The main difference between Julia and python is that most of the "core" python ecosystem has had a lot more dev time put into it. Google, Facebook, and Microsoft all have hundreds of full time developers on major python packages.
Makes sense. I guess the author's contention is that if Julia had those formal features the author wants, it would need very significantly less dev time to reach python's levels of reliability?
It's of course plausible, that's what those sorts of features are intended for, but I'm not certain I'm absolutely confident. At any rate, python demonstrates it is not the only path, as the author seems to be suggesting ("it is not obvious to me the problem can be solved" without these features, says the author. But it's not obvious to me that those features are necessary to solve the problem, or sufficient to solve the problem...)
Python's reliability here comes because it is a much less flexible language in some ways. If you write your own array type in python, and pass it into tensorflow, you would expect it to error. If you do the same thing in Julia, you would expect it to work.
