Academia in general is screwing itself. The main reason for anyone to go into it is to work on interesting problems in a self-directed fashion with amazing job security. You probably wouldn't get rich, but you'll have a nice upper-middle class salary and almost no way of getting fired.
Now the available tenure-track positions are declining every year in favor of adjunct quasi-slave labor. Even if you do win the lottery and get a tenure-track position, from what I understand you'll spend most of your time writing grant proposals to chase funding, and at least 5-7 years trying to please the tenure committee.
So, why exactly would anyone spend 10-15 years making a fraction of what they could in industry for a vanishingly small chance of the eventual payoff of tenure?
If you're going to slave away for 10-15 years anyway before you get to do what you want, you may as well do it in industry where you could put away enough money to be financially independent in that time. The only remaining obstacle is that you still kind of need an institutional affiliation to be taken seriously as a researcher and publish, but that might be slowly changing as the Internet makes it easier to publish.
In computer science, your criticisms are very very valid for graduate students: they make ~30k/year when they could be making ~200k/year at, say, Google, and they usually have less autonomy than a professor. But they don't really apply to tenure-track faculty. If grad school is already a sunk cost for you, (or it was valuable to you in other non-monetary areas), a tenure-track position can actually be quite a lucrative choice.
Firstly, CS professors make a decent salary. A professor at Berkeley (all salaries are publicly available for California state schools) make ~160k/year. Someone like Michael I. Jordan makes ~300k/year. And if you work in a field directly applicable to industry, such as machine learning, this figure does not include the substantial consulting/advising revenue that is available to you.
Furthermore, tenured faculty can go do a startup and then come back at any time for their old job.
Granted, a professor in CS could probably be making millions per year in finance or something, but my point is in absolute terms you can make a lot of money.
Is it really that easy to enter finance at that level (or at all), or even get a job at Google for $200k/year? I don't think it's that automatic unless your background is one that includes the "correct" college, an affluent background, and more.
Many people end up pursuing an advanced degree in this field because they want to break into a new industry, make themselves more appealing to employers and make more money. If you can do a master's while working, you don't end up with 2+ years of lost salary as well.
Maybe I'm doing it wrong, but I don't have a computer science degree and I currently find myself considering a master's for those reasons. It would be nice to avoid having to go that route.
> Maybe I'm doing it wrong, but I don't have a computer science degree and I currently find myself considering a master's for those reasons. It would be nice to avoid having to go that route.
Whatever reason you have for going or not going is a good reason.
I have a master's in CS. Getting it did help me break into a new industry. It may have earned me a better chance at interviews. It didn't get me a higher salary in the near term. I'd have earned more by sticking with my first job. For money alone, experience is best.
And I knew that going in. Personally, I felt comfortable with my expected future earnings before getting the degree. I wasn't comfortable with the rate of my learning at my job. I was headed towards plateauing on a very specific proprietary programming tool (called Ab Initio) whose skills, I felt, weren't easily transferrable to other areas of programming. It turns out I was right and wrong.
Anyway, later in my career, this degree may distinguish me somehow, but in the near term I don't find that it makes much difference to anyone but me. Ultimately, the interview is about how you sell yourself, and a degree doesn't sell itself. It may get you an interview but it doesn't make you a better fit for more organizations.
The reason I went back was I missed classroom learning and wanted exposure to new fields of programming. I ended up studying machine learning. That was in 2006-2008. Now, I try to continue learning using available resources and communities online. Focus is tough though. School was great for forced focus. I thought a lot about the pros and cons of going back to school and decided it was right for me at that time. I'm glad I did it, I learned a lot, met some really cool students and professors who I wouldn't otherwise know, and it gave me some insight into what a PhD and life in academia entails.
> Is it really that easy to enter finance at that level (or at all), or even get a job at Google for $200k/year?
I think it is when you condition on the fact that someone is a professor at Berkeley/CMU. Roughly 5% of applicants are accepted to top PhD programs, and less than 20% of people with PhDs at top universities (like Berkeley/CMU) get tenure track positions. Of those with tenure track positions, a minority (<50%) get tenure, and of those who get tenure, a fraction become full professors.
So the professors at Berkeley/CMU are well into the top 1% of people with computer science degrees, which makes it entirely plausible that their next best alternative is getting a high level finance/tech job.
I agree with that. I do think those limits just makes it a more obvious argument, but I thought the discussion was more general than an obvious subset of people who can have their pick of opportunities.
Even "lesser" schools without top tier academic stars are having trouble retaining and attracting enough talent to accommodate the current demand, and this is even with salaries starting in the six figures. Which implies the market for people with graduate level degrees possibly has a higher demand, and should translate into a higher salary.
> Is it really that easy to enter finance at that level (or at all), or even get a job at Google for $200k/year?
