I think Python is simple to use but difficult to scale. It can be useful to think of programming languages as tools. Assembly is the workbench where tools are made and raw materials are handled. C++ is like your standard set of machining tools. Java is like a bag of tools, hammers, handsaws. Python is like an electric nail gun, or perhaps a gas chainsaw. It does many of the things the other languages say that you can do directly out of the box and often in a manner that’s more sensible to people initially using a tool. But when it comes to building a house and all you have is a chainsaw? You’re gonna have some very cool log cabins, and if we go outside the analogy for a moment: code that uses tricks to accomplish what others would do with better building, architecture, or execution. Now that said, how many people do you know who might go off and build a log cabin vs. a modern home on their own?

It's because Python is both simple and easy to use, and complicated and hard to understand.

It's a Janus of a programming language. There are lots of complexities that alternately tie software engineers up in knots and let them go into raptures of blissful hackery. But they generally aren't all that visible in the face it presents to data scientists and devops.

And it's hard to make sweeping statements about "big or performance critical". A couple times in the past few years years I've seen or been involved in projects that successfully replaced big Java applications with Python implementations that were 1% the SLOC and had better performance. They were special situations, to be sure, though more so, I think, in terms of explaining the performance improvement than the cost savings.