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Well, you could read the article :^)

As a summary, though:

PyTorch has become dominant in research because of its API (both its stability + having eager mode).

TF has become dominant in industry because A. it came out several years before PyTorch and industry is slow to move, B. It supported a lot of production use cases (mobile, serving, removing Python overhead) that PyTorch didn't for a long time.

well, for one pytorch added the python overhead over an otherwise very clean lua jit

PyTorch wraps THNN, not Torch. Moreover, even if this was true, it wouldn't matter at all. Practically 0% of the overhead is related to Python in the first place, all of the time is dominated by the underlying C implementation.

If only articles were as concise as your summary is I would enjoy reading them but as long as they are many pages long I have no time to read beyond the titles, abstracts, conclusions and comments.

BTW I've also read (here on HN) PyTorch learns much faster than TensorFlow does.

Then it wouldn't be an article, it would be a summary :)

100% anecdotal --

The Keras interface for tensorflow makes it easy & fast to make "good enough" models. That is often a driving factor

I've personally found pytorch easier to use than Keras. It's undoubtably easier to use than Tensorflow, of course.

research: the API.

industry: tensorRT.