There were a few places where the text sounded weird.
> He had a plum position, and he was years away from tenure review. It’s hard to walk away from a place like that after a lifetime of striving.
The nominal academic career is ~45 years, as successful academics rarely retire early. In most fields, getting a tenure-track faculty job signals the transition from early to mid-career. But this seems to be about CS in the US, where it's common to get a faculty job with minimal postdoctoral experience. Getting tenure is then the true starting point for mid-career, and this was more likely about an early-career researcher choosing to leave the academia.
> Academics for their part tend to lean into this by playing the “let’s see how quickly I can destroy this entire presentation” game.
There are some toxic fields of research, but I don't think this attitude is particularly common in the academia. There are many fields with a true sense of community. People generally realize that it's better to be nice to your colleagues, even if you are competing against them, because you will be stuck with them for decades.
> Academia is characterized by well-trodden problems, hashed over for decades, and negligible novel data for resolving them.
Here my experience is the opposite. The academia is characterized by world's top experts in a narrow niche investigating speculative problems few people have any idea of. More often than not, that research turns out to be a dead end, "wasting" years of work.
But that is quite normal, isn't it? The (perceived) need to put other people down ultimately stems from uncertainty about ones own work. And having only bad models available makes it incredibly challenging to think of your own work as "obviously worthwhile", making it more important to improve your own standing by putting down others.
Former polisci PhD, now in an economics department. I’m gonna offer a qualified defence of the savagery.
1. Social science matters. If we predict inflation wrong, or mistake what causes development, we screw up the world. (Hello Gunnar Myrdal.)
Therefore, if there’s a tradeoff between a nice atmosphere and getting things right, we should always prefer getting things right.
2. There is a rightness-maximizing level of savagery, which is strictly positive.
Bad ideas must be challenged. Sometimes bad ideas get entrenched. Then they must be challenged loudly and persistently.
Fields where this doesn’t happen tend to degenerate into back-scratching cliques. (Hello, humanities.)
Now clearly there is some level of savagery which is TOO savage. If you destroy a PhD student’s confidence, that’s hurtful and you’ve lost their future contributions. Aggression from the top can be used to defend entrenched but mistaken ideas.
And there’s certainly toxic behaviour in economics. I’ve seen it and experienced it!
3. Nevertheless, overall, econ does better at removing bad ideas than other social sciences. Our models can be critiqued… other soc scis often just have verbal models which are too vague to be wrong! Our focus on good research design to uncover causality is stronger than many other fields, also.
Lastly:
4. There’s more than one kind of toxicity.
There are many stories of bad abuses of power - eg sexual harassment - in supposedly “warm”, and highly feminist, fields like Eng Lit. Doubtless there are many reasons for any differences between fields. But I think one underestimated factor is that when there’s no ruthless, hard-faced empirical rigour, when theories are vague and evidence is narrative, then getting published comes to depend on academic politics - on alliances and cliques. That is a fertile breeding ground for abuse of power.
Honestly, I wasn't particularly impressed with the rigor of said savagery - it mostly came off as a combination of senior people wanting to flatter their own ego and accepted rudeness.
I've been completely dismantled in a presentation before, and public health has fairly high stakes as well, but I let people finish their sentence first. The number of interruptions that were then addressed with "That's on the next slide" was staggering.
I should be clear that I mostly favor the savagery with the caveats you mention, and look back on it with fondness. God made Econ brownbags to train the faithful.
It really isn't "quite common". There are some places that take pride in those things, but they are a small minority. People that have only been in the "top" departments are probably not aware of that though.
The top 20 US Economics Departments train a majority of PhD economists so “quite common” seems fair. Also, using scare quotes for top in Economics is, ah, questionable. There is a very visible ranking and you’d get substantial agreement on it from most academic economists. The strong core agreement on techniques and axioms is part of why economists are more influential in policy than the rest of the social sciences put together.
> The Superiority of Economists
> There exists an implicit pecking order among the social sciences, and it seems to be dominated by economics. For starters, economists see themselves at or near the top of the disciplinary hierarchy. In a survey conducted in the early 2000s, Colander (2005) found that 77 percent of economics graduate students in elite programs agree with the statement that “economics is the most scientific of the social sciences.” Some 15 years ago, Richard Freeman (1999, p. 141) speculated on the origins of such a conviction in the pages of this journal. His assessment was candid: “[S]ociologists and political scientists have less powerful analytical tools and know less than we do, or so we believe. By scores on the Graduate Record Examina- tion and other criteria, our field attracts students stronger than theirs, and our courses are more mathematically demanding.”
