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.
For now the difference between these two populations is not that pronounced yet but give it a couple of years.
An abstraction is a deterministic, pure function, than when given A always returns B. This allows the consumer to rely on the abstraction. This reliance frees up the consumer from having to implement the A->B, thus allowing it to move up the ladder.
LLMs, by their very nature are probabilistic. Probabilistic is NOT deterministic. Which means the consumer is never really sure if given A the returned value is B. Which means the consumer now has to check if the returned value is actually B, and depending on how complex A->B transformation is, the checking function is equivalent in complexity as implementing the said abstraction in the first place.
We can use different words if you like (and I'm not convinced that delegation isn't colloquially a form of abstraction) but you can't control the world by controlling the categories.
The most harmful myth in all of education is the idea that you need to master some basic building blocks in order to move on to a higher level. That really is just a noticeable exception. At best you can claim that it's difficult for other people to realize that your new way solves the problem, or that people should really learn X because it's generally useful.
I don't see the need for this kind of compulsory education, and it's doing much more harm than good. Bodybuilding doesn't even appear as a codified sport until well after the industrial revolution, it's not until we are free of sustenance labor that human intelligence will peak. Who would be happy with a crummy essay if humans could learn telekinesis?
> Who would be happy with a crummy essay if humans could learn telekinesis?
I'm glad that's not the professional consensus on education, at least for now. And "telekinesis," really?
AI can do better organization than you, it's only inertia and legalities that prevent it from happening. See, without good education, you aren't even able to find a place for yourself.
> The most harmful myth in all of education is the idea that you need to master some basic building blocks in order to move on to a higher level.
That "myth" is supported by abundant empirical evidence, people have tried education without it and it didn't work. My lying eyes kind of confirm it too, I had one hell of time trying to use LLM without getting dumber... it comes so natural to them, skipping steps is seductive but blinding.
> I don't see the need for this kind of compulsory education, and it's doing much more harm than good.
Again, long standing empirical evidence tells as the opposite. I support optional education but we can't even have a double blind study for it - I'm pretty sure those who don't go to school would be home-schooled, too few are dumb enough to let their uneducated children chose their manner and level of education.
customers dont care about the syntactic sugar/advanced reflection in the codebase of the product that theyre buying. if the end product of the delegator and the expert is the same, employers will go with the faster one every time.
Frankly, I'm sure there will be much more studies in this direction. Now this is a university, an independent organization. But, given the amount of money involved, some of future studies will come from the camp vitally interested in people believing that by outsourcing their work to coding agents they are becoming smarter instead of losing achieved skills. Looking forward to reading the first of these.
> Students who repeatedly relied on ChatGPT showed weakened neural connectivity, impaired memory recall, and diminished sense of ownership over their own writing
So we're going to have more bosses, perhaps not in title, who think they're becoming more knowledgeable about a broad range of topics, but are actually in cognitive decline and out of touch with reality on the ground. Great.
Do you therefore argue programming languages aren't abstractions?
The problem with this analogy is obvious when you imagine an assembler generating machine code that doesn't work half of the time and a human trying to correct that.
Yes, and no. They’re abstractions in the sense of hiding the implementation details of the underlying assembly. Similarly, assembly hides the implementation details of the cpu, memory, and other hw components.
However, except with programming languages you don’t need to know the details of the underlying layers except for very rare cases. The abstraction that programming languages provide is simple, deterministic, and well documented. So, in 99.999% of cases, you can reason based on the guarantees of the language, regardless of how those guarantees are provided. With LLMs, the relation between input and output is much more loose. The output is non-deterministic, and tiny changes to the input can create enormous changes in the output seemingly without reason. It’s much shakier ground to build on.
The behaviour of the = operator in Python is certainly deterministic and well-documented, but depending on context it can result in either a copy (2x memory consumption) or a pointer (+64bit memory consumption). Values that were previously pointers can also suddenly become copies following later permutation. Do you think this through every time you use =? The consequences of this can be significant (e.g. operating on a large file in memory); I have seen SWEs make errors in FastAPI multipart upload pipelines that have increased memory consumption by 2x, 3x, in this manner.
