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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.
For the past few years I've grown increasingly concerned about AI replacing entry level employees and destroying the pipeline, but this year I've noticed that the interns have finally learned how to properly utilize AI such that they're a genuine value-add again. They seem to be getting better at using AI faster than the AI itself is improving as a practical tool (or at least faster than me, which is the same thing). I'm not comparing them to AI's theoretical capabilities; I'm comparing them to my AI capabilities and they've gotten fast enough (with AI assistance) that the balance has shifted back to where asking an intern for a research memo is more effective than asking and massaging the AI myself. I'm still concerned, but the kids are giving me hope.
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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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Maybe we finally have a use case for keeping kids locked up in school for 12-15 years. Instead of having them do pointless busywork, we can use that time to get them to a mid/senior level, so when they get to using AI,it will be a bit more than mindlessly pushing a button.
I half-agree. We should teach school students more real-world skills (finance, socialization, avoiding deception) and less general knowledge, and give college students (at least those planing for industry in their field) more internship experience and less theory they're unlikely to use.
But I think entirely eliminating math, science, English (or whatever native language), history, and psychology will cause problems. Without math and science, people won't understand necessary concepts like finance and preventing disease. Without English, people will communicate even worse than now. Without history and psychology, people will vote and understand each other worse than now. I'm sure there are more subjects, including some not commonly taught today, that almost everyone should know the basics of.
We should even be "teaching" work ethic, otherwise graduates will be too lazy to accomplish anything, but I'm sure there's a better way than "pointless busywork" in the subjects mentioned above. Maybe sufficient work ethic can be achieved entirely through physical education; though I don't recommend boot camp levels of exercise, and schools/colleges should accommodate naturally weak/unathletic students, students should definitely have more than the ~1 hour "gym class" in my school where we sometimes didn't even exercise, and compulsory fitness should continue through college.
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Why should they be worse at this? There's parts of the process that AI optimizes, and people don't need to be good at those things anymore. That just makes the remaining human input more valuable. Word processing and computer graphics also increased productivity. I bet most grad students couldn't chart a plot by hand these days.
Can't you just ask the AI? If there's no practical applications, well, I'm told that that goes for a lot of human research today, so seems like a win if we can do that with fewer people.
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Question, are you using Opus 4.8 Max ?
People's experience with claude is heavily dependent on which model you use.
100%. I see this exact pattern in coding as well. Senior engineers are empowered, junior engineers create more slop and headaches.
Pyramid schemes, by definition are immune to touching grass. They are fraudulent from day 1. It's like saying "now that we found a fully intact fossil of a dinosaur, support for creationism will collapse". If the pyramid scheme of Academia was to collapse, it would have happened by now.
Every layer of abstraction takes you away from the raw experience of the science itself. It's like playing football by mashing buttons in Fifa.
I worry about a 'medium is the message' style collapse of science. Scientists will only investigate topics that feel amenable to ai-agents. In such a world, computation gets reduced to combinatorics. I hate it. In a real numbers shaped world, AI would never intent complex numbers. In a classical physics world, AI would never suggest a relativistic model. These are not combinatoric outcomes. They're ...... 'romantic'. Ai-agents are another step in what's a century long erosion of romance from science. (statistics -> RCTs -> computers -> internet -> ai).
I use opus exclusively at work. I can't honestly say I've noticed any meaningful difference between 4.6-4.8. 4.6 was the last one I used where I was like "Yeah this is just better".
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It's all so tiresome. On top of that, opus 4.8 is seriously reviled by half of the llm addict community.
Isn't this mostly the half that use Opus for ERP?
Seen some serious researchers who are not a fan of 4.8
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If AI becomes "Real" AI then...good?
Automated high quality research and scientific improvement is in all likelihood going to be a great thing for mankind as long as alignment is dealt with.
If AI remains like shitty LLMs.....don't worry?
The biggest use case in medicine for LLMs is for ambient note generation. It sucks for this. Far below the ability of a human scribe or an actual physician, however people like it because it's fast and many choose not to spend the time proofreading. But if you do then it doesn't really save much time (read: value).
The same thing occurs for quite a few of the uses case. It doesn't work or it requires so much verification that it isn't really that helpful.
The economy is often more or less run by tech and finance, and it does seem actually useful in those disciplines, that doesn't mean it really is going to have much use outside those when stress tested without improvements (and if it improves who cares).
People will realize how to reallocate Human Resources or get mad at the problems with the technology.
It's not specific to AI, but technological solutions that solve problems cheaply and poorly have a tendency to price the existing solutions that solve problems well out of the market. A non-AI example that people complain a lot about is self-checkout machines (although opinions are mixed on whether they're actually worse). A common AI complaint is that AI-based transcription and translation software is not great, but because it exists, no one will pay humans to do it well anymore, instead assigning humans the job of "fixing" the output, which is not significantly less work (and sometimes more work) than just doing it from scratch but paid significantly less.
AI getting pushed heavily everywhere means this quality degradation is happening in a lot of domains. Failure to train the next generation of competent people just amplifies this effect down the road.
The academy as it exists now is a very modern and new invention with some good parts and bad parts. Either the system will correct itself or we'll have a new phase of the academy.
People in the middle are going to be fucked but that isn't new.
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I wrote this story in 2022. I did not write it about LLMs. It's a problem that dates back to the invention of calculators, or arguably to the development of writing.
I think it's more relevant today than four years ago.
We have solutions, of a kind. Programmers pick up (and build) esolangs, hardware hackers hunt for antiques that may never have been documented, sysadmins can learn to install archlinux without archinstall, woodworkers try random crap with random scrap, mechanics do custom work, so on. Math Olympiads and spelling bees can bypass the development of calculators and always-on spell-check. But these are solutions to the old versions of the problem.
In the real FOOM case, we don't care. It's the Minds' universe, or CelestAI, and either we live with it or we don't and it doesn't really matter.
If the extant development is bounded, or even unbounded in many ability but constrained in others, it gets significant more bad.
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