Python software is to a large extent either doing things in not-python (c, c++, rust, etc.) or doing things that are not cpu bound (io bound, async, etc.). If you're cpu bound then you can either take a 2x jit improvement or take a 10x non-python improvement. There's few companies of a scale where the non-hot path cost of 2x cpu is so massive as to be worth caring about.
The python overhead of launching big ML jobs is nontrivial, so I think speeding that up would be meaningful. (I mean the initial tracing and other setup, not things once the GPUs are actually doing the work).
Also you can use projects like numba https://numba.pydata.org/