Yes. from personal experience, if you can get into a good graduate program, you can also get an offer from {google,amazon,microsoft} for $150-$200. the predicate "get into a good graduate program" means you have a CS degree with a decent GPA from a well considered school and some research experience, which usually translates into some kind of job experience that you can talk up in your interview.
Well first of all the only advantage an affluent background will give you to getting a job at Google is the same advantage it'd give you for any other employer or any other industry (better school growing up, able to focus on that school instead of working, etc).
And we're already presupposing that the person in question is sufficiently hard-working and intelligent that they could be a tenure-track professor at a major research institution. I would argue that the vast of majority of folks in that position could get a very good job at Google provided they had the correct skill set.
And I don't know what part of CS you're most interested in, but my degree is in Political Science and after the first shitty one I haven't had any trouble finding development work in my tiny city.
Honest question: where do you learn advanced stuff if not in a graduate school? Academia undoubtedly sucks if one mainly takes money or career prospects into account. But among people who do challenging and sexy stuff (including in the industry) phd seems to be the norm, not the exception. One might object that phd has become unnecessary because nowadays all the relevant information is online but learning without the help of a community and a mentor is hard.
You absolutely do not need a PhD for industry unless you want an R&D job in a handful of domains. A PhD is not simply learning more facts about a particular topic. It's an apprenticeship for conducting independently directed academic research. There are so many topics that you can not just learn from reading some online sources e.g. most experimental work. Most of the time spent in a typical PhD program is spent trying to solve problems that have no easy answers and no easy guidelines to follow. While you can definitely gain similar knowledge and experience in an industry setting, you almost never have the freedom to take the 3+ years often necessary to explore a narrow topic, struggle and fail repeatedly, be faced with and overcome crushing doubt and frustration, and do so in a generally supportive community.
> You absolutely do not need a PhD for industry unless you want an R&D job in a handful of domains.
I know Phd.s at {MSFT, Google, Uber, Deepmind, GE, P+G} research divisions. I don't know anyone who works at those who does not have a doctorate. So I would say this applies to more than 'a handful of domains' -- If you want to do research, you should plan on having a doctorate, even in industry. The majority of data scientists I know have doctorates, even if that strikes me as (generally) overkill.
Otherwise, I agree with the rest of your statements.
You'd have to find a community adjacent to your interests.
I like programming languages and functional programming and haven't had trouble finding things to learn and people to help me, mostly through the Haskell community. You have everything from completely disorganized but perfectly accessible help like IRC channels and mailing lists to things like type theory meetups and study groups (if you live near enough people with those interests). It'll be easier in the Bay Area or New York, but moving here has lots of other benefits and isn't nearly as constrained as going to grad school.
Once you know enough on your own you can parlay that into a "sexy" job if you're so motivated. I interned at a company that had their own internal language, for example, and one of the main people working on their compiler had a philosophy degree and (unless I'm misremembering) no grad school whatsoever. It's more difficult because interesting jobs are relatively rare and a lot of places (especially ones close to academia) are still disproportionately concerned with credentials but it's possible.
As long as you can concretely demonstrate your capabilities, you have a chance at progressive companies without credentials. This still isn't true within academia itself, at many government roles or at big rules-bound corporations, but it is possible within the "core" tech industry.
>where do you learn advanced stuff if not in a graduate school?
From a degree-less "researcher" whose published in neuroimage, i'd say, wherever you can and as you learn more you get better at knowing where to start when searching/who to talk to/who to work with.
>Academia undoubtedly sucks if one mainly takes money or career prospects into account.
Or tired of trying to get your lab to work on more on the cutting edge of the technology vs the grant hamster wheel and status games or sucking up to people who clearly don't have any understanding of modern technology/physics/mathematics/techniques on a intimate level and squandering resources because they can, and figure you can get what you want done faster with more resources you can more directly allocate.
>…learning without the help of a community and a mentor is hard.
It's going to be "hard" no matter what path you take imo, but that what makes the journey of knowledge fun, because of that small chance you might actually figure something out you never knew before is worth ones time?
learning while getting your PhD is maybe 1/10 learning from classes and 9/10 creating new knowledge and reading papers; I think I am being generous to classes here. You become the world expert by going extremely deep in a very narrow topic and actually creating the knowledge around the topic. You don't need to be a PhD candidate to do this.
For credibility, I was a Computer Science PhD candidate 4 years ago.
I think this is good advice in general. But I doubt that the most effective way to follow it is to devote evenings after your 200k+ corporate job to studying.
The most amazing part of this Q&A to me is that people will go into industry, apparently increase their worth by millions of dollars, and then come back! When you have an opportunity where people are willing to forego that sort of wealth, then it seems to me that you are doing something incredibly right.
Not really. It originated to describe high level employees who take a job and then exploit their personal network to hire away friends. This is super-common and expected, and is also the reason non-competes became popular. It wasn't until later, the tech boom, that "poaching" was repurposed to be more generalized luring of high potential employees from wherever using whatever ($, perks) means available.
