Over here in Germany, professors' job is "research and teaching". According to the internet, the author's university is a publicly funded university as well. I can see how AI can make you faster on the research side, but you give up 100% of the teaching/developing people part.
As a tax payer, I am very concerned if the people I fund with my taxes to do a job unilaterally declare they are no longer going to do the half of it.
Teaching and research should be decoupled. Professors are hired and granted tenure primarily based on their ability to produce original research. The skillsets are different; often good researchers are bad teachers, and good teachers are bad researchers.
There is a case to be made that teaching improves the understanding and insight of the teacher which in turn can increase their research ability. For starters, it provides a less boring way of drilling fundamentals. But more importantly, having to answer questions from students which very likely will be coming from odd and unexpected directions, helps the teacher clarify their thinking. It could well be that one of these odd questions, the answer for which the teacher takes for granted, may actually hold some insight or raise questions into what they are working on outside of class.
In a similar vein, it is recommended that if you are in a business meeting you hear what the junior positions have to say about something first and work your way up the chain of command rather than the other way around due to the junior positions being less familiar with internal processes and thus more likely to flag or suggest something completely out of left field that the higher ups might miss.
I tend to agree that teaching can clarify one's ideas, but I don't think the benefits are equal across the board. I think the argument for benefits to research are stronger when it comes to supervising graduate students and teaching seminars. I'm far less convinced that we should have math professors teaching Calc 1 if they're not really passionate about it, and I'm especially not in favor of tying up their salary and performance evaluation with it.
Note, I'm saying all of this as someone outside of academia who is passionate about science and had a very mixed bag of teachers in undergrad.
This used to be the case: research was conducted mostly at academic institutions that did not provide degrees [1]. The "research university" is a relatively new thing
Interesting read. I always wondered from where did the idea about "thesis" & other "extra-circular" activities come from, for both students and professors.
Nowadays, promotions of professors for different levels (Assistant, Associate, Professor) is solely dependent on number of papers they are publishing in Q1 journals.
But the research maybe entirely bogus, same ideas repurposed hundreds of times by different professors.
The entire concept about "systematic knowledge" has gone downhill.
Even more important than the papers is whether you can raise the money required to fund your lab which produces your prestigious journal papers. And the further you go down the league table the less important the "prestigious" part gets.
Absolutely not. You could argue this for entry level lectures, but not at the PhD level. PhD is learning how to do original research, how could you separate teaching that from doing that?
That's not entirely the case in Germany. Applicants need to give a lecture which is public. Usually members of the student union will be present and will have a say later within the hiring committee about the quality of teaching.
But I do agree that the ability to produce and procure research is not at all coupled with the ability to teach.
I would suggest 'more loosely coupled' perhaps, which in many cases they already are. Students may not want a surly, disinterested professor forced upon them because of a teaching requirement, but they won't want to miss out on great teachers forced to choose between teaching and researching. And as expressed by others, teaching and research reinforce each other to some degree. Teaching tests conceptual understanding and research incentivizes maintaining an understanding that tracks the knowledge limits of the field.
But this exists in many educational + research institutions already. Where it runs into problems is in resource constrained environments, where there aren't the budgets to support research-only positions that aren't 'less than' roles or the institution can't support the grant ambitions of highest 'performing' teaching researchers, stuck that they don't go to those institutions and more less research-focused (or at least smaller grant value) teachers populate those schools.
I'm trying not to make any value-judgements here, so please ignore and bias vibes that gives off.
This is like saying peasants growing vegetables in the field should not mix with philosophers questioning the secrets of the Universe.
Problem is most research is just pissing in the wind. No real results. Show me the cure for cancer. Show me the warp engine.
So it's very nice to sit in their ivory tower doing ivory tower stuff while the peasants feed them with the vegetables they grow plowing the fields.
In reality, let them also teach. That's real, palpable work. I can't do all nice things and never touch shit work, so should professors because unless they cure cancer or invent the warp engine now, they are not a privileged cast.
A lot of academics like teaching and think they benefit from it though. Richard Feynman thought so, and many academics I have met seem to as well. Not all.
I think in some subjects (e.g. literature) the greater prestige of research leads to a lot of pointless research and we need more teaching.
Of course, there are many good researches who are bad teachers. I am not so sure about vice-versa, but, nonetheless good teaching should be rewarded more, as should the ability to communicate knowledge in other ways (e.g. by writing books).
I've worked with good and bad at both. Some of the most difficult problems when you have students who have had excellent teachers and then get dropped into the real world. If they don't learn themselves how to apply what they're learning (the other side of the coin of training) then they're often no better than an llm stuck in a loop, they know the textbook but don't know the gray areas...
Also professors and researchers are required to be able to communicate otherwise they're useless to the field. They need to better.
I'm not saying every lecturer will hold any interest in every lecture course. I've had the ones who are there lecturing core material to avoid the dept losing its accreditation and I've done electives where the professor is off the wall and spends half of the time going on about their research instead of the address material (fun but painful come exam time).
I agree. I studied at one of Europe's top "research universities". The most important courses often had the most famous profs, who also were either bad teachers or put no efford into their teaching.
On the other hand the course I learnt the most from was taught by a passionate lecturer who isn't even a professor, and he gets the highest ratings every year. (Yes we have a "rate my prof" event)
Imagine a future classroom defined by elaborate plays performed by curious parents, all on advanced adjacent learning paths themselves. An intertwined learning structure that just keeps going up. At higher levels, instead of having the researcher with their head in the books communicating, they’ll have a whole team of people translating their knowledge into a production fit for antiquity - directors, diverse range of talents, charismatic performers, etc.
Assuming we have time to do this in some post-having-jobs world, of course.
In this sort of case the “teaching” happening with graduate assistants is teaching how to do research. That’s inextricably part of the job of a research professor, is to teach others how to do the job.
So, I'm no professor, but as an ten year post-doc (unfortunately) I can say that most university groups benefit from both types. Again, the problem fundamentally is funding and the wrong incentives, as it always has been from before I entered grad school till now.
"Teaching" in OPs context probably doesn't mean lecturing, but 1:1 sessions with Junior Researchers from Master's Thesis upwards to PhD Candidates and Postdocs.
You probably need to step outside of your US-centric bubble if you are to comment on how university works outside of the US. There was a fairly large clue in the parent comment.
"Often good researchers are bad teachers, and good teachers are bad researchers" is a statement about humans, not a specific country, as far as I can tell. Sure, I happened to use the word "tenure" which is generally used in a U.S. context but you should be able to take a charitable reading of what I said and understand the broader point.
To my knowledge the view is correct for places outside the US.
UK universities do currently hire people to do research and teach. And tenure is based on research not teaching. Teaching is seen as something that funds the operation to an extent. Some are excellent teachers. Some merely provide the material.
