That's not correct. Python will never be as fast as hand-optimized assembler, but it certainly can be much (5-10x) faster that what it is right now for most workloads. Pypy is a living proof that it can be done.
PyPy is faster for pure Python code, but that comes at the expense of having a far slower interface with C code. There's an entire ecosystem built around the fact that while Python itself is slow, it can very easily interface with native code (Numpy, Scipy, OpenCV) with very little overhead.
So sure, you can make Python much faster, if you're willing to piss off the very Python users who care the most about performance in the first place (the data science / ML people and anyone else using native extensions).
https://www.youtube.com/watch?v=qCGofLIzX6g&t=31m44s
PyPy is faster for pure Python code, but that comes at the expense of having a far slower interface with C code. There's an entire ecosystem built around the fact that while Python itself is slow, it can very easily interface with native code (Numpy, Scipy, OpenCV) with very little overhead.
So sure, you can make Python much faster, if you're willing to piss off the very Python users who care the most about performance in the first place (the data science / ML people and anyone else using native extensions).