Comment by camelmel
18 hours ago
I have some sympathy for these kids. If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well; many of them can no longer brainstorm, code, think deeply, or write without an LLM present doing 90% of the work. Many of them can no longer sit quietly for even 30 minutes just thinking on their own, which is a required skill for producing original thought.
For adults the cognitive decline won't be as measurable since there's no exams, and overall output volume will still be fine due to LLM help. But I do believe it's already happening absolutely everywhere around us. Honestly, I wanted to be in denial about it before but it's too obvious to ignore now.
I’m not noticing the decline in my own abilities any more than I had before using them. I finished undergrad 20 years ago and my once sharp math skills had been severely diminished within only 5-10 years. Just simple arithmetic and percentages that I could rapidly do in my head became dependent on calculators/spreadsheets. For all other trivia type knowledge, my brain has offloaded it to the internet RAM in my pocket. It’s a familiar feeling of when some question comes up and I think “oh, I used to know that, let me look it up”. Maybe I just already hit my personal floor of stupidity before LLMs.
However, I personally feel a huge mental burden of the state of communication. The contemporary version of it where I have a million threads and conversations im juggling at any given time. Emails, voicemail, chat, online, texts, personal, business, home, children, other family, friends, then there’s the variants like Messages, Messenger, WhatsApp, etc. And as overwhelming as it is for me, I’m super under connected than everyone else I know. I quit following most news and all sports, as I just don’t have the bandwidth for it.
My brain was molded preinternet and I feel like it’s reaching its max on the analog to digital conversion. Or at least it’s just a really lossy process.
Yeah, I'm 45 and I'm like you - no social media, relatively under connected, and still feel swamped constantly by emails and calls and especially texts. They eat up half my productive time every day, and most of them are things I'm looped in on that I don't even need to respond to.
Okay so let's say that's the new cognitive burden. The new escape hatch is "AI". Now you don't need to read your mail or write responses! Let an LLM handle that for you! And now your friends and coworkers will send you AI generated mail anyway, so if you're actually taking the time to read and respond to it yourself you're a chump, right?
Noise machines. Humans are noise machines. Ever try to sleep till noon and notice that everyone else seems like they can't feel alive unless they wake up and make the maximum amount of noise and racket possible? What could be better for a gibbering species of ground dwelling apes than a miraculous machine that gibbers for them, to point back and forth at each other?
> And now your friends and coworkers will send you AI generated mail anyway
This hits close. I realized one of my friends was using AI to message me and I took it kind of hard. It's weird to be worth the effort for them to set up a chat bot to talk to me but not worth the 2-3mins a week to actually read/respond to my messages.
Right now, I just basically ghosted him, but I have teh feeling this is the start of an emerging issue.
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I think some people are okay with communication that’s less involved. Like meme-y BSing where everyone involved knows everyone else is putting like 12% of their thinking power into sending a response.
I don’t really enjoy that, so I find having that many threads stressful and annoying.
I just take a hard line and will unilaterally downgrade communications (while politely letting the other party know). I have all my family group chats muted because my mom uses “Send” the way you’d use Enter on a desktop. End of a sentence? Send text. Next bullet point in a list? Send text.
I muted the chats and told her that I want my ringer on in case there’s an emergency, but I got 30 something notifications in 5 minutes during an interview and it’s unfair to the candidate or other people in the meeting. Internally I rationalize it as revoking someone’s ability to make noises on my phone at whim. They can still text me, they just can’t interrupt me anymore.
It helps a lot, even if only temporary. I’ve muted people for a few hours or a couple days before when I’m already stressed and they’re really chatty.
We have to normalize being on silent all the time and making people wait hours for a response. Return to the primordial monkey of 1800s-era high-latency comms.
At first, some people will be offended. "Why didn't you let me ping and buzz you and interrupt you all day? You didn't respond immediately each time :'((". Some people with unrealistic expectations may even stop talking to you entirely.
But eventually (years maybe) they will get overwhelmed too. No one can handle this madness indefinitely. I've seen giga-texters get broken down and turn into lazy texters like me, or at least learn to tolerate my long response intervals and recognize it as a coping mechanism rather than rudeness.
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As an aside to this I mute ALL notifications on my phone. I still get notifications of course, but they never ping or vibrate.
For important threads like calls or messages from important people/group chats, I have my watch vibrate.
Otherwise, I just go through my notifications once I have downtime.
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Agree. I mute every group chat and notifications for almost everything. Same reasoning. My wife just talks to me when something reaches a point of me needing to know. Broader holiday planning or group travel planning chatter, it seems like any family gathering requires a minimum of 1000 messages.
I'm noticing some decline of skills I don't practice regularly and LLM is just one of reasons why one stops practicing. Switching to another area of work gives a comparable decline. If you want sharp skills you have to use them.
True. People don't do it though, because keeping skills sharp and using them takes effort, and we have a predisposition to be as efficient as possible with how we spend our effort; if there's an easier way to do it in our awareness, we will naturally gravitate towards that. LLMs are often a universal crutch or swiss-army-knife that significantly take away workload for many abstract tasks, so all kinds of atrophy in abstract thinking is to be expected.
However, when looking at muscle, once you have it you don't need to use it as much in order to maintain it. I wonder if the same is true for skills; in that case, some kind of regiment where you still use the skill you delegate once a week or so could maybe help with avoiding this loss of skill for most part.
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Most/all of my university-level math knowledge is gone, atrophied from never having needed to use any of it professionally. I don't even really recall needing it for any of my CS coursework, honestly. It was just required for the degree.
I used linear algebra to implement PageRank in my Information Retrieval class. I also used it extensively in my AI and ML classes. You can't pass a ML class without a good foundation in linear algebra. Not to mention, discrete mathematics is the fundamental building blocks of CS. Surely you were using algorithms and graphs. I hope you computed an algorithm's efficiency with big O notation. I hope you have used probability before.
I don't think it's just you or your age, per your pre-internet comment. People that grew up in this just don't understand why they're overwhelmed. And I don't think they're even aware of what their missing out on in terms of focus or mental acuity.
Good point. I do have context and self awareness that it all seems unhealthy. Feels like a common sense evaluation to me but I can’t properly place myself in a younger generations experience.
I’m not noticing the decline in my own abilities
…said every drunk person ever.
That you don't notice it doesn't mean it isn't happening. By the time you notice it, it's too late.
That's why elderly people who are worried about their brains play chess and do puzzles like mad.
I too was and wanted to only blame communication overload. Especially with work the hardest thing in ai times seems to be the overload of stuff/shit to read that is too easy to write.
The reality is I agree with the op and I see the loss of reasoning power in myself. I've been using native Emacs on android for a bit and finally have gotten serious about config for it. I got lazy and had Claude do some of it. Which was great untill things don't work because there's not going to be my crazy ask in the data. It was painful for me to sit down and think through my configuration and the problem but I did it.
I am absolutely torn on the technology still two years after adopting it.
There is a massive difference between remembering how to do something and learning how to do it.
Congrats your getting older. Welcome to the club. Find hobbies and keep them, it doesn’t matter what they are it’s important as we age.
It’s a really lossy process. Mostly due to most humans and all models treating sign meetings as determined at the moment of softmax crystallization. Signs (words included) are no more determined than the speed of light is. It’s all reflexive and we should stop lying to ourselves it can be determined.
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It's a really underrated problem. I don't think my actual cognitive skills have declined by using AI, but I do notice that my patience and attention span are a lot lower.
I'm learning a new code base for a new job right now, and I'm finding AI to be a really double edged sword for it. One one hand, it's extremely valuable for asking questions about the code base. On the other hand, if I'm not careful and I just let it apply the fix before I even investigate it, I'm really not learning the code base well at all. I find I need to actually write new code in a code base to exercise the necessary mental muscles to actually retain understanding.
Incidentally, I do find that this large new code base I'm learning also shows the limitations of AI. There's no way I can vibe features on this without understanding and not introduce a lot of issues. Even targeted bug fixes have a lot of unintended consequences the LLM doesn't see. This isn't a bad code base at all, but it's definitely at the size where even frontier models struggle. So to me that tells me that the argument that I should just use more AI to solve my AI issues and not bother to understand the code base isn't viable at the moment.
> I don't think my actual cognitive skills have declined by using AI
I'm not speaking about you but... I know most people would not have much awareness of their cognitive decline. I know this because that awareness gap is there with or without LLMs, across all age groups and cultures.
Especially since attention -- which the parent commenter says has been diminished by LLMs -- is a key part of cognition.
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True, I guess I try to have some objective measures like my chess elo and maybe some canaries like what books I'm reading. But it would be really hard to tell.
I used Google Translate to not learn French in collage. Fortunately for me it was bad enough I had to carefully review all its outputs, but that still didn't help and I managed to pass two semesters without ever developing even basic language skills.
Something radical needs to be done. When I was in high school there were still a lot of "no calculator" restrictions in my math classes that I chaffed at because I hated doing longform arithmetic and felt like it got in the way of learning. So I can certainly understand how students would chafe at some kind of paper-only education system but I also don't see how you can learn anything when you have a high-quality homework machine just sitting there.
I wish that would have worked for me - we had oral tests. 2 years of French in high school and one semester at college - what an absolute waste of time. How much French do I know now? Basically none. The same goes for everyone in my life that did Spanish instead in high school.
Part of what we could do during this upset is re-prioritize.
All that's needed is a tight feedback loop between learning and applying those skills ... the thing that Google Translate helped you evade. AI can be a tool for evading or optimizing that loop, like a knife can cut your sandwich or your throat. Your choice.
It's the "your choice" that's the problem. The quality of a society is dominated by the choices that other people make.
Why do you think you didn't do similar when you were a student? We've seen this exact issue with other technology before: calculators for math; typewriters and then word processors for writing assignments; audio books for literacy. In those cases we've collectively realized there's a benefit in getting the manual skill and understanding how to use it before shortcutting things with the technology, even if most of the time you'll just end up using the technology. My best guess is that for most people in those classes who failed because of ai use, they don't care about the understanding (usually) required to get a good grade, they just care about the grade itself and the doors that opens for them.
It's because modern AI promises to relieve you of the tedium, leaving you to consider the important things like higher structure. It actually does deliver on this, but in contrast to older tools, it is unlimited in scope.