It works as is because researchers are not meaningfully impacted by having to do a few hours a week. And student get access to people in touch with the field. But it is not optimal having people who often are not good at teaching and/or don't particularly want to do it, taking lectures and tutorials.
My experience at a research university, albeit 12 years ago, was that many of my professors loathed teaching. Some openly expressed disdain for the time they wasted teaching when they could be researching. I think a better framework in the future would be to have researchers, and lecturers/teaching professors separate. One is to teach, the other is to research.
I disagree. Coming froma PhD background, the researchers that spend all of their time investigating the intricacies of their field are the most qualified to train up the next generation of researchers. This isn't primary and secondary schooling, where the syllabus evolves at a slow pace. To teach how to research, you need people doing the research.
If we split them up, then the teachers will only be able to teach what they have theoretically learned from literature only. What we need is for institutions to reward teaching, reward students who excel and most importantly, reward teachers who produce excellent students.
Disdain for teaching should not be the norm. After all, what are they doing if not teaching when they publish a paper, or give a talk at conferences? Might as well be a hermit scientist then.
In the classic division, "teaching" consists in giving undergraduate classes, and "research" consists in the whole spectrum between working all on your own and managing a PhD factory (3+ students a year).
So this article is really not saying anything controversial in the strictly ontological side of things, in fact it's already a relatively common stance to prefer supervising few (or, more rarely, none at all) students.
This researcher is saying "when I consider hiring someone as a workhorse, I might prefer AI instead"; what's the harm in that? Too many PhD students are used as disposable cheap labor, seeing little personal growth in their PhD journey and being generally neglected and abused.
I feel like people undervalue the learning experience of just being a workhorse for a while. It's a lot easier to do, make and correct errors when you start with the simpler stuff under guidance
The authors itself writes:
>I would recruit a graduate student into my lab and allow them to run with the project, providing guidance along the way.
You say to many phd students are used as disposable cheap labor, but what is the amount of people still learning stuff maybe bigger?
Teaching an undergraduate class or even a graduate class is still teaching. The author does not say he won't do that anymore.
The problem is about the fresh talent pipeline for researchers (i.e. PhDs). In many ways, elementary school and a Master's degree are more alike than a Master's and a PhD in the sense that you're learning prior art with clearly defined exam/project assessments and no expectation of making something truly novel in both elementary school and the Master's, while a PhD is all about discovering something nobody uncovered before. So, calling this a problem of not wanting to teach isn't quite right.
IMO, the article is rather highlighting a different problem; the former problem in this area was that only a tiny sliver of the best engineering/CS undergrads wanted go into research given the far more lucrative industry careers, and now the supply part of that market is about to vanish too due to agentic AI. This will basically kill the concept of an academic career as we know it and the point of the article is that we need to find a different model of advancing and funding science.
You've clearly never worked the academic sector. Calm down most researchers are hyper focused on their research productivity because at times 90%+ of their time is consumed by teaching for months at a time. This is an almost universal constant for all decent institutes globally.
Taking on extra cheap labor in the form of grad students used to be the only way to do this but every single time this turns into onboarding someone for months to get weeks of work out of them. Great when you can hide it in your other side of your job, but most of the time you can't...
Students are not only workers, they are also disciples of your work and, once forced to read it, will likely use it in the future even when they leave your lab.
Even completely egoistically replacing students with AI is shooting yourself in the foot in the long term.
I think also at the end of your career in science, you are going to care a lot more about the people you've uplifted and turned into flourishing scientists themselves vs however many papers you've gotten your name under. At least for every professor I've come to be close with in my years in academia that is certainly the case.
It says a lot about US academic culture that they think in terms of hiring. There is an important educational commitment requirememt to the role of professor, at least in Europe. Hiring is to the betterment of your own goals and almost orthogonal to the educational mission. A lot of unethicalities fond their root in this schizophrenic mission statement of doing professional competitive scientific research and at the same time education of graduates.
On the contrary, my experience of US academia has been that (graduate) students are very much students, who take a lot of classes, are graded seriously with the possibility of failing, are mentored rigorously (the author even says "the classic one hour a week meeting", which I also witnessed there), and in fact enroll in a program more than they are hired directly.
I did my PhD in France where we were legally employees like any other and did 100% research with like 100 hours training over the three years which could be 5min MOOCs counting for hours or classes the professors would sign us off on. We were hired by a specific researcher for a specific topic, unlike US students who join a broader program and explore their own directions more. My mentoring was drinking coffee with my advisor and colleagues and the odd e-mail exchange the day before turning in a paper.
I believe Germany and quite a few other European countries are similar. Any country that does 3 years PhDs is bound to cut on the student part of things.
This sounds misguided. In the little experience I had, I've seen that models get basic knowledge so absolutely wrong that giving them any sort of independence will not result in publications that positively impact a professor's reputation, or contribute to science. Or at least the reviews and papers I read that had AI content did not give me the impression that we should have more of this. And they require much more supervision, with the added issue that they cannot learn in the long term through your interactions, and without the enjoyment of teaching something to someone.
They're really good at finding papers though. Perhaps because navigating search engines has become a pain. Perhaps this will be the case in the future, but saying you're tempted right now is like saying you're being tempted to replace your HPC with quantum computers. It's a bit early.
> The issue is not whether my students are valuable. In the long run, they are invaluable. The issue is that their value emerges slowly, whereas AI delivers immediate returns.
I had the thought that it's more like hiring only autistic/on-the-spectrum employees that will on whims do exactly what their interpretation was, or possibly worse literally what you said without considering further consequences.
You think? I will get banned from HN if I bring up that these models are fundamentally theft but we just don't put them in jail because they had the foresight to bribe the trump admin like everyone else who wants favor did.
Also 90% of citations generated by AI are wrong or straight up don’t even exist. It’s got such a long way to go to be able to reliably write credible papers.
I think you missed the point. Yes it was meant to be humorous, and also to emphasise one of the reasons AI-generated citations are completely untrustworthy, especially with the growing number of AI-generated (junk) papers being published.
No, I had no intention of trying to offer a real source for the accuracy of AI generated citations. It is not hard to Google, search HN or even (ironically) use AI to search, to find numerous relatively recent studies discussing the problem or highlighting specific cases of respected journals/conferences publishing papers with junk citations.
It feels like allowing fake citations in the output from the AI means that you didn't do even the barest minimum of verification (i.e. tell the AI to verify it by sending a new AI to download the pdf that matches that DOI and verifying that it matches what the citation says).
Yeah I tried building such a tool. The problem was two fold:
1) Automated fetching of papers is difficult. API approaches are limited, and often requires per-journal development, scraping approaches are largely blocked, and AI- approaches require web fetch tools which are often blocked and when not, they consume a lot of credits/tokens very quickly.
