Relying on the cpython gil and releasing the gil are two different thing:

  1. releasing the gil means multithreading is opt-in for a given code section in NumPy. Only very specific parts of the code need to be threadsafe.
  2.  not relying on a gil in cpython runtime means multithreading becomes opt out. Now all the code by default needs to be threadsafe, including the libs you depend on.

A lot of C/C++/Fortran scientific code is not thread safe, and the whole scientific python ecosystem depends heavily on those codebases.