A calculator - let's expand this to maps, thesauruses, dictionaries, and other lookup tools - was used for a pretty narrow set of problems, and you had to transcribe the result to whatever context you needed.
An AI can be all the calculators together, and transport the output of one to the input of the next. You're meant to have the overview, but it's just so enticing to let it simply do that as well.
> If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
I agree - I would have been toast. I wonder if the teachers/colleges need to change the way they teach and assess. Let the students use the AI tools they like (perhaps guide them how they can use them professionally), but test regularly and early on the skills/knowledge they're meant to be gaining offline and in person. Oh and don't give Fs for cheating - suspend them.
I read a few years ago about a teacher (I think highschool) who put his lectures on YouTube for students to view in their own time and then used the in class hours for interaction, questions, tests.
EDIT: Claude beat my Googling: This was 2 chemistry high school teachers in 2007 - The Flipped Classroom https://fltmag.com/the-flipped-classroom/
Absolutely university has to change. But it's not a simple change. I say this as a professor for Physics:
My colleagues say "We must fully embrace AI as a tool". I agree. But how do you teach it? It's a moving target, and you can't even give homework like: "Research <this topic> with an LLM of your choice, and submit the transcript" because they can do that, or they can just copy the task into an LLM and have the LLM do it. It becomes meta quite quickly.
And independent what and how we teach, we have to change how we assess a students learning result:
The first thing we have to change is that homework needs to be completely ungraded. Reviewed and corrected, yes, but not part of the grade. That's the only way to make sure that people who don't want to cheat have to cheat anyway to compete with those that do.
Second, all exams have to be in person. Online, cheating is so trivial it's not even funny (many students are so stupid about it that we have a pretty clear idea what's going on). In person, we have maybe 2-3 years until we have to make sure its proctored and people's glasses are checked. I think in less than 10 years, local mobile AI will be good enough so even a Faraday cage will not help.
Maybe we have to go to oral tests only.
Of course, none of this scales. Some of our intro courses have a thousand students.
Any ideas are much appreciated.
> Maybe we have to go to oral tests only.
"Perfect homework, blank stares: Why colleges are turning to oral exams to combat AI" https://apnews.com/article/college-oral-exam-ai-chatgpt-7795...
I feel like German university was already well "LLM proof". Come to class or not, no ones keeping track and it doesn't matter for grade. There are regular exercises, but they are not graded; you can submit them if you want it corrected to check your understanding, but there is also gonna be a short presentation going through it in class. Your entire grade is a 2-3h written exam at the end, no materials, no remote, no books, no multiple choice.
>Of course, none of this scales. Some of our intro courses have a thousand students.
Any ideas are much appreciated.
Oral exams graded by LLMs? Scale with the improving models. Based on GPQA Diamond results they're mostly at PhD level for subject trivia anyway.
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>(perhaps guide them how they can use them professionally)
If that's anything like how they guided me to use programming languages professionally...
In my workplace I find systems and policies move too slowly to keep up with how rapidly the LLM world is changing. Colleges are even more glacial. They've barely adapted to video conferencing.
How do they keep up? Honestly it’s too risky to make a serious move on such a fast-paced environment.
The only thing I can see them doing is removing technology altogether. People did just fine 100 years ago.
Want to learn to code? Use a Commodore 64. The company was purchased and rebooted the C64: https://commodore.net/
Traditionally, moving slow with policies was fine with new tech because, outside of the PC revolution it wasn't all that impactful, and things used to rightly be labeled as experimental so you could safely ignore it for a while as a big enterprise and be just fine until thinks shook out.
LLMs were, IMO, pushed out too early and without that clear "this is experimental tech" label. Full public access from day 1, no invite only betas, no research previews for a select few pilot customers/orgs, etc. I've been in IT for a little over 18 years now and I haven't seen anything move this fast before.
I mean, I never though I'd see Microsoft go on stage at BUILD and and announce freaking OpenClaw for Enterprise, and then make it available the same day. This is highly unstable tech and what I'd consider still experimental, being sold to F500s as production ready.
> In my workplace I find systems and policies move too slowly to keep up with how rapidly the LLM world is changing. Colleges are even more glacial.
Perhaps this is rather a sign that you currently shouldn't jump on the LLM hype train, but rather attempt to get a good foundation on the basics. When the whole LLM area becomes much more "stabilized" (I see signs that this is currently happening, if only for the reason that training state of the art models has become more and more expensive), you can still get into LLMs if you want.
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Yes, meanwhile, Claude Cowork was only released this past January. And that was amazing. But I don't know about anyone else but I've already moved on to just using Codex for just about everything (except some Kagi use). Schools work on timescales of years, AI is advancing on the timescale of weeks and months.
Until that situation stabilizes I think the only institution capable of teaching about it is the family -- parents.
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Ancient brains, medieval institutions, godlike technology.
Tristan Harris had some sort of comment like that on a podcast about the challenges posed by AI.
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> I read a few years ago about a teacher (I think highschool) who put his lectures on YouTube for students to view in their own time and then used the in class hours for interaction, questions, tests.
That seems like a smart approach. It reverses the traditional model of "lecture in class, homework outside of class".
A smart approach that does not solve the AI problem - actually flipped classrooms work worse now due to AI usage.
My own experience with flipped classrooms (which seems to be shared by quite a few people who have tried it out): they only work well if all students actually read/watch the materials beforehand. In small, advanced courses, intrinsic motivation may be sufficient - but in most cases you need some extrinsic coercion - such as a mandatory quiz about the materials or hand-written lecture notes that need to be shown at each in-person session.
With AI, some people don't watch the lectures but let ChatGPT give them a summary which they submit. Then these people poison your in-person session with their lack of knowledge and motivation.
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My Cryptography professor did this during COVID, since the classes were split in person. It was an interesting model. I'm not sure if I loved it or not, but it was at least a change of pace. Getting 100% of the class time to ask questions was really nice, but it ended up with him re-teaching most of the online lecture in class because some quarter to half the class just didn't watch the lectures.
If done more stringently (if you didn't watch the lecture, I'm not reteaching it), it maybe would've had a bigger impact, but I'm not sure.
Office hours remained king for serious Q and A for the class.
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I always absolutely hated when a teacher did a reverse classroom and I had to “learn” at home and then practice in the classroom. I think the solution is more engaging lessons and less outside work. I know why homework exists, but homework is a chore that most people want to get done as fast as possible. If kids got to learn something interesting in school and then have their free time after school, there would be less dependence on AI. If they’re interested in the topic, they’ll put more effort into it. If not, they were never going to retain it anyway
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Flipped classroom pedagogy has been the subject of a huge amount of research. Ultimately "one weird trick" solutions don't tend to work in education. Enough students don't watch the lectures that you end up needing to go over the material in class anyway. Funding and autonomy works, but nobody likes to pay more.
How does this scale in practice? We already require students be at school for 7 hours a day. If they now have to watch 3-4 hours of lectures at home every day, then students are left with little time to do anything else.
What about those students who don't have stable home environments? How are they supposed to find multiple hours a day to watch lectures?
How does this address the underlying issue of students off loading work? You've replaced homework with lectures, but haven't solved the problem of making sure the student is actually participating.
Logistically, this could only work if you shortened the school days, but then you would need to adjust the rest of society around that. Many parents structure their work days around their kids school schedules, and if kids need to go school later in the day, or get out earlier, that places a burden on the parents.
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> Let the students use the AI tools they like […], but test regularly and early on the skills/knowledge they're meant to be gaining offline and in person.
I very much doubt there is any agreement on what those skills are.
Creating the idea of “what to learn in the new world” is itself IMO an important academic creation, but there’s no reward for doing it and no way to know if you’re on the right track (you just have to wait and see).
Employers are also just adapting.
Wait until companies are paying unsubsidized “list price” for LLM usage. Then we can have a better idea of the worth of the automation and what skills should stay with humans.
This. The industry is dumping electrical labor at a humongous loss JUST BECAUSE they figure people will immediately atrophy and be unable to do without AI… at any price.
We'll get an idea of the relative cost of the labor, all right. It's just that they are specifically trying to wreck the market, at all costs, to be able to cash in on the upside. It's sensible, if you're a monster.
I'm dumb as a rock and I don't have a PhD, but since ~1 year ago I started forcing myself to do small bits of coding and math manually.
I'm not noticing a "cognitive decline" per se, but I do see I'm a lot "lazier", even stuff that used to be routine when I started coding now feel heavy.
>I'm not noticing a "cognitive decline" per se
The funny thing is, maybe not noticing one can be the actual sign of it :)
Yes, precisely. Assessing your own cognitive skills is dubious. I’m pretty certain I’m less clever than I was when younger but if I find a problem tough now maybe 25 yo me would also have struggled?
That’s the most important thing. If we keep reading, maybe we can hold our own.
> but I do see I'm a lot "lazier", even stuff that used to be routine when I started coding now feel heavy.
Not getting that quick dopamine hit the LLMs give you..
Some say you can re-train your system to get back the dopamine hits you used to get from other things, like the enjoyment of the "old fashioned" manual coding and math. Getting there is hard work. And YMMV.
I just do things manually and ask LLMs to check my work. That seems to be working great for me.
I had the most Russian of Russian bosses when I was in college. My first day on the job he so eloquently stated, "I am not your mother. Do not come to me with problems. Come to me with solutions. I want to know what you tried and what did not work."
His advice has served me well in many areas of life too. I try my best to treat LLMs no differently for domains I care about (not one-off little questions here and there).
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>even stuff that used to be routine when I started coding now feel heavy.
The same weight feeling heavier is a sign that your muscles are weaker :)
There's many areas in life were we look back a few decades and think "people use to do it that awkwardly?" And yet results were better. I think the process of removing friction have just served to destroy our ability to concentrate and tolerate difficulty.
Absolutely this, I'm the same as you.
And I'm just afraid this is what cognitive decline feels like from inside the deteriorating mind.
I do a similar version of this, where if I notice a mistake in generated code, I fix it manually (or at least attempt to) instead of telling Claude to fix it.
This is the right balance for me as well.
I use an agent to generate a first-pass attempt, and then (deadlines willing), I manually read every line at least once so I understand what the code actually does.
