What You Need to Know Before Considering a PhD
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I loved grad school. Loved loved loved it. I wouldn't give up the worst day I had in grad school for almost anything. And I love having the skills it taught me. I am a much better engineer and researcher than I could possibly have been had I taken almost any other route. I was given freedom in grad school that just would not have been present in most industry jobs. I was given a hardware project - a submersible robot - that I was completely in charge of on day 1. I had to teach myself machining, how to do electrical and electonicd works, how to do embedded programming, how to tune a PID loop. How to work with other students. How to give a persuasive presentation. How to come uo with my own ideas, how to convince other people that they were worth pursuing, how to quickly become an expert in a topic.
That being said, I am not under any illusions about the financial loss I experienced. I spent ten years making a pauper's wage, when if I had chosen to go into industry I would have been a software engineer... in the Valley... in 1993.
I also was in a remarkable lab in grad school. It made top-ten lists of “coolest college lab”. And that wasn't hype. The caliber of student and of professor was off the charts. And they were not only smart, the vast majority of them were good people. A lot of places aren't like that. And as the srticle correctly said: a toxic graduate school environment is worse than most toxic work environments. In any practical sense, students don't have HR protections. If you can't get your advisor to write you a recommendation, getting into a different program is nearly impossible. You can be worked 100 hours a week. You can be blackballed for getting sick, for taking vacations, for taking maternity or paternity leave (even if it is - and it almost certainly will be - unpaid).
The key is to find an advisor who is doing good work and who is sane and moral. If you can find that you're golden. If you can't you may be completely screwed.
As far as practical advice for finding good advisors, track records of previous students can be a helpful first filter once you've made a list of people who wrote papers you like. Probably the most helpful thing is talking to current students at the admitted students day and listening to them. Bad advisors usually have at least one notable case, and there will probably be at least one person who'll take you aside and tell you about it. Listen to them carefully. Even great advisors can have at least one unsuccessful and bitter student through no fault of their own...but they usually don't show up to the admitted student events unless there's a real cause for animus.
Grad school was one of my best decisions. I went to a great school that was what I call the Goldilocks size, big enough to have a great faculty, equipment, and decent funding but small enough so that collaboration was the norm and the crazy horror stories of maniacal hours and/or cutthroat competition were normally self induced. My PI was an incredibly good guy and still a close friend. I met my business partner and co-founder while working with him the lab and we're now building a company that expands on the work we did in grad school.
That being said, I saw plenty of people not having the experience I did. This was almost always because i) they didn't really like research and didn't know it until they were there or ii) they picked a PI (PI = professor/boss) that was a really bad match for their work style and personality. Finding a lab & PI that matches your personal expectations about the PhD I would say is more important than the research focus. Don't choose something you'll hate learning about but ultimately the PhD can be more about learning how to teach yourself than the skills you learn during research.
YMMV
Grad school was far, far better. Completely different league.
I spent my undergrad learning the theory, and then spent my PhD applying it to real problems, working with actual geniuses, being paid a reasonable salary to do it, and getting to discuss it with external colleagues in nice places. The experience will stay with me forever.
The main function of HR is not to serve employees. The main function of HR is to protect the employer from legal liabilities.
[0] https://www.amazon.com/Corporate-Confidential-Secrets-Compan...
It's just good to remember HR is not an agent of the employee but of the employer, and you can't lean on the HR representative as you would for example on your own lawyer.
> A toxic graduate school environment is worse than most toxic work environments
I agree with this 100%. I would even extend this to overall academia pre-tenure (and maybe tenure as well). Moving to a different employer is usually easy and non-traumatic; in PhD program you are stuck -- leaving will mean abandoning your half-written thesis and starting from scratch, etc. Thus it is critical to avoid bad programs, either with a toxic environment or those that treat grad students as long term slaves.
> A lot of places aren't (expand) ... with vast majority of smart and good people. ... You can be worked 100 hours a week. You can be blackballed for getting sick, for taking vacations, for taking maternity or paternity leave
With this I disagree. I had friends in many schools and while there were a few exceptions by and large the environment was very good: supportive and enabling without kid gloves. I was on the theory side, which surely made things easier (no expensive hardware or purchases to pay for), but I basically wasted third year of 5 of my PhD on aimless wandering: I could get no traction on any problem, would try something and drop it at the first challenge, etc. And I heard no complaints from my adviser -- he was checking in, asking if I want feedback or suggestions on problems to look at, but otherwise let me be. No 100 hour weeks, etc. I knew he was not thrilled, but he let that disease (or growth) run its course.
I much later spoke to other folks doing theoretical PhDs and found that this is not that uncommon: transition from doing great in classes (learning along an externally designed sequence) to planning and doing your own research may not go smoothly.
I would speculate that this is largely because students spend most of their attention on pre-planned classroom-like work and do almost no research-like work during the first 16+ years of their formal schooling.
If I had to do it all over again, I would.
Recovering from a difficult graduate school experience, like the one you perfectly described, is insanely difficult. Financially, emotionally, and temporally.
The caliber of student and professor was off the charts like you said. Everyone was infinitely smarter than I was. They had some of the nicest equipment available. I worked in a 3 research labs for 3 years in college, under 3 different professors.
My first grad mentor, I took everything for granted. Didn't really realize how great he was and how much of an effort he put foward.
My second mentor ... didn't give a shit about me at all. Actually, this lab was the most interesting too, which made it worse. He saw me just as someone who clearly was wasting his time everyday.
My third mentor, I started to appreciate the value in a good teacher. Not the greatest but made efforts in making the concepts much easier to understand. I cared way more about my final honors dissertation in this lab than previous research I did. I wouldn't even call the other 2 labs research, I just read papers all day and did lab tech work.
In the end I realized research is not for me. Its not fun, and painstakingly slow to see results. Still learned a lot.
People should do PhD if they are genuinely interested in doing scientific research. If you are doing PhD under pressure or in hope of getting better paying jobs you will be dissopointed. It is an arduous process and taking up your precious years but it gives you opportunity to have freedom to explore and work on your interests for rest of your life. You won’t be coming to office everyday doing assigned task on your backlog and reporting your status in scrum meeting. Instead you will be reading about new creative work that was literally published yesterday, mulling over that in lunch with colleagues and apply your original ideas to actually get published under your name. The downside could be lower pay and/or no stock bonuses for many outside of hot areas like AI. But in general, you have much better chance of doing cutting edge work that you are truely passionate about if you have PhD in that area.
