A lot of Python use cases don't care about CPU performance at all.

In most cases where you do care about CPU performance, you're using numpy or scikit learn or pandas or pytorch or tensorflow or nltk or some other Python library that's more or just a wrapper around fast C, C++ or Fortran code. The performance of the interpreter almost doesn't matter for these use cases.

Also, those native libraries are a hassle to get to work with PyPy in my experience. So if any part of your program uses those libraries, it's way easier to just use CPython.

There are cases where the Python interpreter's bad performance does matter and where PyPy is a practical choice, and PyPy is absolutely excellent in those cases. They just sadly aren't common and convenient enough for PyPy to be that popular. (Though it's still not exactly unpopular.)