Like everything else in our life, cognition is "use it or lose it". Oursourcing your decision making and critical thinking to a fancy autocomplete with sycopantic tendencies and incapable of reasoning sure is fun, but as the study found, it has its downsides.
Over the last three years or so, I have seen more and more posts where the position just doesn't make sense. I mean, ten years ago, there were posts on HN that I disagreed with that I upvoted anyway, because they made me think. That has become much more rare. An increasing number of posts now are just... weird (I don't know a better word for it). Not thoughtful, not interesting (even if wrong), just weird.
I can't prove that any of them are AI-generated. But I suspect that at least some of them are.
I wouldn't call it "cognitive decline", more "a less deep understanding of the subject".
Try solving bugs from your vibe coded projects... It's pain, you haven't learned anything while you build something. And as a result you don't fully grasp how your creation works.
LLM are tools, but also shortcuts, and humans learn by doing ¯\_(ツ)_/¯
This is pretty obvious to me after using LLMs for various tasks over the past years.
I am offended by coworkers who submit incompletely considered, visibly LLM generated code.
These coworkers are dragging my team down.
> 83.3% of LLM users were unable to quote even one sentence from the essay they had just written.
> In contrast, 88.9% of Search and Brain-only users could quote accurately.
> 0% of LLM users could produce a correct quote, while most Brain-only and Search users could.
Reminds me of my coworkers who have literally no idea what Chat GPT put into their PR from last week.
Could a person, armed with ChatGPT, come up with a better solution in a real world problem than without ChatGPT? Maybe that's what actually matters.
But how can they discuss any content if even the "writer" does not remember what they wrote.
I think a return to the apprentice style of institution where people try to create the best real world solution as possible with LLMs, 3D printers, etc. Then use recorded college courses like our grandparents used books.
Given that AI is literally just words on a monitor just like the rest of the internet, I have a strong prior it's not "reprogram[ming]" anyone's mind, at least not in some manner that, e.g. heavy Reddit use might.
We have decades of research - brain scans, studies, experiments, imaging, stimuli responses, etc - proving that when a human no longer has to think about performing a skill, that skill immediately begins to atrophy and the brain adapts accordingly. It’s why line workers at McDonalds don’t actually learn how to properly cook food (it’s all been procedured-out and automated where possible to eliminate the need for critical thinking skills, thus lowering the quality of labor needed to function), and it’s why - at present - we’re effectively training a cohort of humans who lack critical thinking and reasoning skills because “that’s what the AI is for”.
This is something I’ve known about long before the current LLM craze, and it’s why I’ve always been wary or hostile to “aggressively helpful” tools like some implementations of autocorrect, or some driving aides: I am not just trying to do a thing quickly, I am trying to do it well, and that requires repeatedly practicing a skill in order to improve.
Studies like these continue to support my anxiety that we’re dumbing down the best technical generation ever into little more than agent managers and prompt engineers who can’t solve their own problems anymore without subscribing to an AI service.
My point is that I don't see LLM's effect on the brain as being anything more than the normal experience we have of living and that the level of drama the headline suggests is unwarranted. I don't believe in infohazards.
Might they result in skill atrophy? For sure! But it's the same kind of atrophy we saw when, e.g. transitioning from paper maps to digital ones, or from memorizing phone numbers to handing out email addresses. We apply the neurons we save by no longer learning paper map navigation and such to other domains of life.
The process has been ongoing since homo erectus figured out that if you bang a rock hard enough, you get a knife. So what?
Now, you could argue that, when we use AI, critical thinking skills are more important, because we have to check the output of a tool that is quite prone to error. But in actual use, many people won't do that. We'll be back at "Computers Do Not Lie" (look for the song on Youtube if you're not familiar with it), only with a much higher error rate.
Because of studies like this we know the burning of fossil fuels is a dead-end for us and our climate, and due to that have developed alternative methods of generating energy.
And the study actually proved that LLM usage reprograms your brain and makes you a dumbass. Social media usage does as well, those two things are not exclusive, if anything, their effects compound on an already pretty dumb and gullible population. So if your argumemt is 'but what about reddit', thats a non argument called 'whataboutism'. Look it up and hopefully it might give you a hint why you are getting downvoted.
There have been three recent studies showing that:
- 1. 95% LLM projects fail in the enterprise https://fortune.com/2025/08/18/mit-report-95-percent-generat...
- 2. Experienced developers get 19% less productive when using an LLM https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
- 3. LLM usage makes you dumber https://publichealthpolicyjournal.com/mit-study-finds-artifi...