I would also mention teaching: some academic staff hate it because it takes time away from research. This is true but my experience is that if you put time into being a good teacher then you get to take your pick of the brightest students to help you with your research, which can lead to interesting Masters projects which lead on to PhDs.
I don't have a degree and I have that. Also I know a lot of people with a PhD who do not have anything remotely resembling freedom.
The author might be biased but it's good to hear it from that side once in awhile, rather than be deluged with "you can't do science without a PhD" and similar sentiments (I work parallel to academia and get plenty of shit from their ivory towerism, hence my comment).
In what way is the author "biased" exactly? She did this course, was in both a PHD and tech, and tells people why she thinks it's worth considering not getting a PHD, listing various (valid IMO) reasons.
Where does bias come in exactly?
Including former colleagues I probably know a couple of hundred PhDs who do this exactly the same as mere Bachelors. Outside of a few niches a PhD is barely recognised in industry. Sorry, but that’s the truth.
This condition is necessary, but not sufficient.
After finishing my PhD in quantum information, I turned to data science. I couldn’t be happier about this transition! Compared with academia, data science world looks to me like a wonderland.
tl;dr: faster pace, more freedom (sic!), way less bureaucracy and politics, etc
Longer version: https://p.migdal.pl/2015/12/14/sci-to-data-sci.html
I have found this to be good advice for people who are thinking of starting companies too.
However, I can't help but think that this mentality does scare away certain personality types that would otherwise make for great researchers. I fall into the category of people who ignored advice and did it anyway, but I try to be welcoming to other types too.
It depends of course in hwo the "no" is worded.
A PhD system trains you to think about unsolved problems in an given domain deeply with a larger time runway. The end goal is not a tangible product that reaches millions of people, but rather a set of ideas that can take a crack at the unsolved problems in your field in a novel way. A good work should inspire others in the field, and eventually a larger audience to pick them up and expand and build on top of it. To give a small example, a majority of the fundamentals of machine learning was charted out by many, many PhD works over the last 40 years. Implementing a linear classifier is 2 lines of code in 2018, but many Bothans died to bring us this information :-) .
The goals of industry are more immediate. Expect for a privileged few research labs in industry, your work is expected to be monetized, and rightly so. The goal is for you, if you run the business, else your management team to first figure out a problem of high relevance and monetary value. Build products/solutions for that problem, that can be used by someone who is less versed/ambivalent of your technical solutions. Efficacy of solving that particular problem often defines the merit of your contribution.
The fundamental of choosing the PhD or industry should be taking stock of what kind of contribution you want to make as an individual. If it is a few set of ideas to science, which on a later date might become something fundamental in our understanding of the world, then PhD is a good path. If it is a set of contributions towards a product/solution that eases the pain of many users then go into the industry first.
In other words, don't do a PhD if you're in it for the money.
It may open a few more interview doors for you, but honestly, at least in my experience, I wasn't even aware of which of my coworkers had PhDs. When I eventually found out, they were all just slightly older than me working at the same pay grade.
That said, I am seriously considering going back on the PhD route, because I think I’d like to spend more time teaching down the line. Kind of silly, but I have only a master’s and it seems like most higher ed institutions do not consider hiring you as a professor unless you have the magical piece of paper.
Having worked in research at a few major tech companies now, when hiring research staff I’ve found they typical want someone in a strong position in the research community, typically evidenced by a strong publication record in the field they are recruiting in. While this can certainly be done without a PhD, I find it to be a bit rare. Our current research group is around 10 people (in a ~2K person company), 9 have PhDs.
In my experience this is not true except unless you are super genius. Most folks without PhD often keeps making same naive mistakes, for example, not studying previous state of the art, not recording experiments properly, heuristics instead of rigorous analysis and so on. PhD trains to avoid all these. It allows you to build network, identity great researchers in the field as role models and understand what scientific scrutiny entails. It is not unusual to identify paper written by someone not experienced vs someone experienced. For example, a person without PhD would often neglect to mention scale in the graphs, compute variances in findings, describe figures properly and so on. These might look minor cosmetic things but it often goes long way in overall rigor.
??? They taught all of us that in undergrad.
a person without PhD would often neglect to mention scale in the graphs
... and they taught us about the scale of a graph in secondary school ...
That is all well and good, but I can't name a single person in my research field who has a decent publication record without a PhD (finished or in progress).
Of course you can do what you love also without a PhD, but I’d argue that a PhD will eventually allow you to explore fields and things that are not economically viable.
In fact I would say that it’s just plain sad that we have to choose something based on how economically viable it is (industry) rather than purely for its interesting properties.
However I’d rather have a system that would still allow me to get some sort of basic income (to afford a living) while doing, say, archeology studies in Ancient Rome. Such system is academia right now.
I grew up in a below-average income family in a below-average city in America, and I carved this out for myself despite zero undergraduate degree. This meme that you have to be well off or that everyone who does it had benefactors is kinda silly.
I realize I am a sample size of one, but I really don't care for being wholly discounted in these arguments as if such a path is impossible. Was it exceptionally hard? Yes. Would it have been easier if I just finished my BS and an accepted-to MS? Also yes.
But I didn't for various reasons, and it still worked out because most of the factors that make a successful entrepreneur, scientist, and employee are exogenous to formal education anyway.
I advise people regularly to continue on with formal education; it is not like I think this is a very good track to take. But it is one that is available to those who self-study their ass off and don't mind working menial jobs to put food on the table for themselves and their families with great sacrifices.
What kind of research do you do?
So I guess the land you need to be in is the United States of America, at least in my case anyway. Might be true elsewhere but I haven't tested my luck.
But I absolutely agree if you 'just' want to be involved with some research projects, publish a bit on the side and perhaps teach a few courses then there are many ways to reach that goal that don't involve becoming a professor.
Edit: I actually don't know what you are arguing, I think the point is that you won't get a job as a Professor without a PhD, getting teaching offers is completely different and happen to people in industry without PhDs all the time.
Well is that because you aren’t in a job track that has anything that would need a PhD?
You do a PhD to scratch that itch, hopefully once you graduate you can keep scratching those itches, even if often that doesn’t work out.
It's a degree with a high opportunity cost that won't pay off financially in the long run, even with no tuition or debt. In the past I think everybody understood this but now I'm slightly more worried.
However eventually the next AI winter will come and the old lessons will be relearned again.