> The top 20 US Economics Departments train a majority of PhD economists so “quite common” seems fair.
Perhaps if I had commented about PhD students. I specified "places" because there are lots of places to work, and the miserable folks are mostly found in a small percentage of places you can work as an academic economist.
> There is a very visible ranking and you’d get substantial agreement on it from most academic economists.
Others are free to define "top" as they wish. I am under no obligation to accept their definition.
You are of course free to have your own definition of top Economics departments, just as you are free to decide that you do not need to pay taxes. Social facts exist.
Yeah but it also attracts students a lot worse than actual sciences. And yet here we are, listening to people with two straight lines intersecting talking about “supply and demand” and writing the world based on it.
It seemed like this was about someone transitioning to industry data science, not CS. From the people I have worked with that came to DS by way of academia, the post sounds extremely accurate. It may be that you can get a faculty job in CS without much postdoc experience, but it’s not true for psychology, political science, cognitive science, urban planning, economics, etc (all backgrounds of PhDs I have worked with in big tech data science).
Post docs are not normative in economics academia and I don’t think they are in political science either. Psychology and Cognitive Science I’m sure do have post docs as the academic job market has fierce competition and I don’t think there’s much private sector demand for their skills.
At my FAANG job, we happily hired CogSci PhD’s, and I also worked with multiple political science PhDs who’d left or turned down postdocs. I’m unsure about any of the Econ PhDs that I worked with, but I strongly suspect you’re right as it did seem like they were drowning in job options relative to others (conditional on getting a FAANG job in the first place, I suppose).
> There are some toxic fields of research, but I don't think this attitude is particularly common in the academia.
You are grossly underestimating toxic culture in academia. Although, in his case I think it more of a case of not enjoying the work for the amount of effort put.
Toxic, or rigorous? To my understanding, this kind of questioning is how the search for truth tries to keep itself from veering off into bullshit, and is fundamental to the whole project.
I work in the corporate world where we incubate a great deal of bullshit; projects, careers, and entire teams enjoy wild success on the backs of purported contributions that do not withstand a moment’s scrutiny. It makes a polite and non-confrontational environment, sure, but also comes with its own kinds of psychic injuries.
Toxic. Most professional environments are non-confrontational but not nice. The academic ideal is being nice but sometimes confrontational. Being nice matters, because you can't hide from the same people behind your professional role for decades.
Academic research is fundamentally based on trust. Trust that the others know what they are doing. Trust that they have taken something obvious into account even if they don't mention it explicitly. Trust that they are presenting their work honestly. And so on. If you choose not to trust the other party, you can almost always poke holes into their argument, as long as you are reasonably familiar with the topic.
You should always be critical of your own research and offer constructive criticism to others. Your job is not shooting down their arguments but finding what can be salvaged and guiding them towards stronger arguments. And sometimes you have to accept that their arguments are already good enough, even if you think further work could improve them.
Besides, everything is bullshit anyway. Our job is just finding something interesting in it.
Don’t you think it matters whether the claims in the paper are true? Won’t other people be relying on them in the future, depending on how they’re received in the field? It would seem important for other researchers to digest the evidence offered and update their worldviews accordingly, and it further seems like this would be more likely to converge towards reality if researchers talked openly about the process rather than keeping any serious flaws they find to themselves.
I’m not a scientist, so I was relying on secondhand accounts. Am I way off base about what science is, here?
You can be rigorous without being toxic or--sadly--toxic without being rigorous. In fact, I'd say they are almost unrelated and the same critique can be leveled either way.
I don't think @jltsiren meant that you have to have unconditional trust in someone else's work. Instead, you should approach it with a bit of humility. At a talk, the speaker is usually presenting work they've spent years on, whereas most of the audience saw it for the first time fifteen minutes ago. In most cases, you can trust that they've thought a bit about the obvious, show-stopping objection that immediately popped into your head.
You can--and should--verify this assumption too, but there are light-years between:
- "Could you walk us through your experiment design again? In particular, is there a wash-out period to account for any potential carryover effects?"
- "These results are hot garbage. I don't trust any fMRI stuff and as for the rest, I can't believe you'd run a crossover experiment for this because there are obviously carryover effects that you've glossed over in your talk."
In academia, it's open season on attacking ideas. There's nothing wrong with this; indeed, that's what we expect our intellectuals to do. Unfortunately, some cannot separate (both as critics, and when criticized) the attacking of ideas from attacking of people.
