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Maybe old news to this forum, but I stumbled upon two articles. The first is a Harvard professor using Claude to write a paper that second year graduate student could, and the second is a Less Wrong post about how Go players have disempowered themselves to AI.
I bit the AI bullet finally a couple weeks ago: first using it to help me fix up my software package that had some pretty annoying bugs, and later to help me make a couple Anki add-ons I had sitting in my brain. Even the free version of Claude is very good, and although it made mistakes, at least for coding, it's as least as smart as me and works 100s of times faster and doesn't need to take breaks. With some back and forth I can get it to make something functional it a tenth of the time it would take me to do it by myself. This is almost exactly the experience the Harvard Physics Professor has with trying to get Claude to write the theoretical physics paper: it takes about two weeks of back and forth, but eventually Claude manages to produce what the author and other experts think is a pretty solid paper, in 20x the time as a graduate student.
This is all great for experienced faculty: at least in theoretical disciplines they can already greatly speed-up the research process without spending more or time on pesky grad students with the bonus that Claude doesn't mind if you're mean to it. For younger faculty, postdocs, and graduates students not so much. Not only do these students/researchers not have the experience or knowledge to critique Claude in the way to actually produce usable research, the fact that more experienced faculty can use AI in this way means that there's less demand for educated individuals in general, and slowly but probably surely, the pyramid scheme of Academia will collapse.
Which brings me to the second article. In contrast to chess, which has been a "solved" game for a long time and thus has had time to develop antibodies against heavy AI/computer use, the Go world has been taken completely by storm by AlphaGo, which has led to rampant cheating with AI and rapid deskilling of the player-base. The author of the article highlights that this was a choice made by the community, both not to punish cheaters, and to care more about abstract values like ranking and points rather than having fun or development of some kind of genuine skill.
How does this relate to the first article? I worry that this Harvard professor is missing the large scale implications of wide-spread adaption of his AI practices on the university system, and how on emphasis on one aspect of the system (solving problems efficiently) can destroy the whole thing if not left unchecked. Many people both inside and outside academia take the purpose of the academy to be the generation of new knowledge, mainly in the form of research papers. This is certainly its most important role, at least to society at large. But academia also needs to perform two other key functions: disseminate that knowledge to industry/the general public so it can actually be used by society, and to reproduce itself so knowledge can continue to be generated. AI puts both of these secondary functions in grave danger.
Consider two scenarios. In the first, AI agents get no better than they are now at solving scientific problems. In this case they are still useful to higher level faculty in producing novel research output. Due to enhanced productivity at the top, there is a lesser need for graduate students, postdocs and younger faculty, and the ones that do remain in the system receive inferior training because of heavy reliance on AI to pass coursework and generate their own novel research questions. Over time as traditionally trained professors retire the effectiveness of the system declines because the new professors aren't as competent and are thus unable to use AI as effectively. In the long-term output shrinks, and less knowledge is able to be translated to the general public.
In the second scenario AI continues to improve its capabilities. In this scenario research output continues to go up indefinitely, but we begin to lose the ability to comprehend what much of it means or how much of it can be applied. Academia basically stops needing to exist at all, and we are reliant on the proper alignment of AI to get anything of value out of the research it performs.
In both scenarios, academia has basically signed its own death wish. For the reward of extremely high-productivity for very AI-savvy professors over the next few years, the system that brought us so many world-altering discoveries will basically be dismantled. Like the Go players who have seen all enjoyment and skill taken out of the game by heavy AI use, I think we are going to see the same in academia, except at the very top. And if you're not effectively training students to take your place, the system can't last very much longer.
Luckily being in biology I still have quite a bit of time left before AI can automate away my hands, but this whole thing has made me very personally scared.
It's the same problem in industry where companies are replacing junior developers with AI. I do think we should keep training young interns and academics even if they're not immediately useful, for the same reason we still have schools.
Fortunately, I think it's easier to align the incentives for academia than industry, and I doubt PhD or postdoc positions will completely go away at least because of this. Although there's a stereotype of the professor who's a genius and talented researcher but hates mentoring and teaching, plenty of genius talented researchers love to teach, and would gladly accept more teaching responsibilities for less research output. "Publish or perish" is the bigger obstacle, but I don't see why citations must be weighed stronger than student outcomes (if anything, the latter translate more directly into profit, because a student who greatly succeeds may donate back), and my understanding is that most tenured professors are already judged by how many PhD students they graduate. If AI makes it so most academics can't meaningfully contribute until late postdoc, I'm confident there will still be plenty of professors willing to mentor them, and if universities can keep funding pure math and social sciences, I'm confident they can keep funding this mentorship.
Disagree. Teaching currently sucks pretty hard because of culture. Students come in with a mindset that they deserve whatever degree they're going for, and it's your job to get them there. The university admin doesn't care about standards, they just want you to pass as many students as possible bc it's pure $ for them. Politics is still dominated by boomers who believe that more years of education automatically means people get smarter. As a teacher, absolutely every incentive pushes you towards just passing everyone while barely even bothering to go through the motions. Which makes the students become even more cynical & entitled: If even plenty of the teachers clearly don't care, why should they?
Likewise, it's pretty hard to miss this development, so the opinion of teaching among faculty goes down. Less people WANT to do teaching, so the only ones doing it are those who get it pushed unto them, which further erodes the quality of the teaching.
It's a pretty impossible doom-loop consuming the universities. The only exception are electives, which are organized by genuine researchers for the purpose of selection, which means that they actually have an incentive to care about standards.
I agree with everything except the doom loop. Teaching right now is seriously flawed, it degraded before but AI is accelerating it.
However:
Even basic general knowledge, some students are intrinsically motivated to learn (it's not basic to them until they learn it), and many teachers like to teach students who are intrinsically motivated regardless of the subject.
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