I have a PhD in physics. At the time that I finished college, and I think it's still somewhat the case today, the relevance of a PhD varied from one field to another. Maybe scientists just take longer to ripen. As an example, I've noticed that startup founders tend to be older in science than in computer programming.
Don't do a PhD if teenagers are getting rich in your field. Instead, decide if you belong in that field, i.e., if it's the kind of work that you actually want to do.
I suppose it could be unique in a way that their PhDs were sponsored by large companies and hence well funded, but I worked 4 years a C++ programmer and never caught up with their pay.
That was in the UK btw.
Bottom line: do a Ph.D if and only if you want to do the work. Don't do one in order to get the qualification. Focus on the journey, not the destination.
I know this is a large generalization, but I could comfortably say this is a predominant trait among maybe 70-80% of the folks I've worked with. On the surface it seems like there's something in the training for PhD staff that seems to kill the ability to self-regulate that's a very good thing when pursuing the unknown, but an excruciating pattern to deal with in industry where budgets and shipping times are primary importance.
Seeing this in action, and knowing I have no interest in working in academia, has been the primary reason I haven't pursued one myself. I don't want to be "broken" by training.
I get to pursue all of the R&D I can handle already working in an R&D lab -- with the usual publication, patent, ship to customers that it all entails. So I'm not starving for interesting things to pursue.
Do a PhD for the jobs that it unlocks (professor or researcher, mostly) or the type of freedom that it provides (it was 6 years of mostly unstructured time that I got to explore things that interested me while being paid). If all else fails, you can still go join a big tech company and make more than enough money to live a good life.
I'm a professor now and love it! Couldn't have happened without a PhD first.
Could you talk about what life is like as a professor? I looked at your research interests and they lean heavily on the applicable-to-industry side - is that by purpose?
BTW - I think it's so cool that a professor is posting on HN. I think back to my professors and I couldn't imagine anyone of them being nearly as hip.
I am a new professor but I can give you a short summary of what it is like. It is very unstructured. No one tells me how to spend my time, but I have to balance many different things: teaching a course, working on multiple research projects, writing multiple papers, writing grant proposals, reviewing papers for journals/conferences, traveling to conferences, recruiting and working with student researchers, etc. Some people like to describe it like running a startup.
Also, most engineering PhD's are bogus because most engineering "research" is actually not deep research - it's building prototypes that aren't quite useful but not quite that novel or interesting either. If you're in a PhD program and you're not doing something really interesting and fundamental, you're definitely in a tough spot.
As a guiding principle, I think deep research is answering questions that no one has approached in quite the same way before. In my own PhD work, I used the time to learn how to answer questions sufficiently (methodology) as well as how to recognize shape/distill questions in a way that they can be approached. Developing these skills don't require a PhD, but the dedicated time helped me.
I've used these skills to learn deeply and answer questions to great extent in business roles and drive some solid change in a few large orgs. It doesn't mean I do a job better than someone without a PhD. Also, the PhD signals that I have answered some deep questions to the satisfaction of others that answer deep questions (PhD committee).
ML is an Applied Research discipline and all the better for it.
I've never been to any academic presentation where they start like that. In-fact, more often they complain about the huge compute resources in industry.
The deeper into a field you get the more you realize that the parts which seem deep research aren't, and the parts which seem incremental improvements are actually very deep.
I can think of a number of things in machine learning which appear hard which are easy, and vice versa.
Theoretical justification for GAN improvements (eg, the WGAN paper): elegant but obvious, even though I'm not a mathematician.
Generative models for text including entities that remain coherent for longer than a sentence? We barely know how to even start thinking about this problem.
Deep research maximizes uncertainty reduction (or information gain in other words). Uncertainty here could be model uncertainty if you are developing models, or a shift in the probability distribution for a particular question more generally. E.g., "Does P=NP?" would be an example of the latter.
It might be very general, applicable in many fields. Or it could be targeted at a particular field, but in a way which answers many questions.
Bayesian experimental design can do what I think is the easy part of the problem: maximizing information gained for a particular experimental problem statement. In my view, most of the time you can reasonably guess what Bayesian experimental design might tell you by looking at a state space of your exprimental data. So the math may not be strictly necessary. Unfortunately, not all research is experimental. And it won't tell you, for example, if you are missing a variable.
Framing the problem (which questions to ask, and how to answer them) seems like the most important part to me. Or at least it has been in my almost complete PhD.
These thoughts are in flux. I may have a different view in a year.
The first was a researcher who talked to us a few times (just a few hours of meetings) then returned a few months later with a pile of MatLab code and a "problem solved!" attitude -- his simulations showed that things were working great. We looked at the code and while it taught us some things, it was obviously not shippable. None of his stuff wound up in the product, though it did point us down some interesting paths (some good, some bad).
Another researcher got a desk smack in the middle of the product team and spent 18 months sitting with us full-time, porting and dramatically improving his algorithms. I'd say that he learned just as much from us as we learned from him. His first few months were rocky, but he eventually became a productive and supportive member of the team. I hope that MSR treated him well upon his return.
Personally I think that research is great, but it's fantastic if you can occasionally ship your work to real customers.
Hello! Engineering researcher here. I toy with things that are both wildly theoretical (information bounds for algorithms, inference in stochastic dynamical systems, etc.) to things that are fantastically and directly useful (design of photonic structures for LIDAR, (much) better AR/VR lenses, etc.).
While you're possibly right that I might be "building prototypes that aren't quite useful but not quite that novel," I disagree that they're "[not] interesting." In fact, I'd be absolutely surprised if in a few years, much of the applied "engineering" work our lab does (in contrast to the theoretical work) is not in constant use for on-chip photonics and fabrication of optical structures.
1) Do FAANG companies hire non-PhDs for machine learning positions? Most seem to require a MS or PhD
2) What are the interview questions like at FAANG companies for machine learning positions? Is the interview different if you don't have a PhD?
3) For non-PhDs applying, what are the math requirements for the job?
4) For people that have a PhD working in ML at a FAANG, do you feel like you use your PhD level skills day-to-day?
> 4) For people that have a PhD working in ML at a FAANG, do you feel like you use your PhD level skills day-to-day?
5) For people who work with a mix of PhDs and non-PhDs in the same field, do you notice a difference in output quality?
I'm not sure where choices come in here. I specifically mentioned a mixed team; the choices have already been made, so the performance of the existing team is what I'm asking about.