Then I manually fix the inevitable slop that is mixed in with the good stuff, and only once the code is up to my personal standards do I send it.
This probably reduces my “AI performance boost” to 30-50% instead of the huge gains reported by others. But I retain the ability to reason about the codebase and use AI much more precisely when I’m trying to troubleshoot production outages or subtle bugs — something I notice the rest of my team struggles with, since adopting “agentic workflows” everywhere.
I think actively working to retain some cognitive flexibility and “muscle memory” around coding tasks is going to be rather advantageous in the long run.
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Same, but also because it feels like it takes longer for an LLM to do it. I think that's something people who are into gathering personal metrics should do - measure how long it takes to type a prompt / have the LLM fix things vs just doing it yourself.
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“ I'm not noticing a "cognitive decline" per se, but I do see I'm a lot "lazier"”
These are correlated - it just hasn’t happened in a large enough amount for you to have clearly noticed it yet.
LLMs are making me smarter. I have more code to read!
Counterpoint, I think this is true for some archetypes of people, but certainly not everyone. I personally use it like the socratic method. I am an intermediate user, I spend a ton of time with LLMs at work and personally, both prompting and letting some crappy agents try to automate boring work. I primarily use Gemini and ChatGPT models, along with some Chinese smaller weight models (eg qwen) locally.
If you treat the model like an excellent bluffer, it has never been more fun to challenge a model. To me, there is something deeply intellectually satisfying about "proving" it incorrect, and I like being deeply critical of what the model spits back out. I find that refinement process (with the constant sycophancy turned down in the system prompt) creates a really good loop of critical evaluation that would be hard to get in anywhere else. You can treat it just like the Socratic method, but instead of a benevolent teacher, you get a probabilistic bullshit artist. Lots of fun, highly recommend.
This, I will use Obra Superpowers brainstorming skill to propose/refine a few viable solutions for a feature or bug I'm trying to solve. After it asks me clarifying questions and presents a spec, I will say "well what about X or Y". The I'll run the grill me skill on the spec to tighten it up, clarifying any assumptions made.
I find it to be a really tight loop and results in very high quality code at a high velocity.
My two modes of using LLMs has been to try it for 1) natural language search queries where traditional search engines have failed and 2) occasionally as a sounding board using the socratic method.
Inevitably, it fails frequently at both. Any "reasoning" it is doing is merely rehashing ideas that someone else has already posited. This helps some of the times, but the vast majority of the time it just chooses a biased perspective (frequently the most common) and then regurgitates tired old talking points. This contrasts greatly to speaking with others who often have more intuitive notions that tend to be less polished and rote.
I'd love for LLMs to be better sounding boards, but so far they fail miserably far too often for my tastes. To each their own though.
Are you also one of those people who believes that advertising works on other people, but not you?
Advertising is fine (not great), especially when highly targeted and relevant. Spam, misleading, or predatory advertising is not.
> If you treat the model like an excellent bluffer, it has never been more fun to challenge a model. To me, there is something deeply intellectually satisfying about "proving" it incorrect, and I like being deeply critical of what the model spits back out. I find that refinement process (with the constant sycophancy turned down in the system prompt) creates a really good loop of critical evaluation that would be hard to get in anywhere else. You can treat it just like the Socratic method, but instead of a benevolent teacher, you get a probabilistic bullshit artist. Lots of fun, highly recommend.
Yes, but eventually the intellectual whack-a-mole gets tiresome unless you get really, really good at simultaneously cornering it and not letting it concede to your point.
new session. It's easy to lead a model into getting the response you want, deliberately or accidently.
The point is not to literally win an argument (it doesn't matter), it is to use the model like a partner to poke holes in your own understanding. Once it's poked a hole, it has served its purpose. Plus, you eventually run out of context or the model trails off into babbble.
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We used to prosecute cheating and plagiarism for the moral failure that it is.
If we allow lying, cheating and stealing - why bother being a schmuck that does the work.
> For adults the cognitive decline won't be as measurable since there's no exams, and overall output volume will still be fine due to LLM help
The leading indicator for me is the amount of emails and, god forbid, more personal messages (like birthday wishes!) I see that are obviously AI generated. It just keeps on rising. If you’re not able to dash off a quick message without the help of AI I have to assume you’re using it heavily elsewhere too.
I have sympathy for the university students too, we’re all bombarded with rhetoric about AI being the future. And I remember being incredibly nervous emailing my lecturer (am I phrasing this right? Is it respectful enough?) that I can imagine leaning on AI myself had it been available back in the day. But I’m glad it wasn’t, it’s an important skill to work out this stuff. They’re going to land an in person interview when they graduate and stumble around unable to effectively answer the questions they’re asked in real time.
> They’re going to land an in person interview
Not necessarily. It can be either an AI interview or a record that will be analysed by AI later. So there’s a chance to cheat here as well.
> If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
So, as long as you are not under time pressure (which you in some degree courses unluckily are), there is simply no need to "speed up" any homework assignments.
If, on the other hand, LLMs help you with making much faster progress in understanding the subject that you study (which is only loosely correlated to homework and tests), I guess it's fine to use them. Just always keep in mind that very often the pain of attempting to understand the topic on your own often makes you smarter - something that you will miss when you take an "LLM shortcut".
> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
This is probably not true for majority of people. Most go to school because it is mandatory, pushed by parents and society, and university gives you credentials and better job opportunities. Homework and tests are a way to get a number grade on 'how well you memorized something', it doesn't really measure a deep understanding of the topic.
> Homework and tests are a way to get a number grade on 'how well you memorized something', it doesn't really measure a deep understanding of the topic.
As I said: they are goalposts.
Typically homework and tests are sufficiently easy (yes, there are exceptions) that if you fail them, you can assume that you didn't make sufficient progress in improving your understanding.
But I do agree that at least sometimes the difference between being good and exceptional at homework and tests can indeed be rote, "unnecessary" memorization.
Uni grading Brownian-walks around edu trends, but misses the point that improving one's (and humanity's) lot depends on a tiny loop:
- doing - failing - discovery>learning - remembering
With learning predicated on both failing and remembering it's unfortunate uni scores on 100% successful doing but doesn't teach failing well, and scores for remembering but not for learning well.
> You go to a university because you are deeply interested in understanding the subject that you study.
Echoing the other comments here, at least in the US, this is generally untrue. I went because my parents made me, because the choice was that or get kicked out of the house. It was beaten into my head since I was in grade school that "people in this family go to college" and "you can't get a good job without a college degree."
I hated every moment of it and I was glad to take my BSc and never look back once it was over (University of Houston, c/o 2000). And, indeed, without the degree I wouldn't have had the jobs I've had.
But I didn't go because I was "interested." I went because it was an effectively mandatory life-path objective. I'm very happy for you if your lived experience is different, but it is also—at least in the US—both extremely uncommon and extremely privileged.
> You go to a university because you are deeply interested in understanding the subject that you study.
This has not been true for something like 70 years now. People go to university because it is expected that that is what you do after high school.
It wasn't even true before...
Universities have always had people not interested in the subject, but who went because its elite training school. I was reading something about Issac Newtons college in the 1600s, many of his classmates didn't care about studying and drank all the time.
> This has not been true for something like 70 years now. People go to university because it is expected that that is what you do after high school.
In Germany, many people indeed say if you are not deeply into the topic that you study, you should rather get a vocational training (Ausbildung), or attend a different kind of tertiary education than a university such as
- Fachhochschule
- Berufsakademie
(these words have no good English translation). Basically these are kinds of tertiary education that are more applied than the much more scientific training that you get at a university.
Specifically for mathematics (I guess the same holds for physics), a lot of people say that if you don't consider it to be an ideal life to think about math exercise sheets when you sit in the bathtub while other people are having fun at some party, you simply are not made for studying mathematics and should change your degree course as soon as possible.
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It is really about time we thought about what universities are for in the 21st century, since there has been significant scope creep wrt labour markets, particularly roles which do not actually require university education but do require a degree for CV reasons. It is nonsense in the 21st century to require a bachelor's degree for such roles. Not to mention the huge societal pressure that you have mentioned, which no 18 year old can really be expected to see through.
With CS students this is one thing. Medical students? Air traffic controllers?
That is to say, there is a huge gap in the educational integrity of degrees, and this is probably partly driven by people who do not really want to be at university for educational reasons (and, believe it or not, there are other ways to party in your early twenties) and for whom a degree in XYZ is not rationally connected to 80% of their options after school. And there are many such people.
This really needs to be thought through, because education is expensive, and it is an enormous waste of money to pay for a couple of years of university and end up failing out or being sanctioned for AI cheating, or being educated for something you do not really want or need to be taught. That is true whether or not education is paid for privately or by the public.
ETA that when I graduated from school the idea of not going to university was really discouraged by the guidance counselor. It seemed like vocational courses were not really a worthwhile option unless you were a poor (significantly below average) student. There was a lot of emphasis on ‘getting a degree’ probably related to (nonsense) job requirements. Not a lot on what career you should pursue, or why you should consider university. It was more like why would you not consider university, since it was the de facto default. It was, I guess, unseemly for the school to end up with fewer university entrants and more apprentices.
At the time, there was somewhat of a social stigma with apprenticeships. The people that pursued them seemed to only genuinely have been set on the idea, and there were few if any that were diverted thereto. Now, of course, ‘the trades’ pay much better than a middling office job. Egg on my face.
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Rest doesn't make sense either with the parent's tautologies and self-counters. They seem to argue for it and raise the challenges.
~"Speed doesn't matter unless you need it."
~"LLMs can be good, but if you don't use them properly™, then they become a crutch."
It's hard to deny that "cognitive offloading" via LLMs is becoming a more acute problem [0]. The intelligentsia were supposed to be immune.
[0] https://www.bbc.com/future/article/20260417-ai-chatbots-coul...
> You go to a university because you are deeply interested in understanding the subject that you study.
I don't think I've met anyone who fits that description. The ones deeply interested in the subject would likely skip college anyway if not for future economic prospects.
> The ones deeply interested in the subject would likely skip college anyway if not for future economic prospects.
