My PhD work is emblematic of this. I created an algorithm (PGE) that improved the SOTA in Symbolic Regression by 1000x+ by stitching together other algos (bfs, A*, nonlinear regression, CAS, memoization, NGA). Without the fundamentals, this would not have been possible.
I also find the fundamentals invaluable in understanding where bottlenecks are in production systems, which can appear anywhere in the stack from software to hardware
In terms of making progress, we think of the breakthroughs mainly, but daily, incremental progress is made. Look at almost any changelog in open source
It's mostly just the virtue of a public shared asset that the most demanded optimizations are done the earliest. If I wanted to push the state of the art forward, I'd have to move halfway across the country, get a new major, and then get paid peanuts to work in a TSMC factory. No thanks, I'll keep my Herman-Miller and Starbucks allowance working on boring old software.