Programmers love to save an hour in the library by spending a week in the lab
Or you can just throw more hardware at it, or use existing native libraries like NumPy. I don't think there are a ton of real-world use cases where Python's mediocre performance is a genuine deal-breaker.
If Python is even on the table, it's probably good enough.
Instead, there are big companies, who are running let's say the majority of their workloads in python. It's working well, it doesn't need to be very performant, but together all of the workloads are representing a considerable portion of your compute spend.
At a certain scale it makes sense to employ experts who can for example optimize Python itself, or the Linux kernel, or your DBMS. Not because you need the performance improvement for any specific workload, but to shave off 2% of your total compute spend.
This isn't applicable to small or medium companies usually, but it can work out for bigger ones.
Think about it: if you have some Python application that's having performance issues you can either dig into a foreign codebase to see if you can find something to optimize (with no guarantee of result) and if you do get something done you'll have to get the patch upstream. And all that "only" for a 25% speedup.
Or you could rewrite your application in part or in full in Go, Rust, C++ or some other faster language to get a (probably) vastly bigger speedup without having to deal with third parties.