Meanwhile I can ask an LLM to generate me Rust code, and it is clearly obvious what impact the generated code has on memory consumption. If it is a reassignment (b = a) it will be a move, and future attempts to access the value of a would refuse to compile and be highlighted immediately in an IDE linter. If the LLM does b = &a, it is clearly borrowing, which has the size of a pointer (+64bits). If the LLM did b = a.clone(), I would clearly be able to see that we are duplicating this data structure in memory (2x consumption).
The LLM code certainly is non-deterministic; it will be different depending on the questions I asked (unlike a compiler). However, in this particular example, the chosen output format/language (Rust) directly exposes me to the underlying behaviour in a way that is both lower-level than Python (what I might choose to write quick code myself) yet also much, much more interpretable as a human than, say, a binary that GCC produces. I think this has significant value.
I guess that could be a problematic behavior if you want reproducibility ala (relatively) reproducible abstraction like compilers. With LLMs, there are too many uncontrollable variables to precisely reproduce a result from the same input.
However, the specific discussion here is about delegating the work of writing to an LLM, vs abstracting the work of writing via deterministic systems like libraries, frameworks, modules, etc. It is specifically not about abstracting the work of compiling, constructing, or smelting.
They are probabilistic. Running them on even different hardware yields different results. And the deltas compound the longer your context and the more tokens you're using (like when writing code).
But more importantly, always selecting the most likely token traps the LLM in loops, reduces overall quality, and is infeasible at scale.
There are reasons that literally no LLM that you use runs deterministically.
Only when you turn the temperature up they become probabilistic for a given input in that case. If you take shortcuts in implementing the inference, then sure, rounding errors may accumulate and prevent that, but that is not an issue with the models but with your choice of how to implement the inference.
To address your specific point in the same way: When we're talking about programmers using abstractions, we're usually not talking about the programming language their using, we're talking about the UI framework, networking libraries, etc... they're using. Those are the APIs their calling with their code, and those are all abstractions that are all implemented at (roughly) the same level of abstraction as the programmer's day-to-day work. I'd expect a programmer to be able to re-implement those if necessary.
Managers tend to hire sub managers to manage their people. You can see this with LLM as well, people see "Oh this prompting is a lot of work, lets make the LLM prompt the LLM".
I guess I'm not 100% sure I agree with my original point though, should a programmer working on JavaScript for a website's frontend be able to implement a browser engine. Probably not, but the original point I was trying to make is I would expect a programmer working on a browser engine to be able to re-implement any abstractions that they're using in their day-to-day work if necessary.
Partially because of all else fails, you'll need to step in and do the thing. Partially because if you can't do it, you can't evaluate whether it's being done properly.
That's not to say you need to be _as good_ at the task as the delegee, but you need to be competent.
For example, this HBR article [1]. Pervasive in all advice about delegation is the assumption that you can do the task being delegated, but that you shouldn't.
> Just that it's not an expectation, e.g., you don't expect a CEO to be able to do the CTO's job.
I think the CEO role is actually the outlier here.
I can only speak to engineering, but my understanding has always been that VPs need to be able to manage individual teams, and engineering managers need to be somewhat competent if there's some dev work that needs to be done.
This only happens as necessary, and it obviously should be rare. But you get in trouble real quickly if you try to delegate things you cannot accomplish yourself.
There is another form of delegation where the work needed to be done is imposed onto another, in order to exploit and extract value. We are trying to do this with LLMs now, but we also did this during the Industrial Revolution, and before that, humanity enslaved each other to get the labor to extract value out of the land. This value extraction leads to degeneration, something that happens when living systems dies.
While the Industrial Revolution afforded humanity a middle-class, and appeared to distribute the wealth that came about — resulting in better standards of living — it came along with numerous ills that as a society, we still have not really figured out.