2) AI generates so many hallucinated citations it’s very hard to know what a given citation was even supposed to be. Sure you can verify one link, but when you start trying to verify and correct 20 to 40 citations, you end up having to deal with hundreds or thousands of citations just to get to a small number of accurate and relevant ones, which rapidly runs you out of credits/tokens on Claude, and API pricing is insane for this use-case. It’s not possible to just verify the link, as “200 Status” isn’t enough to be confident the paper actually exists and actually contains the content the AI was trying to cite. And if it requires human review anyway, then the whole thing is pointless because a human could more quickly search, read and create citations than the AI tool approach (bearing in mind most researchers aren’t starting from scratch - they build up a personal ‘database’ of useful papers relevant to their work, and having an AI search it isn’t optimising any meaningful amount of work; so the focus has to be on discovering new citations).
All in all, AI is a very poor tool for this part of the problem, and the pricing for AI tools and/or APIs is high enough that it’s a barrier to this use case (partly due to tokens, and partly because the web search and web fetch tools are so relatively expensive).
Interesting, tools like Zotero seem to have sorted out the pdf fetching (and metadata + abstract fetching even without institutional access to the pdf). Did you try building the fetching on top of that?
I meant for point 1. Zotero will accept a doi/arxiv link (among other) and download the public metadata (authors, journal, abstract) for you so you don't need to build something for that end. AI cites a paper, copy DOI into Zotero, analyze info Zotero returns.
10 years from now, the people that stopped hiring novices and juniors are going to be deeply regretting their past decisions. The people that kept hiring are going to be working with their newly-promoted-to-senior colleagues and be making significantly more progress than those that didn’t keep hiring.
(IBM figured this out a couple of months ago, and explicitly announced tripling their hiring of juniors/grads in order to avoid ending up with a massive gap in the management/senior layers in future).
> “The companies three to five years from now that are going to be the most successful are those companies that doubled down on entry-level hiring in this environment,” Nickle LaMoreaux, IBM’s chief human resources officer, said this week.
Will they? Just because you developed them that doesn't guarantee they will stay with you. It's been always the same issue tbh, but big companies could accept the risk because they pay the most competitive salaries anyways.
Except they won't. They will just hire those new people away from the firms that trained them. That's what happens now and there's no reason why it won't happen in the future.
This is why firms that do actual training have clauses written in the employment contract that says if you receive x months of training from them then you have to work for them for at least y number of years otherwise if you leave then you have to pay them for the cost of training you (which is written as a dollar amount in the contract).
Companies that don't have that kind of clause in the contract are going to get screwed over when their newly trained employees get poached by other firms.
I started my career with a graduate program from a larger company. I stuck around in that company for close to 5 years and would have liked to stay longer. My reason for leaving were the absence of a career progression. The first 3 years, the company had a great career progression path. Clear outlines what it needs for a promotion, fair and transparent pay, etc.
That changed and despite hitting/exceeding my goals, I was denied a promotion twice with no good reason. My boss, who is fantastic, told me that he cannot give me a good reason because he himself did not receive one. So I left.
Generally speaking, my cohort of the program was part of the company much longer than most employees. I don't think a single person left in the first 3 years. Attrition only started now that there was a general shift in the companies culture and communication.
It might happen, but there are risks. The obvious one is that the existing employers will make an effort to keep the best (promotions and pay rises) so people hiring away from them will get the people they do not need to keep.
Those sorts of clauses are not legal everywhere. They would certainly be at least heavily restricted in the UK (on the other hand there are subsidies for some employer training and education here - which is why my daughter has an engineering degree without paying any fees). The author of the article is in Israel, and as an academic is in a different position to people in businesses.
It honestly seems a little control freakish to think this way. People leave companies and that’s a good thing, they explore the industry and generally become more capable. If you leave on good terms there’s nothing holding back a renewed relationship, now with the added benefit of new perspectives; maybe meeting at conferences or working on a project. My gut is telling me these companies don’t part on good terms with their employees.
> The issue is not whether my students are valuable. In the long run, they are invaluable. The issue is that their value emerges slowly, whereas AI delivers immediate returns.
This is like the classic "I'll do it myself because its quicker".
In the current environment and likely more so into the future, those that hire and develop graduates skills are going to be looked at more favorably. Furthermore, the people you work with and coach become peers and typically help build a network of people who can bat for you.
It's tricky and its a balancing act and I appreciate that AI is becoming the easier quicker less fuss option
That’s just not really not true from my professional experience or my industry in cyber security. There is of course a level of experience required of a junior but it’s still junior level experience.
In my line of work I was coaching and now I am senior I am expected to delegate tasks and coach, not to increase my own workload for doing simpler tasks myself.
>In the process, they may bypass the valuable experience of struggling through early tasks and learning from their mistakes. Students, I worry, could simply become an intermediary between the raw idea and the AI’s output.
Even if all AI progress grinds to a permanent halt today, there's already enough utility in its current capability to force these questions. As a result, how we train and educate graduates and young people needs to change.
I have no doubt you need to have actual experience to be able to ensure AI output is at a production standard but if we accept that reality, then a shift in how we educate and train young people could make an enormous difference in ensuring employers still see value in hiring people with no real commercial work experience.
This is something that will have to be solved through the way research is funded.
At least for publicly funded work, it was always an assumption that you would need students to hit some goal; so by funding it you would get both the outcome, and more people skilled in that field. If the scope of what one team/senior can handle has grown with ai, we will either need explicit staff numbers as a requirement or bigger scope to the point where the ai can't handle it.
Or we find that AI can do so much the whole system implodes...
And here we see you’ve hit upon Jevon’s paradox. The scope of work will grow to use more than it did before, now that human labour achieves more for the same money. Employment will ultimately go up not down (over the long term - we are seeing a lot of short term instability and noise, although there’s much said about AI without it yet showing up in the data, as per articles recently shared on HN about employment figures across the US and the world).
Even the author eventually admits the problem is fundamentally academic funding and the stupid publishing culture we have. I wonder if the author had the courage to actually expound upon that as opposed to leaving it as a triffling mention at the end if it would have been published as a cute little op-ed in science in the first place.
Why not hire a graduate and empower them to use AI? Much better interfacing with an actual human who will then go and do the work using all AI tools at their disposal.
> Why not hire a graduate and empower them to use AI?
A lot of my work with AI involves questions where I have an intuitive direction and sense of the data or model, but where explaining why takes almost as much work as doing it. (Commonalities: weird interdisciplinary nexuses and idiosyncratic data sources.) Adding a human translator, much less someone without field experience, seems worse than giving the task to a human or AI wholesale.
