Highlights:
My point is that it took a long time for people to realize that it was OK to work with a high-level language, and that doing so didn’t make you a worse programmer. When you use a high-level language, your programs might run a bit more slowly, but that’s often an acceptable compromise.
In today's world, computers are cheap, while people are expensive.
Let’s assume that a Python program runs twice as slowly as the equivalent Java program, and thus requires two servers instead of one server. In today’s world, that server difference will probably cost a few hundred dollars per month. If the programmer writing the software is 5x as productive, then that server is more than paid for by the increase in efficiency.
This doesn’t mean, of course, that you don’t need to worry about slow code, or that there’s no need for C++ programmers in the world any more. But the need for speed is increasingly balanced by something even more important: The need for maintainable software.
One of the reasons I love Python is that the code is clear and readable, allowing me to join a new project and dive in, because the code is written similarly to all of the other Python code I’ve read and written over the years.
Better to save your colleagues (and company) money by making things more efficient for people, rather than for computers.
Your 1st comment on this article Note: Really interesting insight with the switch to a high level language to save people time and make debugging easier instead of saving server resources. It might not always be the right equation like in our case where the biggest expense are the servers but in many cases it would be true that human price > server price