I wrote a logistics optimization program (think travelling-salesman++) in Python and it gradually took over from the v1 version written in Java.

People generally compare "speed of execution" to "speed of coding".

I'll add "speed of troubleshooting". When something goes wrong and the clock is ticking, you need tools to identify quickly if the data is inconsistent, a config set wrongly or the algorithm stuck at a local optimum. And then decide if things can be mitigated with a patch or we should we grovel and buy time from the client... All of this is much easier in Python (bar the grovelling).

LLMs are changing things though. I recently ported a complex part of the kernel to C++. 90% of it was creating the scaffolding before the actual algorithms were ported. Very tedious work but Claude can just chug through. Wouldn't like to maintain it just yet.