Where humans still reign supreme is in interacting with other humans. Paradoxically, this might make grad students’ roles attending staff meetings as their professors’ proxies and/or filling out paperwork.
It's interesting that this dilemma (of getting quick and easy wins) is occurring at multiple levels. Even as a junior researcher, its often tempting to hand off actuall thinking and reasoning about one's research to AI (e.g. blindly accepting AI code) to quickly make 'progress'.
Apparently the same question is being asked at different levels and abstractions...
Just like DEI, sustainability efforts, I predict we will see new initiatives for forced hiring of Juniors.
Implementation can differ (e.g. ratio of interns vs total headcount and so on), but it is the time for governments to intervene and force corporations to train people, humans are resource for the government, they need to polish that resource to thrive.
> Research grants are given by governments mainly to first TEACH students
Government's goal is obvious and correct, but if you have done a research and tried to get a grant you should know grants are very "political" as well, if you are researching a thing which is not trendy or takes another 10 years to yield results, but there is another lab who is telling we are researching LLM, it will be very difficult to get a grant even if you promise to TEACH/hire 20 students for that research.
Justifying long term benefits is difficult problem
The difficulty the researchers who developed RNA vaccines faced in getting funding is a good example of how bad the system is. Safe and unambitious is preferred,
As someone who both recoils at DEI and is at least a decade too old to benefit from a policy like this personally, I have to say this honestly sounds like a great idea.
Both avoids the tragedy of the commons (why would a corporation pay to train a junior when they can just let their competition do it then poach the experienced senior) and gives more opportunity to a new generation that are frankly getting economically screwed over enough as-is.
Yeah, could be, some problems I see with this implementation:
1. the wait time is too long for the company to fill a position, it is difficult to predict what happens in the next 4 years
2. difficult to match the students with companies. For example, you are interested in CS, but company wants specifically React developer (assuming there was no AI and there was still demand), would the student change all their courses based on the requirements and live like a robot who is forced to take courses they are not much interested in. Now imagine when gap is higher between topics (CS vs React is closer, compared to MBA vs procurement, both are somewhat subset of same topic)
The point is that we will still need senior level employees, but the way fresh grads get to that level is generally through entry level positions, experience and mentorship. I don't think we can expect the university system to start pumping out senior level graduates.
> I’m not sure where that will leave students who start with no research experience.
What is wrong with this guy? Of course he knows where that will leave those students. Why did he even choose to be in the business of developing people? Nobody forced him. Anyway, the ladders were pulled up in 2020–2021.
Reading the piece I _hope_ they are trying to make a point and not really thinking they are not going to help novices become juniors. But who knows, nowadays...
It's one thing to be forced to use the damn things, but this guy gives it very serious thought, much more than others I've seen and known, he even writes a science.org article about it, and ultimately chose wrong.
We measure scientific output as nr of publications. And that is the cause of bs like this.
These institutions have a duty to educate humanity. PhDs are also supposed to be able to help the public understand complicated science. To guide ethical decisions.
But no, we measure the number papers, and not even their quality (very well).
It's all a matter of incentive alignment, what gets measured gets done. The state of academic science is sad in most places. This contemplation by OP being case and point.
I am an academic at a big Australian university. I can't disagree more with this article. Students are not just machines that convert ideas to papers (and nor should faculty), they are one of the true joys of academia in the first place.
Unfortunately, as other comments here have pointed out, the incentive structure for academia right now is misaligned, leading some faculty to focus just on publish or perish. This is a huge shame on so many counts.
My hope and also slight expectation from the AI era is that academic writing becomes so commodified that we see a total devaluing of papers as a metric, even in prestige venues. Perhaps this would help the community find better things to focus on.
On the other hand, if it can detract PhD factory foremen from hiring students for the sake of publishing, and let them focus on advising fewer students better, I can't say I see the harm in that.
Either he is talking about ML in general, and it has been used for one decade now and it's a fucking joke, or he is talking about LLM and it's a fucking joke
The title is not relevant to the article, not even for a single line. The author straightup assumes, does not answer the 'why', cause I was here to give Lady Lovelace argument to Turing, that you would NEVER (hire an ai instead of a student) unless you making directionless slop. You can share goals, but not the vision, and mission is different. Ai learns from experience, humans are needed to build that experience due to their extremely large 'context windows' going as deep as the constant evolution of the DNA(as long as it serves human-centric goals, which circles back to the mission part).
The article really is about "education seems directionless without economic goals", and again as comments have pointed out, it only seems so.
This is morally wrong and it should be embarrassing to publish such an article.
It doesn’t surprise me to see such articles coming from academia, in which juniors are treated like dirt to such an extreme that is unimaginable in any other industry, save for maybe Michelin star cuisine.
Over here in Germany, professors' job is "research and teaching". According to the internet, the author's university is a publicly funded university as well. I can see how AI can make you faster on the research side, but you give up 100% of the teaching/developing people part.
As a tax payer, I am very concerned if the people I fund with my taxes to do a job unilaterally declare they are no longer going to do the half of it.
Teaching and research should be decoupled. Professors are hired and granted tenure primarily based on their ability to produce original research. The skillsets are different; often good researchers are bad teachers, and good teachers are bad researchers.
There is a case to be made that teaching improves the understanding and insight of the teacher which in turn can increase their research ability. For starters, it provides a less boring way of drilling fundamentals. But more importantly, having to answer questions from students which very likely will be coming from odd and unexpected directions, helps the teacher clarify their thinking. It could well be that one of these odd questions, the answer for which the teacher takes for granted, may actually hold some insight or raise questions into what they are working on outside of class.
In a similar vein, it is recommended that if you are in a business meeting you hear what the junior positions have to say about something first and work your way up the chain of command rather than the other way around due to the junior positions being less familiar with internal processes and thus more likely to flag or suggest something completely out of left field that the higher ups might miss.
I tend to agree that teaching can clarify one's ideas, but I don't think the benefits are equal across the board. I think the argument for benefits to research are stronger when it comes to supervising graduate students and teaching seminars. I'm far less convinced that we should have math professors teaching Calc 1 if they're not really passionate about it, and I'm especially not in favor of tying up their salary and performance evaluation with it.
Note, I'm saying all of this as someone outside of academia who is passionate about science and had a very mixed bag of teachers in undergrad.
This used to be the case: research was conducted mostly at academic institutions that did not provide degrees [1]. The "research university" is a relatively new thing
[1] https://asteriskmag.com/issues/10/the-origin-of-the-research...
Interesting read. I always wondered from where did the idea about "thesis" & other "extra-circular" activities come from, for both students and professors.
Nowadays, promotions of professors for different levels (Assistant, Associate, Professor) is solely dependent on number of papers they are publishing in Q1 journals. But the research maybe entirely bogus, same ideas repurposed hundreds of times by different professors.
