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My first reaction was pretty much identicle, right now you can do well at almost anything purely based on conscientiousness, including video games, work, school, and social interaction. I don't know of any good way to measure general talent, but when I learn most things I tend to be quite bad at them until I enter tsukoku naritai mode. Perhaps this should influence my career decision somewhat, its hard to tell if talent or effort is more crucial for programming.
Effort. Always assume effort. Talent will speed up the learning process in the early stages, is likely to make effort easier (because it is more fun) and at the extreme upper ends of of performance probably gives a higher limit. But in general effort plus social politics skill will determine your career success.
Despite what they are taught likely to be about themselves, what they might think of themselves, and what western culture expects of them, programmers are more creative artists than analytic engineers.
The difference is most tangible from the management perspective since motivating programmers is less like motivating chemical, mechanical, or any other sort of engineer and more like motivating commercial artists with less pretense, who were never led to believe they were meant for something greater. Dissatisfaction from programmers grows in much the same way it grows in commercial artists as well, though they programmer is less likely to specifically identify his or her complaint and the artist is more likely to complain about having sold his or her soul.
Common responses to criticism of work among programmers align more with those among artists than those among engineers. Again, I learned this from a managerial perspective.
The most important advice that may be given to starting artists (excluding all the low-hanging fruit advice that is best for everyone in general, of course) isn't about discovering your own inner talent or anything similar, instead it is about discipline: "Ideas are not swords you can brandish about in triumph. What matters most is the Sit Down, Shut Up And Get It Done. Only there will you find the true steel for your craft. Only there, will you know if you are worth the words out of your mouth."
I would suggest talking to some programmers.
My intuition is that there's something of innate talent involved in programming, so that you can divide people into two populations: those whose brain makeup causes them to find programming intuitive and fascinating and cool, and those to whom it just doesn't make sense. If you're considering it as a career, presumably you fall into the first category. Beyond that, I would guess that conscientiousness is the biggest predictor–my one-semester programming elective was enough to show me that it's really time-consuming.
But I'm not a programmer by specialty. An unusual percentage of LWers are, though, so maybe someone can give you advice?
"The Camel Has Two Humps", which IIRC has been linked here before, does purport to find a bimodal distribution between people who can and can't program. I'm not at all sure if that has anything to do with inborn talent, though, at least beyond basic general intelligence.
At various points in my career I've found reasons to teach people programming skills, and my n=1 impression is that the ability to internalize basic programming has little to do with personality (though conscientiousness helps, and I suspect openness to experience might too) and a lot to do with the student's level of comfort with mathematical thinking. Not necessarily advanced math (you don't need anything more complicated than algebra to program except in specialized domains), but you do need to be very comfortable with a certain level of abstraction. I suspect that might have more to do with the distribution in the linked paper than the "geek gene" concepts I've heard tossed around elsewhere: at the level of the math prerequisites for CS 1 it's still possible to do well by solving problems mechanistically without a good grasp of the abstractions involved, but that won't cut it in computer science. And it'd probably be difficult to teach that in a semester.
Camel seems to fail replication: http://www.gwern.net/Notes#the-camel-has-two-humps
The thing that wasn't replicated was their attempt at a predictive test of the distribution (based on a particular explanation they thought applied), not the existence of the distribution itself, which is something that was observed in grade patterns in CS compared to other subjects (though I don't know how rigorously established it is).
Isn't the predictive part the interesting thing? I wasn't aware that bimodal grade distributions were unique to CS.
Well, their original paper claimed that (eg) math grades are typically a bell curve, whereas CS grades are typically bimodal (with examples from one university). But again, I'm not sure if this is something that's been rigorously demonstrated.
Good to know. I thought it had a bit of a questionable odor to it, but I wasn't able to find any replications in the brief time I spent looking into it.
I don't think it has much to do with personality either, except, like you said, willingness to work hard (especially if you're someone who starts out finding it very difficult.) But I think that a lot of people, even people who can work up to the level of calculus in math, go at it with the mindset of "memorize that Formula X gives Answer Y" instead of trying to understand how and why Formula X relates to the underlying structure of the problem so that it's obvious that it should give answer Y, but gives Answer Z in a different context... You can get by with memorizing formulas in math classes, at least the way they're currently taught and tested. It's a lot harder to get by with that habit that when you're programming.
(On the whole, the people I've known whose minds appear to work like this aren't noticeably "lower" intelligence, however you define that. They just don't think of math as something where they should be applying the analytic part of their mind.)
If you define "talent" as a product of your current ability to produce and visualize mental models of complex systems, especially "from nothing", then it is the most defining factor for the higher maximum awesomeness of programs you can code at present.
This "talent" can be enhanced and self-improved through effort, however, in a very similar manner to making oneself more "luminous".