We reached a stage where people on the internet mistake their opinion on a subject to be as relevant as a study on the subject.
If you don't have another study or haven't done the science to disprove this study, how come you dismiss so easily a study that actually took time, data and the scientific method to reach to a conclusion? I feel we gotta actively and firmly call out that kind of behavior and ridicule it.
If the Victorians had scientific studies showing that, you might have a point. Instead, you just have a flawed analogy.
And, why the scare quotes? If you can point to some actual flaws in the study, do so. If not, you're just dismissing a study that you don't agree with, but you have no actual basis for doing so. Whereas the study does give us a basis for accepting its conclusions.
N=54, students and academics only (mostly undergrad), impossible to blind, and, worst of all, the conclusion of the study supports a certain kind of anti-technology moralizing want to do anyway. I'd be shocked if it replicated, and even if it did, it wouldn't mean much concretely.
You could run the same experiment comparing paper maps versus Google Maps in a simulated navigation scenario. I'd bet the paper map group would score higher on various comprehension metrics. So what? Does that make digital maps bad for us? That's the implication of the article, and I don't think the inference is warranted.
But didn’t pocket calculators present the same risk / panic?
>Everyone Is Cheating Their Way Through College. ChatGPT has unraveled the entire academic project.
https://nypost.com/2025/08/19/world-news/china-restricts-ai-...
"That’s because the Chinese Communist Party knows their youth learn less when they use artificial intelligence. Surely, President Xi Jinping is reveling in this leg up over American students, who are using AI as a crutch and missing out on valuable learning experiences as a result.
It’s just one of the ways China protects their youth, while we feed ours into the jaws of Big Tech in the name of progress."
https://www.scmp.com/tech/policy/article/3323959/chinas-soci...
Sure you do, and maybe its really an actual benefit for ya. Not for most though. For young folks still going through education, this is devastating. If I didn't have kids I wouldn't care, less quality competition at work, but I do (too young to be affected by it now, and by the time they will be allowed to use these, frameworks for use and restrictions will be in place already).
But since maybe 30% of folks here are directly or indirectly dependent on LLMs to be pushed down every possible throat and then some more, I expect much more denial and resistance to critique of their little pets or investments.
My optimistic take is that the rise of AI in education could cause more workplaces to move away from "must have xyz degree" and actually determine if the candidate has the skills needed.
For this reason, I don't feel as optimistic as you do. I worry instead that equality gaps will widen significantly: there will be the majority which abuses AI and graduates with empty brains, and there will be the minority who somehow manage to avoid doing that (e.g. lucky enough to have parents with sufficient foresight to take preventative measures with their children).
LLMs may end up being both educationally valuable in certain contexts for certain users, and totally unsuitable for developing brains. I would err towards caution for young minds especially.
Let's say I'm a writer of no skill who still wants attention. I could spend years learning to write better, but I still might not get any attention.
Or I could use AI to write something today. It won't be all that interesting, because AI still can't write all that well, but it may be better than I can do on my own, and I can get attention today.
If you care about your own growth (or even not dwindling) as a human, that's a trap. But not everyone cares about that...
Don’t sugarcoat it. Tell us how you really feel.
Probably both are true: you should try them out and then use them where they are useful, not for everything.
None of my professional life reflects that whatsoever. When used well, LLMs are exceptional and putting out large amounts of code of sufficient quality. My peers have switched entire engineering departments to LLM-first development and are reporting that the whole org is moving 2x as fast even after they fired the 50% of devs who couldn't make the switch and didn't hire replacements.
If you think LLM coding is a fad, your head is in the sand.
I have no doubt that volumes of code are being generated and LGTM'd.
It used to take me days or even multiple sprints to complete large-scale infrastructure projects, largely because of having to repeatedly reference Terraform cloud provider docs for every step along the way.
Now I use Claude Code daily. I use an .md to describe what I want in as much detail as possible and with whatever idiosyncrasies or caveats I know are important from a career of doing this stuff, and then I go make coffee and come back to 99% working code (sometimes there are syntax errors due to provider / API updates).
I love learning, and I love coding. But I am hired to get things done, and to succeed (both personally and in my role, which is directly tied to our organization's security, compliance, and scalability) I can't spend two weeks on my pet projects for self-edification. I also have to worry about the million things that Claude CAN'T do for me yet, so whatever it can take off of my plate is priceless.
I say the same things to my non-tech friends: don't worry about it 'coming for your job' yet - just consider that your output and perceived worth as an employee could benefit greatly from it. If it comes down to two awesome people but one can produce even 2x the amount of work using AI, the choice is obvious.
