Certainly if you ignore those papers, you'd likely have no issue in practice - I suspect many of them are about more theoretical concerns. Perhaps I'll take a look at/post a list tomorrow.

Either way, I believe that our original discussion was on why somebody should bother. I provided a list of (admittedly) somewhat niche reasons. My personal opinion is that Jax will stick around, and at the very least, provide some neat ideas for Pytorch to ... independently come up with :)

>>> I'd place my bets on Jax

Hey hey hey context! Pytorch is currently dominant in research, so who could supplant it? Anecdotally, since I published my article (https://thegradient.pub/state-of-ml-frameworks-2019-pytorch-...) there has been more momentum towards Pytorch (preferred networks and openAI).

So if not Tensorflow, then who? I think Pytorch represents a local optima and is "good enough" for most people. So any newcomer framework needs to bring something new to the table, even if it's niche. I think Jax looks the most promising.