In corporate, it's always an attack on a person, because ideas don't matter in corporate. In theory, ideas can matter insofar as they can drive a P&L, but in practice, corporate is not about P&L at all so much as it is about how P&L are attributed to oneself personally. This does mean people are less ready to criticize an idea (or a person) in the open, although that's mostly because the real gaming happens behind the backs of those being gamed.
>The academia is characterized by world's top experts in a narrow niche investigating speculative problems few people have any idea of. More often than not, that research turns out to be a dead end, "wasting" years of work.
I believe this is very much dependent on where and with whom you are working with. By definition, not everyone is a top expert. Even rarer to publish a top paper. Sometimes entering a narrow niche field makes it possible to work in an insulated silo where niche's favorite problem statements and research programs can escape critique from experts of other disciplines.
As a practical, though not quite disastrous example, I did an applied maths MSc with focus on ML and data science, and then spent some time in bioinformatics oriented data science grad program. It was only after I entered the pharma industry that I found a field where it was expected to have serious interest in doing ones best with causal inference while acknowledging its limitations ... with methods which apparently have been bread and butter of econometrics and maybe some biostatistics for decades. On the other side of fence, typically only people exposed to particular statistics textbook or ML fields are interested in running LOO/cross-validation model validation checks for their model fits. I see some more communication between the disciplines could absolutely improve the work of everyone involved. And these are big fields. Small niche fields with niche problems where everyone publishes in a niche journal can become worse.
The world is small, but there are many things to study. If you want to make a career in academic research, you have to build a profile other people in the field know you for. That profile quite likely makes you a top expert in something.
I'm all for anti-academia content, but a lot of this did not seem relatable to me. A lot of the advice concerns may be a subset of academics but hopefully not all, because I was specifically advised NOT to be like this. For example, I am not afraid to ask questions, if anything my academic experience taught me to ask questions without hesitation. Same for presenting your work for a specific audience, the "no one cares that you're smart." Every presentation I gave was framed with that in my mind, who your talk / presentation is for. The academic speak really isn't because it makes you sound smart, it's honestly just easier than speaking either for a general audience or for someone for whom the results that are distilled down to be used for a decision or something else, as they said. THAT is much more difficult but it's what you learn as an academic, at least it's what I learned.
Also, taking "assignments" is probably the thing I relate to least. I feel like a lot of research groups out there are top-down like that but I'm lucky I've never been in one.
Same here - academia has plenty of problems, but the characterization here seemed to mostly invent ones I don't think exist. The notion of 'assignments' is quite irrelevant for any PhD student once they are done with classes, not to speak of professors.
The assignments thing probably makes particular sense in engineering disciplines? They’re generally in a pretty well defined context throughout their academic career and often as groups. Just trying to guess at context.
Otherwise I did have a similar feeling in academia studying math. Especially given the general requirement courses. I often wished I could spend more time on certain topics than a course allowed or found entire topics just not taught locally.
As much as I love content that critiques academia, this one was really cringe and inaccurate for most of academia.
> Academia is characterized by well-trodden problems, hashed over for decades, and negligible novel data for resolving them. Industry is by comparison a mass of green field areas of inquiry with large budgets, minimal bureaucracy, and ample data.
These two sentences neither describes academia (science) nor industry.
Science expands the realm of what’s possible, and industry focuses more on scale and bringing a specific service to the masses.
Therefore, science is the realm with novel data, while data from industry is larger in quantity but more focused towards a customer or service.
In general I hate to the “academia bad, industry better” types of articles. It’s a high dimensional comparison with lots of overlap. There are many places where academia is preferable to industry, and many places where industry is preferable to academia.
I just dont think this is true in practice. One of my industry-linked professors really turned me on to the stark difference in data availability in the private sector vs what academics (in social science to be clear!) have access to and it's night and day.
Economists frequently just rehash the same tired quarterly data sets with marginally different models or methods; meanwhile Amazon generates petabytes of incredibled detailed and realtime sales data every--what, month? And indeed they're making use of it: https://www.amazon.science/publications/new-goods-productivi...
Your model of science seems highly idealized compared to the reality that I experienced; and this isn't an ontological question, it's straightforwardly empirical. Social media sites have billions of users and graphed networks; academic psychologists have groups of 30 undergraduates. Economists have surveys and Fed data while Amazon has . . . Amazon's entire dataset, along with what was when I last checked the largest concentration of economics PhDs outside the Federal Reserve System.
I agree that industry isn't universally preferable to the academy and I definitely wasn't trying to make that claim.
I think one fallacy I see when people critique academia vs industry is that they take an example like Amazon from industry and show how it is better than the average output of academia.
To do a proper comparison one should compare Amazon’s products with top products from science: eg ontogenetics, CRISPR, gravitational waves, large hadron collider, and solar voltaics.
