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.
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.
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?
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.
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.
No.. this depends on how much muscle you have. The appropriate comparison is mass and density of knowledge/understanding vs muscle. There’s not a chance in hell you will retain mass and dense muscle without pushing the body hard. Just in the same way you will not retain very deep understanding of things unless a) you’ve been reciting it for over 10 yrs b) you go back and push the understanding continuously for it to remain as part of your being
And if you went 3 years without exercising, you'll be able to get your muscles back much quicker than had you never had the muscle before.
It's pretty comparable to skills. You don't need to practice as hard to maintain a skill than you do to build it. And if you let the skill atrophy, it's much easier to recover the skill compared to building it from scratch.
This very much depends on age. I went on statins about 18 months, which destroyed about 15lbs of muscle over the course of a year (160->145). Along with that muscle loss came about a halving or more of the weights I could lift in any given exercise. I interpreted the "do you have any weakness on this medication" question as inability to function levels of weakness, it wasn't until I showed my training logs to my physician that she asserted that I was having weakness.
It's been a year since I went off them and I'm still lifting barely what I could in high school. I'm exploring some different training plans, but AFAIK, there isn't much research into if different weight/volume breakdowns work better for older guys.
I’ve got 20 inch lean arms - I know far more about muscle building and retention than you. I train just as hard to maintain them as I did to get them there.
The people who say “oh it’s easy to maintain” LOL it’s easy to maintain 16 inch arms.
I am a competitive bodybuilder…
> I train just as hard to maintain them as I did to get them there.
Are you enhanced? Were you enhanced when you built the 20” arms? If so, yes I agree.
Edit: With 20" arms, there's nearly 0% chance you're natural. You can't compare your enhanced experience to naturals.
I noticed it myself with cycling. Took 8 years off the bike, when I started up again I was nearly back to my old FTP in about 2 months despite starting from basically zero. Muscle memory is real, where I am now as a returning cyclist would take a pure beginner cyclist at least 4+ months to get to, fitness wise.
That said, you do have to work somewhat hard to maintain. With cycling, just 2 weeks off the bike is enough to see a VO2 max drop of anywhere from 4 to 7%. After just 4 weeks, your glycogen storage capacity decreases and you start rapidly losing fitness. After 2 months, you are basically now out of shape.
Detraining happens faster than most people think. And therein lies the danger with over reliance on LLMs for your cognitive skills. Detraining there happens just as fast, skills atrophy in a matter of weeks, not months or years.
Also like some people hinted at this in sibling threads, I think it's different between purely abstract skills, and skills that involve muscle memory. For instance, I could probably stop using my bicycle for a very long time, and still not unlearn how to use it, or learn it again really quickly. Maybe it is because abstract skills are inherently more complex and require more cognitive effort and connections to knowledge overall, and are therefore more fragile.
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.
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'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.
The funny thing is, maybe not noticing one can be the actual sign of it :)
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.
And I'm just afraid this is what cognitive decline feels like from inside the deteriorating mind.
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.
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.
These are correlated - it just hasn’t happened in a large enough amount for you to have clearly noticed it yet.
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/
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.
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.
That seems like a smart approach. It reverses the traditional model of "lecture in class, homework outside of class".
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.
Just have a quiz every day. In fact, have _TWO_ quizzes, one at the start of class and one at the end, and take the higher of the two scores. In between the first quiz and the second, work through problems with the students designed to help people that bombed the first test figure out how to pass the second.
We had no lectures, the teacher just gave us a short, concise textbook to read a chapter of every week.
In class time was devoted to discussing and problem solving.
But yes, it only worked because we were a small class of 15 math students
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.
College is different, because theoretically you should be taking classes that are relevant to your field (although there are still "core" requirements that are somewhat high-school adjacent).
Worksheets certainly are. But good homework, even if it's challenging, is what makes a reasonably fast-paced course even possible. In a well-paced university course you're typically spending proportionally several times as much time working on it out of class than you are in class. Then class time is both preparation and catch-up, similar to office hours.
This was true of my most demanding humanities courses (sometimes reading 100 pages a week directly from academic journals, not easy reading) as well as my most challenging math courses (group theory, ring theory). Once the pace gets fast, there just isn't enough time for you to learn everything you need to inside the classroom anyway.
And in those classes, where homework was really essential for learning at the required pace and depth of mastery, my instructors didn't even need to factor the homework into my grades at all. In some of them, we could get "feedback" on homework but it was never officially recorded in our grades... and yet, anyone who didn't do it would fail the next test. If homework doesn't have that characteristic, it probably doesn't need to be assigned at all.
If "flipped classroom" means that students are expected to do all of their homework in class, then indeed it'll feel like a waste of time to many of the smarter kids, and it will also just be unfeasible for advanced courses (which theoretically should be most courses in a university, though it currently isn't). But if it means "we don't even have time to lecture you on every single thing you need to learn, therefore you must arrive already having done the reading and the exercises, and we'll use this time to help clear up misunderstandings"... that's already how classes for grown-ups are in universities.
Kids get to learn lots of interesting things in school. The problem is that they're kids! They want immediate gratification from phones/games/recess, not to do the hard work of learning.
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.
