My guess is that's because ML researchers typically have not spent much time with statically typed languages. I was also once convinced that static typing was

My guess is that's because ML researchers typically have not spent much time with statically typed languages. I was also once convinced that static typing was not so valuable - when I was working a lot with js, python and ruby, but the more time I spend with static typing the more I like it. There is an "activation energy" to overcome with static typing: when you first start it feels like a tedious burden to have to add type annotations, when your python code ran just fine without them. But in my experience, with a little bit of practice the type system starts to feel completely effortless, and it saves you from all kinds of strange runtime issues you run into when nobody is checking your types. Also type systems encode a lot of intention into the code. If I look at a function signature in Swift, I know exactly what has to be passed in, and exactly what will come out. In python, I have to rely on documentation, or even maybe I have to dig into the source to see what this function expects. In my experience, the real difference is seen when you come back to a weakly-typed language after working with types for an extended period. Returning to Python after spending time with Rust or Swift feels like walking down the street with one of my senses missing. There are whole categories of dangers I now have to watch out for which the compiler would have caught for me automatically. It is not pleasant to go back.