Falcon Heavy is certain an impressive engineering feat, but it doesn’t compare to the Voyager 1 (40 years ago!) in terms of new science and new frontiers.
Industry is better than academia when it comes to getting to scale and reducing costs. But usually new frontiers and new science does not often have those constraints.
> meanwhile Amazon generates petabytes of incredibled detailed and realtime sales data
The problem is that you can't use Amazon's sales data to answer research questions about maternity leave policies, UBI, or the criminal justice system. All of that data is great but it benefits a relatively small part of social science.
this deserves its own post, but the other advantage working with tech datasets on the regular is developing perspective on the methodological shortcomings of what's done at universities.
what i mean is that once i started running product experiments at scale in really hyperoptimized environments, my sense of what's realistically possible with small data sets collected by humans describing meatspace circumstances contracted fairly dramatically. I don't just mean replication crisis issues, although those are real; im instead skeptical of nearly all of the empirical work being done in nearly every social science field.
that is, im not arguing that tech does a great job facilitating research into eg maternity leave policies. rather, im saying i dont think anyone is learning much about them at all.
im happy to acknowledge that this is a minority view.
Are you mainly concerned about the quality of the data that is collected, or the econometric methods that are applied to them, or the way in which respondents are sampled?
I think that the direction that is promising is combining data sets like those that are built up at Amazon with administrative data that come from other sources. It is a combination of such data sets that opens up answers to important research questions. But I am doubtful that Amazon wants to fund the pursuit of such questions. That is not what will increase shareholder value.
> Academia is characterized by well-trodden problems, hashed over for decades, and negligible novel data for resolving them. Industry is by comparison a mass of green field areas of inquiry with large budgets, minimal bureaucracy, and ample data.
IMO I thought it was the opposite.
Academia is filled with people going out of their way to do something "novel", even if it's useless and/or over-complicated. Because a) that's how you get papers, and b) moreover most PhD programs literally require you to make a novel discovery to graduate. Case in point: all those theory papers with weird techniques, limited use cases, and demos which barely work; any "game" made by academics.
In contrast, industry has a ton of resources, but they don't like allocating them to anything which isn't expected to make a project. Most industries are just "new" approaches to old problems which are really just improvements. Industries use tried-and-true tools and technologies, and don't use the theoretical cutting-edge stuff until enough research is done and tools are created that it's no longer cutting-edge (e.g. linear types in Rust). Case in point: another ride-sharing app, database-management tool, Twitter introducing medium-style notes, Dropbox introducing Patreon-like pay-for-access, AAA games.
This is a pessimistic take: there are plenty of academics who try to work on truly practical problems even if there's not much "novelty", and startups who work to create something radically new. And then there's corporate research labs like Microsoft Research whose goal is essentially to make stuff that's a) novel and b) practical.
But those fourty-year-old problems and techniques you learned in undergrad? Those aren't what you're working on once you get into a PhD. And I would not paint industry as "minimal bureaucracy", maybe some industries but definitely not FAANG.
Just because they do novel work doesn't mean that the power dynamics and even bureaucracy is much worse than industry.
> even if it's useless and/or over-complicated. Because a) that's how you get papers,
Isn't it opposite? You can't do useless or overcomplicated things because you have to get paper out. In industry I have seen people slacking for years and without any issues.
As someone who left academia for industry, and then made the somewhat crazy decision to return to academia, I agree with a lot of this. Especially in terms of academia, you are often expected to be independent and able to figure things out yourself, which can sometimes lead to you avoiding asking for help when you should. One thing that really shook me out of this in my first company was when they switched to an Agile process and I was literally forced to talk to people everyday.
I'm curious about your journey. In another thread you mentioned to me that you're currently debating leaving a faculty position for industry research. To go back and forth twice seems really unusual and I'm curious to hear more.
When I was a young lawyer, I worked with a group of four attorneys, three of whom had PhDs in other fields. The most senior lawyer, who had no PhD, commented on the challenge of leading such a group: "each man thought he was the smartest guy in the room".
But at the same time, if you humble yourself too much, people will walk all over you, and your work might have less impact than it should have. For instance if they assume that the things you say are ignore-able when they are actually correct and important.
Nobody walks over me. For example, while I play stupid, many people in the room know I'm asking dumb questions to get the presenter to admit the unspoken problems with their presentation. Think of a person giving a VC pitch. There's always somebody in the back asking "Oh, hey, I guess I'm slow because I didn't have my coffee yet, but I'm really confused. You said earlier that your monetization depends on conversions, but the advertising strategy is designed to maximize impressions. I don't get, how can you turn an impression into a conversion?"