For secondary school, I do agree with you - homework load can be problematic for some students. But at the same time, my honors classes all came with hours of homework and I’m not sure I would have been as prepared for uni without it.
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.
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.
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.
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.
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".
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.
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.
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.
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.
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.
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)
Some students do not have this privilege and implicitly see university as first and foremost a funnel into a paying career.
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 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.
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.
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.
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.
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.
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.
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.
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.
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"
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.
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.
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.
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.
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.
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.
Taste in coding is a combination of insight, experience, native talent, technical skill, and flair. Tasteful coding produces clever but straightforward minimal elegant solutions that an average developer can't imagine but can adapt and maintain.
This is why "critical thinking" is a meme. Being a critic takes no skill. I want far fewer critics and far more constructive thinking. GenAI being the ultimate constructor is a bonus.
Taste implicitly requires discipline of what one chooses to expose themself to and what not to.
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.
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.
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".
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.
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.
> 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
That doesn't happen for me anymore to the same degree.
*speaking of things I should be doing less of...
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.
(* I can count on one hand the number of time I've used an AI tool.)
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?
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.
The part I find weird is all the claims that LLM usage leads to less thinking and exploring and just grabbing the first result. I constantly find myself going off on tangents and pulling on threads when I’m working with these tools. Is it really that different than before when my “peers” weren’t able or willing to be curious about their craft? They didn’t explore other programming languages out of curiosity or for fun? That covers literally 95% of all software developers I’ve worked with in the last 24 years across many domains. To them it’s just a job. Their only goal is to deliver tickets assigned to them and go home. They rarely go out of their way to learn something new unless the company assigns them some mandatory courses. Largely the LLM is capable of producing better and more consistent results than they ever could in the first place.
I don’t know how to cultivate curiosity in the work force. Maybe it’s not possible and you have to filter aggressively at the hiring step. But then your pool of hireable candidates shrinks to a few thousand developers most who are probably not actively looking for work.
The only distinction I wanted to make is that the learning doesn’t come by default. Yet that was largely true when people copied mystery solutions from stack overflow and used black box libraries for 90% of the complex work their programs facilitated.
Perhaps not much has changed but we’re now operating at a much larger scale and the opportunity to not be curious is actually more present than ever.
People who are curious are massively benefited by this tooling, in my opinion. Like you’re saying, if you want to investigate and learn, there has never really been a better time. If you’re sincerely applying yourself and pulling all of those threads, there has never been a better teacher.
I’ve wondered about the matter of finding and cultivating curiosity too. I’ve come to believe most humans, let alone programmers specifically, are not all that curious. A lot of us are path-followers and we’d rather not get into the weeds most of the time. Then some of us see weeds and dive in, even when it’s not pragmatic to do so. I don’t know how much it can be cultivated or even removed from a person who has more than enough.
I've heard LLMs can be helpful in limited targeted ways. But not as some kind of "game changing" accelerant.
The downvotes are just a sign of the times. It's also something to observe and think about..
Other fields may be different. YMMV
Use-it-or-lose-it is the evolutionary principle, both for cognitive and physical abilities.
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.
> 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.
But now we delegate thinking itself, so I wonder what is left.
A proper nights sleep is massive! I'd put 99% down to this..
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 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 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.
>> 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.
The systems are too large and self-propulsing for anyone to really control. Consider the rainforest. How many millions of variables interact, nobody is in charge, everything influences everything in a billion different ways. You might say, well we can cut it down, so kind we can control it. Allright, let's continue to spiral. You might build a city there after a few years. Still in charge right. But it get's too hot because there's no vegitation, so you have to change again. And then we find that people keep getting strangely sick, and scientists find some special mushroom that survived and apparantly thrives on the mix of cut trees and diesel fumes and their spores in the air are poisonous. I made that up, but you get the idea hopefully.
The Whispering Earring: https://croissanthology.com/earring
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.
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.
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.
I still did well, but I had gaps for which there was no help outside of the internet available.
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.
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)
At least now we know why we will start watering our plants with Brawndo.
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 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?
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.
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.
The problem is that it sounds like many people are just using it for everything.
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.
Asking suggesting or arguing to go deeper is impossible. There is a new path of least resistance and it saddens me.
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?
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.
---
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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Sorry, but I highly doubt that. Has a very "old man yells at clouds" vibe.
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.
Not saying everyone else is immune, but those a few years older have also had a period without it.
Most people definitely can't meditate for 30 minutes, so if you can do this, it's very impressive. Regardless, being able to think about poorly-defined problems and build completely new mental models from nothing is genuinely a really hard and uncomfortable task. If you don't use the skill you'll lose it.
Maybe not traditional meditation, but I have no problem taking a 30 minute plus walk with nothing but my thoughts. It’s actually when I do most of my thinking. The other is in the shower/sauna where devices don’t work anyway.
> apparently some of the smartest people in the world have lost the skill?
> But the commenter haven't
> why?
Perhaps because a correlation you assumed was there (more smartness = more ability to sit still alone with one's thoughts), is not actually as strong as you thought? If one does not start with that assumption, there is no inherent conflict in the 3 pieces of evidence you cited.
Or perhaps because you are smarter than you give yourself credit for :)
(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.)
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.