But for it to be useful, you have to already know what you're doing. You need to tell it where to look. Review what it does carefully. Also, sometimes I find particular hairy bits of code need to be written completely by hand, so I can fully internalise the problem. Only once I've internalised hard parts of codebase can I effectively guide CC. Plus there's so many other things in my day-to-day where next token predictors are just not useful.
In short, its useful but no one's losing a job because it exists. Also, the idea of having non-experts manage software systems at any moderate and above level of complexity is still laughable.
https://edition.cnn.com/2025/08/27/us/alaska-f-35-crash-acci...
Like any new tool that automates a human process, humans must still learn the manual process to understand the skill.
Students should still learn to write all their code manually and build things from the ground up before learning to use AI as an assistant.
personally I think everyone should shut up
1. This is arxiv - before publication or peer review. Grain of salt.[0]
2. 18 participants per cohort
3. 54 participants total
Given the low N and the likelihood that this is drawn from 18-22 year olds attending MIT, one should expect an uphill battle for replication and for generalizability.
Further, they are brain scanning during the experiment, which is an uncomfortable/out-of-the-norm experience, and the object of their study is easy to infer if not directly known by the population (the person being studied using LLM, search tools, or no tools).
> We thus present a study which explores the cognitive cost of using an LLM while performing the task of writing an essay. We chose essay writing as it is a cognitively complex task that engages multiple mental processes while being used as a common tool in schools and in standardized tests of a student's skills. Essay writing places significant demands on working memory, requiring simultaneous management of multiple cognitive processes. A person writing an essay must juggle both macro-level tasks (organizing ideas, structuring arguments), and micro-level tasks (word choice, grammar, syntax). In order to evaluate cognitive engagement and cognitive load as well as to better understand the brain activations when performing a task of essay writing, we used Electroencephalography (EEG) to measure brain signals of the participants. In addition to using an LLM, we also want to understand and compare the brain activations when performing the same task using classic Internet search and when no tools (neither LLM nor search) are available to the user.
I would describe the study size and composition as a limitation, and a reason to pursue a larger and more diverse study for confirmation (or lack thereof), rather than a reason to expect an "uphill battle" for replication and so forth.
Maybe. I believe we bout agree it is a critical gap in the research as-is, but whether it is a neutral item or an albatross is an open question. Much of psychology and neuroscience research doesn't replicate, often because of the limited sample size / composition as well as unrealistic experimental design. Your approach of deepening and broadening the demographics would attack generalizability, but not necessarily replication.
My prior puts this on an uphill battle.
4. This is clickbait research, so it's automatically less likely to be true.
5. They are touting obvious things as if they are surprising, like the fact that you're less likely to remember an essay that you got something else to write, or that the ChatGPT essays were verbose and superficial.
Science should become a marketplace of ideas. Your other criticisms are completely valid. Those should be what’s front and center. And I agree with you. The conclusions of the paper are premature and designed to grab headlines and get citations. Might as well be posting “first post” on slashdot. IMO we should not see the current standard of peer review as anything other than anachronistic.
Absolutely not. I am an advocate for peer review, warts and all, and find that it has significant value. From a personal perspective, peer review has improved or shot down 100% of the papers that I have worked on -- which to me indicates its value to ensure good ideas with merit make it through. Papers I've reviewed are similarly improved -- no one knows everything and its helpful to have others with knowledge add their voice, even when the reviewers also add cranky items.[0] I would grant that it isn't a perfect process (some reviewers, editors are bad, some steal ideas) -- but that is why the marketplace of ideas exists across journals.
> Science should become a marketplace of ideas.
This already happens. The scholarly sphere is the savanna when it comes to resources -- it looks verdant and green but it is highly resource constrained. A shitty idea will get ripped apart unless it comes from an elephant -- and even then it can be torn to shreds.
That it happens behind paywalls is a huge problem, and the incentive structures need to be changed for that. But unless we want blatant charlatanism running rampant, you want quality checks.
[0] https://x.com/JustinWolfers/status/591280547898462209?lang=e... if a car were a manuscript
The only advantage to closed peer review is it saves slight scientific embarrassment. However, this is a natural part of taking risks ofc and risky science is great.
P.s. in this case I really don't like the paper or methods. However, open peer review is good for science.