There exist a lot of things that are much "easier" or even (currently) only possible to learn by attending a university because, for example,
- for the access to various devices and experts,
- you walk a much more "established" and "time-tested" hike for getting good in the subject,
etc.
>The ones deeply interested in the subject would likely skip college anyway
Spoken like a true software engineer ;), there are jobs where you have to have a degree to get the job. "Real" engineers with sign-off responsibilities, Medical Doctors, etc.
Then you either really haven't tried very hard to notice them or have been in an academic environment with severe defects.
Does college even work for future economic prospects, by the way?
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> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
Some students do not have this privilege and implicitly see university as first and foremost a funnel into a paying career.
The main reason to go is you need that piece of paper.
I needed the structure more than anything. Nothing I learned in university was some secretive knowledge. Anyone could buy the same textbooks and learn the same materials.
However, I am not one of those people. I lack(ed) the self-discipline required to learn anything with moderate to long feedback loops, thus it was far wiser for me to outsource all the necessary planning and assessment to a structured program.
Student loan debt is a blight on society, but I'd be lying if I said the looming repayment was not a great motivator to attend classes and do well in school. Some things just hit differently when there is skin in the game.
What a delightful fantasy world you live in. Doesn’t sound very predictive of actual human behavior though.
> What a delightful fantasy world you live in.
I can really certify that this was my lived experience. In the math degree course, basically everyone who was not incredibly passionate about mathematics (NB: "passionate" does not necessary imply "great academic achievements") changed their major or decided for a different kind of tertiary education.
Former co-students who attended the same university and degree course had the same experience.
I guess the reason was that it was a decent university in a "boring" town where learning for your studies was one of the more exciting things that you could do.
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> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
There is only one classmate in my class who came to study CSE because they are interested in CSE. And since we all enrolled after AI became somewhat good at everything none of them know how to code. After two years of study I had to explain someone how to swap two number by drawing boxes. This are the things you learn in the first week if you're interested in programming.
My point is very tiny percentage of people study something because they're genuinely interested in that subject.
> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
I think this was true a long time ago. Perhaps with LLMs this can become true again in the future. But definitely that was not why I went the first time, nor most of my classmates. (Second time I did post-secondary, sure, 100% -- but I was almost 30, not an average student)
It's easy to fool oneself into thinking one knows the subject because somebody explained it well / demonstrated skills, and it made perfect sense.
Unfortunately that, on its own, very much does not translate to being able to explain it all oneself, or to having the skills.
Ease and norms of outsourcing to software invites and amplifies this trap, I think.
> You go to a university because you are deeply interested in understanding the subject that you study.
You must come from a wealthy background because what you described is far beyond the vast majority of people's means - at least here in the US.
Most of us go to college because it's the only reliable way to get a tollerable job that pays well. Only a few of my college courses aligned with my interests. The rest were just the price paid for the degree.
> You go to a university because you are deeply interested in understanding the subject that you study.
This is a bit of a naive or maybe affluent take? Like, theoretically, I agree. And I myself was curious. But most people, by and large, are going to university because they know they need a degree to get a job, unlike their parents or grandparents. And even "the degree" is quickly becoming devalued in this current AI age.
I would guess that if all basic needs were met through UBI, the fraction of individuals going to school would drop and the makeup of subjects they pursue would change. Probably more cooking and art classes and less stem. Although, if UBI existed and AI did not, we'd probably see more educated individuals in the first place so maybe there would be an uptick in stem attendance and general curiosity in such a utopian world.
> > You go to a university because you are deeply interested in understanding the subject that you study.
> This is a bit of a naive or maybe affluent take?
Concerning the "naive" aspect, I wrote something at https://news.ycombinator.com/item?id=48397759
Basically, this was really my lived experience, which might have been amplified that it was a decent university in a "boring" town where learning for your studies was one of the more exciting things that you could do.
Concerning the "affluent" aspect, I can clearly assure you that neither I am nor my parents were.
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> If, on the other hand, LLMs help you with making much faster progress in understanding the subject that you study
My experience is that they uncomfortably do both. You can "understand" something conceptually quicker -- like you have a new brain-muscle-thing that lets you cut through the hard difficult tedious corners to get to the meat of the matter.
But then you also can become reliant on it, and have difficulty doing the mechanistic rote work of working through it yourself.
Like the really big powerful calculator that it is, really.
It's two fold. They're learning and understanding more things, but at a very surface level and without the nuance and ability to actually use the knowledge because they have none of the muscle memory and hard work associated with learning it.
You can use AI or the internet to learn the basics of how a gas engine works in a couple of minutes. But you'd be incapable of actually working on a gas engine or designing one.
Surface level knowledge gets you surface level functionality. You don't become good at something from surface level knowledge, but you might think you're good at it.
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LLMS didn’t invent cheating just made it easier. When you cheat you’re the one who cheats yourself because the point of an education is to learn, not complete the assignments and get high marks on tests alone. No one benefits and no one other than you is materially hurt by cheating, but you are absolutely the one who is hurt.
There’s no way to learn than to force the brain into adaptation which it is resistant to do through challenge and stress, just like your muscles. Similarly you can’t play e sports and get into physical condition any more than you can use LLMs to do your homework and learn.
It’s going to be a hard adjustment for a lot of people to recognize that letting the machine think for you is as healthy as smoking brain cigarettes.
The smart student uses the LLM as a proctor or provide challenges and feedback on attempts rather than an easy button. They make great tools for learning if they’re used as an adversarial or editorial tool. The future belongs to those who work to use the tools in ways that make themselves more efficacious, not those who use efficacious tools so they don’t have to work.
>The smart student uses the LLM as a proctor or provide challenges and feedback on attempts rather than an easy button.
Yeah, this is how we used wolframalpha for Math as students. Whatever we had to do, we did it ourself as a group of three. Afterwards we checked with Wolframaplha to see if we were correct. If there were any difference between us, we went line by line to find where the error appeared.
It was helpful, because we did it ourself, but because the work was graded, we had the security, that it is not a total failure.
To say that students don’t benefit from getting good grades using LLMs is incredibly naive. Learning is only about the third or fourth most important “benefit” for students, after getting a degree, getting good grades, and making connections.
These matter about getting the first job you get, at which point what you learned begins to dominate the rest of your career and life. The connections you made at school matter less and less as your connections in your career dominate, and they are built on what you can do, which is based on what you’ve learned.
Does anyone look at GPA on a resume? I’ve hired thousands of people I’ve never once looked at GPA. (N.b., my resume has “summa cum laude” ok it and no one has ever once mentioned it or presumably noticed it, despite the fact you only really get it if you can BOTH learn the material AND get perfect grades)
The problem with AI in an educational setting is when one is graded versus their students on things and things genuinely depend on those grades. Group projects also force those willing to do things without AI to go along with others in their group who'll use it regardless.
My son just finished his first year in college, and had no trouble getting decent grades without using AI while many of the kids around him were using it. At least in his humanities track, class participation is a lot of his grade, and he said the "AI kids" tended to suck at participation because they hadn't actually thought about the material, and couldn't dynamically work with it in class. He also said their AI assisted writing that he'd read was dull and unoriginal, and all sounded the same, which he thought likely helped his essays stand out. His English composition teacher said he was "probably too advanced for this class" when he told her he didn't use AI to write his essays, which made him roll his eyes, as he has clinically diagnosed dysgraphia (learning disability in writing).
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You're right.
But I like to add artwork to my presentations. My artistic skills have not advanced beyond 2nd grade. So I'll make a line sketch, and give to AI to "fix" it.
The results are nice and I use them.
I have no interest in learning how to do art well myself, so using AI for it is appropriate.
But I still write my code myself.
>> The results are nice and I use them.
I haven't seen your presentations, so I can't speak to them. But I do know at work there's a lot more illustrations in docs and presentations and such, and they almost all have an AI art "tell". I find them grating and distracting from the actual content. Very rarely do they add anything useful to the doc other than the knowledge that the owner burned some GPU time and tokens for a distracting, low value illustration.
I can only imagine how an actual artist or graphic designer feels about it.
Actually I don't have to imagine; there's some serious vitriol over on some of my favorite webcomics about it.
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> But I still write my code myself.
Not for long, if you so easily have caved in to using AI elsewhere. People are lazy. If you see that the 'results are nice', it's game over for your programming/thinking.
Waiting for the day the advice will be to "enjoy AI assistance in moderation"
What does this have to do with LLMs?
They stopped requiring SAT and ACTs in order to get a student population more representative of the population in general. This obviously allowed students that were not prepared for college into the system.
If you do well in your math SATs you'll likely do well in math college. SAT scores and college GPA are highly correlated. No idea why anyone thought it was good to ignore probably the strongest signal of success in college.
Isn't GP talking about PhDs here? If they're PhDs I imagine they've already had good enough GPAs/were smart enough to be selected for the program.
I broadly agree with the premise. As a PhD student in Computer Science, I feel there are some significant upsides to my work routine. LLM access has made many new domains more "accessible" to me which I otherwise would be too hesitant in investing my time in. For example, my area of research is computer systems which involves operating systems, distributed systems and more recently systems for AI. Within these, there is a wide breadth of topics/techniques one can employ and up until now, I have not gone deep into theoretical aspects of things like scheduling etc. But with access to LLMs, I feel like I can at least brainstorm from a high-level about these sub-areas that I am not well-versed in and the responses give me some relevant pieces to start exploring on my own, depending on what interests me more or the amount of time I want to spend on that sub-branch of a larger tree of ideas. However, the one thing I do have skepticism is the lack of awareness of blind-spots when dabbling into areas that I am not an expert in, and taking the LLM's lead in applying such techniques to some systems problems that I am working on. I often feel that I am not aware of what alternatives exist that the LLM has not explored for me, or if the directions it has proposed really do apply or have corner cases/assumptions that break in what I am doing. On the other hand, when working on something I have good intuitions about, I am often correcting the model's assumptions and it back-tracks what it told me. Unfortunately, I cannot do that comfortably with topics I don't have good intuition about which limits my confidence in "if this is the right direction to pursue."
As someone with a PhD in CS focused on NLP (I started my PhD in 2018 just as Transformers were introduced), and with a strong background in distributed systems owing to the fact that I was a lead developer of an MMO before starting my PhD, I can definitively say that any surface-level understanding you get by interacting with an LLM, is just that: surface level.
