It seems to break down a bunch in real use cases though. For example, we found a differential equation benchmarks the Jax devs were optimizing, where it was still 6x slower than Julia (https://gist.github.com/ChrisRackauckas/62a063f23cccf3a55a4a...). Then the gradient calculations don't mix well with GPUs, at least for ODEs (https://github.com/google/jax/issues/5006). That lack of composability compared to Julia is the main issue that then creeps up in the bigger problems.