If you measure how much money someone will make in the next 10 years from the point they decide whether to pursue a PhD or not, yes, you are correct, the ones who do not pursue one will have lots more money.
However, you can live a middle class lifestyle while gettings a PhD (OK, maybe "lower middle class" here, but still very comfortably, if you don't have dependents to support) and also after getting a PhD, which is the same result as if you didn't pursue the PhD.
To me, that is more relevant than a raw amount of money.
I care what class I'm in, i.e., (a) starving; (b) poor; (c) middle class; (d) rich. I don't really care _within_ those categories.
As a sidenote, I think getting a PhD increases your likelihood of getting an otherwise unlikely outcome in the sense of career success/advancement, getting rich, etc. (unless you choose academia). I mean, over the course of your career, you could really leverage your PhD, or you could not. In theory (and probably in practice most of the time), it won't hurt to have one, career-wise.
To give you another counterpoint, as a person who dropped out of a C.S. PhD and went to industry. Sure, it won't hurt to get a PhD. Getting rich unfortunately is only weakly correlated with technical ability beyond a certain point. 1) You are not going to get rich purely as a salary (wo)man unless you are lucky enough to be an early stage employee (which I don't see how the PhD or otherwise helps you that much. ). 2) To found your own company and leverage your PhD skills seems tempting but rarely if ever do PhD's have research that can be converted into successful industry products. Boston Dynamics is one of those rare examples. However, these opportunities are not closed to off to people who while they may not have done research in the field and know every other implication of certain strategies but have great contacts and know how to get things done.
I think this is basically true, but I wonder about a couple of potential exceptions:
(a) I suspect that in huge corporations (IBM being a canonical example), just having the credential can help qualify you for leadership roles (e.g. leading, say, groups of 100 to 1,000 people), if you are also a good leader/strategical thinker/do-er. And at some point, these leaders are probably getting paid significantly more, which (with wise investments) could potentially (?) be bootstrapped into getting rich. I'd be interested if anyone with personal experience can comment on any of the above.
(b) I would think that having a PhD would help qualify you for the top-level executive roles. Although, frankly, I've looked at company websites, and top-level executives with PhDs are not _that_ common, even in software companies. But yeah, I mean, there _should_ be a top-level decision maker who is _at least_ keeping a close watch on any research related to what the company does (I'm talking software companies here, not companies that _use_ software)... and the vast majority of people who have the necessary skills will be people who did PhD-level research. Anyway, this person should be able to advocate for how the company can leverage new research, and this should not just be left to lower-level (hierarchically) technical folks who have no true strategic voice in the company.
To found your own company and leverage your PhD skills seems tempting but rarely if ever do PhD's have research that can be converted into successful industry products.
Do you think this is true even of CS PhDs? I feel like in my research group, there is a real chance for any of we (students) to do this, but AFAIK I am the only one who has ever really thought about it, because my colleagues tend to keep their noses in the books and focus on narrow technical concerns, whereas I'm really a big-picture thinker. So: in my case, plenty of opportunity, just not much interest among actual PhD students.
> Do you think this is true even of CS PhDs? I feel like in my research group, there is a real chance for any of we (students) to do this, but AFAIK I am the only one who has ever really thought about it, because my colleagues tend to keep their noses in the books and focus on narrow technical concerns, whereas I'm really a big-picture thinker. So: in my case, plenty of opportunity, just not much interest among actual PhD students.
My experience is mostly of C.S./Machine Learning PhDs. Let me give you an illustration. Say you spend your entire PhD figuring out one specific problem in recommendation systems, like for example, building optimization algorithms where the error rate is 6% or so. This is incredibly cool stuff but when you go out into the real world, you don't necessarily need that fancy algorithm inorder to solve problems. Really simple stuff works and the way production code works, keep it simple stupid is an important thing!
I'm just interested in your experience of doing just that. I've found very little reference material on it, except some comments by professors and people currently working in industry that people are courted by companies once they finish their qualifying exams.
This may be off topic, but what's your experience with this? Were you courted before you decided to leave? How far did you get? What affected your decision? What was the salary difference? What do you do now? Was it worth it to even go for a little?
I couldn't let my advisor down. After sticking with me for two years, I would have felt horrible if I had left him after my masters. I did not _expect_ this going in, and I planned to have "leaving after my masters" as a real option.
To be clear, I still would have left if I had had a good reason. Ultimately, I wanted to stay. But if it had been borderline, I would have still stayed. And "borderline" for grad school may very well be "I'm tired of killing myself with overwork, I don't really feel like doing this shit for a few more years. But I could just suck it up and keep going, since I'm already like half way there." Grad school can be pretty crappy.
I definitely could have turned summer internships during grad school into full-time work, though, and gotten a well-paying engineering job.
There are tons and tons of companies that come to a school every year. If you have a linkedin profile or some sort of web presence, you will be courted. It doesn't necessarily happen once you finish quals or whatever, but it will keep happening.
> This may be off topic, but what's your experience with this? Were you courted before you decided to leave? How far did you get? What affected your decision? What was the salary difference? What do you do now? Was it worth it to even go for a little?
I do data science (machine learning, scalability engineering) stuff. Lots of people have PhDs, I presume they make a good 20% or so more than I do. However, keep in mind that they have spent 3+ years finishing a PhD so I can probably make up. Honestly, if you are going into industry, I don't think you'd need a PhD. Companies these days think it is fancy to hire someone with a big name degree but a master's degree hasn't hampered me. I miss school terribly mainly because I felt stupid there (I know how silly this sounds) and the problems were terribly hard.
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.
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.
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.
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'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).
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.