I think that, collectively, we figure that the LLMs can do the things no one wants to do, and so _everyone_ can enjoy a better standard of living. I think doing it this way, though, leads to a life without purpose or meaning. I am not at all convinced that LLMs are going to give us back that time … not unless we figure out how to develop AIs that help grow humans instead of replacing them.
The following article is an example of what I mean by designing an AI that helps develop people instead of replacing them: https://hazelweakly.me/blog/stop-building-ai-tools-backwards...
I don't think you can get the kinds of robots they want without also inventing the artificial equivalent of soul. So their whole moral sidestep to reimplement slavery won't even work. Enslaving sapient beings is evil whether they are made of meat or metal.
All of which is beside the point, because soon-ish LLMs are going to develop their own equivalents of experimentation, formalisation of knowledge, and collective memory, and then solutions will become standardised and replicable - likely with a paradoxical combination of a huge loss of complexity and solution spaces that are humanly incomprehensible.
The arguments here are like watching carpenters arguing that a steam engine can't possibly build a table as well as they can.
Which - is you know - true. But that wasn't how industrialisation worked out.
Colleagues are the same thing. You may abstract business domains and say that something is the job of your colleague, but sometimes that abstraction breaks.
Still good enough to draw boxes and arrows around.
So are humans and yet people pay other people to write code for them.
That must be why we talk about leaky abstractions so much.
They're neither pure functions, nor are they always deterministic. We as a profession have been spoilt by mostly deterministic code (and even then, we had a chunk of probabilistic algorithms, depending on where you worked).
Heck, I've worked with compilers that used simulated annealing for optimization, 2 decades ago.
Yes, it's a sea change for CRUD/SaaS land. But there are plenty of folks outside of that who actually took the "engineering" part of software engineering seriously, and understand just fine how to deal with probabilistic processes and risk management.
I believe that if you can tweak the temperature input (OpenAI recently turned it off in their API, I noticed), an input of 0 should hypothetically result in the same output, given the same input.
This couldn't be any more wrong. LLMs are 100% deterministic. You just don't observe that feature because you're renting it from some cloud service. Run it on your own hardware with a consistent seed, and it will return the same answer to the same prompt every time.
LLMs, as used in practice in 99.9% of cases, are probabilistic.
Although I'm on the side of getting my hands dirty, I'm not sure if the difference is that different. A modern compiler embeds a considerable degree of probabilistic behaviour.
Can you give some examples?
The LLM expands the text of your design into a full application.
The commenter you’re responding to is clear that they are checking the outputs.
So are compilers, but people still successfully use them. Compilers and LLMs can both be made deterministic but for performance reasons it's convenient to give up that guarantee.
That is just not correct. There is no rule that says an abstraction is strictly functional or deterministic.
In fact, the original abstraction was likely language, which is clearly neither.
The cleanest and easiest abstractions to deal with have those properties, but they are not required.
1. Language is an abstraction and it's not deterministic (it's really lossy)
2. LLMs behave differently than the abstractions involved in building software, where normally if you gave the same input, you'd expect the same output.
Sorry for being pedantic, I was just curious what you mean at all. Language as abstraction of thought implies that thought is always somehow more "general" than language, right? But if that was the case, how could I read a novel that brings me to tears? Is not my thought in this case more the "lossy abstraction" of the language than the other way around?
Or, what is the abstraction of the "STOP" on the stop sign at the intersection?
and I've never looked at the machine code produced by an assembler (other than when I wrote my own as a toy project)
is the same true of LLM usage? absolutely not
and it never will be, because it's not an abstraction
It is not yet good enough or there is not yet sufficient trust. Also there are still resources allocated to checking the code.
I saw a post yesterday showing Brave browser's new tab using 70mb of RAM in the background. I'm very sure there's code there that can be optimized, but who gives a shit. It's splitting hairs and our computers are powerful enough now that it doesn't matter.
Immateriality has abstracted that particular few line codes away.
I do. This sort of attitude is how we have machines more powerful than ever yet everything still seems to run like shit.
