The future of Deep Learning frameworks

the author got a couple of things wrong, that are worth pointing out:

1. PyTorch is going all-in on torch.compile -- Dynamo is the frontend, Inductor is the backend -- with a strong default Inductor codegen powered by OpenAI Triton (which now has CPU, NVIDIA GPU and AMD GPU backends). The author's view that PyTorch is building towards a multi-backend future isn't really where things are going. PyTorch supports extensibility of backends (including XLA), but there's disproportionate effort into the default path. torch.compile is 2 years old, XLA is 7 years old. Compilers take a few years to mature. torch.compile will get there (and we have reasonable measures that the compiler is on track to maturity).

2. PyTorch/XLA exists, mainly to drive a TPU backend for PyTorch, as Google gives no other real way to access the TPU. It's not great to try shoe-in XLA as a backend into PyTorch -- as XLA fundamentally doesn't have the flexibility that PyTorch supports by default (especially dynamic shapes). PyTorch on TPUs is unlikely to ever have the experience of JAX on TPUs, almost by definition.

3. JAX was developed at Google, not at Deepmind.

A more accurate title for the OP would be "I hope and wish PyTorch were dead, so Jax could become the standard."

Leaving aside the fact that PyTorch's ecosystem is 10x to 100x larger, depending on how one measures it, PyTorch's biggest advantage, in my experience, is that it can be picked up quickly by developers who are new to it. Jax, despite its superiority, or maybe because of it, can not be picked up quickly.

Equinox does a great job of making Jax accessible, but Jax's functional approach is in practice more difficult to learn than PyTorch's object-oriented one.

It's not about the syntax, it's all the knowledge, tools, existing code, etc that make Python so attractive.

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).