As far as not getting an unbiased answer, that's why I'm asking HN at large -- hopefully there are enough people in enough environments to give an interesting and informative combination of answers. :)
No, individual differences outweigh any pattern that I've seen.
1) Having a PhD will make it easier to get an interview, but it is not necessary. Relevant experience counts just as much. I only have an MSc.
2) That will vary a ton from one team to another and from one position to another. The interviewers are not going to change their questions depending on your education.
3) The same requirement as for PhDs. It will depend on the role, but in general I expect people value hands-on experience more than theory.
Except for some particular hiring managers with strong opinions, a PhD is not going to be a hard requirement for nearly any job. However, a PhD in a relevant area is going to be useful, just like any other relevant experience you may have.
That depends on what you mean by "machine learning positions" and what your bar for normalcy is. For research roles - these generally have distinct titles like "Research Scientist" or "Quantitative Researcher" - it is extremely difficult to get an interview without a PhD, let alone an offer. It happens, but rarely, because there are many capable people with PhDs and other relevant experience applying for the same roles.
If instead you relax the bar to also include software engineers who work with research scientists on model implementation and optimization then yes, people with "only" an MSc are routinely hired for these positions. These engineers are still credited on papers published as a result of their collaboration, and they still need to have a firm understanding of how the models work. The difference is that they don't tend to have leadership roles and don't develop novel theory - they are responsible for supporting the core research team and helping the research output become production ready software.
Both of these types of roles require strong coding skills, but the "hard" research roles require significantly stronger mastery of linear algebra and probability theory. As an example, compare the roles for Research Scientist[1] and Research Engineer[2] at Facebook. You'll find a similar bifurcation at other industry labs like Google, Microsoft and IBM.
If you have the opportunity to do either and you're optimizing your career for wealth maximization or research impact, it's better to obtain a role as a research scientist. That being said most PhDs do not end up at Google Brain or FAIR, so it's not a cut and dry decision. You can't just choose to trade n years of your life and an easier-to-obtain role on the periphery of research for the ability to do theoretical research at the best tech companies in the world later on.
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1. https://www.facebook.com/careers/jobs/a0I1H00000Mp2ZCUAZ/
2. https://www.facebook.com/careers/jobs/a0I1H00000LJm3MUAT/
You know how you've take a course before where the professor was just surprisingly awful at teaching? These professors are often some of the most knowledgeable people in a subfield of the subject you are taking, yet their teaching ability is severely lacking and you have to scramble to learn the material some other way (or just never learn it).
During a PhD, there is a decent chance that your adviser is similarly a bad manager. Unfortunately, having a bad manager for 5-7 years of your life can be a fairly awful experience. You will work with someone who you, on the one hand, look up to, but on the other hand, who seems to not care at all about your mental health, your possible career desires outside of academia, your work/life balance, or the exact reason why this week was a rough week for research in your (human) life.
I have a lot of other thoughts on the matter, but I thought I'd try to keep this post more concise =).
I wouldn't call that being a bad manager, but rather being an asshole.
As a professor, I often think that one of my biggest weaknesses is indeed management skills. After all, we suddenly find ourselves having to manage people without any training in the matter, and when our true call is typically science, not management.
But at least I'm not an asshole.
Wholeheartedly agree. Aspiring PhDs discount what industry can teach them. The problem is compounded by undergrads who have zero industry experience when they graduate.
If you spend time working in a lab with grad students, listen to them. Heed their points about the field and their boss.
Research is research, and maybe you'll have some idea of what the technical details of the field are. But the only way you know what your life will be like is to pay attention. It's not bad, but you're trading something real to have "Dr." on your magazine subscriptions.
My biggest concern is more people getting PhDs, and the process becoming the New Bachelor degree, particularly in STEM.
In the majority of industry "science" appears to be a dirty word and "evidence" means "oh my buddy did this so it must work". The bar is depressingly low.
People in my country usually start PhD. at 25 and take at least 6 years to finish, because the universities use them as cheap workforce and aren't incentivised to allow students graduate quickly.
uh ? which country are you in ? In france like you said you have to do a master before BUT a phd is only 3 years. I'm 26 and I finished my phd - had my defense earlier this year when I was still 25.
It was wrong from me to generalise to whole Europe. I didn't want to be too specific in my comment.
On the other hand, the upside is that we do not have to pay a single dollar for education here as opposed to US.
In his particular institute, the better you were, the LONGER it often took because the professors found stuff they wanted you to help with unless you were really good at boundaries. My husband took 7 years; a very kind, very bright friend of his needed 8.5.
Now, students must submit their thesis within five years (one of my students very nearly failed his PhD because of this), and there are funding restrictions for continuing beyond three.
At least I knew this when I started. And it motivated me to get finished in 3 years.
Someone did give me the advice in this article before I started. Which was good. I wasn't in it for the money. It was the freedom to work on things that interested me that mattered.
What no-one told me later, when I started a post-doc, was that the chances of getting a full-time academic job were close to zero... now that did feel like 3 years wasted.
The article makes a few resonant points, but overall I think the "you can get into research if you jump straight into industry" pitch it tries to make is very weak. As someone who very much wants to do research (mostly hard-to-monetize research about comparing exotic models of computation to one another, but I'll listen to a pitch for applied research too), I'd very much like to see someone lay out a highly plausible roadmap for getting into a research position without a PhD. I don't think this article is that.
So having an incomplete PhD, would I do it again? Probably. I would be a bit more cautious about the topic and ensure my supervisor(s) were focussed on the area before beginning. With hindsight I would be more aware of the risks associated with external sources (hardware) that could delay the project for whatever reason. What I did learn though was the ability to manage my own time and collate information from various data sources in order to back up my side of a discussion. The ability to manage my own time I think is something that separates me now from my peers who did not do a PhD, but I do find when applying for jobs that I lack the necessary years of commercial experiences for roles where the hiring manager does not understand the nature of working on a PhD. So whilst it was definitely a great learning experience, I think it has set back my career slightly.
Do I regret doing a PhD? Absolutely not. Sure it was stressful and frustrating dealing with problems out of your control. But I learnt alot about myself and how to manage my own time, as well as how to stay motivated when presented with problems that are outside of your control.
The author is running a business whose main purpose is to sell educational content marketed towards people that want to learn Machine Learning (and claiming you don't need a PhD to do it).
I only have a good impression of fast.ai, but perhaps the author is not in the best position to give career advice on this topic? The author didn't even do a PhD in ML/CS, but in mathematics which arguably less applied/practical.