The idea that they need non-competes to retain talent should still set off some alarms that maybe companies aren't paying market rate salaries.
Most people don't leave a company for less money and no other benefits, and there's nothing wrong with employees being lured away for more pay. That's how it's supposed to work.
An that use of the term "poaching" [high level employee hiring away friends] originated with the earlier/original meaning of the term i.e. "hunting animals illegally". The deer (for example) were the property of the King - Robin Hood was a poacher.
It's great language for shifting blame as well. Instead of a business admitting they couldn't retain an employee, which would imply that they need to look at what they are doing. By calling it poaching, they are now a victim of being poached from, and less responsible for what happened. I put this next to the people who say 'can't find anyone who does x' but who never admit it is because of the salary they are paying.
Whatever the history, it's psychologically tied up with a lot of nasty baggage, and makes it seem inherently dirty, like there's something wrong with bidding up the value of labor by seeking to offer better compensation for workers.
Whatever the technical "correctness" of the term, it comes of as a sort of apology for setting better wages and we should use less loaded terms, like "recruited" or "bid/hired away".
I always thought the scenario they meant to describe was something like, I work for Company A, and Company B makes some partnership with them to deliver some product, but then I'm the main guy so Company B just hires me away
That was my immediate reaction to this title as well. Even if you discount the ownership thing as a historical artifact of the language, the term is clearly used as a way to reduce competition for labor and drive down salaries.
CMU has dropped significantly in my personal ranking of universities. None of that was related to faculty being hired away. All of it is related to activities like attacking the Tor network or accidentally admitting 800 students. Maybe they have a PR problem but my overal view of them is pretty meh even though they are one of the more elite CS schools (and I think Pittsburgh is a fairly nice city, never been there though).
I did research in machine learning in college (not at CMU). CMU remains the top overall university in the world for machine learning and robotics (depending on specific subfields of ML/robotics the rankings will switch, but at that fine granularity the difference is just 1-3 professors).
Your metric for ranking universities is in no way based on how productive the university is in research or the quality of the graduating students.
Even if you consider the things you mentioned as proxies for the quality of the university, they are extremely weak ones and there are better metrics available to judge the quality of a university.
Am I wrong in viewing this comment as a flawed attempt to attack the university? Open to any counterarguments.
Presumably OP's ranking cares about things besides just research quality in CS or graduating students? Those other things aren't proxies for the quality of the university, they're components of it.
Unfortunately creating one's own idiosyncratic ranking holds no value to the world if it doesn't conform to what the world values. (Open to counter arguments on what the world values)
People generally use rankings to gauge how strong a university is perceived. OP's comments do nothing to change my perception of the university's strength.
It's a personal judgement that is independent of the actual research performed. It's only a personal ranking to answer the question "would I want to work there".
I also downgrade universities that have a hard pro copyright stance, do heavy military research etc. It's a personal ranking and not one that I'd tell anyone to follow. People should rank by their own criteria.
While the research was unfortunately performed under the CMU name, the SEI was more or less founded by the government to do this type of research and is not related to the rest of the school.
I wrote to the ethics board about it when the story broke, but I doubt anything will be done, as it's more or less a separate institution.
Well, I'm glad to hear CMU is hiring 17 new faculty and that the fields of robotics, computer vision, and machine learning are all booming so rapidly (though let's hope it's not a bubble)!
But as to the concept of "poaching", well, to quote the economist Homer Simpson, "Money can be exchanged for goods and services."
Just curious: How many out there are yinzers? I knew there was a nice technology scene here but am surprised to see Pitt/CMU on HN several times over the past couple weeks.
CMU professor here. Currently on leave at Google for the 2015-16 academic year, returning in August. (Not, in my case, for the money - I actually left a startup I co-founded a few months before it was acquired because I decided that I'd probably learn more at Google. Ask me in a few years if I regret leaving that money on the table -- but thus far, I'm exceptionally glad about the decision. Having a total blast. Sabbaticals/leaves are one of the under-advertised benefits of being a faculty member. Which is useful, since the salary delta to industry sucks. :)
No one, here or in the article, claimed they were 'poor'. But most are not as wealthy as similarly-qualified peers in industry. That's all. CMU's CS and AI profs are among the best in the world and companies are keen for these skills.
Now the available tenure-track positions are declining every year in favor of adjunct quasi-slave labor. Even if you do win the lottery and get a tenure-track position, from what I understand you'll spend most of your time writing grant proposals to chase funding, and at least 5-7 years trying to please the tenure committee.
So, why exactly would anyone spend 10-15 years making a fraction of what they could in industry for a vanishingly small chance of the eventual payoff of tenure?
If you're going to slave away for 10-15 years anyway before you get to do what you want, you may as well do it in industry where you could put away enough money to be financially independent in that time. The only remaining obstacle is that you still kind of need an institutional affiliation to be taken seriously as a researcher and publish, but that might be slowly changing as the Internet makes it easier to publish.