The entire concept about "systematic knowledge" has gone downhill.
Even more important than the papers is whether you can raise the money required to fund your lab which produces your prestigious journal papers. And the further you go down the league table the less important the "prestigious" part gets.
Absolutely not. You could argue this for entry level lectures, but not at the PhD level. PhD is learning how to do original research, how could you separate teaching that from doing that?
That's not entirely the case in Germany. Applicants need to give a lecture which is public. Usually members of the student union will be present and will have a say later within the hiring committee about the quality of teaching.
But I do agree that the ability to produce and procure research is not at all coupled with the ability to teach.
I would suggest 'more loosely coupled' perhaps, which in many cases they already are. Students may not want a surly, disinterested professor forced upon them because of a teaching requirement, but they won't want to miss out on great teachers forced to choose between teaching and researching. And as expressed by others, teaching and research reinforce each other to some degree. Teaching tests conceptual understanding and research incentivizes maintaining an understanding that tracks the knowledge limits of the field.
But this exists in many educational + research institutions already. Where it runs into problems is in resource constrained environments, where there aren't the budgets to support research-only positions that aren't 'less than' roles or the institution can't support the grant ambitions of highest 'performing' teaching researchers, stuck that they don't go to those institutions and more less research-focused (or at least smaller grant value) teachers populate those schools.
I'm trying not to make any value-judgements here, so please ignore and bias vibes that gives off.
>> Teaching and research should be decoupled.
This is like saying peasants growing vegetables in the field should not mix with philosophers questioning the secrets of the Universe.
Problem is most research is just pissing in the wind. No real results. Show me the cure for cancer. Show me the warp engine.
So it's very nice to sit in their ivory tower doing ivory tower stuff while the peasants feed them with the vegetables they grow plowing the fields.
In reality, let them also teach. That's real, palpable work. I can't do all nice things and never touch shit work, so should professors because unless they cure cancer or invent the warp engine now, they are not a privileged cast.
A lot of academics like teaching and think they benefit from it though. Richard Feynman thought so, and many academics I have met seem to as well. Not all.
I think in some subjects (e.g. literature) the greater prestige of research leads to a lot of pointless research and we need more teaching.
Of course, there are many good researches who are bad teachers. I am not so sure about vice-versa, but, nonetheless good teaching should be rewarded more, as should the ability to communicate knowledge in other ways (e.g. by writing books).
No. You should move students around more imo.
I've worked with good and bad at both. Some of the most difficult problems when you have students who have had excellent teachers and then get dropped into the real world. If they don't learn themselves how to apply what they're learning (the other side of the coin of training) then they're often no better than an llm stuck in a loop, they know the textbook but don't know the gray areas...
Also professors and researchers are required to be able to communicate otherwise they're useless to the field. They need to better.
I'm not saying every lecturer will hold any interest in every lecture course. I've had the ones who are there lecturing core material to avoid the dept losing its accreditation and I've done electives where the professor is off the wall and spends half of the time going on about their research instead of the address material (fun but painful come exam time).
I agree. I studied at one of Europe's top "research universities". The most important courses often had the most famous profs, who also were either bad teachers or put no efford into their teaching.
On the other hand the course I learnt the most from was taught by a passionate lecturer who isn't even a professor, and he gets the highest ratings every year. (Yes we have a "rate my prof" event)
Imagine a future classroom defined by elaborate plays performed by curious parents, all on advanced adjacent learning paths themselves. An intertwined learning structure that just keeps going up. At higher levels, instead of having the researcher with their head in the books communicating, they’ll have a whole team of people translating their knowledge into a production fit for antiquity - directors, diverse range of talents, charismatic performers, etc.
Assuming we have time to do this in some post-having-jobs world, of course.
In this sort of case the “teaching” happening with graduate assistants is teaching how to do research. That’s inextricably part of the job of a research professor, is to teach others how to do the job.
So, I'm no professor, but as an ten year post-doc (unfortunately) I can say that most university groups benefit from both types. Again, the problem fundamentally is funding and the wrong incentives, as it always has been from before I entered grad school till now.
"Teaching" in OPs context probably doesn't mean lecturing, but 1:1 sessions with Junior Researchers from Master's Thesis upwards to PhD Candidates and Postdocs.
No, they’re usually rated on the ability to bring in grant money.
You probably need to step outside of your US-centric bubble if you are to comment on how university works outside of the US. There was a fairly large clue in the parent comment.
"Often good researchers are bad teachers, and good teachers are bad researchers" is a statement about humans, not a specific country, as far as I can tell. Sure, I happened to use the word "tenure" which is generally used in a U.S. context but you should be able to take a charitable reading of what I said and understand the broader point.
To my knowledge the view is correct for places outside the US.
UK universities do currently hire people to do research and teach. And tenure is based on research not teaching. Teaching is seen as something that funds the operation to an extent. Some are excellent teachers. Some merely provide the material.
It works as is because researchers are not meaningfully impacted by having to do a few hours a week. And student get access to people in touch with the field. But it is not optimal having people who often are not good at teaching and/or don't particularly want to do it, taking lectures and tutorials.
As mentioned in another comment, the US-centric view of how university and professorship work is certainly not the case in Germany.
My experience at a research university, albeit 12 years ago, was that many of my professors loathed teaching. Some openly expressed disdain for the time they wasted teaching when they could be researching. I think a better framework in the future would be to have researchers, and lecturers/teaching professors separate. One is to teach, the other is to research.
I disagree. Coming froma PhD background, the researchers that spend all of their time investigating the intricacies of their field are the most qualified to train up the next generation of researchers. This isn't primary and secondary schooling, where the syllabus evolves at a slow pace. To teach how to research, you need people doing the research.
If we split them up, then the teachers will only be able to teach what they have theoretically learned from literature only. What we need is for institutions to reward teaching, reward students who excel and most importantly, reward teachers who produce excellent students.
Disdain for teaching should not be the norm. After all, what are they doing if not teaching when they publish a paper, or give a talk at conferences? Might as well be a hermit scientist then.
.01% lifetime earnings from all student directly to the teacher for life
If this gets implemented I suspect those auditorium style lectures will get a lot more teacher interest.
In the classic division, "teaching" consists in giving undergraduate classes, and "research" consists in the whole spectrum between working all on your own and managing a PhD factory (3+ students a year).
So this article is really not saying anything controversial in the strictly ontological side of things, in fact it's already a relatively common stance to prefer supervising few (or, more rarely, none at all) students.
This researcher is saying "when I consider hiring someone as a workhorse, I might prefer AI instead"; what's the harm in that? Too many PhD students are used as disposable cheap labor, seeing little personal growth in their PhD journey and being generally neglected and abused.
