With the release of pytorch mobile, people building products will want to use stuff from the Pytorch universe, while researchers who just want to prototype an idea and want a numpy like accelerated interface will look at jax.
I talk about Jax in the article. It's very cool, especially if you need higher order derivatives. However, it's not meant to be a full neural network library, and unless Google invests significantly into it, it won't take off significantly imo.
At some point you stop caring about being able to import a set of imagnet pretrained weights and start caring about extreme flexibility. Think about implement
ting, say "Scene Representation Networks" https://arxiv.org/abs/1906.01618 in each of the three frameworks. Tf is a pig, pytorch is slow, and Jax is going to crush the problem.
The lack of say, keras.applications is a shame, but it won't last, and if you have a GPU or 8 the power of optimized (p/v)map definitely makes up for it.