Ironically, I am waiting for AI to start automating the process of teasing apart obvious pencil whipping, back scratching, buddy-bro behavior. Some believe its in the 1% range of falsified papers and pencil whipped reviews. I expect it to be significantly higher based on reading NIH papers for a long time in the attempt to actually learn things. I've reported the obvious shenanigans and sometimes papers are taken down but there are so many bad incentives in this process I predict it will only get worse.
So it's possible to be both skeptical of how well these results generalize (and call for further research), but also heed the warning: AI usage does appear to change something fundamental about our congnitive processes, enough to give any reasonable person pause.
I don’t know the percentage of people who are still critically thinking while using AI tools, but I can first hand see many students just copy pasting content to their school work.
Our bodies naturally adjust to what we do. Do things and your body reinforces that enabling you do even more advanced versions of those things. Don't do things and your skill or muscle in such tends to atrophy over time. Asking LLMs to (as in this case) write an essay is always going to be orders of magnitude easier than actually writing an essay. And so it seems fairly self evident that using LLMs to write essays would gradually degrade your own ability to do so.
I mean it's possible that this, for some reason, might not be true, but that would be quite surprising.
What is reported as cognitive decline in the paper might very well be cognitive decline. It could also be alternative routing focused on higher abstractions, which we interpret as cognitive decline because the effect is new.
I share your concern, for the record, that people become too attached to LLMs for generation of creative work. However, I will say it can absolutely be used to unblock and push more through. The quality versus quantity balance definitely needs consideration (which I think they are actually capturing vs. cognitive decline) -- the real question to me is whether an individual's production possibility frontier is increased (which means more value per person -- a win!), partially negative in impact (use with caution), or decreased overall (a major loss). Cognitive decline points to the latter.
The problem is that a headline that people want to believe is a very powerful force that can override replication and sample size and methodology problems. AI rots your brain follows behind social media rots your brain, which came after video games rot your brain, which preceded TV rots your brain. I’m sure TV wasn’t even the first. There’s a long tradition of publicly worrying about machines making us stupider.
Which I believe still does have a large grain of truth.
These things can make us simultaneously dumber and smarter, depending on usage.
Your reminded me of this (possibly spurious) quote:
>> An Assyrian clay tablet dating to around 2800 B.C. bears the inscription: “Our Earth is degenerate in these later days; there are signs that the world is speedily coming to an end; bribery and corruption are common; children no longer obey their parents; every man wants to write a book and the end of the world is evidently approaching.”[0]
Same as it ever was. [1]
Most importantly, I did not remember anything (which is a good thing because half of the output is wrong). I then switched to Stackoverflow etc. instead of the "AI". Suddenly my mental maps worked again, I recalled what I read, programming was fun again, the results were correct and the process much faster.
> 83.3% of LLM users were unable to quote even one sentence from the essay they had just written
Not sure why you need to wire EEG up, it's pretty obvious that they simply did _not_ write the essay, LLM did it for them, and likely didn't even read it, so there is no surprise that they don't remember what didn't pass through their own thinking apparatus properly.
The idea that I would say 'write an essay on X' and then never look at the output is kind of wild. I guess that's vibe writing instead of vibe coding.
>the gentle, childlike Eloi and the subterranean, predatory Morlocks.
Seems like a nice metaphor for the current two political parties we are provided with.
Wikipedia lists several. Do you recall which you read?
Because the people around you affect your life. Presumably you don’t want to live in a world of stupid people who are incapable of critical thought or doing anything which are not direct instructions from a machine. Think about it every time you are frustrated by your interaction with a system you have no choice but to use, such as a bank or a government branch.
John Greene has a quote which I think fits, even if it’s about paying taxes for public education rather than LLM use: https://www.goodreads.com/quotes/1390885-public-education-do...
There will always be people who misuse something, but we should not hurt those who do not. Same with drugs. There are functional junkies who know when to stop, go on a tolerance break, take just enough of a dose and so forth, vs. the irresponsible ones. The situation is quite similar and I do not want AI to be "banned" (assuming it could) because of people who misuse LLMs.
People, let us have nice things.
As for the article... did they not say the same thing about search engines and Wikipedia? Do you remember how cheating actually helps us learn (by writing down the things you want to cheat)? Problem is, people do not even bother reading the output of the LLM and that is on them.
Internet was supposed to be this wonderful free place with all information available and unbiased, not the cesspool of scams and tracking that makes 1984 look like a fairytale for children. Atomic energy was supposed to free mankind from everlasting struggle for energy dependency, end wars and whatnot. LLMs we supposed to be X and not Y and used as Z and not BBCCD.
