And this attitude, my friends, is the reason why so much software out there is so bad.
We need more of a math mindset when developing software. What can we be sure about, what are the invariants, what can we prove? There is so much crap out there that somebody lacking understanding just tried to wing, and I'm constantly ashamed of it.
Number theory had no applications for centuries. Now, cryptography is based on it and the modern internet would be unthinkable without.
Foundational research does often not provide immediate applications. Still, if we don't do it, out understanding of the world is lacking and it hurts us later down the road.
While there certainly exists math for the sake of math, there is a trickle down effect that is quite real (there’s also a trickle up effect that is real but that’s unrelated). Someone does some math for the sake of math. Later on, someone who is slightly more applied sees a link between that math and a more applied problem they’re working on. If the idea is truly useful, it propagates down all the way to application-focused practitioners. Researchers exist on a spectrum, generally, between pure theory and pure application.
Math has no application until you find an application for it. Differential equations are just equations until you pair them with physics. Formal logic is just an abstract discussion of human reasoning until you build a circuit, etc.
One wonders if trickle down mathematics is any more efficient than trickle down economics. It seems like we might be better off not funding pure math, as forcing function to coerce those minds to work on more applied problems directly, instead of relying on this random serendipity.
It seems like I might be better off picking the winning lottery numbers directly instead of relying on the random serendipity of guessing them and most of the time being wrong.
PhD is granted for novelty, not practicality.