If that allows you to target your deep dives better, then great. If instead your deep dive into a topic is purely through prompting an LLM, that will almost certainly end with little functional domain expertise.
The absolute best experience you can get is by trying, failing, then improving upon your past failures. Remove that friction at your peril.
At this rate humans will become avatars remotely controlled by LLMs. Ironic conclusion of the consciousness debate.
We are already remote sensors and manipulators for the corporate and economic structures we operate under. You can't see it, but we are ants in a superorganism.
More evidence of the philosophical concept of 'technology is a life form.' Humans would be the perfect host, at least for the time being. They are certainly a willing host.
Yes, ask AI to produce a plan for me to do x like design an travel itinerary. It creates the plan and I execute it.
obligatory https://smbc-wiki.com/index.php/2014-12-17
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Distopian, what an insightful frame
When LLMs and ChatGPT first came out, it struck me as obvious and dangerous to a deep thinker or a knowledge worker the answering capacity. So, from my initial use I did not ask them questions, I have always "done my own work" and then asked the LLMs to criticize that work. This has been an exponential ladder of learning, and my cognitive growth is personally noticeable. I'm not hesitating to scribble out calculus and work it out, as I need for my work, where in the past I'd have found some other way because I felt uncomfortable with my tip-of-my-tongue calc skills. Don't ask AI, do your own work and ask for criticism, and them improve your own work yourself. This creates a learning ladder that you will climb.
That's nice except when you work somewhere where more and more developers are pushed to pump out slop generated by AI as fast as possible. So far I am not there yet but I have plenty of friends in the industry who are basically 'not allowed' to code manually anymore.
I've been wondering if there would be a benefit to inverting how we teach subjects now. Previously we would teach from the bottom, and build up. Semi-colon goes here, curly brace goes there, and then build up to architecture, systems, etc.
But this doesn't seem to make sense when someone comes to a topic with an LLM in-hand. They need to know high-level techniques, architecture, best practice, etc. As they pursue the topic they start to get down into the details, although probably never learn to do it fully independently.
I quite like this view because it paints a somewhat optimistic way forward from where we are now.
You can ask LLMs about high-level techniques, and their answers will usually be good enough. What you can't get from LLMs is the taste and judgment, which you can only obtain by having a strong CS base and coding manually for years.
High-level techniques were never a problem. You could Google tens of articles on this topic. They are useless too, it's like learning how to drive a racing bicycle from reading a book. Sure, you will know a lot about nuances, but you will fail miserably when it comes to a real race.
The other day I just wanted to loop through characters in a std::string to copy data to a new string with a few escape characters (sending to peripheral device). Simple enough task for AI. I got a coroutine monstrocity back, with copies to std::array and a range based iterator, since I specified C++23. If I specified C++11, I would have received a: char p = src.data(); while (p) { … p++; }
I had the experience to keep calling out AI to simplify and downgrade the solution to something primitive, which ended up smaller, faster, easier to maintain. Juniors with real world experience would not bother, they’ll take the first working AI result.
taste and judgment, which you can only obtain by having a strong CS base and coding manually for years.
I disagree, the definers of taste; art and food critics, movie and book reviewers, don’t need to have learned the craft by doing. Taste is a separate skill.
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> which you can only obtain by having a strong CS base and coding manually for years.
I hope this isn’t the case. It is the route I took, but it also doesn’t seem to be a likely route going forward. Strong CS grounding is feasible for sure, but I have a hard time believing that a meaningful number of people will be spending the requisite years coding manually.
Exactly. Repeating or rephrasing a definition is trivial, teaching someone is not.
I can't speak for other disciplines, but for math and CS, both with a really heavy focus on abstraction, the final result of learning is to build a nice intuition on top of the abstractions we find useful/expressive. And to build the intuition, the old, usual, and perhaps the only way is to see and practice a lot of concrete examples, after which the motivation of building some abstraction can be understood, and after which the abstraction itself can be fully grasped.
e.g. The "group" abstraction requires one see a lot of int, polynomial, modular arithmetic etc. before knowing why we want such a thing. It's unskippable.
This idea sounds good at first, but if you look closer, it would just make workers, not experts who really understand. What we could do, and already do, is tweak the learned abstractions. In our field, it's easy to see: most of us first learned about computing abstractions, not how processors actually work, or started with Java, not assembler. Plus, you can't teach math from top to bottom.
> As they pursue the topic they start to get down into the details, although probably never learn to do it fully independently.
It's hard to claim one has mastered a subject without independent command of its fundamentals. A less charitable take on this future is that students only learn to hand-wave answers and correspondingly cannot evaluate statements beyond "sounds about right".
> Semi-colon goes here, curly brace goes there, and then build up to architecture, systems, etc.
If that's happening, that would be a weird way to teach CS in my opinion.
In my undergrad program, languages and syntax were learned on your own. Class material and lectures were all conceptual.
I keep trying to convince people that English majors and Philosophy majors will benefit the most from LLMs. English majors in particular, have been trained to be VERY exact in how they word things.
That awareness of how to structure the English language, it will benefit those who use LLMs.
Then again, maybe someone will just make a LLM that’s built to turn poor English and poor reasoning into excellent English and excellent reasoning. Maybe this is just a technical puzzle that needs solving.
I disagree with you, for the very reason you give:
> Then again, maybe someone will just make a LLM that’s built to turn poor English... into excellent English
That's already been done, for some (pretty weird) definition of "excellent".
I work with, or at least in the vicinity of, someone who is very good at getting work out of LLMs. He has a whole system of CLAUDE.md files and skill files and things. He makes TONS of typos. When I first saw that, I was itching to go in and fix them all, it seemed viscerally wrong to be adding an extra layer of correction required between the instructions and the LLM's behavior. But in practice, I don't think it mattered at all. The LLM didn't care. Typos in particular might require a bunch of RLHF in the chatbot, but my hypothesis is that the LLM is already mapping messy human input to the nearest surface of some high-dimensional manifold and the added noise of typos is inconsequential to where it ends up (as long as there isn't any real ambiguity -- though even there, you could probably construct cases where that would help rather than hurt!)
Typos are different from sloppy writing, but I think the AI companies have put a lot of work into training these chatbots on dealing with typical non-English major writing with all of its imprecision. Also, it's easier to construct cases where that imprecision and sloppiness would help rather than hurt: a mistake in the input that is common enough to show up in the training data is going to be a good match for the needed correction as well as associated corrections. The precise language could easily result in the LLM overestimating the user's competence.
That doesn't address whether an English major's careful composition would help for hard tasks where getting the specification right really matters -- perhaps that was your point? I guess it's an open question whether "boiling away the typos" and "boiling away a poorly articulated specification" are related enough.
I don't think you can learn high level techniques or architectures without first understanding the basics first. This means boring boiler plate coding.
I’m not sure. We’ve always had to pick the level of abstraction we start teaching at. Voltages, transistors, registers, assembly, C, etc. This feels like it could just be a progression of that.
Then its reasonabel to expect someone who is not using LLMs to have an edge in their cognitive abilies. Or will it be overshadowed by the shear magnitude of bruteforcing that LLMs are capable of. I tend to side with the former. If that would be true, then not using LLMs would give an edge in solving novel problems. But we have been dependent on tools, cognjtive and physical, since forever. We cant imagine a world without tools. Why would LLMs be discriminated as a tool
I have observed this in myself when I began to over-leverage AI in my workflows. I've since become more deliberate with what kinds of tasks I will use it for, although I still slip up.
With writing:
Things like brainstorming a plot line for a book with a custom GPT or Claude project that has all of my prior books in its knowledge? Works great.
Things like asking it to write a paragraph or chapter for me - I can rapidly feel my own writing skill, motivation, vocabulary, and ability to grasp/remember the resulting plotlines deteriorating. I don't use it for that anymore.
With studying:
I've been taking a couple of evening uni courses and the thing I found so great is that I've been forcing myself to think through the problems, and take my own notes in every lecture. I may then still get ChatGPT to help explain and reason through some of the concepts with me. And I have it review and 'grade' my assignments. But I refuse to ask it to start drafting answers.
With programming:
This one is tougher. When I am not very personally invested in a problem or codebase it becomes too easy to offload more parts to Claude, and when the company encourages 'vibing' to speed up velocity and you're reviewing and writing a higher influx of lower quality PRs, investment goes down. I still sometimes catch myself committing solutions I only _mostly_ grasp and the rest is hand-waving. A big part of it is a work culture thing.
For my own projects I make sure to understand and have a back-and-forth with the planning agent for each task, or write the first plan myself to go off of. When it comes to producing the code, I have to admit it is much easier to properly review parts of the codebase I am extra interested and knowledgeable in (backend in my case). The frontend I'm less well versed in and also admittedly less interested in, so I do sometimes fall into the trap of "Ehh it works, just commit it" with the goal of doing a thorough quality pass before actual release.
With all of the above, I can feel my ability to think, plan, reason, focus (and my vocabulary) suffer if I go over the line too much into agent offloading. For me keeping that balance is as much about maintaining my own long-term brain health as it is about producing good output. I imagine younger people growing up with AI today won't even know what that more capable (in my opinion) brain state feels like - to them, the AI-using brain will be the norm.
The place I've come to with AI for writing is to have an idea for a chapter/article/etc, which I take to AI, and tell it to either ask me a bunch of clarifying questions, or try to blow holes in it/challenge it. I'll keep talking to AI and answering questions/handling challenges until the AI runs out of steam, then I'll ask the AI to write out a condensed outline with all the pertinent details of the conversation.
Once I have the condensed outline, I'll re-order stuff, clean it up/tune it up, then do the final writing. This keeps my voice and logical train of thought while avoiding blank page syndrome and some of the organizational mess of condensing notes into an outline manually.
> many of them can no longer brainstorm, code, think deeply, or write
I believe this is the real crux of the issue. We often turn the target to things like "Can johnny Add, Read a book, or recite dates" which are only proxy measures for important things like "Can johnny solve a numerical problem presented to him, can he synthesize information, or can he think critically about what is occurring around him?" .
If students use AI to accomplish goals I do not see it an issue. If they cannot figure out how to use tools, or what their goals are-- that is a major issue!