I feel like people undervalue the learning experience of just being a workhorse for a while. It's a lot easier to do, make and correct errors when you start with the simpler stuff under guidance
The authors itself writes:
>I would recruit a graduate student into my lab and allow them to run with the project, providing guidance along the way.
You say to many phd students are used as disposable cheap labor, but what is the amount of people still learning stuff maybe bigger?
Teaching an undergraduate class or even a graduate class is still teaching. The author does not say he won't do that anymore.
The problem is about the fresh talent pipeline for researchers (i.e. PhDs). In many ways, elementary school and a Master's degree are more alike than a Master's and a PhD in the sense that you're learning prior art with clearly defined exam/project assessments and no expectation of making something truly novel in both elementary school and the Master's, while a PhD is all about discovering something nobody uncovered before. So, calling this a problem of not wanting to teach isn't quite right.
IMO, the article is rather highlighting a different problem; the former problem in this area was that only a tiny sliver of the best engineering/CS undergrads wanted go into research given the far more lucrative industry careers, and now the supply part of that market is about to vanish too due to agentic AI. This will basically kill the concept of an academic career as we know it and the point of the article is that we need to find a different model of advancing and funding science.
You've clearly never worked the academic sector. Calm down most researchers are hyper focused on their research productivity because at times 90%+ of their time is consumed by teaching for months at a time. This is an almost universal constant for all decent institutes globally. Taking on extra cheap labor in the form of grad students used to be the only way to do this but every single time this turns into onboarding someone for months to get weeks of work out of them. Great when you can hide it in your other side of your job, but most of the time you can't...
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Students are not only workers, they are also disciples of your work and, once forced to read it, will likely use it in the future even when they leave your lab.
Even completely egoistically replacing students with AI is shooting yourself in the foot in the long term.
I think also at the end of your career in science, you are going to care a lot more about the people you've uplifted and turned into flourishing scientists themselves vs however many papers you've gotten your name under. At least for every professor I've come to be close with in my years in academia that is certainly the case.
It says a lot about US academic culture that they think in terms of hiring. There is an important educational commitment requirememt to the role of professor, at least in Europe. Hiring is to the betterment of your own goals and almost orthogonal to the educational mission. A lot of unethicalities fond their root in this schizophrenic mission statement of doing professional competitive scientific research and at the same time education of graduates.
On the contrary, my experience of US academia has been that (graduate) students are very much students, who take a lot of classes, are graded seriously with the possibility of failing, are mentored rigorously (the author even says "the classic one hour a week meeting", which I also witnessed there), and in fact enroll in a program more than they are hired directly.
I did my PhD in France where we were legally employees like any other and did 100% research with like 100 hours training over the three years which could be 5min MOOCs counting for hours or classes the professors would sign us off on. We were hired by a specific researcher for a specific topic, unlike US students who join a broader program and explore their own directions more. My mentoring was drinking coffee with my advisor and colleagues and the odd e-mail exchange the day before turning in a paper.
I believe Germany and quite a few other European countries are similar. Any country that does 3 years PhDs is bound to cut on the student part of things.
This is a non-US scientist at a non-US university.
Israel is more a member of the US than Wyoming is. Their PM is from philly!
This sounds misguided. In the little experience I had, I've seen that models get basic knowledge so absolutely wrong that giving them any sort of independence will not result in publications that positively impact a professor's reputation, or contribute to science. Or at least the reviews and papers I read that had AI content did not give me the impression that we should have more of this. And they require much more supervision, with the added issue that they cannot learn in the long term through your interactions, and without the enjoyment of teaching something to someone. They're really good at finding papers though. Perhaps because navigating search engines has become a pain. Perhaps this will be the case in the future, but saying you're tempted right now is like saying you're being tempted to replace your HPC with quantum computers. It's a bit early.
Upon reading this:
> The issue is not whether my students are valuable. In the long run, they are invaluable. The issue is that their value emerges slowly, whereas AI delivers immediate returns.
I had the thought that it's more like hiring only autistic/on-the-spectrum employees that will on whims do exactly what their interpretation was, or possibly worse literally what you said without considering further consequences.
Sounds a bit like externalising the learning cost (of AI models) is preferred to investing the time into training the students.
You think? I will get banned from HN if I bring up that these models are fundamentally theft but we just don't put them in jail because they had the foresight to bribe the trump admin like everyone else who wants favor did.
Also 90% of citations generated by AI are wrong or straight up don’t even exist. It’s got such a long way to go to be able to reliably write credible papers.
[Source: https://www.reddit.com/r/AskReddit/comments/o6hlry/statistic... ]
Your source is a 5 year AMA post that it itself claims is made it.
While funny, it does nothing to prove your assertion.
>While funny, it does nothing to prove your assertion.
Unless that citation was generated by AI.
I think you missed the point. Yes it was meant to be humorous, and also to emphasise one of the reasons AI-generated citations are completely untrustworthy, especially with the growing number of AI-generated (junk) papers being published.
No, I had no intention of trying to offer a real source for the accuracy of AI generated citations. It is not hard to Google, search HN or even (ironically) use AI to search, to find numerous relatively recent studies discussing the problem or highlighting specific cases of respected journals/conferences publishing papers with junk citations.
It feels like allowing fake citations in the output from the AI means that you didn't do even the barest minimum of verification (i.e. tell the AI to verify it by sending a new AI to download the pdf that matches that DOI and verifying that it matches what the citation says).
Yeah I tried building such a tool. The problem was two fold:
1) Automated fetching of papers is difficult. API approaches are limited, and often requires per-journal development, scraping approaches are largely blocked, and AI- approaches require web fetch tools which are often blocked and when not, they consume a lot of credits/tokens very quickly.
2) AI generates so many hallucinated citations it’s very hard to know what a given citation was even supposed to be. Sure you can verify one link, but when you start trying to verify and correct 20 to 40 citations, you end up having to deal with hundreds or thousands of citations just to get to a small number of accurate and relevant ones, which rapidly runs you out of credits/tokens on Claude, and API pricing is insane for this use-case. It’s not possible to just verify the link, as “200 Status” isn’t enough to be confident the paper actually exists and actually contains the content the AI was trying to cite. And if it requires human review anyway, then the whole thing is pointless because a human could more quickly search, read and create citations than the AI tool approach (bearing in mind most researchers aren’t starting from scratch - they build up a personal ‘database’ of useful papers relevant to their work, and having an AI search it isn’t optimising any meaningful amount of work; so the focus has to be on discovering new citations).
All in all, AI is a very poor tool for this part of the problem, and the pricing for AI tools and/or APIs is high enough that it’s a barrier to this use case (partly due to tokens, and partly because the web search and web fetch tools are so relatively expensive).