Is there anyone in this thread done a phd that late? Obviously my motivation is different now to study - to really learn the subject. I am financially self sufficient and will continue to be, and hence making a living out of my phd is not a consideration.
Edit: I would like to be able to study in a university setup (not distance education). Main reason is to soak in all the related conversations / workshops and also I like being in a young environment.
The hard part I imagine will be hanging out with mid-20-year olds everyday when I'll be in my late 40s already :)
I'm curious about this scenario from the advisor or departmental perspective: If a PhD candidate is financially self-sufficient, does this mean that a potential advisor has one less mouth to feed when competing for grants? How does the power dynamic change between advisor and financially independent PhD candidate? What if the PhD candidate is capable of funding a whole (or good portion) of a lab themselves - is there a conflict of interest somewhere there between donor vs. principal investigator vs. PhD-candidate roles? Do financially independent PhD candidates have more, less, or no competitive advantage during the selection and admissions process for an R1 institution?
So many questions...
"...I deeply admire everyone I’ve listed, and I am not arguing that a PhD is never useful or never works out well" but never really gives examples of skills that PhDs do provide.
And there are absolutely career paths where a PhD is not required, but since many of the practitioners have one, can often be selected for (data science, biotech, biostats, lots of engineering research etc.) So without a PhD you might have a harder time rising as far as you would like in one of these positions (again being realistic that it's not all about talent, it can often be politics/perceived competence, which a PhD can augment).
It's just important to be honest about both pros and cons when writing advice articles like this.
In CS you can earn over 50k a year with your stipend plus summer internship, you have a lot of unstructured time to explore your interests, and there are a lot of tenure-track positions without doing a postdoc.
Of course, you can work on cutting edge problems without a PhD but probably do it better with PhD. A PhD teaches a lot of intangible things like being comfortable with unknown problems , working patiently towards an end goal, get over the fear of failure etc. One thing it does not teach is how to earn money :).
In this context I would like to highlight unique cases where you do an "Industrial PhD" where you are working in a company and chose a relevant problem for a PhD. These have the "best of both worlds" and one is not bogged down with typical pressures in a regular PhD like those pointed by the author. That said, the final word is that a PhD is merely a conduit. Its more of what you chose to do with it than what it is , which matters at the end.
Also, the metric of success seems to be here how much you earn and how many startups you have founded. If these are your success metrics then don't ever even consider doing a PhD or moving out of Silicon Valley.
There is of course value in the critique that PhD programs can be oppressive with a toxic culture and depression. One has to consider how big this risk is within your program and if one is willing to take it. Thinking you can tackle these issues within your department is beyond naive.
With regards to learning new stuff and finding and scoping interesting problems of your own: People don't seem to realize that it is your PhD program. You are in the lead of the direction your research is taking* . Advisors are there to advise you and not the other way around. If you are just following orders when doing a PhD you are doing it wrong.
To conclude my tangent rambling, if you are going to do a PhD do it for the right reasons. Money, fame, titles and number of startups founded are IMHO not valid reasons to consider doing a PhD. If you are doing a PhD you can shape your research the way you want and be advised by your advisors, not the other way around.
* Terms and conditions may apply. You are still (usually) working of course within a grant to solve one huge problem, but the way you solve it is mostly up to you.
I'd really appreciate if anyone reading this comment have something to suggest. Thanks a lot HN community, you've been a great support.
I am in a similar position as you. My PhD. topic is focused on the same research area as my job in tech company so I can do both together.
If it is not possible I would advise not pursuing PhD. and continue self-learning. The opportunity cost of PhD. is not worth it in my opinion.
"Career Guide for Engineers and Computer Scientists" by Philip Greenspun
A safe option?
A (funded!) PhD is an opportunity to focus on a subject you are really interested in, that you probably can't work on outside of academia and spend a lot of time, three to four years, free of all other responsibilities but producing a short book at the end describing your work.
It is the only three years in your life you can spend directing your own research, choosing your own goals and creating new knowledge with nothing but the power in your little hands and your little brain. "Safe"? There is nothing scarier than staring at the darkness of a new path to knowledge, stumbling around in the unknown trying to find your way where neither google, nor Stack Overflow have gone before and there's noone to hold your hand while you make it up as you go along.
A PhD is an opportunity to become a world-class expert in your field. Not because you get an empty title at the end, but because if you do it right there should be literally no-one else on this sweet earth who knows more than you do about your chosen subject (well, except perhaps than your annoying thesis advisor who's always been there and done that and can nip your best ideas in the bud with but a couple of words. But I digress).
A PhD should be a time for your mind to open wide, like a blooming flower, like the hippies of old thought LSD would do to them. It should be a time to become a strong man or woman of knowledge, to drag yourself over and above the mean and look at the stars and say "oh, sweet lord, I get it! I G E T I T !!".
Well of course if you try to take the safe path you'll be bored out of your head and disappointed. What do you expect? "Safe"? "Prestigious"? It's not public office, man! It's a PhD! Focus on the knowledge! That's what it's all about.
Yes, of course you'll learn a ton if you stay in industry, too. But, in industry, it's always someone else who chooses what you need to know. In a PhD, it's your game. A PhD is knowledge, coupled with freedom.
This is not at all the case in Computer Science. Especially permanent positions at CCs and lecturing at universities, there are more roles than people at the moment. Mostly because the worst-case alternative is taking one of the plentifully available 100k-200k industry jobs. Especially in ML.
Generally, "don't get a Ph.D. planning that you'll be a professor" is good advice. CS at the moment is the exception that proves the rule. Especially outside of R1.
My experience has been great. Straight out of grad school I got several tenure-track offers from R1 universities. I'm not a super star and graduated from an unranked department.
Seems to have all the upside and none of the downside, you keep your industry pay, continue racking up experience and after a few more years than normal get a PhD to hang on your wall.
A PhD is too long, narrow, and frustrating to do just to get a slightly fatter paycheck.
For some reason, that deep dive clicked with me, and I’m grateful for all the personal growth that came from that. But for the vast majority of people, I think it could easily end as an exercise in frustration.
Between classes, teaching, and genuinely getting things done, it's not a fast experience.
Also, you probably arent making a ton more unless you go for an industry job...which is a little bit the opposite of the Platonic independence supposedly at the heart of the training method.
But very few people I know want to be a PI.
When I finished up in the 90s, the market was flooded with applicants from the former soviet union as well as locals. I remember speaking to people I knew on hiring committees who told me of 1000+ applicants per open tenure track position at tier 2 and tier 3 schools.
