I was there when it was chosen all those years ago.

At the time (2007-2009), Matlab was the application of choice for what would become "deep" learning research. Though it had its warts, and licensing issues. It was easy for students to get started with and to use, also as a lot of them were not from computer science backgrounds, but often from statistics, engineering or neuroscience.

When autograd came (this was even before gpu's), people needed something more powerful than matlab, yet familiar. Numpy already existed, and python+numpy+matplotlib give you an environment and a language very similar to matlab. The biggest hurdle was that python is zero-indexed.

If things went slightly different, I reckon we might have ended up using Octave or lua. I reckon Octave was too restrictive and poorly documented for autograd. On the other hand, lua was too dissimilar to matlab. I think it was Theano, the first widely used python autograd, and then later PyTorch, that really sealed the deal for python.

We chose Python for Theano because Python was already the language of choice for our research lab. If it had been my choice, I would probably have picked Scheme (I was really into macros at that time) or Ruby (I think it's better designed than Python). But if we had done it in another language than Python, frankly, I'm not sure it would have taken off in the first place. Python already had quite a bit of inertia, likely thanks to numpy and matplotlib.

You were there 30 years ago, when the strength of men failed?

Not only is their argument circular but it is wrong. There is no need to use 50 million lines of Python, Pytorch, Numpy, Linux, Cmake, CUDA, and god knows how many other layers of madness to do inference.

It is literally on the order of tens of thousands of lines of code, instead of tens of millions, to do Vulkan ML, especially if you strip out the parts of the kernel you don't need.