An analogy of my point is that I don't want to focus on cursive in the age of computers keyboards, and I dont want to focus on abacus skills when a pocket calculator is like $5.
If students are allowed to use AI to accomplish their goals, then I think the real question is why should they go to an expensive university for four years to learn how to ask AI to do something?
very fair question. But that's on the university not the students, as in the faculty shouldn't be complaining about the students, but adapting with the times.
We’re in a world where LLMs are basically going to be extensions of how we think. An additional thing we use to do a lot of thinking tasks.
As a piano player, it’s important to work hands separately. Sometimes your right hand will carry the melody and your left hand the harmony, sometimes vice versa. Sometimes there may be more than just two “voices”/melodies/lines between your two hands. Even as a very good (as in getting paid to do it) sight reader, I learn a lot working all the voices/melodic lines separately.
Singers do similar things like singing only the vowels to keep themselves in the right placement. Learning handstands, you have to work your wrists, rotator cuffs, core (which is many things), etc. separately. Yoga, Pilates, and running also help us learn to break problems down this way.
Anyway, all that to say: If LLMs are gonna be a natural extension of how we think, we need to understand what parts of problem-solving LLMs are good for, and what parts our brains are for. The nice thing about working these bits “separately” is that one side is done for us. So we just need to consciously practice using our brains.
As programmers that means, maybe we conscientiously practice writing things ourselves sometimes. Remembering that this even if this sacrifices short-term “velocity” (whose measurement is problematic, but I digress), it preserves our long-term ability to do good work. And I think any of the above physical/artistic practices (or countless others), worked in these ways, will help reinforce this entire mindset.
I think kids of the coming generation will be sharply divided on their ability to conscientiously practice things separately. It’s been happening, but I suspect LLMs will accelerate it unless how we actually teach kids can catch up.
> We’re in a world where LLMs are basically going to be extensions of how we think
If that's the case then we're in trouble based on my experience. This week I've been using ChatGPT to help figure out some old linux platform that I need to resurrect. It's very good at quickly searching and surfacing relevant information online, and that's helpful, but if I did not have a lot of experience at linux administration to be able to see where it was suggesting the wrong thing, or initially dismissing the right thing, then I'd just be thrashing.
The LLM is helping me because I know what I need, and it can search and read faster than I can. But it's not really very smart.
> An additional thing we use to do a lot of thinking tasks.
Which is to say, an additional thing you're going to be forced to pay a lifelong tithe to a trillion-dollar company in order to do a lot of thinking tasks.
I’m rather optimistic about the future of smaller open-source models and market competition actually doing its job here, honestly. I myself, again, err on the side of doing things with my own brain. But there are many things LLMs are useful for, and they’re definitely better than a “rubber duck” if you don’t trust them blindly.
> Many of them can no longer sit quietly for even 30 minutes just thinking on their own
Plummeting attention spans has been a trend for much, much longer than LLMs and is more the result of constant digital interruptions and these days overwhelmingly social media and doomscrolling: https://www.apa.org/news/podcasts/speaking-of-psychology/att...
The effects on children have gotten most of the, err, attention, but the effects on adults are no less deleterious.
In 2002 I spoke with a lecturer in the humanities and he told me about how nobody was learning French at university level (in the UK). My own course had been cancelled due to the cost of teaching it, and the era of 'easy degrees' had set in during the early 90s.
Before that, I also noticed the decline in newspaper readership in the 80s.
It is easy to blame this general decline on the latest tech (or moral panic), whether that be LLMs or even the existence of the internet, however, the trend in dumbing down has been going on for decades.
In the context of a declining empire and financialised economies, this makes a lot of sense.
I think we are talking about two different trends that have similar symptoms. I do agree there has been a noticeable anti-intellectual trend for a long time, especially in the West. (See also: Grade Inflation.) But that is separate from the drop in attention spans, which is relatively recent has been pretty strongly linked to digital stimulation, constant multi-tasking, and now short-form social media.
LLMs are an entirely new dynamic with significant cognitive implications, but I fear it will be hard to discern their impact from the falling attention spans and other long-term trends that have led to things like grade inflation.
I dunno, I used wolfram alpha a lot during calculus classes. However my uni didn't require any homework assignments to be done and they did not contribute to grades. Only the exam mattered.
Maybe the problem is that doing assignments contributes to your grades? The answer from wolfram alpha wasn't so much to get the homework done, but to understand how I would be screwed in the exam.
I don’t buy it. Properly leveraging LLMs to generate stable and extendable systems is mentally exhausting (i.e. highly demanding of intelligent thought), especially given the poor quality and churn within the harness ecosystem.
Now, if you’re creating trivial, unstable, or nonextendable systems maybe this doesn’t apply. And maybe I have long overestimated the work that SWEs have done.
i use claude a lot and i find that it is best applied in domains in which i am already a master. I tried applying it to domain's im unfamiliar with and i found that i produced stuff but as time went on i understood what i produced less and i almost felt like i do after binge watching a netflix show, 2 weeks later i barely remember any of the details. I wonder how much you need to "do" to learn and remember. LLM's give you a shortcut to doing and so you probably aren't learning either. It's like when you watch a professor write a proof and it makes sense while listening to the professor but at home you have difficulty deriving it. LLM's give me the same sort of feeling. I think the way forward is still going to be doing things manually to learn and using LLM's once you've mastered an area and people who don't understand this fact are going to slowly descend down a hill and forget how to depend on their own thinking.
As much as I hate to admit it, using agents for too long makes me less able to think for myself. I am dedicating 30 mins to 1 hour everyday writing and thinking without LLMs.
> I am dedicating 30 mins to 1 hour everyday writing and thinking without LLMs.
Before you did this, was literally every hour of your waking time spend thinking about LLMs?
I don't think I could do that even if I tried, and I spend all my development hours with agents, but during meals, showers, walking the dogs, enjoying a coffee outside or whatever, naturally I get time to think about other stuff, sounds out of the ordinary (to me at least) to have to dedicate 1 hour to not think about something. Reminds me of when I was addicted to amphetamines way back when.
> Before you did this, was literally every hour of your waking time spend thinking about LLMs?
They said "writing and thinking without LLMs", not "not thinking about LLMs". I think they're talking about setting aside time for fairly focused thought/work.
Shouldn't it be the opposite? 1h max LLM per day?
Perhaps, but I'm not sure my boss would appreciate that though.
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I wonder if AI or something else changing (developing anxiety, etc) has made the pay-off of the degree less certain. If you're confident that your years of effort will pay off, it's probably easier to see it through. If you're worried that AI will wreck your industry before you hit the workforce, maybe the equation changes and you're more inclined to gamble with shortcuts?
Yes, and this is going to hurt everyone. If everyone knows that you can skate through a college degree without doing any work, it is not going to have much value as a credential.
I totally agree about school-level homework: it was many years before my pre-frontal cortex developed enough that I could have forced myself to do the work.
That said, though, one thing I don't understand about the heavy users of AI in academia and software development is that the thinking and coding is the fun part. And that's the part so many people seem to be so keen to automate away.
I'm right there with you. The thinking and the coding is the fun part. I'm pretty relieved that all of this is happening near the end of my career. To me, AI is just not fun. And constantly signaling how productive I am and having to show "my value" is exhausting. This is only my subjective experience, of course, but in many ways the world seems like the fun is getting sucked out everywhere, not just from AI. Like the type of people that become managers are taking over everything.
Isn't the fun part having the thing work how you wanted it to? Why shouldn't I be keen to automate the process away?
Depends on the person. I find that it's extremely satisfying to figure out a tricky problem on the way to that end result - to struggle with something for a bit, then finally fix it or fully wrap my head around it. So to me, it's a mixture of both. What I want is the end result, but in the past sometimes that came with thinking in the shower about an approach... Or a wild thought while going to bed that makes me jump up and grab my computer.
That doesn't happen for me anymore to the same degree.
I've read enough comments* on HN to know that there are different camps. Some people don't really enjoy the process of development and just want results. Meanwhile, telling me to automate away the problem solving aspect of software dev is like saying "you know you can just copy the answers to the crossword from the back of the book?"
*speaking of things I should be doing less of...
different strokes for different folks. I'm def. in that end result camp, i get the biggest thrill out of seeing something work. For me, coding agents are awesome because i can bring a lot more to life in much shorter of a time frame. I do enjoy the process and problem solving of coding, it relaxes me. On the other hand, i really really enjoy when an idea i have is on the screen and working.
No. The fun part is the process of getting there. The end result doesn't excite me at all.
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I'd argue that this is an adjustment period that society has to go through. The way we are using electronic devices today, in some years it will probably be looked at like smoking cigarettes. And I'd argue that a lot of the "decline" is due to a shift of skills away from things that mattered more in the past toward other things that are not measured/perceived by the older generation.
Interesting analogy. I believe regarding addictiveness they may be compared.
> a shift of skills away from things that mattered more in the past toward other things that are not measured/perceived by the older generation.
Do you have any ideas what these things might be? As someone in his twenties, I’m sometimes saddened by observing that some of the skills I acquired over a long time (e.g., writing, coding) may become obsolete or won’t be respected anymore just now that I‘m finally getting good at them.
Thinking is the skill that becomes obsolete.
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it happens, things change and the change is only speeding up. I think the real skill to have going forward is the ability to acquire new skills. I tell my boys "get good at learning and you don't have to get good at anything else".
Ages ago I had similar thoughts. Everything changed when I came to terms with the concept of change being the only constant. A bit of a cliché, perhaps, but profoundly true.
Eh, I think it's less like a cigarette and more like the car. We're not going back. Americans are famously less healthy the more car dependent they are, and now people walk/run as an explicit task to be healthy. People will start going to a "thinking" gym, or engaging in additional manual mental activities for sport, like we do with chess today.
Or when the Internet came and made memory kinda obsolete. Why remember facts if you can simply index them and then lookup on demand.
But now we delegate thinking itself, so I wonder what is left.
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I wonder who would be working in these thinking gyms? Nice idea. Extra mural studies for the age of agentic ai.
Funny that you mention that. A month ago I started the Duolingo chess course, and just yesterday I noticed that my brain is clearer, more capable of deep thought than it has been in years. It's like stepping out of a fog. I also started CPAP recently, so it's hard to attribute the change to either, but I feel certain that the chess helped.