Around that time I was looking at the postdoc train, saw where it (didn't quite) led, and chose a different path.
In the UK and Europe, shorter PhDs are much more common, in part because you're expected to do a master's before a PhD.
Even for two or three years, I don't think getting a computer science PhD is a good bet if your primary goal is making more money.
I tell students to expect 5-6 depending on whether you want to go to industry or academia.
That’s true of Europe but it’s quite common to go straight from a Bschelor’s to a doctorate in the U.K. and many other former British Empire countries.
I took eight years but I also got married, worked full time, and started a company before I finished. By the time I graduated, the CS department had started taking a much firmer stance on timelines, with a desire for most students to graduate within six years or sooner.
The discrepancy here may be that "PhD time" in the US usually includes a masters, while it is counted separately elsewhere. That said, even if you break it down, my MS took 2 years and the PhD took another 5.
My BS took 4 years. My MS took 2 years. My PhD (computational physics) took 7 years.
I was the fast one at my school. Some of my peers (high energy physics, nuclear, etc.) took 9+ years.
Then again, I met my former business partner (not at the time) while in grad school. I was 2 years into my research, and he was a fresh new assistant prof in CS. About 9 weeks younger than me. Ph.D. in CS in 3.5 years.
At my university, I learned very little in my degree that was noticeably useful in industry but then academia is not supposed to be about industry, usually, but about increasing knowledge that may or may not have an application.
In the college I attended before university, I learned an enormous amount of information useful for Industry but it didn't give me much of a leg-up for the work I was about to do at university.
Most industry work I have seen is not massively academic and does not require academic qualifications. In certain jobs, however, they make a point of requiring qualifications when they could probably run some tests instead to demonstrate that you understand the basics of comp-sci or whatever else.
A Doctor of Philosophy should be an individual capable of critical thinking, developing their own experimental process, and helping others do the same. Unfortunately, many professors are just running a race chasing publications and grants while their students are employees, not apprentices in the art and practice of science.
I have no idea how have managed to swing that, and if this arrangement will last for long. He's only been employed here for about a year.
A solution would be to have a letter of recommendation and appreciation from a school.
As long as you are pursuing any education, you should either get the degree automatically, or just get some certificate that you went in some school.
It should be up to schools to select their students, merit in education doesn't seem to work, as it doesn't anywhere anyway.
Love the experiences shared here. I see a lot of them are centred around pay (PhD vs. non-PhD). I think it is important to remind ourselves that seeing earnings as a race against others and against time rules us out from doing anything interesting in life.
Just as the OP describes, if the advisor is great, it's fantastic. If they aren't, you are in a world of pain.
I don't regret choosing to do a PhD, but I deeply regret choosing my advisor.
http://www.johnskylar.com/post/107416685924/a-career-in-scie...
Postdocs definitely deserve be paid (and treated) better.
Have a plan and resources commited to how you’ll deal with the depression. This means actually building out a support network and asking them if they’d be available to talk during such a situation.
Not easy but yeah weigh the cognitive costs!
I'll try to give an answer and a response more generally to the material in the OP.
Background. I got a BS in pure math with nearly a second major in physics and worked in computing and applied math for problems in US national security around DC. Jobs were very easy to get; at one time my annual salary was 6 times what a new high end Camaro cost; much of the work was challenging for both the computing and the applied math; I was learning a lot of both computing, e.g., algorithms in Knuth, and applied math on the job and also especially math in independent study on evenings and weekends. Soon I got a call from a college friend to join FedEx. I used my background in computing, with a little applied math, to write some software, in six weeks, to schedule the fleet. The results pleased the BoD, enabled crucial funding, and saved the company. Later the BoD wanted some revenue projections. I formulated and solved
y'(t) = k y(t) (b - y(t))
for time t, revenue y(t) at time t, rate of growth y'(t) = d/dt y(t) at time t, and full revenue potential b. The BoD was pleased, and ..., to make a long story short, I saved the company again.
Then I got a Ph.D. in applied math from the engineering school of a famous, high end research university.
Now I'm doing an Internet startup, a Web site, basically a new and very different search engine -- for the, IMHO, very large part of search handled at best poorly by the existing Web sites and well known techniques.
For the startup, my applied math background is crucial: The crucial, enabling core of the startup is some original applied math I derived based on some advanced pure math prerequisites I got both in grad school and in independent study. I already knew enough computing except had to learn how to bring up a Web site that has been easy for the user interface but due to the core math somewhat tricky on the server side. I learned Microsoft's Visual Basic .NET (for the programming language -- I like it), ADO.NET (for the Web pages), and ASP.NET for the (relatively meager) use of SQL Server. The main difficulty was working through 5000+ Web pages of documentation. The first code is the first production code, 100,000 lines of typing, 24,000 programming language statements, and lots of documentation. The code seems to run as intended, but I need to add some data.
Some lessons:
(1) Math. IMHO, the key to powerful, valuable, new applications of computing is applied math. That is, if we accept that the big opportunity is to exploit and apply current computing, then we might notice that whatever we code to put out data users will like, as information, entertainment, whatever, is necessarily mathematically something, understood or not, powerful and valuable or not. So, in some sense, from 100,000 feet up, it should help to proceed mathematically, with possibly advanced prerequisites, some new results focused on the application in mind, and complete with theorems and proofs. Just IMHO. But I don't know anyone with a yacht over 100' long that did that; I suspect that very few people agree with me. Maybe what I am saying is a hopeless wild goose chase or a great green field opportunity -- you judge.
(2) Getting a Ph.D. In a nutshell, the three most important parts of getting a Ph.D., in no particular order, are research, research, and research. Yes, there can be courses, credits, grades, teaching assistant positions, weekly research seminars, qualifying exams, etc., but at a research university what can "cut through", dominate, and trump all or nearly all of that is research. The research should be publishable in a good peer-reviewed journal of original research; if there is any question, then send it in.
The criterion for a Ph.D. dissertation may be something like "An original contribution to knowledge worthy of publication." -- so, in case of some doubt, publish the thing.
The usual criteria for publication are that the work be (i) new, (ii) correct, and (iii) significant.