I think of it as jeopardy rules: any time you are correcting someone you must phrase it as a question. If they’ve made a mistake, it lets them save face. And if you are actually wrong, you save face
I’ve been back in academia for a few years now. I’m lucky to be permanent and my colleagues are generally lovely and supportive. There’s a big push on Equality Diversity and Inclusion here so people are aware of treating students better. I don’t doubt there is some serious toxicity here and I’ve seen severe cases of it elsewhere.
My problem with the whole thing is the constant rejection when it comes to securing research funding. You have all these high achieving people being subject to endless failure often with useless feedback or none at all. You feel people doing incremental research (or sometimes what looks like the same research they’re always do) get big grants. When you propose, in your self inflated opinion of yourself, something novel but risky you get nothing.
Research money should not fund safe and guaranteed to work, it is always a gamble on something out there. If something is that obvious and safe its not academic research. So there comes the time where you start to lose you ambition and find the safe boring research that will funded and allow you to progress in your career.
Sometimes I think academia is ripe for UBI. Pay PhDs and PostDocs a UBI and give academics a guaranteed but small amount of research money per year. Let them self organise so this money can fund lab equipment and let them research whatever they want. Success can be measured based on the output achieved and that can drive promotion and then the ability to attract others to work with you.
Amen. The balance between fixed funding and grants is totally broken. The growth in the number of postdocs on short term dead end contracts is a direct result of the model too. So much time is wasted on grant applications.
There are a lot of problems with academia, but this post shows such a rosy-eyed view of industry, I have a hard time taking it or its conclusions seriously.
An industry job is fine if you're protected from the politics and valued for your knowledge and intelligence, but that's rare, and it usually doesn't last. The people making major decisions don't value intelligence or curiosity and, worse yet, they often have malevolent intentions. Getting "screwed" in academia means getting a B because of a tricky question on a final, or having to publish in a less prestigious journal than you think you deserve. Getting screwed in industry means you lose your right to an income and might never get it back.
The thing is, people in industry (meaning for-profit businesses that expect every member to work on some line-of-business concern... I'm not talking about research labs) have better social skills than academics are used to, so a lot of ex-academics jump into it thinking they're getting into a non-toxic environment, because the corrosive behaviors are not immediately visible. It takes a few years before people realize they haven't just entered "industry"--they've gone into literal corporate America.
> Getting "screwed" in academia means getting a B because of a tricky question on a final, or having to publish in a less prestigious journal than you think you deserve.
Actually, getting screwed in academia is:
- Never getting a tenure track position because your advisor has bad mouthed you (common)
- Not getting tenure and never getting another chance at it.
> Getting screwed in industry means you lose your right to an income and might never get it back.
Everyone I know who got fired got another job and bounced back. Lose tenure? You'll only get a second chance if you are particularly good.
Most of the times, in industry, getting screwed just means not getting a promotion.
The obviously realistic assessment is: it depends.
Sure, there are politics in industry, but most large corporations have large R&D organizations were politics are manageable and predictable, at least for individual contributors and team leads, simply because this is a crucial environmental factor and critical for retaining talent and getting products shipped. Politics primarily happen on higher levels and in particularly competitive parts of the organization (like Sales).
In academia (in contrast), the governance structures that are in place essentially ensure that with the exception of the particularly lucky (or: 'genius') few, one always has to watch one's back to ensure the fight for employment, funding, research time, top-venue acceptance, tenure, etc. is successful.
With academics, I think the problem is to some degree self-created. In the Boomer days, they copped the attitude that their research was the only thing that mattered and that teaching was unimportant grunt work to be delegated as much as possible. This resulted in under-educated, condescended-to politicians reducing their funding, and this combined with administrative bloat (another Boomer trick) to create a truly awful job market.
Government research labs tend to have decent cultures, but it seems like the replacement of old, decent noblesse oblige companies by new-style psychotic McKinsey-esque startup-culture ones is complete. What I've heard is that places like Google X are ultra-political (which isn't surprising, because all FAANGs are nasty places run by nasty people). I've had decent R&D jobs; the problem is that they're unstable. You are a resource to be pulled into someone else's war.
A lot depends on your field. The academic fields that are moderately OK are the ones where there's a lively job market in industry competing for talent. I know professors in CS and Medicine, who are quite happy.
