A language like Ruby can be very productive for someone who has climbed the learning curve to learn all its ins and outs. However, in my experience, this turns

A language like Ruby can be very productive for someone who has climbed the learning curve to learn all its ins and outs. However, in my experience, this turns into a large productivity drain the moment someone else (who is less of an expert) has to touch it. For large projects with multiple developers, readability should win over writability every time, most code are read more than they are written (my hypothesis). You can see evidence of this, given the success of languages like Python and Go. That said, for scientific compute, in a lot of cases, writability matters way more, as your job as a scientist is to produce results as fast as possible, code quality be damned. However, only a small number of scientists are expert developers and have climb the learning curve and can write code with ease. The vast majority of them are junior at best, and Python's approachability (which is rooted from its readability of course), wins. With most of the people using Python, the ecosystem develops and there are no other viable alternatives. In the long run, I suspect even languages like MATLAB and Mathematica will die out as the open source stack becomes more mature and (eventually, if not already) significantly more capable. Julia might be a wildcard due to its (potential) performance advantages, but the aesthetics of the programming language is simply not in the minds of 99% of the scientific compute users out there.