Now for a non-standard observation and recommendation: Go for applied math in an engineering school. Start with a real problem, hopefully a significant real problem, likely from outside academics, hopefully identified before or early in the Ph.D. program. Do some new math to get the first good or a much better solution to the real problem. So, the math is "new" -- got (i)! Since the work is math, with theorems and proofs, it's easy enough to check for "correct" -- got (ii)! Since have the first good or much better solution for the real problem which is hopefully significant, get "significant" -- got (iii). Note: The math may not, just as stand alone pure math, be seen as significant -- so, likely have not proved the Riemann hypothesis, shown that P = NP, etc. But compared with a lot of pure math, are already ahead by one point -- have one real world application!
In my case, I started with a problem I had identified in industry before grad school. I found an intuitive and rough solution on an airplane ride. In my first year in grad school, one course I took let me make solid math out of my intuitive solution; I did that independently in my first summer, walked out of the library with an 80 page manuscript that had all the actual research for my dissertation.
Then I encountered some of the nasty nonsense as in the OP: There was a prof who didn't like me. He thought of rows, columns, and layers, lines, stay within the lines, rules, etc. and resented that I'd basically written my dissertation independently within 12 months of arriving on campus and before taking the qualifying exams.
Well, a course had a question but no answer. I did a good enough literature search and saw that likely there was no answer known -- since it was a very narrow question, it was easy to do the literature search. I got a reading course approved to address the question and write a paper, maybe just expository and maybe without a solution. Just before getting the reading course approved, in a few evenings I found a rough solution. So, got the course approved -- shook hands with the prof. Then I cleaned up my first solution, mostly sitting beside my wife on our bed while she watched TV and I worked on the problem, and found a much better solution and a general result that was surprising, even shocking, and settled some related questions. That took two weeks into the reading course. I submitted my manuscript of about 20 pages, and I was done with the reading course. Fast course. The course also had three credits and gave me the last credits I needed for an MS. News of my work spread through the department; my favorite prof walked up to me in the hall, "I heard about your result. It also says that ....". Yup, clearly it did.
The result was clearly publishable; later I did publish it -- no problem, accepted right away.
The biggie, practical result was that suddenly I had a halo and a coat of Kevlar armor against any criticism; any of the faculty would have loved to have done what I did. To defend against the abuse as in the OP, I recommend -- do some publishable RESEARCH.
(3) Handling Ph.D. Qualifying Exams. At least at one time, the Web page of the math department at Princeton stated, IIRC (if I remember correctly):
"Students are expected to prepare for the qualifying exams on their own. Graduate courses are introductions to research by experts in their fields. No courses are given for preparation for the qualifying exams."
In part, the qualifying exams are like a foot race, but in this race you can get a head start and be 1 foot from the finish line when the starting gun goes off.
I consider that Princeton policy to be somewhat wise. So, prepare for the qualifying exams on your own (to be able to do this in math, a good ugrad pure math major should be sufficient to let you know how to study and learn, make good progress, and not get stuck or lost). To do this preparation, get the best, focused information you can on what will be asked. So, get recommended texts, copies of old exams, maybe chat with some profs and some students who have taken, hopefully passed, the exams, syllabi of any relevant courses, etc., maybe all before applying to the Ph.D. program. Then study. Use some judgment on how deep to cut and how many proofs to memorize -- cut deep enough but not so deep you take too long or just quit.
Then show up as a first year Ph.D. student, do well in some courses, do well on the qualifying exams, complete your research, listen to Pomp and Circumstance, get your degree, and LEAVE.
(4) Academic Career. If you want an academic career, then maybe don't leave the grad program so soon. Instead, publish some papers, get some streams of promising research going, meet people at research seminars and conferences, get known by the Editors in Chief of the journals or conferences where you publish, if invited to give a talk at a conference, do so, do the usual meet and greet and publicity, build your own professional network, etc., hopefully some of your profs will help you get some job interviews, etc. Learn how the academic games are played -- there are some really important academic games, and you very much should learn how they are played. Then you will be in an academic career.
One possible prof slot is in a B-school. Consider that. Someone with a good applied math background has a heck of an advantage in a B-school. Papers that make progress on practical problems are commonly considered good research in B-schools. People who want to hire consultants tend to regard B-school profs as more practical , i.e., motivated by money, than more pure profs.
My wife was fatally injured in her Ph.D. program. The OP outlines a lot of just what happened to her. To have time to try to help her, for a while I took a slot as a B-school prof. It didn't work -- lost her anyway.
I never for even a milli, micro, nano, pico, femto second wanted to be a prof. Instead, I wanted to be solving problems in business, the money making kind.
(5) Non-Academic Career. Then I tried to get my career going again, outside academics. Bluntly, that didn't work very well.
I made a mistake: I should have returned to DC and gotten back into applied math and computing for US national security. I guessed that there would be opportunities as an employee in business; I was wrong.
Bluntly, my view is that US business and Ph.D. holders mix less well than oil and water.
Part of why:
(A) Business is still a lot like Ford in Henry's day: The manager knows more, and the subordinate is there to add muscle to the work of the manager. A manager has no use for a subordinate who knows much and resents or feels threatened by such a person.
Supposedly lawyers have a solution: A working level lawyer should work only for a lawyer. Period.
Well, a working level Ph.D. should work only for another Ph.D., and that criterion would eliminate nearly all jobs for a Ph.D. in business.
Not even a CEO wants a Ph.D. around except maybe tucked away in some side organization, out of the main work of the business. E.g., the CEO is plenty sure that he is the only really important person in the company and, thus, certainly doesn't need a Ph.D. or some academic background he (the CEO) doesn't have!
(B) Business regards Ph.D. holders as blue sky dreamers out in the ozone who refuse to contribute to the business, who really want to publish a lot of papers and get a prof slot in academics.
(C) If a Ph.D. person does anything original relevant to anything in business, usually the business will regard this person as a threat.
(D) Suppose a Ph.D. takes on a practical business problem:
(i) If the Ph.D. successfully uses their advanced knowledge to get a good solution, e.g., one that makes a lot of money for the business, then everyone else in the business, even the CEO and the BoD, will feel threatened and/or jealous.
(ii) If the Ph.D. fails to get a good solution, then everyone else will take the opportunity to denigrate both the person and the Ph.D. degree -- "I always thought that a Ph.D. was just a useless, hopeless, worthless impractical dreamer out in the ozone, and now we know for sure.".
(6) A Ph.D. in a business research division. Yes, some businesses, say, ones with some loose cash, might set up a research division, hire a Ph.D. as the director, and hope for something good. If nothing good happens, well, the company could afford the wasted money.
