If you're talking about the industry, machine learning and data science projects are inherently experimental processes that rely on setting up quick feedback lo

If you're talking about the industry, machine learning and data science projects are inherently experimental processes that rely on setting up quick feedback loops to analyze your data and see what works. 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.