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The interesting thing about jogging is I do my best thinking while jogging. I've found it impossible to do deep thinking while driving, as driving evidently requires higher functions of the brain. Jogging doesn't require any of that, I can jog deep in thought and have no recollection of the previous mile.
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You do realise most people aren’t in shape right?
The idea that most people have the discipline to keep themselves mentally in check is false. We already know this! Millions and billions of people who spend hrs a day consuming media on platforms such as instagram.
> I'd argue that this is an adjustment period that society has to go through.
I used to think like this until social media proved there are some tech innovations we just can’t adjust to. 10 years ago you would’ve never caught me supporting any sort of age based social media ban. Now? I don’t think it goes far enough. Fake news (actual fake news) and misinformation has only gotten worse with it as well. It’s so destructive.
The human is designed to interact with small groups, to understand several smaller groups, and perhaps to imagine a big group of smaller groups. In a literal sense, let's say 100 people per group. At that level the human can actually know and interact with them still. In a city of 100.000 it's still managable to feel you are related and involved to this group-of-groups. In a city of a million, you'll revert to only your own small group and have lost the connection to the collective.
The same goes for speed and quantity of input, as to what the human is designed for (not literally designed). Be it social media with it's infinite scrolling, cars racing by as opposed to looking out the window a few times per hour because you see someone/something, constant sound input if you live anywhere remotely busy or work in a busy office.
The point I'm trying to make is that the world used to be comprehensible for the human. Some understood a little complexer things, some only the simpler things. Now there is an overload of everything. So, most humans are in survival mode wether they know it or not. Hence the many seekin mindfullness etc
No matter, it's an observation, not a judgement or opinion on it. The world will just keep rushing forward. Some have a slight hand in the direction it goes for better (never) or for worse, but spiral it will.
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This is also why AI isn't going away. People will (and already are) leaning hard on it, and will pay in the future for that crutch.
LLMs have killed my facility but not my knowledge.
I can still read code and write it, I just need to look back at docs a lot more, when I used to just know things. I also have to sit and try to recall how to do things and what abstractions are involved more. I also have more "writer's block" when starting with a fresh program/document if trying not to get AI to seed it with a baseline implementation, where I have to sit for a while thinking about what I really want to build.
Yeah. I have no doubt that I would have used LLMs “just this one time” to help with problem sets or papers when I got behind or wanted to do something else.
Do any of said PhDs gain anything positive from LLM usage? Or does it only lead to declining thinking skills in your view?
Yes, I can churn out a lot more stuff as can most of my peers. Experiments etc are all way faster to run with coding agents. But I think the overall creativity and originality is a lot lower. I think this is what many people are facing, if you don't use LLMs your short term productivity is worse.
There’s the saying that we overestimate what we can achieve in the short term, but underestimate what we can achieve in the long term. Optimizing for the short term is therefore counterproductive if it impairs us for the long term.
“ But I think the overall creativity and originality is a lot lower.
Therein lies the trade off. Your implicit gamble is that you expect machines to continue to get better in the future. What if they don’t?
They're incredibly more productive. LLMs are amplifiers, so where they'd have branched and tried out N things, they can easily try 5N pathways of RnD. LLMs are extending the frontiers of science fast -- math -> phy -> chem -> bio in that order.
In my own experience, the only path I truly gain intellectual benefits is the one where I work closely with the LLM, test very narrow hypotheses, and leverage it for learning over producing.
Trying 5N paths is useful and sometimes yields interesting insights I’ll retain, but it’s not the rich, challenging, deeply engaging kind of process I find I need in order to develop useful knowledge and skills.
So yes it’s an accelerant for people who want stuff from me, but that doesn’t map directly to learning and building skills. I think that mismatching is really important.
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I'm hearing different from PhDs. The bottleneck with much research isn't "trying out ideas" so much as it's all the bureaucratic minutiae, grants, mentoring PhD candidates, collaboration with other researchers, etc.
I've heard LLMs can be helpful in limited targeted ways. But not as some kind of "game changing" accelerant.
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It’s creating a daemon and machine spirit filled world of Warhammer 40k. We already scarcely understand how the world works, but LLM use actively degrades cognitive ability that way it is used by a majority of people (The bringing a forklift to gym analogy).
The AI is among us.
To me it is crazy that you are being downvoted. My experience in academia was that an incredible amount of time was devoted to data cleansing analysis, coding, etc., which were completely non-core to the actual underlying academic pursuit.
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It depends on the field, but an Economist with a PhD is a huge red flag and anything they say should be ignored.
Other fields may be different. YMMV
I've avoided using AI* for precisely this reason. I don't want my brain to get lazy and rot.
(* I can count on one hand the number of time I've used an AI tool.)
I don't understand why people take shortcuts in school. You pay a LOT of money to be there to learn. Taking shortcuts seems completely counterintuitive to me.
- Time is a scarce resource. Students do what they can to learn what they can, but if they're under the gun, they'll take the path of least resistance to make it to the next day (totally not like the business world, right?)
- In the interest of having well-rounded students, a lot of degree programs include subjects the student didn't want to sign up for, but have to. Even in something like CS, I knew a lot of people who liked the hardware side of it, but didn't like the software side and vice versa. So I can imagine a student justifying taking shortcuts that way.
- Psychological reasons like wanting to protect their ego. Maybe they had always done well in school and are now struggling, but don't want to ask for help, so they think why not just take a shortcut here and promise to do better next time, etc., etc.
A lot of people view it, rightly or wrongly, as paying a lot of money to earn a degree that opens up certain opportunities, while learning is secondary, so minimizing effort is worth it.
And to some people, it's not even a lot of money.
In many ways, schools are just the modern day peerage system.
This is all assuming tests measured anything valuable in the first place. In my experience standardized tests were always flawed and most of my peers knew shit about the subjects they passed in top % a year after. If AI breaks the current education system that's a win in my book.
> I wanted to be in denial about it before but it's too obvious to ignore now
Use-it-or-lose-it is the evolutionary principle, both for cognitive and physical abilities.
> Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well
I noticed this before LLMs became a thing. It was by accident. We had a team of programmers. All decent at what they do. The management said 'hey you want to learn another language we are going to be using it for these upcoming projects'. So we set up a self learned at your own pace class curriculum. Maybe 10-20 hours of school work if you sat and really dug in. Maybe 3 to 4 hours if you breeze thru it and do not care much. We set up weekly check-ins doing about 1 hour a week. Easy. Watch a 20-30 min of vid 20-30 mins of do homework come to check-in and talk about what you learned and help others if needed.
Now this is where I was disappointed. The first 'class' was 40 people. By the last there were 3. Those 3 I noticed always are the ones who dug in. The rest wanted a proctored classroom and someone to tell them what to do.
Actual genuine curiosity is rare I think. We have a lot of people who are decent at what they do. But do not really care about it. IF you do not care you are going to just push the button and get the answer.
I could see myself dropping out even if I was interested in learning. I'd suspect that the time spent would end up with me needing to stay late to make up for it or being penalized in some other way.
I think it varies tremendously from one role to the next. I'm a senior software engineer and LLMs, the way I'm using them, improve almost everything I do. I use them to write most of my code now, but first I spent twenty years writing code before LLMs came into existence and second writing code is like 5% of my job. Most of my job is research, investigation, and architecture. I treat LLMs just like a junior engineer. I give them clearly defined jobs that I could do on my own just fine, that I already spent years doing. The problem here is that students are using LLMs to automate everything BEFORE they become proficient at it themselves. Letting college students use LLMs for homework is like letting kindergarteners use calculators instead of counting on their fingers.
You cannot tell me that letting anyone do something for you does not affect the skills that you outsourced, unless you are some sort of a superhuman.
As an example, I have been drawing portraits for quite a few years now, and whenever I go on a hiatus and come back after a few months, I can notice my skill not being anywhere close to where it was before I stopped using it.
Sure, after 2 or 3 portraits they mostly come back because of the previous experience, but skill rust is a real thing, and if you think your coding skills are the same because you used to code 20 years but haven't coded for some time, you are probably just lying to yourself.
Yeah he’s living in denial.
His skills are slowly eroding. Given that he spent 20 yrs building it up it won’t happen overnight. But the trade off is happening in real time.
My "digging for roots to eat" skills have also atrophied. Fortunately I don't need those much anymore because of modern agriculture.
I wonder how much of this "you are gonna lose your skills!" stuff matters. And if knowing how to properly iterate a for loop with my eyes closed matters all that much anymore.
On the contrary, with the amount of times I went to ask for help and was failed pedagogically, plus not being able to afford tutoring like my peers had, I think access to an LLM would have genuinely boosted my grades.
I still did well, but I had gaps for which there was no help outside of the internet available.
The risk or difference is that tutoring helped people learn which they can use to do the work, whereas with only one or two different words an LLM will do the work (that proves you have learned) for you. A tutor has limits, but an LLM needs to be asked to set limits. And especially younger people are less likely to "punish themselves" like that.
You're using it to help you study and think, which is great, but the original post was about how many people are bypassing that step entirely.
I recently switched back from a Tesla to an older car without permanently having a map visible. Suddenly my brain has to think about routes again and it definitively feels like my brain has to put in more effort again to handle it.
This likely varies person by person or the way people adapted AI. For me AI replaced the boring part of writing code, but has not replaced the fun part of thinking about code and problem solving.
We also had exercises for which the solutions were given, and we didn't reach for them immediately...
“Better for you if you take me off.”
The Whispering Earring: https://croissanthology.com/earring
Maybe you've discovered the great filter.
before AI was around we blamed Covid for doing this to us, and now we blame LLMs... and before that we blamed social media. I'm pretty sure this downtrend has been happening for decades.
> I have some sympathy for these kids.
where do you see kids? This is a university. These are adults. 100% their fault.
No, that's the quality of candidates. I wish I was joking as a PhD holder for only 15yr.
A lot of skill of is getting bled into the private sector because getting the PhD in a lot of regions doesn't mean the step up it used to. A lot of that comes from awarding them to layabouts doing "a gender critical analysis of ...".
Industry doesn't how/what/why they just wanted the 3 letters as a performance barrier to hire competants.