For what population loses overall, compared to whats gained (really, what? a mild increased efficiency sometimes experienced on individual level, sometimes made up for PR), I consider these LLMs are a net loss for whole mankind.
Above should tell you something about human nature, how naive some of the brightest of us are.
If it is a human nature issue (with which I agree), then we are in a deep shit and this is why we cannot have nice things.
Educate, and if that fails, then punish those who "misuse" it. I do not have a better idea. It works for me quite well for coding, and it will continue to work as long as it is not going to get nerfed.
Well cheers to even bigger gap between elite who can afford good education and upbringing and cheap crappy rest. Number of scifi novels come to mind where poor semi-mindless masses are governed by 'educated' elites. I always thought how such society must have screwed up badly in the past to end up like that. Nope, road to hell is indeed paved with good intentions and small little steps which seem innocent or even beneficial on their own, in their time.
Rather than coming up with the right answers?
Wouldn't that be the expected result here? Less knowledge, more questions?
When I use LLMs, it’s less about patching holes in my memory and more about taking an idea a few steps further than I otherwise might. For me it’s expanding the surface area of inquiry, not shrinking it. If the study’s thesis were true in my case, I’d expect to be less curious, not more.
Now that said I also have a healthy dose of skepticism for all output but I find for the general case I can at least explore my thoughts further than what I may have done in the past.
I don't have a dog in this fight, but "asking more questions" could be evidence of cognitive decline if you're having to ask more questions than ever!
It's easy to twist evidence to fit biases, which is why I'd hold judgement to better evidence comes through.
If I’m constantly asking “what does this mean again?” that would signal decline. But if I’m asking “what if I combine this with X?” or “what are the tradeoffs of Y?” that feels like the opposite: more engagement, not less.
That’s why I’m skeptical of blanket claims from one study, the lived experience doesn’t map so cleanly.
But if I'm teaching a class, and one student keeps asking questions that they feel the material raised, I don't tend to think "brain damage". I think "engaged and interested student".
Personally, I find myself often asking AI about things I wouldn't have been bothered to find out about before.
For example I've always these funny little grates on the outside of houses near me and wondered what they are. Googling "little grates outside houses" doesn't help at all. Give AI a vagueish description and it instantly tells you they are old boot scapers.
Maybe there is a movie in the back of my head or a song. Typical search engine queries would never find it. I can give super vague references to a LLM and with search enabled get an answer that’s correct often enough.
When writing was invented, societies started depending on long form memorization less, which is a cognitive "decline". When calculators were invented, societies started depending on mental math less, which is a cognitive "decline".
I'm sure LLMs are doing the same thing. People aren't getting dumber, they are just outsourcing tasks more, so that their brains spend more time on the tasks that can't be outsourced.
Also, domesticated dogs show indications of lower intelligence and memory than wolves. They don't have to plan complex strategies to find and kill food, anymore.
But humans need jobs, and jobs need to capture value from society. So we do actually still have to stay sharp, whatever form "sharp" takes.
People who maintain a high level of curiosity or a have drive to create things will most assuredly benefit from using AI to outsource work that doesn't support those drives. It has the potential to free up more time for creative endeavors or those that require more deep thinking. Few would argue the benefit there.
Unfortunately, anti-intellectualism is rampant, media literacy is in decline, and a lot of people are content to consume content and not think unless they absolutely have to. Dopamine is a helluva drug.
If LLMs reduce the cognitive effort at work, and the people go home to doom scroll on social media or veg out in front of their streaming media of choice, it seems that we're heading down the path of creating a society of mindless automatons. Idiocracy is cited so often today that I hate to do so myself, but it seems increasingly prescient.
Edit: I also don't think that AI will enable a greater work-life harmony. The pandemic showed that a large number of jobs could effectively be done remotely. However, after the pandemic, there was significant "Return to Office" movement that almost seemed like retribution for believing we could achieve a better balance. Corporations won't pass on the time savings to their employees and enable things like 4-day work weeks. They'll simply expect more productivity from the employees they have.
Maybe it's my natural ADHD tendencies, but having that implementation/process noise removed from my workflow has been transformational. I joke about having gone super saiyan, but it's for real. In the last month, I've gotten 3 papers in pre-print ready state, I'm working on a new model architecture that I'm about to test on ARC-AGI, and I've gotten ~20 projects to initial release or very close (several of which concretely advance SOTA).
Passive AI use where you let something else think for your will obvious cause cognitive decline.
Active use of AI as a thought partner, and learning as you go yourself seem to feel different.
