I think the main issue with Python tends to not be performance (though it can be hard to speed up certain bottlenecks), but rather there's a point where maintaining it goes from very easy to very difficult due to lack of static typing. Where this occurs can be pushed further back with very careful programming discipline (or by adopting mypy et. al from the start). But I could see a world where something go-like with a little more expressivity could've become that glue language instead of python.
This is a software engineering practices problem rather than a Python problem. Python has great tooling and language support for type annotations. I work on large Python codebases with ease because I leverage these things. My IDE is able to do static analysis and catch potential typing errors before runtime.
The problem is we have researchers with no SWE expertise writing huge codebases in Jupyter notebooks inside a bespoke, possibly unreproducible Anaconda environment. That is going to be a maintenance disaster no matter which language is being used.
And if you force your researchers, who are using to being productive prototyping in Python, R, etc. to use a statically-typed language, they are going to complain and be a lot less productive at their job.