HN is going to eat up this garbage
Yeah, it's a scary thought. I feel the pull of it every time I'm stuck on a code problem that I don't want to search solutions for and hand-code... and I also feel myself wanting to reach for the crutch of an LLM when I just have something boilerplate and easy to do. It's incredibly tempting to just ask the question and have the "thinking" done for you. Until you have actual skin in the game and realize that it doesn't reason, and its "thinking" is utter shit. Then it's like: you got addicted to cigarettes and now you have to quit, because this habit is poisonous. It really does lead very quickly to cognitive decline if you rely on them, or even think about asking them while you're writing code.
The LLMs will gradually cramp more and more adds in their responses in an effort to make a profit.
At least now we know why we will start watering our plants with Brawndo.
> Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well; many of them can no longer brainstorm, code, think deeply, or write without an LLM present doing 90% of the work. Many of them can no longer sit quietly for even 30 minutes just thinking on their own, which is a required skill for producing original thought.
This was my experience even pre-LLMs though (about my own PhD thinking skills too). I blame the amount of random stuff work now involves more than LLMs.
I used "AI" in the 2000's to increase my homework assignments, and to correct them.
As in I wrote code to generate random exercises, with solutions, using many tricks, to get myself hundreds of problems instead of 1 or 2.
Often spent more time on getting these programs right than on the problems. Still did better than the class. Oh and it was AI in the 1980s IBM sense. Ie. it was based around a python version (which I wrote) of a LISP math system based on maple. I even attempted (and largely failed) to rewrite it in C++.
Even attempted to have my homework read to have the computer correct the actual pages, but I never got convnets to reliably read entire lines (yes, I understand, well now, why a convolution would mostly not realize whether 2 pieces of text are on the same line or not and so get very confused if you go deep enough for recognition to work well)
You make good points here. But I want to point out some issues that I have with what you are saying, because I see a few assumptions that I would not myself make.
I graduated from RPI with a degree in Management and a concentration in Information Systems. I began in Computer Science, and didn't like it because RPI CS at the time was loaded with professors who were mathematicians who had transitioned over to CompSci and because the 100 and 200 level courses were excessively math-heavy in my view.
Since this was the late 80s, there may not have been an easy way to teach B.S.-level computing without it being heavily math-based, but I digress.
No matter what degree we achieved or what work we ended up succeeding at, we have a tendency to look back at people rising in the ranks below us, see differences in their experiences and struggles, and say, Look! That is evidence of a lack of rigor or a lack of understanding of fundamentals that we had to learn in order to succeed.
The only thing is that some of what we learned to become successful just isn't necessary to be learned when we learned it.
I do a fair amount of low-level software engineering with Claude Code now that was above my level of understanding of data structures and algorithms because I never took those CS courses at RPI because I switched to Management IT.
But as someone who could be described as a solopreneur at some level, my new system designs reach a certain level of complexity or code maturity, and I hit problems that I would not hit if I had more understanding of data structures and algorithms.
So-- I end up having to learn aspects of those disciplines at that point, rather than before I actually needed them.
I run into these situations often enough where I now say to myself, gee, I wish I had taken Data Structures. And I think, could I effectively take Data Structures at this late date and get better at specifying how I want data stored, or perhaps knowing the shortcomings of simplistic database structures that are the ones I end up with initially because of my lack of spec-writing skill?
Aren't many of the less experienced folks who come up now, whatever age they are, going to hit problems that show them their weaknesses in this fashion?
Is the issue that these people will never get jobs because the seniors and managers who are interviewing them will design interview questions that keep people with their level of understanding out of the workforce?
What happens when somebody who sucks at the fundamentals but is really motivated bangs their head against their shortcomings and eventually succeeds in building something that takes off? Aren't those people great assets because they learned some of their critical skills the hard way?
> If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
As a counterpoint, I was once a physics grad student. I didn't finish the PhD because at some point I discovered that I was not going to be the next Richard Feynman and this was too much for my ego at the time. But I think that if LLMs were available, I might have finished.
Part of my problem was that at some point the math transitioned from stuff I understood to symbols and notation that I knew how to manipulate but didn't really understand. LLMs could have helped bridge that gap.
On the other hand, it's hard to imagine I wouldn't have used it for Jackson, etc. but we got Jackson solutions from previous students and the internet anyway. Using LLMs probably would have been more effective, used correctly.
This is also where I had issues advancing in math. For so long I was able to build intuition around mathematical concepts easily. They fit in my head and made sense. I couldn’t understand why my peers were so bad and slow at picking up the concepts. Until my first calculus class where there was absolutely no focus on the intuition or practical utility. It was just formulas for the sake of formulas as exposed by our teacher.
It wasn’t until I was curious enough to learn about calculus outside of the classroom that I was exposed to things which helped develop that intuition and made the calculations something other than just symbols and equations to memorize.
I think this would be fine as an adult, if it meant using LLM to churn out the boring work required of you at a corporate gig, to spend more brain cycles on something you actually want to work on.
The problem is that it sounds like many people are just using it for everything.
> For adults the cognitive decline won't be as measurable since there's no exam
I think this is true of every affliction that adults criticize children and teenagers of
I’ve been out of university for a very long time, and I took a community college course and for the first few sessions I couldn't focus or sit still at all. Fortunately I knew that was abnormal and how to conform to a prior version of myself, but I don’t think children have a frame of reference.
I see this too. There is this theme where people are more and more going only as far as the ai does.
Asking suggesting or arguing to go deeper is impossible. There is a new path of least resistance and it saddens me.
>Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well;
tomorrow most regular people's thinking skills will definitely be weaker than those of the LLMs of tomorrow. And physical skills in most cases will be weaker than those of the robots. That leads to the question - what would most people do?
I've got mixed feelings on AI assistance. I'll relate 2 anecdotes.
1 - When I was in grad school (before AI), we had to use Canvas for a class. One day, I got an obvious spam/phishing email in the internal Canvas system. It was so strange. The writer just would randomly hit the capslock button and keep typing away, no salutation, no signature, just a real mess. They were asking for a particular professor to come to their house to teach them about ... something? Again, real strange.
So, I email IT and say 'Hey, somehow a spammer got into the system, do your thing'.
They email back and go 'Nope, it's a student, that somehow managed to CC the entire system, sorry about that'.
Dear Reader, the message was pure garbage. Literally, it looked liked it was written by a 3rd grader without any shame. [0]
I happened to know the professor of the class. So later on, I talked with them over symposium coffee about it. They said that they remembered that particular email because of all the IT back and forth. It was for an upperdivision class in the Engineering department. The email itself was not particularly notable otherwise. In that, they saw such emails all the time, in terms of quality. This was a top 100 ranked (whatever that means) university, by the by.
Shocking.
2 - My grandfather was an officer and a mechanic for the USAF. A bit of an odd combo, but he was partly responsible for instituting many preventative maintenance checks and protocols, novel in those early days of the AF. His aptitude and memory were quite sharp for many mechanical things. Until the strokes from decades of smoking caught up, he could tell you exact measurements and torque values for a variety of airplane related things (I can no longer remember what exactly, the memory skills did not transfer to me).
I do vividly remember standing in that light blue garage of his and him all but yelling at me once. We were looking at the brakes on an old car he was 'restoring' (getting away from Grandma for a little bit). He pointed at the old drum brakes on the axel.
He asked me how tight the pads should be on the inner rim of it.
I had no idea.
So he asked where I might find out.
I figured I'd ask him.
But what if Grandpa wasn't there?
We'll I'd have to look it up somewhere (they had no internet).
Fantastic. Now, what about the next time you're working on the brakes?
Well, just make sure that the pads are at that spec.
And that when Grandpa hit me with the nugget of hard won wisdom: No, you look it up every time. Because these are brakes, and if you are wrong then they might fail, and they might fail when the driver has their whole family in the car at 100 mph. And then because you were lazy, half a dozen people die.
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These two times stand in my head when it comes to AI.
For the first one, yes, AI would be such a boon to that very clearly struggling student reaching out for help. It would get them back on the path to the real struggle of getting their degree. That level of assistance would be like a wheelchair to a paraplegic.
For the second anecdote, AI is condemning people to death. Using it in life critical situations and care, letting it hallucinate or skip over critical values, that's a recipe for disaster.
Where do we set the fine line of using AI and not? For brakes and X-ray machines, obviously not. For helping kids learn to write emails correctly? Sure, sounds great.
Unfortunately, I feel the old adage about regulations is going to be true here like it is with every new technology: The rules are written in blood.
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How many took the COVID vax? /s
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> Many of them can no longer sit quietly for even 30 minutes just thinking on their own
Sorry, but I highly doubt that. Has a very "old man yells at clouds" vibe.
I doubt that you doubt that. I think you actually believe they're speaking truthfully and in good faith.
I doubt it. I'm stupid and I use LLMs a lot but I can still meditate for 30 minutes.
But apparently some of the smartest people in the world have lost the skill? But the commenter haven't, because why, they're 15 years older and thus immune to the same LLM-effects?
Plus, the issue with people having trouble sitting still for 30 minutes precede LLMs with decades.
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I feel the opposite. My brainstorming has increased rapidly. I can now just throw ideas at an LLM to rapidly validate.
Are you saying you leave it up to the LLM to judge whether your idea is good or not? Are you even human anymore?
(I am not saying LLMs can't be a good tool in evaluating ideas. To me, it sounds like you're firing off ideas all over, letting the LLMs judge what's good and what's not. Insane.)
Not judge no. Implement and create a working MVP, yes of course.
And yes, I fire off ideas all over. Many require predicting the future to decide what to focus my individual effort on. This is a terrible way to do things because humans (and LLMs) are notoriously terrible at predicting the future. The gold standard is to try everything and eliminate what doesn't work. This is impossible using human labor. With LLM labor, it's simply a matter of relatively cheap money.
It's amazing. Technical problems are now no longer having to predict what the best implementation is. You can just try each one.
Again, no need to have an LLM judge, because the metrics that define 'better' are well-defined, and this is the interesting part of computer science, not the implementation.
When reading and writing became prevalent, the ancients bemoaned our reduced facility to memorize long texts. Are we now “less smart” because of that technology?