Gold star for you :) The NumPy implementation is basically a for loop. The only cost is the very brief inspection of the dtype to dispatch on.

Why can't normal pythons sum do that?

Because CPython has no JIT :)

Because python lists can contain different types, unlike numpy arrays

Exactly. To further elaborate, try the following in python, if you have the memory:

    >>> x = list(range(100000000)) + ["break"]

After some time, you get a list with a hundred million integers with a string at the end.

    >>> len(x)
    100000001

Now take the sum:

    >>> sum(x)

On my machine, I get a traceback, but after about half a second of hesitation. Python's been iterating through each of those elements, testing to see if it's an integer and building a running sum, only to throw an exception because it didn't expect the string at the end. That means that in order to avoid memory corruption (or other terrible fates worse than an exception), Python must double-check each element of the list as it goes; it cannot take shortcuts.

Can't Python notice that a list always has the same types and do something special?

That could have been implemented but it is not. I dont think it will be because it is not that usefull and would be a lot of work/code for the small benefits. If you want faster python use pypy, if you want homogenieus lists you probably could use numpy arrays.