[jandrewrogers]

Python became the de facto glue language for supercomputing a very long time ago because you could easily bind C code into it. If you needed linear algebra etc to run on a massive supercomputer, there were highly optimized Python libraries for that so the researcher didn’t have to write C/C++/Fortran. This massively improved iteration times for a lot of scientific computing efforts with only a modest loss of performance. By the time data science/ML/AI/etc became a thing these tools were already very mature and also relevant.

The tl;dr: Python had the advantage of a mature legacy in supercomputing doing many of the same types of computations done in AI/ML. Those libraries and bindings provided a massive leg up versus other scripting languages that did not have this kind of capability effectively built-in.