I don't doubt that, but I'm specifically talking about new languages. I've seen far more enthusiasm from ML researchers for Mojo, which doesn't even do automatic differentiation, than for Dex. And to recycle an old HN comment of mine, people are much more eager to learn a functional programming language if it looks like NumPy (I'm talking about JAX here).

Is Mojo actually getting significant uptake in research? I haven't been following closely but new tooling at that layer seems much more useful when cost-optimizing deployment.

Mojo is interesting, because I get to keep all my existing Python code and libraries for free. Then when I need to speed things up I can use Mojo syntax.

As far as I understand, you will only be able to speed up code that was previously written in pure Python. This excludes JAX, PyTorch, NumPy and any other Python package written in C/C++/Rust/Fortran.