I'm probably not the foremost authority on AI and ML, but a lot of it seems to be in Python. I'm assuming this is because it is popular in research fields and lots of libraries exist. The progress has been amazing in such a short time to say the least.
Curious that since a lot of things are popularly rewritten in Rust or Zig, why has this not happened on these types of projects?
The ideal setup for that is the interpreted shell/compiled core approach that languages like Python have championed forever. Competitors to Python would be the likes of R, Lua, Ruby, Perl, Javascript; not C, C++ or Rust.
Why Python in particular? The answer is that it has an excellent scientific stack with Numpy, Scipy and Pandas.
R is very competitive for statistical work.
Julia is an interesting, if unproven, alternative.
Once you're done with your model, you write it in C (or whatever) for production, but that's a more traditional software engineering project.
In academia, the incentives are such that most people don't really care about code. Their careers depend on them publishing papers, so that's where they're going to focus their efforts. Code is a way to prove a point for publication, not a product that needs to generate value.
> a lot of things are popularly rewritten in Rust or Zig
Beware of the HN bubble. Rust and especially Zig are tiny, fringe players in the industry, regardless of their technological merits.