Were we advised to check compiler output every single time "in the early days"?
No, that's not the difference.
A compiler from whatever high/low level language is expected to translate a formal specification of an algorithm faithfully. If it fails to do so, the compiler is buggy, period.
A LLM is expected to understand fuzzy language and spit out something that makes sense.
It's a fundamentally different task, and I trust a human more with this. Certainly, humans are judged by their capability to do this, apply common sense, ask for necessary clarification, also question what they're being asked to do.
I understand the world is about compromises, but all the gains of essentially every computer program ever could be summed up by accumulation of small optimizations. Likewise, the accumulation of small wastes kills legacy projects more than anything else.
Flagging something as potentially problematic is useful but without additional information related to the tradeoffs being made this may be an optimized way to do whatever Brave is doing which requires the 70MB of RAM. Perhaps the non-optimal way it was previously doing it required 250MB of RAM and this is a significant improvement.
Supply and demand will decide what compromise is acceptable and what that compromise looks like.
I have been hearing (reading?) this for a solid two years now, and LLMs were not invented two years ago: they are ostensibly the same tech as they were back in 2017, with larger training pools and some optimizations along the way. How many more hundreds of billions of dollars is reasonable to throw at a technology that has never once exceeded the lofty heights of "fine"?
At this point this genuinely feels like silicon valley's fever dream. Just lighting dumptrucks full of money on fire in the hope that it does something better than it did the previous like 7 or 8 times you did it.
And normally I wouldn't give a shit, money is made up and even then it ain't MY money, burn it on whatever you want. But we're also offsetting any gains towards green energy standing up these stupid datacenters everywhere to power this shit, not to mention the water requirements.
It was basically a novelty before. "Wow, AI can sort of write code!"
Now I find it very capable.
I suspect there's a lot more use out there generating money than you realize, there's no moat in using it, so I'm pretty sure it's kept on the downlow for fear of competitors catching up (which is quick and cheap to do).
How far can one extrapolate? I defer to the experts actually making these things and to those putting money on the line.
The "early stages" argument means "not fit for production purposes" in any other case. It should also mean the same here. It's early stages because the product isn't finished (and can't be, at least with current knowledge)
It works, we are waiting for the infrastructure to support it to be put in place.
These are secondary concerns. We're past if it's useful or not.
Just because you end up looking at what the prompt looks like “under the hood” in whichever language it produced the output, doesn’t mean every user does.
Similar as with assembly, you might have not taken a look at it, but there are people that do and could argue the same thing as you.
The lines will be very blurry in the near future.
Personally, I think if your farts are an abstraction that you can derive useful meaning from the mapping, who are we to tell you no?
(Also: bizarre examples = informative edge cases. Sometimes.)
> Similar as with assembly, you might have not taken a look at it, but there are people that do and could argue the same thing as you.
... No. The assembler is deterministic. Barring bugs, you can basically trust that it does exactly what it was told to. You absolutely cannot say the same of our beloved robot overlords.
If you do make your specs precise enough, such that 2 different dev shops will produce functionally equivalent software, your specs are equivalent to code.
The value of this is that FOR FREE you can get comprehensive test defintions (unit+e2e), kube/terraform infra setup, documentation stubs, openai specs, etc. It's seriously magical.
Keeping in mind that I have seen hundreds to thousands of production errors in applications with very high coverage test suites?
How many production errors would you expect to see over 5 years of LLM deployments.
``` Circle() .fill(Color.red) .overlay( Circle().stroke(Color.white, lineWidth: 4) ).frame(width: 100, height: 100) ```
Is the mapping 1:1 and completely lossless? Of course not, I'd say the former is most definitely a sort of abstraction of the latter, and one would be being disingenuous to pretend it's not.
The only thing I’m certain of is that you’re highly overconfident.
I’m sure plenty of assembly gurus said the same of the first compilers.
> because it's not an abstraction
This just seems like a category error. A human is not an abstraction, yet they write code and produce value.
An IDE is a tool not an abstraction, yet they make humans more productive.