Interesting, tools like Zotero seem to have sorted out the pdf fetching (and metadata + abstract fetching even without institutional access to the pdf). Did you try building the fetching on top of that?
AFAICT Zotero relies on scanning what you browse, not on suggesting citations based on a draft paper. It’s not solving the same problem.
I meant for point 1. Zotero will accept a doi/arxiv link (among other) and download the public metadata (authors, journal, abstract) for you so you don't need to build something for that end. AI cites a paper, copy DOI into Zotero, analyze info Zotero returns.
10 years from now, the people that stopped hiring novices and juniors are going to be deeply regretting their past decisions. The people that kept hiring are going to be working with their newly-promoted-to-senior colleagues and be making significantly more progress than those that didn’t keep hiring.
(IBM figured this out a couple of months ago, and explicitly announced tripling their hiring of juniors/grads in order to avoid ending up with a massive gap in the management/senior layers in future).
Link? Source?
https://news.ycombinator.com/item?id=47009327
> “The companies three to five years from now that are going to be the most successful are those companies that doubled down on entry-level hiring in this environment,” Nickle LaMoreaux, IBM’s chief human resources officer, said this week.
Will they? Just because you developed them that doesn't guarantee they will stay with you. It's been always the same issue tbh, but big companies could accept the risk because they pay the most competitive salaries anyways.
Ten years from now the people making these decisions have moved on to different companies and cashed their quarterly bonuses.
Except they won't. They will just hire those new people away from the firms that trained them. That's what happens now and there's no reason why it won't happen in the future.
This is why firms that do actual training have clauses written in the employment contract that says if you receive x months of training from them then you have to work for them for at least y number of years otherwise if you leave then you have to pay them for the cost of training you (which is written as a dollar amount in the contract).
Companies that don't have that kind of clause in the contract are going to get screwed over when their newly trained employees get poached by other firms.
Yes and no.
I started my career with a graduate program from a larger company. I stuck around in that company for close to 5 years and would have liked to stay longer. My reason for leaving were the absence of a career progression. The first 3 years, the company had a great career progression path. Clear outlines what it needs for a promotion, fair and transparent pay, etc.
That changed and despite hitting/exceeding my goals, I was denied a promotion twice with no good reason. My boss, who is fantastic, told me that he cannot give me a good reason because he himself did not receive one. So I left.
Generally speaking, my cohort of the program was part of the company much longer than most employees. I don't think a single person left in the first 3 years. Attrition only started now that there was a general shift in the companies culture and communication.
It might happen, but there are risks. The obvious one is that the existing employers will make an effort to keep the best (promotions and pay rises) so people hiring away from them will get the people they do not need to keep.
Those sorts of clauses are not legal everywhere. They would certainly be at least heavily restricted in the UK (on the other hand there are subsidies for some employer training and education here - which is why my daughter has an engineering degree without paying any fees). The author of the article is in Israel, and as an academic is in a different position to people in businesses.
That dynamic is nothing new. Years of experience to become a senior engineer is not “training” and not covered by what you’re describing.
The shortage of senior engineers will be even worse than it is today.
Not sure your argument really holds any water over a 10+ year period as I originally described.
It honestly seems a little control freakish to think this way. People leave companies and that’s a good thing, they explore the industry and generally become more capable. If you leave on good terms there’s nothing holding back a renewed relationship, now with the added benefit of new perspectives; maybe meeting at conferences or working on a project. My gut is telling me these companies don’t part on good terms with their employees.
> The issue is not whether my students are valuable. In the long run, they are invaluable. The issue is that their value emerges slowly, whereas AI delivers immediate returns.
This is like the classic "I'll do it myself because its quicker".
In the current environment and likely more so into the future, those that hire and develop graduates skills are going to be looked at more favorably. Furthermore, the people you work with and coach become peers and typically help build a network of people who can bat for you.
It's tricky and its a balancing act and I appreciate that AI is becoming the easier quicker less fuss option
Companies stopped training people many decades ago, they expect you to arrive on the job trained now.
i.e. they shifted the cost of training from the employer to the employee.
What makes you think that will suddenly reverse course, or that society will suddenly start to care?
People want the cheapest, fastest shit possible. Companies too, generally.
That’s just not really not true from my professional experience or my industry in cyber security. There is of course a level of experience required of a junior but it’s still junior level experience.
In my line of work I was coaching and now I am senior I am expected to delegate tasks and coach, not to increase my own workload for doing simpler tasks myself.
People are you. You can make the change.
To beat to death a well-known quote:
You may be able to go fast with AI, but you can only go far with humans.
First time I'm hearing this quote and I like it a lot
We are definitely seeing a lot of anti-human behavior around AI adoption, because all anyone seems to care about is going fast
Where is the quote from? A web search revealed only your comment
It is a play on that quote, isn't it?
> If you want to go fast, go alone, if you want to go far, go together.
AI hits the sweet spot for the 'bring me a rock' management style.
>In the process, they may bypass the valuable experience of struggling through early tasks and learning from their mistakes. Students, I worry, could simply become an intermediary between the raw idea and the AI’s output.
Even if all AI progress grinds to a permanent halt today, there's already enough utility in its current capability to force these questions. As a result, how we train and educate graduates and young people needs to change.
I have no doubt you need to have actual experience to be able to ensure AI output is at a production standard but if we accept that reality, then a shift in how we educate and train young people could make an enormous difference in ensuring employers still see value in hiring people with no real commercial work experience.
I wonder if this could be solved by student worker unions?
That would "solve" the problem in the sense that it would make students even more uncompetitive with AI.
It's interesting in the ways AI mania and psychosis manifest
This is something that will have to be solved through the way research is funded.
At least for publicly funded work, it was always an assumption that you would need students to hit some goal; so by funding it you would get both the outcome, and more people skilled in that field. If the scope of what one team/senior can handle has grown with ai, we will either need explicit staff numbers as a requirement or bigger scope to the point where the ai can't handle it.
Or we find that AI can do so much the whole system implodes...
And here we see you’ve hit upon Jevon’s paradox. The scope of work will grow to use more than it did before, now that human labour achieves more for the same money. Employment will ultimately go up not down (over the long term - we are seeing a lot of short term instability and noise, although there’s much said about AI without it yet showing up in the data, as per articles recently shared on HN about employment figures across the US and the world).
Even the author eventually admits the problem is fundamentally academic funding and the stupid publishing culture we have. I wonder if the author had the courage to actually expound upon that as opposed to leaving it as a triffling mention at the end if it would have been published as a cute little op-ed in science in the first place.
Why not hire a graduate and empower them to use AI? Much better interfacing with an actual human who will then go and do the work using all AI tools at their disposal.
> Why not hire a graduate and empower them to use AI?
