The choice of language usually comes down to the packages. In any of the three aforementioned languages one can easily and quickly manipulate matrices short of an unwillingness to learn. Julia is nice because it's fast with native code. Python is nice because of Scipy. Matlab is nice because it decides how to spend your money without cause.

I'm an AI researcher / practitioner. For me code accompanying papers is very useful and usually this code is in Python. Occasionally it's Matlab but let's be honest, who cares about those papers :). I'd love to use Julia but the package support just isn't there. Ironically people like me are supposed to be writing this code but with a demanding job and a family it's not likely I will be improving their DataFrame effort anytime soon.

Anyway the MAIN reason I use open source software is because if it isn't working correctly I simply fix the code myself. This isn't possible in the proprietary world. Why would you trust your research or production work with code you can't see and edit?

There's been a lot of talk about documentation. Docs are secondary sources, like WIRED, read the code if you're serious about being correct. Even (especially) hired hands make mistakes and fail to write good tests.

This article reminded me of the fictional Simpson's news article "Old Man Yells at Cloud". It's funny, and he may have a point, but it has no relevance.