Lovely post, love the call out to design vs scientific experimentation! I feel like @jonathoda talks about this! http://tagide.com/blog/academia/research-in-programming-languages/

And here’s the first itchy point: there appears to be no correlation between the success of a programming language and its emergence in the form of someone’s doctoral or post-doctoral work. This bothers me a lot, as an academic. It appears that deep thoughts, consistency, rigor and all other things we value as scientists aren’t that important for mass adoption of programming languages.

In turn, I'm kind of bothered by this paragraph. Apparently academics are the only capable of producing "deep thoughts".

Being a professional Rubyist taught me that consistency and rigor are unnecessary for making a language user friendly. Instead Matz and the community optimized for "what I would expect to happen here."

Academics don't have any exclusivity on producing deep thought or research. But they are, ostensibly, devoted to achieving it. A if B, not A iff B.

The whole premise of "deep thoughts" just seems pretentious. The objective of academy is producing new knowledge.

I see where you’re coming from @Emmanuel Oga but I’d push back on one thing. When academic research is done well, deep work is essential, and being a full-time scientist is (although that’s dying now too with increased demands) one of the few places where success isn’t possible WITHOUT this type of deep focus and concentration. Especially for non-experimental fields like math and computer science. Cal Newport, author of Deep Work and a theory professor himself, talks about this allll the time on his blog and his podcast. The way I see it, new knowledge is the ‘goal’ of academic research. Deep thoughts & deep work are what’s necessary to produce said new knowledge. Really getting to the bottom of things, understanding why X doesn’t work but Y could work, and chasing those ideas for the sake of them. The author probably hasn’t interacted with many FoC people 🙂 so they default to the place / arena they know well (academic research)

@Emmanuel Oga New knowledge most often arises in the context of deep thinking, so... 🙂 @Srini K I find the characterization of the difference in development process between older and newer languages in this post a bit odd. For example, the first Lisp interpreter was a weekend hack by Slug Russell and C was bashed together quite quickly by non-academic practitioners to solve their own problems. Most of the grand pseudo-academic committee languages of yesteryear were popular failures, like PL/I and ADA, and the ones that succeeded did so largely because of massive corporate propaganda efforts (Cobol, Java).