[rwallace]

> I think getting a PhD increases your likelihood of getting an otherwise unlikely outcome in the sense of career success/advancement, getting rich, etc.

I can't see how it could fail to decrease it, once you take opportunity cost into account. The most likely way to get rich in a technical career is doing a startup. During the five or six years you were spending on your PhD, you could have founded two startups, had them both fail, learned from the experience, taken a third shot, and be seeing your latest startup taking off, all by the time you would have been putting your PhD in your pocket and wondering what to do next.

[javert]

What about going into quant finance? :P I've heard it said that that's a better way to become a multimillionaire than trying to do startups.

But regarding startups... doing a technical startup that leverages PhD research (or just background knowledge), or even just a "highly specialized" software consultancy, is probably a better strategy than trying to build the next stupid app that anybody who can program can build.

One key step to me going into grad school was realizing that I could always do startups, but I could only go to grad school while I was young. (Technically you can always go, but for me, there was a very strong preference to do it "now" or do it never.) So... you can have both, but probably only if you do grad school first, not startups first.

[rwallace]

(It's not giving me a link to reply to javert's latest comment, any idea why not? Replying here.)

Quant finance is an option, though I suspect once you actually got into it, you'd find making serious money that way wasn't really easier than doing a startup. Besides it's mostly a zero-sum game these days, and wouldn't you like to make the world a better place as well as getting rich?

I know of people who've gone back and done a PhD in their forties or fifties. Strikes me as probably more practical than doing it in your twenties and putting the rest of your life on hold.

The probability of being able to use your PhD thesis in a startup is negligible. Technical knowledge that you pick up on the way, sure, but that's a very inefficient way of obtaining that kind of knowledge.

[javert]

You couldn't reply to my comment because there is a built-in delay (like 5 minutes or something) before a commen can be replied to. You should be able to do it by now.

Quant finance is an option, though I suspect once you actually got into it, you'd find making serious money that way wasn't really easier than doing a startup.

I don't know, I mean, I wouldn't go solo... I would join up with an established company that wants to hire. And I have definitely seen these companies recruiting CS PhDs in the last 1-2 years.

Besides it's mostly a zero-sum game these days, and wouldn't you like to make the world a better place as well as getting rich?

Might be better to make some good money, and then retire kind of young and focus on whatever else you really want to do with complete financial freedom.

I suspect that traders actually do contribute though, just like every single other sector of the economy. I mean, traders provide liquidity and also "provide" econonic information, both of which help coordinate the economy. And if high frequency traders aren't actually providing any direct benefit to anybody, we should see markets arise that disallow that kind of trading.

I know of people who've gone back and done a PhD in their forties or fifties. Strikes me as probably more practical than doing it in your twenties and putting the rest of your life on hold.

I've been told by profs that older folks (who are, by implication, settled, fully mature, and have figured out what they want in life), are a much safer bet as grad students than younger folks. So, there is something to this. But if you get your PhD that late, there's not necessarily that much time left in life to do that much with it.

Technical knowledge that you pick up on the way, sure, but that's a very inefficient way of obtaining that kind of knowledge.

I think it's counterintuitive, but I disagree. If you really want to understand the cutting edge and see new opportunities, you have to be carefully reading the research papers that are being published (and understanding them), doing a lot of critical thinking, and talking to people in the field. And it's going to take a few years. A grad student is well-positioned to do this. Anybody else who wants to do it almost might as well just be a grad student (unless they're already a professional researcer or professor, which typically implies having been a grad student).

[rwallace]

> Might be better to make some good money, and then retire kind of young and focus on whatever else you really want to do with complete financial freedom.

That's an attractive idea in theory, but it almost never works out that way in practice. Your brain rewires itself over the years to match what you're doing. Unless you are very unusual, your expenses will drift up to match your income. You are almost certainly much better off to make your plans as though today was the first day of the rest of your life.

> I suspect that traders actually do contribute though, just like every single other sector of the economy.

Absolutely, they do. But it has to be past the point of diminishing returns by now. Cutting the time to move capital from a week to a day was surely a contribution to the economy. Cutting it from a hundred milliseconds to fifty milliseconds? I have a hard time believing that does more for the economy than writing a better poker bot.

> If you really want to understand the cutting edge and see new opportunities, you have to be carefully reading the research papers that are being published

Business opportunities usually arise some way behind the cutting edge. You are right of course that you don't want to fall into writing yet another cat photograph website because you don't know how to do anything else. But neither do you want to waste the best years of your life obsessing about the mathematical properties of some esoteric algorithm that ends up being no better than off-the-shelf algorithms on practical workloads. The sweet spot tends to be somewhere in the middle.