I think many in the comments are misunderstanding what the author mean to say in his post. He is not talking about working in academia or making scientific software. He talks about improving one's skills in basic science and such fields in computer science as A.I. which have historically been entrenched more in academia than industry.
The author of the post started by getting into programming/CS by wanting to earn more/better money, by picking up an Access book and working it out from there, and now that he's more established, he looks down at the young hungry people who picked up an Access book in the hopes of more/better money, running through all the tropes that people who got where they are through knowledge will use.
Yes, it's useful to understand things better, and to know math. And as always since the first CS degree started, CS people gripe that people should Know More Math. Sure it helps. Other, less prestigious, things also help but you don't hear people griping about it. TDD allows the idiots in. Yes, that's effectively why you want TDD, you want to get more mileage and solving more complex or more bug-sensitive problems using the same people. Building software is not about being smart (although that helps on occasions), it's about getting stuff done.
Yes, machine learning and AI are the new kids on the block, and like Web programming, they will see a bloom of increased customer demand, and like Web programming, we'll get a progression from bespoke boutique software to frameworks that make people's lives easier to frameworks that allow any person with the intelligence of a pet rock to do simple stuff productively. Why is that? Because building frameworks is the only way that the smartest people can earn money faster than programming the (N+1)th variation on that theme everyone follows -- frameworks are what make people more productive, or allow you to use a workforce that's more accessible.
As a Wizard With a Pointy Head (aka academic), I'd say that the need for Wizards With Pointy Heads in production work is often overestimated and/or idealized. There is a large number of PhD graduates, and the market happily gobbles them up (indeed, realizing that you can hire PhDs and have them do productive work is one of the things that made Google successful as a company back in the early 2000s).
> We can argue about how to version APIs and how a service is such RESTful and such not RESTful. We can mull over pettiest of things such as semicolon or the gender of a pronoun and let insanely clever people leave our community. We can exchange the worst of words over "females in the industry" while we more or less are saying the same thing, Too much drama.
> But soon this will be no good. Not good enough. We got to grow up and go back to school, relearn all about Maths, statistics, and generally scientific reasoning.
The programming community is large and diverse. We can do all of these things, including the rigorous (what the author seems to call "scientific" [1]) ones and the UI/marketing ones too. I think this diversity is a strength, as long as managers and entrepreneurs find and retain the skill-sets necessary for their domains.
[1] I prefer to use "science" to mean falsifiability, preferably with strong experimental designs. Much of what the author talks about is mathematical rigor (from computer science), which is also important, but not experimental science. See also: https://en.wikipedia.org/wiki/Philosophy_of_science#Defining...