The issue with studying 18-22 year olds is their prefrontal cortex (a center of logic, will power, focus, reasoning, discipline) is not fully developed until 26. But that probably doesn't matter if the study is trying to make a point about technology.
The art of learning fake information from real could also increase cognitive capacity.
This article is focused on essay writing, but I swear I've experienced cognitive decline when using AI tools a bit too much to help solve programming-related problems. When dealing with an unfamiliar programming ecosystem it feels so easy and magical to just keep copy / pasting error outputs until the problem is resolved. Previously solving the problem would've taken me longer but I would've also learned a lot more. Then again, LLMs also make it way easier to get started and feel like you're making significant progress, instead of getting stuck at the first hurdle. There's definitely a balance. It requires a lot of willpower to sit with a problem in order to try and work through it rather than praying to the LLM slot machine for an instant solution.
I also like preparing a draft and using llm for critique, it helps me figure out some blind spots or ways to articulate better.
It’s really convenient. It also similarly rots the parts of the brain required for spatial reasoning and memory for a geographic area. It can also lead to brain rot with decision making.
Usually it’s good enough. Sometimes it leads to really ridiculous outcomes (especially if you never double check actual addresses and just put in a business name or whatever). In many edge cases depending on the use case, it leads to being stuck, because the maps data is wrong, or doesn’t have updated locations, or can’t consider weather conditions, etc. especially if we’re talking in the mountains or outside of major cities.
Doing it blindly has led to numerous people dying by stupidly getting themselves into more and more dumb situations.
People still got stuck using paper maps. Sometimes they even died. It was much rarer and people were more aware they were lost, instead of persisting thinking they weren’t. So different failure modes.
Paper maps were very inconvenient, so dealt with it using more human interaction and adding more buffer time. Which had it’s own costs.
In areas where there are active bad actors (Eastern Europe now a days, many other areas in that region sometimes) it leads to actively pathological outcomes.
It is now rare for anyone outside of conflict zones to use paper maps except for specific commercial and gov’t uses, and even then they often use digitized ‘paper’ maps.
I think any developer worth their salt would use LLMs to learn quicker, and arrive to conclusions quicker. There's some programming problems I run into when working on a new project that I've run into before but cannot recall what my last solution was and it is frustrating, I could see how an LLM could help with such a resolution coming back quicker. Sometimes its 'first time setup' stuff that you have not had to do for like 5 years, so you forget, and maybe you wrote it down on a wiki, two jobs ago, but an LLM could help you remember.
I think we need to self-evaluate how we use LLMs so that they help us become better Software Engineers, not worse ones.
I've had the opposite experience, but my approach is different. I don't just copy/paste errors, accept the AI's answer when it works, and move on. I ask follow up questions to make sure I understand why the AI's answer works. For example, if it suggests running a particular command, I'll ask it to break down the command and all the flags and explain what each part is doing. Only when I'm satisfied that I can see why the suggestion solves the problem do I accept it and move on to the next thing.
The tradeoff for me ends up being that I spend less time learning individual units of knowledge than if I had to figure things out entirely myself e.g. by reading the manual (which perhaps leads to less retention), but I learn a greater quantity of things because I can more rapidly move on to the next problem that needs solving.
I've tried a similar approach and found it very prone to hallucination[0]. I tend to google things first and ask a LLM as fallback, so maybe it's not a fair comparison, but what do I need a LLM for if a search engine can answer my question.
[0]: Just the other day I asked ChatGPT what a colonn (':') after systemd's ExecStart= means. The correct answer is that it inhibits variable expansion, but it kept giving me convincing yet incorrect answers.
What I can comment on is how valuable and energizing it is for me to cooperatively code with LLM's using agents.
I find it sad to hear when someone finds this experience disappointing, and I wonder what could go wrong to make it so.
"Writing is nature’s way of letting you know how sloppy your thinking is." -- Guindon
I would argue that it helps kids learn how to organize and formulate coherent thoughts and communicate with others. I'm sure it helps them do homework, too.
The article actually contains the sentence "The machines aren’t just taking over our work—they’re taking over our minds." which reminds me more of Reefer Madness than an honest critique of modern tech.
Thinking about it myself, and looking at the questions and time limits, I'm not sure how I would be able to navigate that distinction given only 20 minutes. The way I would use an LLM to aid me in writing an essay on the topic wouldn't fit within the time limit, so even with an LLM, I would likely stick to brain only except in a few specific case that might occur (forgetting how to spell a word or forgetting a name for a concept).
