Raymond Hettinger's explanation was more to the point, and he's one of the core developers. I'll expand a bit more on that.
First we have to admit that there is no one size fits all language. Different languages have their pros and cons in different application domains. That's why multiple languages exist and will continue to exist for the foreseeable future.
While there may be no one size fits all languages, Python happens to fit a lot of application domains. It's the "size medium" of computing languages. It's not the fastest, but it's not the slowest either. It's a good balance between speed of development and speed of execution.
The area that it's slow at is things involving tight loops on large arrays of basic data types, particularly integers.
However, as Hettinger states, in most of the sorts of applications where you do that sort of thing extensively, you would tend to be using libraries anyway, such as Numpy or other native libraries. One of the things that Python is really good at is interfacing with existing libraries written in languages such as 'C' used in numerical, scientific, artificial intelligence, and other applications. It's a deliberate design choice that prevents certain optimisations that could be made to the Python run-time itself, but it's considered to be worth it.
If those existing libraries don't suit your needs, there are options such as Cython or Numba which compile Python to C or C++, and thence to native code. At that point there's no difference between a C program and a Python program, because your Python program has been converted to C (or C++). However your development cycle is then slowed down to the pace of a C program, so it's considered advisable to only do this to the performance bottlenecks in your program, if it's necessary at all.
There are multiple implementations of Python, including ones which have JIT compilers which score well on benchmarks. The fact that people stick with the original C-Python system shows that most users don't see significant performance problems with respect to performance which aren't addressed by the tools and libraries which are well known to Python programmers. There's probably a reason why Python is the language of choice in many high performance computing applications.
The actual problems that Python has are lack of penetration into mobile programming, and not having a better cross-platform GUI system integrated into the standard distribution than TKInter. The first problem mainly has to do with the difficulties in jumping through Apple's and Google's walled-garden hoops without a big corporate backer. The second problem is due to lack of interest as people would rather either using the native platform GUI (for which there are library bindings) or they prefer to use QT, which is a third party library. Python comes with the TKInter GUI toolkit, and there just isn't enough interest in replacing it to make the effort worth while.