In other fields, there's no continuity of work beyond someone's job. A professor studying X can be replaced by another professor studying Y. There's no reason to keep you, if someone else can be more productive. And beyond the undergrad level, you depend on the cooperation and goodwill of people who can deliberately or inadvertently make your career vanish in a nanosecond. And then unless you're a superstar, you get start from scratch and hope for a second lucky break amidst a multiple of brilliant competitors.
I got out right after grad school, so I don't even qualify as an academic refugee. My parents had both been industrial scientists, so that career path wasn't foreign to me, and I planned for it while working on my degree.
When I got "screwed" in industry, I was quickly back on my feet. Turning things around, a company making X can hire someone away from a company making Y.
> Getting screwed in industry means you lose your right to an income and might never get it back.
Why is this? It really easy to get hired in industry after you've been fired from one job -- especially if you've been there for awhile. I've seen many postdocs or non-tenure-track professors forced entirely out of academia if their contracts aren't renewed. If they don't have skills valued by industry (not uncommon if you spent 5+ years post-PhD in academia), I've seen former colleagues never get a good job again.
I have had the complete opposite experience. I was in academia for 15 years, and have never seen such a lack of humanity. I was at a small, highly regarded liberal arts college.
I now work for a multinational American corporation - something I never thought I would do. It has been a dream come true. A recent thought: if one wants to experience true diversity, take a job with a multinational corporation!
A couple thoughts, from an academic who doesn't mind working with industry partners:
"In industry, though, no skeptical journal editor will review your work. It’s going to be a bunch of PMs and engineers, who will take you at your word that the analysis is what you say it is."
This is at odds with the tremendous amount of consulting I end up doing essentially double-checking industry analysis. I've had at least three projects where I am functionally cast in the role of "skeptical peer reviewer".
"They may have critiques, and good ones, but they will tend to stem from domain knowledge rather than a deep familiarity with the statistical properties of an estimator."
Similarly, in my work with industry partners, some of the most rigorous methodological discussions I've ever had have been with them.
"Writing for an academic audience, rather than plainly and directly for an audience of sharp non-experts"
This is definitely true. It's also good advice for folks in academia writing grant proposals to industry.
"Academia is characterized by well-trodden problems, hashed over for decades, and negligible novel data for resolving them. Industry is by comparison a mass of green field areas of inquiry with large budgets, minimal bureaucracy, and ample data."
None of this has been my experience. Indeed, most of my work with industry has been "How do we get this done on an absolute shoestring budget?"
"School tends to train people to inhabit a state where they passively wait for assignments."
This is a really weird statement. Any graduate student who has passed their preliminary exams should already be over this. If his friend was a faculty member? It's been years since he had an "assignment".
"You don’t need to ask permission to go to the bathroom and you can seize opportunities of your own volition rather than waiting for them to fall into your lap like an essay due a week from Friday."
Following on this - this is just a weird view of academia, especially given his example is someone at the faculty level.
Yup! I always wonder where these caricatures of academia come from.
Another thing that stood out to me was "you need to generate good-enough results quickly."
I'm sure there are a few lavishly-funded groups where you can spend eons designing the perfect experiment, collecting noiseless data, and writing it up like Shakespeare (Or GRR Martin) . For the rest of us, especially in soft-money jobs, the name of the game is picking one thing to hone for each project and satisficing everything else.
I've seen data scientists who take the "no assignments" idea as an excuse to focus on "interesting" vs "practical" data problems (and IME, like yours, the "no assignments" mindset arises mainly in academia, not industry - I never even finished a MS, but even there saw far fewer academic 'assignments' than industry ones).
And if that project is high-budget or has ample data, the stakes get a lot higher. I've seen teams lose their projects and leaders lose their position because the results weren't there. It doesn't matter if they did the math right or wrong, if the results weren't there due to randomness or outside of their control, etc.... lots of people don't want to spend hundreds of thousands to millions a year on a team that hasn't produced working products.
The worst were cases where the project had an impact, but a negative one (because of an over-focus on specific areas, say, without getting sufficient full-business context). And often these were cases which some more requirements gathering up front could've avoided, but "you can do all the science and math right and still fail and lose your job" is a particular pitfall that I don't think most academic transfers really appreciate at first.
Does eigenrobot—a pseudonymous influencer, as far as I know; I have no clue who s/he is—have an earned doctorate? Comparisons of the academy and industry are better made by people who actually have experience in both realms. This post reads to me as though the author thinks s/he understands the culture of professors and researchers, but in reality does not. The “let’s see how quickly I can destroy this entire presentation” line, for example, is a tell. People tend to lose that attitude by the later stages of a PhD program (if not sooner).
edit: I should add, another funny thing about this post is that much of the advice would also be good for someone intending to stay in the academy. Among the successful scholars that I know, for every genius, there are several normal-smart people who learned how to get out of their own way and keep their projects moving forward on a day-to-day basis.