A lot of my work with AI involves questions where I have an intuitive direction and sense of the data or model, but where explaining why takes almost as much work as doing it. (Commonalities: weird interdisciplinary nexuses and idiosyncratic data sources.) Adding a human translator, much less someone without field experience, seems worse than giving the task to a human or AI wholesale.
Where humans still reign supreme is in interacting with other humans. Paradoxically, this might make grad students’ roles attending staff meetings as their professors’ proxies and/or filling out paperwork.
Exactly! So many commentors are wrongly framing this as an either/or choice, while companies can choose to have both.
It's interesting that this dilemma (of getting quick and easy wins) is occurring at multiple levels. Even as a junior researcher, its often tempting to hand off actuall thinking and reasoning about one's research to AI (e.g. blindly accepting AI code) to quickly make 'progress'.
Apparently the same question is being asked at different levels and abstractions...
Just like DEI, sustainability efforts, I predict we will see new initiatives for forced hiring of Juniors.
Implementation can differ (e.g. ratio of interns vs total headcount and so on), but it is the time for governments to intervene and force corporations to train people, humans are resource for the government, they need to polish that resource to thrive.
> Just like DEI, sustainability efforts, I predict we will see new initiatives for forced hiring of Juniors.
The professor's jobs are to TEACH students.
Research grants are given by governments mainly to first TEACH students and secondly to get something useful.
If they are not doing their job they should be fired.
That's not DEI or anything of the sort. That's common sense.
They can do their research at private companies if it's worth it.
> Research grants are given by governments mainly to first TEACH students
Government's goal is obvious and correct, but if you have done a research and tried to get a grant you should know grants are very "political" as well, if you are researching a thing which is not trendy or takes another 10 years to yield results, but there is another lab who is telling we are researching LLM, it will be very difficult to get a grant even if you promise to TEACH/hire 20 students for that research.
Justifying long term benefits is difficult problem
The difficulty the researchers who developed RNA vaccines faced in getting funding is a good example of how bad the system is. Safe and unambitious is preferred,
https://www.uclsciencemagazine.com/sss8/
As someone who both recoils at DEI and is at least a decade too old to benefit from a policy like this personally, I have to say this honestly sounds like a great idea.
Both avoids the tragedy of the commons (why would a corporation pay to train a junior when they can just let their competition do it then poach the experienced senior) and gives more opportunity to a new generation that are frankly getting economically screwed over enough as-is.
Humans are a resource for the government? Jesus man, slavery has been abolished in the West.
i mean they could train them for free in universities. just pay them to go to majors that actually matter for the economy
Yeah, could be, some problems I see with this implementation:
1. the wait time is too long for the company to fill a position, it is difficult to predict what happens in the next 4 years
2. difficult to match the students with companies. For example, you are interested in CS, but company wants specifically React developer (assuming there was no AI and there was still demand), would the student change all their courses based on the requirements and live like a robot who is forced to take courses they are not much interested in. Now imagine when gap is higher between topics (CS vs React is closer, compared to MBA vs procurement, both are somewhat subset of same topic)
The point is that we will still need senior level employees, but the way fresh grads get to that level is generally through entry level positions, experience and mentorship. I don't think we can expect the university system to start pumping out senior level graduates.
We could but it would be something more like the medical system where education lasts much longer, and expected wages at the end are much higher.
i dont mean to just enroll them in a undergrad. make them work in gov projects
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> I’m not sure where that will leave students who start with no research experience.
What is wrong with this guy? Of course he knows where that will leave those students. Why did he even choose to be in the business of developing people? Nobody forced him. Anyway, the ladders were pulled up in 2020–2021.
Research is a very abstract field, not everyone with an interest also has an interest in teaching for it's own point.
The motivation to take on juniors to grow your long term capabilities equation is shifting, to the point where its harder to justify.
Reading the piece I _hope_ they are trying to make a point and not really thinking they are not going to help novices become juniors. But who knows, nowadays...
“I'm not sure” is academic jargon for “I don't give a damn”.
It's one thing to be forced to use the damn things, but this guy gives it very serious thought, much more than others I've seen and known, he even writes a science.org article about it, and ultimately chose wrong.
This is morally wrong and it should be embarrassing to publish such an article
OTOH, yes.
However, the person is also sufficiently self-aware to share their thought process (and candid about its shortsightedness).
To me, that made it worth reading (even though it has a sad message).
We measure scientific output as nr of publications. And that is the cause of bs like this.
These institutions have a duty to educate humanity. PhDs are also supposed to be able to help the public understand complicated science. To guide ethical decisions.
But no, we measure the number papers, and not even their quality (very well).
It's all a matter of incentive alignment, what gets measured gets done. The state of academic science is sad in most places. This contemplation by OP being case and point.
In other words "why I prefer machines over humans and how I've replaced my moral compass with productivity metrics".
To drive your car, to build your software, to run the government. But not to change the oil in your car. Ya ok.
I am an academic at a big Australian university. I can't disagree more with this article. Students are not just machines that convert ideas to papers (and nor should faculty), they are one of the true joys of academia in the first place.
Unfortunately, as other comments here have pointed out, the incentive structure for academia right now is misaligned, leading some faculty to focus just on publish or perish. This is a huge shame on so many counts.
My hope and also slight expectation from the AI era is that academic writing becomes so commodified that we see a total devaluing of papers as a metric, even in prestige venues. Perhaps this would help the community find better things to focus on.
On the other hand, if it can detract PhD factory foremen from hiring students for the sake of publishing, and let them focus on advising fewer students better, I can't say I see the harm in that.
Reading this article makes me happy for the graduates that are spared from working for an absolute imbecile.
Either he is talking about ML in general, and it has been used for one decade now and it's a fucking joke, or he is talking about LLM and it's a fucking joke
The title is not relevant to the article, not even for a single line. The author straightup assumes, does not answer the 'why', cause I was here to give Lady Lovelace argument to Turing, that you would NEVER (hire an ai instead of a student) unless you making directionless slop. You can share goals, but not the vision, and mission is different. Ai learns from experience, humans are needed to build that experience due to their extremely large 'context windows' going as deep as the constant evolution of the DNA(as long as it serves human-centric goals, which circles back to the mission part).
The article really is about "education seems directionless without economic goals", and again as comments have pointed out, it only seems so.
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Rage bait. AI is incompent compared to the average freshman. If you are struggling to train a grad student the problem is you.
Those who can, research. Those who can't research, teach. Those who can't teach, make inflammatory statements on the press to gain the spotlight
This is morally wrong and it should be embarrassing to publish such an article.
It doesn’t surprise me to see such articles coming from academia, in which juniors are treated like dirt to such an extreme that is unimaginable in any other industry, save for maybe Michelin star cuisine.