely better - state of the art has already quantified the huge gains, and then spent 15 years trying to make the thing runnable in real-time - if I could run hundreds of millions of complex 400x400 SVDs per second or if I could run a maximum likelihood search at the same rhythm).
A 10000x increase in perf puts some exact best answer and not just approximations or 'best we could compute, sorry' in the realm of possibilities.
I'd also say AI itself would not be viable without the huge increase in power and memory bandwidth that allows to play SGD on such a huge search space.
You need months or 200+ DGX servers (8xA100 each) and a team of mlops people babysitting the process, to train a large language model today. Isn't that a direct result of computing power increase?
I'll take any more computing power, especially if there's a 10x or 100x gain on the horizon, if they'll sell it to me.
Yes we need to be clever and not waste the computing power, but for many as-yet-not-over-optimized problems any increase is an instant win.
We already see helpless activities to be better with AI (and a lot of computing) then classical optimisation theory (think solving PDEs) and failing.
Just more computing power will not help (except people whose business model relies on selling you computing power)