A short question: have you ever worked in research? Or know well anyone working in academia?
First, many problems there are much harder just to get to the level that one can do any valuable contribution. And approaching them goes well beyond reading tutorial, playing with it, asking some questions on SE, being expert.
In mathematics, or medicine, it's rather years than weeks.
Second, many deep problems are not that easy to commercialize; even if they may, possibly, save many lives in future, they are not an easy base to make profit in, say, next 10 years.
Third, as projects are way to hard to be run by a few enthusiasts + commercialization is not straightforward - everything works in universities, with an inertia typical for huge institutions (and no 2-3 people startups are available). As a secondary effect, it deters creative people to pursue such option.
And fourth, there is money in programming. When you fail a startup you can still get a great job. If you fail your academic career - it may be harder.
Very good points. I work as a bioinformatics scientist at Novo Nordisk, and we take our jobs very seriously in trying to find medicine for diabetes and other autoimmune diseases - and we feel lucky to have this possibility of success. It is very difficult because of many reasons, especially because each iteration (drug) takes years and costs millions of dollars to try out and hundreds of patients to volunteer to inject it into their body.
And one more point (also: the rationale for regulations in medicine) - in programming you are allowed to risk a lot more. Create a social network and the it fails? No problem! Running out of money so you need to shout down you server? OK!
And in general - approach "we have no idea if it is going to work, but let's try!".
In medicine, a failure may cost thousands of lives (and in past it did, a lot of times), so time and cost overhead is enormous.
First, many problems there are much harder just to get to the level that one can do any valuable contribution. And approaching them goes well beyond reading tutorial, playing with it, asking some questions on SE, being expert.
In mathematics, or medicine, it's rather years than weeks.
Second, many deep problems are not that easy to commercialize; even if they may, possibly, save many lives in future, they are not an easy base to make profit in, say, next 10 years.
Third, as projects are way to hard to be run by a few enthusiasts + commercialization is not straightforward - everything works in universities, with an inertia typical for huge institutions (and no 2-3 people startups are available). As a secondary effect, it deters creative people to pursue such option.
And fourth, there is money in programming. When you fail a startup you can still get a great job. If you fail your academic career - it may be harder.