[NobleSir]

As someone who just finished a ph.d. in (non-applied) math I would agree profit incentives are a large percentage of the reason - in programming, you can learn enough basic skills to make money, and then smoothly transition that to higher skill / profit levels just through the experience you gain doing more actual programming. In math, you can't really make money with a lower level of math skill (your options of making money at a lower math level are maybe accounting or teaching, and neither of these provides a transition to deeper math skill levels). Thus your option to increase your math skill is to tough it out through 5 years of grad school making about minimum wage, and then another three years of a postdoc, making a 1/3-1/2 of the average software developer salary, and then you can start making more money if you are lucky enough in the problems you choose, and work hard enough.

I would guess the more applied math you care about / the closer to software your math is, the better you can do as a self-taught. I actually know of one guy who transitioned from an engineering ph.d. to teaching applied math doing self-taught / working on fluid dynamics in aerospace, but I expect the examples of someone doing this in more abstract / pure areas are extremely few.