Python is notably very popular in two communities: web developer and scientific computing.
The former usually yell loudly every time someone propose to remove GIL. Meanwhile everyone in the scientific computing community had to learn how to workaround GIL which absolutely sucks and sometimes just impossible. (e.g. I have a mostly memory-bandwidth-bound data loading pipeline but it sometimes need multiple cores for doing some trivial data transformation with numpy. This is impossible to do efficiently in Python right now, due to GIL)
Do you mean Python is not the right tool for my job and we should drop numpy/scipy/... and let Python be your shit tool for rendering web pages?
Numpy/Scipy are great for prototyping, but IMO code the performant / concurrent stuff in C/C++/Rust/Julia once your prototype is done. Use the right tool for the job instead of trying to bend the wrong one.
Try to convince those "Data Scientists" to learn Julia/Rust or spend another ten years for learning how to write non-crashy C++ then.
Also, IMO Python is the right tool for glueing optimized C++ implementation of various linalg algos together. And this is exactly how we use Python. And we still have to workaround GIL. Yes, it is that bad.
<rant>
Glue codes could be slow, but they must scale. They should not unnecessarily contend for a stupid lock. GIL is really a scalability bug and I do acknowledge it is a hard-to-fix one due to the world's legacy codebases depends on it. However when someone applies an absurd amount of computer science (the design OP posted is literally based on state-of-the-art PL research), you should at least be sincerely curious why people are being so serious about it.
</rant>
In actual production, corporate environments, performance isn't necessarily the most important thing. Maintainability is often much more important. Sure, a Rust system may be faster than Python, but when your lead engineer quits and your backup is on vacation, and nobody else even knows how the hell the code works, you're going to reach for something like Python or Java.
Previous GIL removal attempts hurts single thread performance and it isn't that scalable, so people are usually by default dismissive.
Most of Python codes depend on subtle details of CPython internal. For example sometimes it is just convenient to assume GIL exists (i.e. simplifies concurrency codes because "you know there are at most one thread running").
I haven't seen any appetite to even consider solutions that break the promises of the GIL and make currently atomic things non-atomic, so that second argument seems weird.
Python is notably very popular in two communities: web developer and scientific computing.
The former usually yell loudly every time someone propose to remove GIL. Meanwhile everyone in the scientific computing community had to learn how to workaround GIL which absolutely sucks and sometimes just impossible. (e.g. I have a mostly memory-bandwidth-bound data loading pipeline but it sometimes need multiple cores for doing some trivial data transformation with numpy. This is impossible to do efficiently in Python right now, due to GIL)
Do you mean Python is not the right tool for my job and we should drop numpy/scipy/... and let Python be your shit tool for rendering web pages?