[cdavid]

Python is my main language, and I think you underestimate the speed different for a same implementation by an order of magnitude. That is, CPU-boud tasks will most likely be around 100x slower.

The realy argument, of course, is that in a given time frame, with people of similar skills, you will not have the same implementation unless your team contains only vulcans. Several people in the scipy community have reported having gone from C++ to numpy/scipy and went faster at the same time - because C++ is so hard to use correctly, people whose job is not even programming ended up doing things very fast but one millions times because they don't understand their code.

This point is surprisingly not understood by a majority of programmers. Most of the time, you see benchmarks for some trivial or even non trivial algorithms, well specified, and get "look, this language is N times faster". But in my experience, this almost never happens in real life - code specification keeps changing, you need to redesign constantly what you're doing.