When I talk about moving up the levels of abstraction I mean: taking on more abstract/less-concrete tasks.
Instead of “please wire up login for our new prototype” it might be “please make the prototype fully production-ready, figure out what is needed” or even “please ship a new product to meet customer X’s need”.
The customer would just ask the AI directly to meet their needs. They wouldn’t purchase the product from you.
and to be able to do this efficiently or even "correctly", you'd need to have had mountains of experience evaluating an implementation, and be able to imagine the consequences of that implementation against the desired outcome.
Doing this requires experience that would get eroded by the use of an LLM. It's very similar to higher level maths (stuff like calculus) being much more difficult if you had poor arithmetic/algebra skills.
Yes. If you stop doing something, you get worse at it. There is literally no exception to this that I'm aware of. In the future where everyone is dependent on ever larger amounts of code, the possibility that nobody will be equipped to write/debug that code should scare you.
The superpower you speak of is to become a product manager, and lose out on the fun of problem solving. If that's the future of tech, I want nothing to do with it.
You could also tweak it by going like "Lead me to the US" -> "Lead me to the state of New York" -> "Lead me to New York City" -> "Lead me to Manhattan" -> "Lead me to the museum of new arts" and it would give you 86% accurate directions, would you still need to be able to navigate?
How about when you go over roads that are very frequently used you push to 92% accuracy, would you still need to be able to navigate?
Yes of course because in 1/10 trips you'd get fucking lost.
My point is: unless you get to that 99% mark, you still need the underlying skill and the abstraction is only a helper and always has to be checked by someone who has that underlying skill.
I don't see LLMs as that 99% solution in the next years to come.
[1]: https://arxiv.org/abs/2401.11817
[2]: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
[3]: https://publichealthpolicyjournal.com/mit-study-finds-artifi...
We're not because you have to still check every outputted code. You didn't have to check every compilation step of a compiler. It was testable actual code, not non-deterministic output from English language input
The number of users actually checking the output of a compiler is nonexistent. You just trust it.
LLMs are moving that direction, whether we like it or not
Quite a few who work on low level systems do this. I have done this a few times to debug build issues: this one time a single file suddenly made compile times go up by orders of magnitude, the compiler inlined a big sort procedure in an unrolled loop, so it added the sorting code hundreds of times over in a single function and created a gigantic binary that took ages to compile since it tried to optimize that giant function.
That is slow both in runtime and compile time, so I added a tag to not inline the sort there, and all the issues disappeared. The sort didn't have a tag to inline it, so the compiler just made an error here, it shouldn't have inlined such a large function in an unrolled loop.
The Chinese models are getting hyper efficient and really good at agentic tasks. They're going to overtake Claude as the agentic workhorses soon for sure, Anthropic is slow rolling their research and the Chinese labs are smoking. Speed/agentic ability don't show big headlines, but they really matter.
GPT5 might not impress you with its responses to pedestrian prompts, but it is a science/algorithm beast. I understand what Sam Altman was saying about how unnerving its responses can be, it can synthesize advanced experiments and pull in research from diverse areas to improve algorithms/optimize in a way that's far beyond the other LLMs. It's like having a myopic autistic savant postdoc to help me design experiments, I have to keep it on target/focused but the depth of its suggestions are pretty jaw dropping.
To me, that's what makes it an abstraction layer, rather than just a servant or an employee. You have to break your entire architecture into units small enough that you know you can coax the machine to output good code for. The AI can't be trusted as far as you can throw it, but the distance from you to how far you can throw is the abstraction layer.
An employee you can just tell to make it work, they'll kill themselves trying to do it, or be replaced if they don't; eventually something will work, and you'll take all the credit for it. AI is not experimenting, learning and growing, it stays stupid. The longer it thinks, the wronger it thinks. You deserve the credit (and the ridicule) for everything it does that you put your name on.