Yes, he has a PhD in Economics from U Washington and has worked in at least two private sector companies. The first was probably Amazon, definitely in Seattle. Don’t know what the current one is.
This is great advice for anyone working at a startup, particularly the part about not waiting for an assignment. "Literally just do things, as long as they're not the wrong things" is the hardest thing to teach or learn.
>Academia is curious in that performance, both relative and cardinal, is evaluated explicitly and sometimes publicly; you develop a reputation quickly, and making errors especially in a classroom or seminar setting can be humiliating. Academics for their part tend to lean into this by playing the “let’s see how quickly I can destroy this entire presentation” game.
This actually just hit me because it highlighted something my (new) boss does that completely caught me by surprise. He goes away and does research on an idea and comes to an extremely strong opinion about something, and only then will he discuss it with you, and I think that is highly related to this (his background is academia).
In some ways this is great, he'll present things he's really thought about, but the flipside of it is that he wants absolutely no discussion. It's always communicated as "I've looked into this and decided x" and the result is that there's no real interrogation of the idea. And since the area we subject area we work in is new to him, it often means there are things he just doesn't have the experience or intuition to reason about. This has resulted in several situations where the team has had to do enormous amounts of work based on questionable assumptions that later turn out to not hold up to scrutiny.
In every other team I've worked in people present their ideas without ego, are open to discussion and feedback and value the different perspectives. It takes a lot of effort to find ways to influence him and highlight issues, you kind of have to create a situation where you're making sure you're giving him feedback and thoughts about ideas before he has stated an opinion.
OK, clearly there are a lot of generalisations in the article - you may or may not recognizes the academic or industry positions in the article, so clearly ymmv.
Perhaps a better simplification is - Life is a Game, Learn the Rules. And in that context recognise that the game you are playing just changed, and there are new riles, and new ways of scoring, so pay attention and learn quickly.
This is true got any job change, but is especially true for the academic/industry switch (in either direction). There are also likely to be a bunch of "local rules" (some unwritten) to figure out.
What works the least is bringing rules from the old game and trying to convince the new team to play by the old rules. That path never ends well.
Yes, I think most people should ask more questions (regardless of industry or academia), yes I think academics get paid up front (grants) regardless of outcomes - industry gets paid by results (revenue) and thats a different set of goal posts.
You might come out of, or go into, an academic, or industry position, with supportive, or abusive colleagues, with or without lots of office politics, with or without smarter, or less-smart peers, who may or may not care.
This particular article seems quite biased to an American working culture. In many working cultures, just doing things and producing results too quickly is frowned upon. Cooperative working where ideas are carefully considered first and resources formally allocated before work begins is also a very normal way of working in many cultures. It's sometimes considered to be slower than the go and do it way of work. But also produces fewer total failures, and creates the kind of slow, steady, sustainable growth that is unsexy to workers in Silicon Valley.
Academics and industry both have their share of problematic personality types of various flavors. If there's a single piece of advice I'd relate to young people going into either academics or industry, it would be to adopt professionalism - which is unfortunately a bit difficult to define precisely and which varies a bit from place to place and job to job.
For example, in an academic laboratory or and industrial laboratory setting, one basic common rule is just don't create problems that other people have to clean up. Clean up your own messes and try to avoid making new ones. This doesn't just apply to dishes in the sink, but also to interpersonal relationships.
Another part of professionalism is to learn to recognize dead-end no-win situations. Typically this would involve an academic adviser who is a dishonest fraudulent con artist who sabotages anyone who points out flaws in their work, or an industry supervisor who is an egotistic twit who plays favorites and needs a daily ego massage, or something like that. Just exit immediately once you realize what's up, don't waste your time fighting battles with such clods, unless you like tilting at windmills. A careful study of the potential employer in academia or industry can help with recognizing the danger signs before you sign up. Again, that's professionalism: don't commit to a situation until you feel good about it, and if not, politely decline and move on.
If you do happen to luck into a decent situation where you're not stuck with working for a psychopathic personality type, still stick to a professional attitude. If you do go to someone for help, and they take an hour of their time to explain things to you, be absolutely sure that you then spend three hours of your own time solidifying that knowledge - don't go asking the same question again because you forgot to take notes or whatever, that's sure to irritate people and make future help less likely.
As far as academics vs. industry, I'd say academics is where the real nasty political infighting is more common, although I'm sure industry has its share. People fight over lab space, imagined or real slights, grants, you name it. Woe to the grad student or postdoc who gets caught up between two pissy professors out to do damage to each other by any means necessary, for example. Really every incoming grad student should have to take Institutional Politics 101 just to know what to watch out for.