Generally, connections about the actual business between the research division and the rest of the company are more awkward than a skunk at a Victorian garden party. The rest of the company doesn't want to be bothered, sees various threats, etc.
Here are some of the reasons for such a research division:
(A) Luster. Use the research division to impress the public, for good PR, to impress customers, to cover the rear exhaust port of both the CEO and the BoD, etc.
(B) As a patent shop. So, the research division can develop a patent portfolio, maybe dozens, hundreds, thousands of patents. Then some specialized lawyers can use that patent portfolio as a, call it, battering ram against any would be competitors. There can be cross licensing deals, revenue, etc.
(7) Career direction. It's your career. In this career, there will necessarily be some directions you will be pursuing. Some directions are good; most are not. It's up to you, and maybe your family, closest, trusted friends, etc. to pick, at least try to pick, a good direction(s).
If you just look for a job, get some offers, and take the best offer, then likely you will be following the direction of your employer, especially your immediate supervisor. That direction was not picked by you; likely it is not a very good direction for you or anyone; likely in that job you will have quite limited opportunities to change the direction to be something good for you.
Bluntly, you will want income enough to provide for food, clothing, shelter, transportation, medical care, insurance against risk, recreation, a house you own, a family, education and other needs for your kids, and retirement, with some security, i.e., low risk, and at least a comfortable life style. That obvious goal is surprisingly difficult to achieve, especially if you are working just for a salary for a manager in a company, small, medium, or large.
(8) Blunt US Fact of Life. IMHO, nearly all the people in the US doing well supporting a family get their money from owning part or all of a business that makes the money needed to pay the bills for that family.
For this, can use some strategy: E.g., run the most popular Italian restaurant in a radius of 50 miles. Then you have:
(A) A strong geographical barrier to entry, that is, a restaurant more than 50 miles away will be little or no competition for you. You have a better "Buffett moat" than any of IBM, Cisco, Intel, etc.
(B) Your business is unlikely to be killed off by changes in technology.
(C) We can be sure lots of people will still want a good Italian restaurant 10, 50, 100 years from now. You have a business more stable than any of IBM, Cisco, Microsoft, Facebook, Google, Intel, etc. Good economy or poor, people will still want to go for a dinner at an Italian restaurant -- you are relatively immune from changes in the economy. You have a very wide variety of customers, i.e., are not vulnerable to some one or few customers going broke, leaving town, etc.
(D) Your family, spouse, children, can help in the restaurant and learn the business and continue running it as you grow old.
(E) Working as an employee, you can be fired by a manager who, for whatever reason, doesn't like you. If you are the owner, then you can't be fired.
(F) No one can please all the people all the time, and some managers can never be pleased. But in a good Italian restaurant, one unhappy customer occasionally can usually be mollified by an apology, a free glass of wine, just tearing up the check, etc. You DO have to do good work and please nearly everyone nearly all the time, but you can't be run out of business by just one unhappy customer.
All or nearly all of (A)-(F) apply with no more than small modifications to a huge range of Main Street US family businesses. In your career, you should aim to do at least that well.
(9) Ph.D. Entrepreneur. Okay, you have a STEM field Ph.D. and want to own your own business. If you work hard and smart, find that your Ph.D. is a great technological advantage (e.g., you can stir up powerful, valuable, new secret sauce), have some good luck, avoid too much bad luck, get well informed, consider strategy, ..., etc. then you might do really well. Your Ph.D. could be a terrific advantage.
(10) Warning. Generally, if want to use your Ph.D. to help you be an entrepreneur in something relatively new, i.e., not an Italian restaurant, then likely you need to be darned careful and insightful.
In this sense, I will say:
(A) I believe strongly in the potential of some original applied math based on some powerful pure math prerequisites.
(B) I regard current work in artificial intelligence (AI) and machine learning (ML) as not very promising. Some people may yet have good careers there, or quickly get rich from some stock, invest the money in an index fund, and essentially retire, but generally my view is that the math is not powerful enough to be very promising and 90+% of what is being done in those fields now is based on wild, blue sky dreams with little real hope and a lot of hype, PR, maybe patent games, etc.
Why: So far too much of the AI/ML work is too close to empirical curve fitting.
(i) For small amounts of data, we've been able to do, and often have done, such curve fitting going back decades to the first transistor computers. At one point in my career, inside GE I did a lot of consulting for that work. So there was, and still are, SPSS, SAS, Matlab, R, etc. I never saw such people in yacht clubs.
(ii) What appears to be new is curve fitting for large amounts of data. Well, we don't expect to have a lot of such data collections and promising corresponding problems.
For more, I'd guess that self-driving cars are not very promising: For now, for current traffic on current roads, driving occasionally, and too often, needs real human intelligence. E.g., chimpanzee intelligence is not enough, and AI/ML are a long way short of chimpanzee intelligence. There is a chance for self-driving cars on roads that have a lot of new engineering, but that will be very expensive, IMHO, for a long time, too expensive. Self driving might work on some large farms, in a big open pit copper mine, some military tasks, and some other situations much less challenging than Manhattan traffic, I-95, etc.
The general lesson: One of the keys to success is good initial problem selection. Most of the problems people have selected are not good. So, we have to try quite hard to select a good problem.
Your mileage will likely vary widely.
My guess is that there are a lot of Ph.D. electronics engineers over 40 who are essentially unemployable at anything close to their past who want an electrician's license so they can install AC wiring in houses.
Maybe the best job they can get is to be a clerk in the electronics department at Wal-Mart.
Imagine, two guys just out of high school. They both mowed lawns as teenagers. Joe wants to continue that as a business, at first still living at home. Tom goes to college, continues, gets an electronics Ph.D., and gets a job in an electronics company.
Then they are both 35: Joe now has five lawn and garden crews, each with a late model, crew cab truck, a $5000 trailer with 4 riding mowers each worth $15,000. He has clients in upper class residential areas and small to medium commercial lots. He does weeding, soil testing, fertilizer applications, mowing, edging, shrubbery trimming, landscaping, etc. Tom's employer has a policy: By age 35, promoted into management or fired. Tom gets fired.
Joe is now much better off than Tom. Tom would do well to look for a job with Joe and rise to managing one of the crews, planning and marketing for higher end parts of the business, etc.
This is not a joke. Besides, it's not funny.
So true - now most students are happy with, "I came into the office every [most] days and did [the thing]. I wrote it up so where's my PhD."
They don't evaluate their own data critically for interesting information - they did the experiment or simulation as told and they think this is their job.