Language abstractions that are not "zero-cost" inevitably lead to worse performance. Python has many such abstractions designed to improve developer experience. I think that's all the person you're responding to meant.
Or the SELF workstation environment at Sun, whose research ideas in optmizing JIT compilers eventually landed on V8.
For example, Python has a drastically simpler syntax in some ways than C++ (ignoring the borrow annotations). In many ways it can look like Python code. Yet its performance is the same as c++ because it’s AOT compiled and has an explicit type system to support that.
TLDR: most of python’s slowness is not syntactic but architectural design decisions about how to run the code which is why alternate Python implementations (IronPython, PyPy) typically run faster.