I'm someone who has been educated in CS, as well as other fields. I both perform and manage industrial-scale programming and research programming, and I hire programmers of varying skill-levels for projects.

In my experience, there are a few caveats to your advice:

1) Strong programmers, fun side projects, and deep understanding comes from the pre-Computer interest which many of the die-hard CS people have. It is rarely, if ever, taught in academic programs and is something I specifically look for in my heavy-duty programmers whether or not their major is or was in CS.

2) My personal take on things is: The best programmers take innate talent and a lifetime of experience and turn it into super-star ability - don't expect to become one by majoring in CS. The second best programmers have a strong mathematics background (i.e. proof writing), because their code requires less debugging, which is the most costly part of software development. Any programmer can do better by analyzing their process and learning from that, but that form of software engineering seems to have become less popular, since it takes more work and makes you vulnerable to bad managers. Algorithm design is, of course, an entirely different matter.

In short, I'd suggest a CS/Mathematics double major. It's a good idea to be able to speak more than one technical language after all.

Oh and, my usual answer to:

When's the last time you heard a math person refer to some real-world situation as "a real elliptic curve"?

Well there was the time Heisenberg came back from his honeymoon where he had, of course, spent the entire time trying to puzzle out quantum mechanics. He was about to give up because of the non-commutative multiplication when he talked to a mathematician friend who said, "Wait, let me tell you about these things we call matrices."

Mathematics gives you very useful models, it just doesn't tell you how to use them. CS gives you the tools to implement solutions, but it tends to leave the solutions rather ad hoc. Bringing the two together is one good mix. I could suggest a half-dozen others - but this will do for now.

Edited to add: This is a mix if you want to achieve the goals laid out in the post. Not suggesting that it is either trivial to get such a pair of degrees or desirable for everyone to head in that direction.

Whoa... you are suggesting that everyone else do what you didn't and the reasoning is a list of ideas with no data? No offense, but how do I know this is not a grass-is-greener, I-wish-I-would-have moment?

I would beware of other-optimizing. As soon as I graduated with a CS degree I realized I should have been in philosophy the whole time. I love my CS major and agree that it is an excellent choice and do not regret the work I put into the process. I also agree with your points. But choosing a major is not a small thing that should be tilted by a post based on anecdotal evidence from someone who didn't do it.

After watching math and physics majors struggle in CS101 I realized that CS is not an intuitive field for everyone. They were not stupid or lazy people. They just didn't "get it". You can graduate with a CS degree and never "get it" and not know jack shit about coding, systems, or any of the other great things you mentioned. I learned about those things. But right now I am working with someone who graduated with a CS degree and did not.

This isn't meant to be a rain-on-your-parade comment; I just want to warn the bloke who reads this post and thinks, "God, math is that useless? I guess I will go CS." The key part in your post they may have skimmed over is this:

You shouldn't be considering a career in academia unless you're passionately in love with your field, unless you think about it in the shower and over lunch and as you drift off to sleep, unless the problem sets are a weekly joy. A lesser love will abandon you and leave you stranded and heartbroken, four years into grad school.

If you are passionately in love with your field, go for it. If you are not, find a different field. CS is not something everyone can fall in love with and think about in the shower and over lunch and drifting off to sleep. Neither is Math, Physics, Engineering, Psychology...

Just to add my own two cents. The market for CS majors is excellent. I guarantee you; you won't have any trouble finding a job if you are a CS major. Nowadays, there isn't a company that doesn't have software people.

So, what's wrong with this article? Is it bad prose, or too hand-wavy assertions, or overly obscure presentation/inferential distance, or too much text, or too obvious a point? Please leave a comment, I really don't understand.

In general, I think that getting a custom of writing some formal review-like comments would be valuable as feedback, not about the subject of the article, but about presentation, especially if the article looks bad and there is much for the author to work on improving.

I haven't read Jaynes's work on the subject, so I couldn't say. However, if he thinks that equal probabilities mean equal obligation to be surprised, I disagree with him. It's easy to do things that are spectacularly unlikely - flip through a shuffled deck of cards to see a given sequence, for instance - that do not, and should not, surprise you at all.

By "bead jar guess" I mean a wild, nearly-groundless assignment of a probability to a proposition. This is as opposed to a solidly backed up estimate based on something like well-controlled sample data, or a guess made with an appeal to an inelegant but often-effective hack like the availability heuristic.

This post confused me enormously. I thought I must be missing something, but reading over the comments, this seems to be true for virtually all readers.

What exactly do you mean by "bead jar guess"? "Surprise"? "Actual probability"? Are you making a new point or explaining something existing? Are you purposely being obscure "to make us think"?

I propose replacing this entire post with the following text:

Hey everybody! Read E.T. Jaynes's Probability Theory: The Logic Of Science!

It sounds like you're arguing that there are increasing returns to rationality in groups.

I am not sure. But I think that it would be helpful to think about what experiment would demonstrate the argument you're making here. e.g. Give a rationality diagnostic exam to a bunch of people, then put them in groups of various sizes and measure how well they perform various tasks.

How have you used rationality in your marriage and family life? Did it help you choose the right partner?

How do you 'imagine a couple that truly understood Aumann'?