So this study likely is applicable to similar timed instances, like letting use LLMs on a test, but that's one I would have already seen as extremely problematic for learning to begin with (granted, still worth while to find evidence to back even the 'obvious' conclusions).
- Learning how to solder
- Learning how to use a multimeter
- Learning to build basic circuits on breadboxes
- learning about solar panels, mppt, battery management system, and different variations of li-on batteries
- learning about LoRa band / meshtastic / how to build my own antenna
And every single one of these things I've learned I've also applied practically to experiment and learn more. I'm doing things with my brain that I couldn't do before, and it's great. When something doesn't work like I thought it would, AI helps me understand where I may have went wrong, I ask it a ton of questions, and I try again until I understand how it works and how to prove it.
You could say you can learn all of this from YouTube, but I can't stand watching videos. I have a massive textbook about electronics, but it doesn't help me break down different paths to what I actually want to do.
And to be blunt: I like making mistakes and breaking things to learn. That strategy works great for software (not in prod obviously...), but now I can do it reasonably effectively for cheap electronics too.
Like you, I don't like watching videos. However, the web also has text, the same text used to train the LLMs that you used.
> When something doesn't work like I thought it would, AI helps me understand where I may have went wrong, I ask it a ton of questions, and I try again until I understand how it works and how to prove it.
Likewise, but I would have to ask either the real world or written docs.
I'm glad you've found a way to learn with LLMs. Just remember that people have been learning without LLMs for a long time, and it is not at all clear that LLMs are a better way to learn than other methods.
> However, the web also has text, the same text used to train the LLMs that you used.
> I have a massive textbook about electronics, but it doesn't help me break down different paths to what I actually want to do.I think the problem was all of the getting started guides didn't really solve problems I cared about, they're just like "see, a light! isn't that neat?" and then I get bored and impatient and don't internalize anything. The textbooks had theory but so much of it I would forget most of it before I could use it and actually learn. Then when I tried to build something actually interesting to me, I didn't actually understand the fundamentals, it always fails, Google doesn't help me find out why because it could be a million things and no human in my life understands this stuff either, so I would just go back to software.
It could be LLMs are at least possibly better for certain people to learn certain things in certain situations.
Here's what I think: AI causes you to forget how to program but causes you to learn how to plan.
Also, AI enhances who you are. Dummies get dummer. Smarties get smarter.
But that's not proven. It's anecdote. And I don't believe anyone knows what is really happening and those that claim to are counterproductive.
I think a better interpretation would be to say that LLMs gives people the ability to "filter out" certain tasks in our brains. Maybe a good parallel would be to point out that some drivers are able to drive long distances on what is essentially an "auto-pilot". When this happens they are able to drive correctly but don't really register every single action they've taken during the process.
In this study you are asking for information that is irrelevant (to the participant). So, I think it is expected that people would filter it out if given the chance.
[edit] Forgot to link the related xkcd: https://xkcd.com/1414/
The age of social media and constant distraction already atrophies the ability to maintain sustained focus. Who reads a book these days, never mind a thick book requiring struggle to master? That requires immersion, sustained engagement, persevering through discomfort, and denying yourself indulgence in all sorts of temptations and enticements to get a cheap fix. It requires postponed gratification, or a gratification that is more subtle and measured and piecemeal rather than some sharp spike. We become conditioned in Pavlovian fashion, more habituated to such behavior, the more we engage in such behavior.
The reliance on AI for writing is partly rooted in the failure to recognize that writing is a form of engagement with the material. Clear writing is a way of developing knowledge and understanding. It helps uncover what you understand and what you don't. If you can't explain something, you don't know it well enough to have clear ideas about it. What good does an AI do you - you as a knowing subject - if it does the "writing" for you? You, personally, don't become wiser or better. You don't become fit by watching others exercise.
This isn't to say AI has no purpose, but our attitude toward technology is often irresponsible. We think that if we have the power to do something, we are missing out by not using it. This is boneheaded. The ultimate measure is whether the technology is good for you in some particular use case. Sometimes, we make prudential allowances for practical reasons. There can be a place for AI to "write" for us, but there are plenty of cases where it is simply senseless to use. You need to be prudent, or you end up abusing the technology.
But it does highlight that this mind-slop decline is not new in any way even if it may have accelerated with the decline and erosion of standards.
Think of it what you want, but if the standards that led to a state everyone really enjoys and benefits from are done away with, inevitably that enjoyable state everyone benefited from and you really like will start crumbling all around you.