> You don’t need to signal brilliance; you need to generate good-enough results quickly.
As a previous academic now working in industry, this stuck out for me. "good-enough", and "quickly". On the whole I understand where the article is coming from, but I think some of the things it brings up, while maybe true, are actually rather unhealthy trends. I don't know for sure, but I feel like things used to be a little less like this 10, 15 years ago.
What I'm referring to is the refusal for companies to properly invest in knowledge. At least in my limited experience so far, I find that I've been asked multiple times now to start a project from scratch, or given a shoddily written codebase as a starting point, and told to add features / generate results as quickly as possible, take as many shortcuts as possible, just deliver some PoC or minimum viable solution so that we claim support for something or put it in our marketing material asap.
Meanwhile there is zero effort to take time between projects and actually develop general knowledge and excellence in a particular field. Many times now I have felt that the company would really, really benefit from just giving the team 3 to 6 months to fully study a topic, not as part of a project, but as a general knowledge gathering exercise, to experiment with different methods and implementations and just explore and see what is possible with the data that we have. The idea being, that when we do start a project for a client, we really know what we're doing and know what are the best tools to reach for to solve the problem quickly and well.
Instead what happens is that every now and then some idea comes down from on high, either from management or sales, that some "concept" must be proven immediately, like give me something in 2 weeks, don't worry about the code architecture for now (3 months later, still hacking on the poorly designed codebase that gets passed on to the next team..) and don't worry about studying the optimal solution, just jump right to implementing something. There is just no sense of, you know, let's invest some time to really properly study this and become proper experts so that we can be the best. Every single time it's just, let's solve this problem from scratch with barely any time to try more than 2 things, and then when we get mediocre results, we'll scratch our heads and have no idea what went wrong because we don't really know what we're doing.
Compared to academia, where if you don't fully understand what you're doing, someone is going to catch you with a bad analysis, or invalid assumptions etc. Instead the "closed" way things work in industry lets one away with hiding poor methods and choices from your competitors, and show publicly a very "shiny" version of what you really have.
Having said all that, there are many positives, like you can actually implement something that will be used, and you can work on interesting, applied problems, and get paid well for it, which is honestly really helpful for motivation. And, as the article says, it actually is less stressful not having to constantly defend your intelligence, or fight for publications and grant money, but just gaining trust in people by delivering good work in a timely manner. In that sense it is far simpler and, frankly, more lucrative, so the upsides do outweigh the downsides imho. But the lack of planning for the future and general long term thinking do rather bother me.
> Recently I met a new coworker, a now-former professor who left academia. Like many, he seemed wounded by the experience. This is his first private-sector job.
> I have to give him respect: he left willingly. He had a plum position, and he was years away from tenure review. It’s hard to walk away from a place like that after a lifetime of striving. But he was unhappy, and he’d grown disenchanted with his research agenda, and didn’t enjoy the labor itself anymore, and it was degrading his ability to enjoy his private life; so he quit. Not everyone is brave enough to do that.
> When I was talking to him about onboarding and getting acquainted, I realized I was speaking to a more-accomplished version of my past self. There are certain pernicious behavioral patterns and outlooks that are instilled in a graduate student. Over the coming weeks I’ll do my best to shepherd my coworker into the private sector and help him overcome what’s been done to him; but today I had only a half hour, and was constrained by professional norms, and could only touch on the surface
> He had a plum position, and he was years away from tenure review. It’s hard to walk away from a place like that after a lifetime of striving.
The nominal academic career is ~45 years, as successful academics rarely retire early. In most fields, getting a tenure-track faculty job signals the transition from early to mid-career. But this seems to be about CS in the US, where it's common to get a faculty job with minimal postdoctoral experience. Getting tenure is then the true starting point for mid-career, and this was more likely about an early-career researcher choosing to leave the academia.
> Academics for their part tend to lean into this by playing the “let’s see how quickly I can destroy this entire presentation” game.
There are some toxic fields of research, but I don't think this attitude is particularly common in the academia. There are many fields with a true sense of community. People generally realize that it's better to be nice to your colleagues, even if you are competing against them, because you will be stuck with them for decades.
> Academia is characterized by well-trodden problems, hashed over for decades, and negligible novel data for resolving them.
Here my experience is the opposite. The academia is characterized by world's top experts in a narrow niche investigating speculative problems few people have any idea of. More often than not, that research turns out to be a dead end, "wasting" years of work.