It's true in scientific computing too... there's plenty of times when a quick Python script that takes 10 minutes to bang out is better than writing a chunk of

It's true in scientific computing too... there's plenty of times when a quick Python script that takes 10 minutes to bang out is better than writing a chunk of C++ in an hour. The lost time isn't necessarily in the salary of the scientist, it's in the time spent. A lot of the scientists working in the big government research labs have one-of-a-kind knowledge and their time is at a premium in terms of other stuff they could be working on. But I agree, if you're writing a massively parallel simulation in Python and running it on a supercomputer it's a net waste of money. There's a big trend in the government projects to build libraries like Trilinos or MOOSE, which work kind of along the same philosophy as NumPy - let specialists implement the parts that really matter (in terms of computational expense) in a compiled language and let the scientists glue together those parts in a higher level language. It's a good strategy IMO, especially for technologies like CUDA where there's a huge benefit but also a steep learning curve.