AI is not really unusual in this manner, other than maybe that it is squarely hitting a group and population like public health policy journalists and programmers that previously thought they were immune because they were engaged in writing. Yes, programmers are essentially just writers.
All the headings and bullets and phrases like "The findings are clear:" stick out like a sore thumb.
Using LLMs to do replace the effort we would've otherwise endured to complete a task short-circuits that exercising function, and I would suggest is potentially addictive because it's a near-instant reward for little work.
It would be interesting to see a longitudinal study on the affect of LLMs, collective attention spans, and academic scores where testing is conducted on pen and paper.
It's like a drug. You start using it, and think you have super powers, and then you've forgotten how to think, and you need AI just to maybe be as smart as you were before.
Every company will need enterprise AI solutions just to maybe get the same amount of productivity as they got before without it.
I've found it both helpful and dangerous, it's great for expanding scope obviously, greater search engine.
But I've also significantly noticed further some of the "harmful patterns" I guess that I would not have noticed about... myself? For example, AI is way too eager to "solve things" when given a prompt, even if you give it an abstract one. It's unable to take a step back and just.... think?
And hey, I notice that I do that too! Lol.
It's helped me realize more refined "stages" of thinking I guess, even beyond just "plan" and "solve".
But for sure a lot of the time I'm just lazy and ask AI to just "go do it" and turn off critical thinking, hoping that it can just 1 shot the problem instead of me breaking it down. Sometimes it genuinely works. Often it doesn't.
I think if I stay way more intentional with my thinking, I can use it to good use. Which will probably reduce AI usage - but it's the first principles of real critical thinking, not the usage of AI.
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These kinds of studies remind me of when my parents told me "stop getting addicted to games" as a kid. Sure, anyone can observe effects, it takes real brains to really try and understand the first principles effects. Addiction went away in a flash once I understood the principles, lol.
Not many people can perform mental arithmetic beyond single-digit numbers. Just plug it into a calculator...
We're at the point of people plugging their thoughts into an LLM and having it do the work for them... what's going to happen to thinking?
I have recently been finding it noticeably more difficult to come up with the word I'm thinking of. Is this because I've been spending more time scrolling than reading? I have no idea.
These days, I'm fairly senior and don't touch code much anymore but I find it really really instructive to get my hands dirty and struggle through new code and ideas. I think the "just tweak the prompts bro" people are missing out on learning.
Alternatively they're just learning building intuition for something else. The level of abstraction is moving upwards. I don't know why people don't seem to grok that the level of the current models is the floor, not the ceiling. Despite the naysayers like Gary Marcus, there is in fact no sign of scaling or progress slowing down at all on AI capabilities. So it might be that if there is any value in human labor left in the future it will be in being able to get AI models to do what you want correctly.
I think the same effect has been around forever in the form of every boss/manager/ceo/rando-divorcee-or-child-with-money using employees to do their thinking as a current information-handling worker or student using an ai to do their thinking.
We just have to get better at identifying risks with using the LLMs doing the grunt work and in mitigating them.
A year or two ago when LLMs popped on the scene my coworkers would say "Look at how great this is, I can generate test cases".
Now my coworkers are saying "I can still generate test cases! And if I'm _really pacificcccc_, I can get it to generate small functions too!".
It seems to have slowed down considerably, but maybe that's just me.
For now the difference between these two populations is not that pronounced yet but give it a couple of years.
Similar thing in the historian's profession (which I also don't do for my job but have some knowledge of). Historians who spend all day immersed in physical archives tend, over time, to be great at synthesizing ideas and building up an intuition about their subject. But those who just Google for quotes and documents on whatever they want to write about tend to have more a static and crude view of their topic; they are less likely to consider things from different angles, or see how one things affects another, or see the same phenomenon arising in different ways; they are more likely to become monomaniacal (exaggerated word but it gets the point across) about their own thesis.
Calculators reduced our capabilities in mental and pencil-paper arithmetic. Graphing calculators later reduced our capacity to sketch curves, and in turn, our intuition in working directly with equations themselves. Power tools and electric mixers reduced our grip strength. Cheap long distance plans and electronic messaging reduced our collective abilities in long-form letter writing. The written word decimated the population of bards who could recite Homer from memory.
It's not that there aren't pitfalls and failure modes to watch out for, but the framing as a "general decline" is tired, moralizing, motivated, clickbait.
Discussion then: https://news.ycombinator.com/item?id=44286277
puilp0502•1h ago
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chychiu•1h ago
On that note, reading the ChatGPT-esque summary in the linked article gave me more brain damage than any AI I've used so far