Failure Modes in People Who Develop Math Skills but Don’t Capitalize On Them via Coding
1) Difficulty grappling with complexity when it grows so big that you can't fit everything in your head. 2) Lack of understanding or willingness to accept practical constraints of the problem and incorporate them into the solution. 3) Getting distracted by low-ROI features/details. 4) Being unwilling to do "tedious" work.
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I know some people who were skilled in math but never really capitalized on it.
Two trends I noticed:
- they typically were not very skilled at coding, and
- they also lacked discipline to work on things that had to be done even if they weren't inherently intellectually enjoyable.
The first trend is sometimes surprising to people: how can someone be good at math but not coding? Doesn’t being good at math naturally transfer over to coding?
There’s definitely a positive correlation, but the correlation is nowhere near as high as you might expect.
Here are 4 failure modes that I’ve seen frequently in people who are skilled at math but failed to capitalize on it in the form of coding.
1) Difficulty grappling with complexity when it grows so big that you can’t fit everything in your head.
Not organizing and naming things well, not understanding or maintaining scope, letting responsibilities bleed too much across scope.
2) Lack of understanding or willingness to accept practical constraints of the problem and incorporate them into the solution.
It’s good to think about the Platonic ideal but you can’t let that become a constraint in the sense of the great becoming the enemy of the good.
3) Getting distracted by low-ROI features/details.
In math there’s typically a really clean line between details that are absolutely critical vs completely irrelevant.
But in reality there’s more of a spectrum, lots of things matter at least a little bit but you have to have a good sense of what things matter a lot and how costly their implementation will be relative to the impact.
Like, what are the things that have a 100x or 1000x impact and ROI relative to other things.
4) Being unwilling to do “tedious” work.
This plays into item #3 because in order to get that good sense of what really matters, you have to get your arms around the problem, which typically requires getting your hands dirty and doing enough manual grunt work to develop intuitions and strong gut feelings.
Mathy people sometimes justify avoiding the grunt work because it’s tedious and they already have it all figured out in their head… but the issue is that the contour of the problem space in their head doesn’t match up with reality.
Their reasoning tends to be sound, but it’s the assumptions that get them. There’s some parts of the real-life problem that they haven’t loaded up in their head. Sometimes there are important things they think are negligible, sometimes negligible things they think are important.
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