So nVidia has a huge software lead because of open source developers like you? Or because people employed by nVidia write closed source high performance drivers and kernels? Or because the people employed by Meta and Google that wrote Torch and Tensorflow built it on nVidia?

I am really sympathetic to the complaints. It would just be incredibly useful to have competition and options further down the food chain. But the argument that this is a core strategic mistake makes no sense to me.

You don't have to beg for good software quality from Nvidia. Everything else is built on top of that foundation. That's all there is to it.

Nit: just writing Torch/TF isn't what made the difference. Having them adopted by a huge audience outside those orgs is, and that's bottlenecked on the hardware platform choice.

AMD has demonstrably not even acknowledged that they needed to play catch-up for a significant chunk of the last 20 years. The mistake isn't a recent one.

Also theano, keras ... nvidia made it easy to develop on so that's what people use.

> if researchers don't get access to hardware X, they write their paper using hardware Y (Nvidia).

There are plenty of research institutions that can easily spend >$250k on computational resources. Many routinely spend multiples of that volume.

They'll be fine.

Many institutions don't.

Look at China. A couple of years ago people thought people in China weren't doing good AI research, but the thing is, there's good AI research from basically everywhere-- even South America. You can't assume that institutions can spend >$250k on computational resources.

Many can, but many can't.

Before they became serious researchers they were once upon a time students learning with what their laptops were capable of.