I think it's better to think of JAX as a more general framework for differentiable programming and PyTorch more focused specifically on deep learning/neural networks.

The beauty of JAX is that basic usage is basically a single function: `grad`.

You just write whatever Python function you want and can get the derivative/gradient of it trivially. It gets a bit trickier when you need more sophisticated numeric tools like numpy/scipy, but in those cases it's just about swapping out with a JAX version of those.

In this sense JAX is the spiritual success to Autograd. However the really amazing thing about JAX is that not only do you get the autodiff for basically free, you also get very good performance, and basically GPU parallelism without needing to think about it at all.

PyTorch is an awesome library, but largely focus on building Neural Networks specifically. JAX should be thought of a tool that basically any Python programmer can just throw in there whenever they come across a problem that benefits from having differentiable code (which is a lot of cases once you start thinking about differentiation as a first class feature).