It's true right now though there are a lot of people working on different ways to get python to have near native performance. I'd say it could be a fixable problem.
I was listening Lex Friedman's podcast featuring Chris Lattner a few months ago. He's is working on Mojo, which is basically a fast superset of python that can be fast (if you opt in to a few things) and worst case just falls back to being as fast as regular python. The intention is to give people enough means that they can optimize such code to be actually fast or just run it as is.
I'm not much of a python developer myself, even though I do have to deal with it occasionally. I liked the point that he made that, for whatever reason, there are just a lot of people using python and getting access to that community of people is a good way to get traction for your tool or technology. He was talking about machine learning specifically. A lot of the experts in that field are using python. Of course all the difficult bits and bobs are outsourced to native libraries. His vision is that a lot of that stuff should be written in mojo/python and that there are no good reasons why that should be any slower.
Probably removing the gil will help (everything blocking is not cool). And the language could use some better primitives for dealing with things like co-routines. They are kind of nice to have in asynchronous code bases like games or UIs. But those are things that could be fixable and might benefit the rest of the ecosystem.