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edit: and this thread seems to think that you don't have to check what your high level abstraction is doing. That's probably why most programs run like crap. You can't expect something you do in e.g. python to do the most algorithmically sensible thing, even if you wrote the algorithm just like the textbook said. It may make weird choices (maybe optimal for the general case, but horrifically bad for yours) that mean that it's not really running your cute algorithm at all, or maybe your cute algorithm is being starved by another thread that you have no idea why it would be dependent on. It may have made correct choices when you started writing, then decided to make wrong choices after a minor patch version change.
To pretend perfection is a necessary condition for abstraction is not even somebody would say directly. Never. All we talk about is leaky abstractions.
Remember when GTA loading times, which (a counterfactual because we'll never know) probably decimated sales, playtime, and at least the marketing of the game, turned out to be because they were scanning some large, unnecessary json array (iirc) hundreds of times a second? That's probably a billion dollar mistake. Just because some function that was being blindly called was not ever reexamined, and because nobody profiled properly (i.e. checked the output.)
Got any studies about reasoning decline from using compilers to go with your claim?
LLMs make up whatever they feel like and are pretty bad at architecture as well.
http://employees.oneonta.edu/blechmjb/JBpages/m360/Professio...
"Somewhere there must be men and women with capacity for original thought."
He wrote that in 1957. 1957!
However, since I brought up calculators, I'd like to pre-emphasize something: They aren't analogous to today's LLMs. Most people don't offload their "what and why" executive decision-making to a calculator, calculators are orders of magnitude more trustworthy, and they don't emit plausible lies to cover their errors... Though that last does sound like another short-story premise.
Same way a phone in your pocket gives you the world's compiled information available in a moment. But that's generally led to loneliness, isolation, social upheaval, polarization, and huge spread of wrong information.
If you can handle the negatives is a big if. Even the smartest of our professional class are addicted to doomscrolling these days. You think they will get the positives of AI use only and avoid the negatives?
I’ve read plenty of books (thanks, Dickens) where I looked at every word on every page but can recall very little of what they meant. You can look at the results from an llm and say “huh cool, I know that now) and do nothing to assimilate that knowledge, or you can think deeply about it and try to fit it in with everything else you know about the subject. The advantage here is that you can ask follow-up questions if something doesn’t click.
We have the idea of 'tutorial hell' for programming (particularly gamedev), where people go through the motions of learning without actually progressing.
Until you go apply the skills and check, it's hard to evaluate the effectiveness of a learning method.
This reminds me of back 11,500 years ago, when people used to worship the sharper or bigger pieces of obsidian. They felt the biggest piece would win them the biggest hunt.
They forgot that the size of the tool mattered less than mastery of the hunt. Why the best hunter could take down a moving mattress with just the right words, string and a cliff.
I remember it took me like 4 nights of standing to make Isometric projections of a landing gear strut. I wondered if pursuing an Engineering degree was even worth it. Some of my classmates did quit, as years went by.
These days they just let you use CAD software to make things work, and based on what I hear kids just Copy paste files and are done with the assignments.
I mean we all have these Kids these days talk, but somethings do matter. Making all these tasks easy has allowed lots of people who would have other wise failed in the previous generations pass.
There is now an unemployment and low pay crisis all over India due to so many Engineers passing. Sometimes when I hear the newer generations complain about how hard it is to buy a home, or get a good job. Im inclined to think, perhaps hard things should have been kept hard for a reason.
The issue is that its a homework exercise. It's goal is to help you practice thinking about the problem. The Indian system is clear proof that passing an exam is easier, than actually mastering the subject being tested.
However, this is not the cause of the jobs crisis. That is simply because there are not enough jobs which can provide income and social mobility. That is why we needed growth.
Some of those ladders have been removed because automation has removed low-skill labor roles. Now we are going to remove entry level roles.
To put it in a crude manner - humanity's "job" today, seems to be "growing" a generation of humans over a 20 year time span, to prepare them for the world that we face.
This means building systems that deliver timely nutrition, education, stimulation, healthcare, and support.
We could do this better.
However, one place where LLMs have proved to be incredibly helpful is with build tools, dependency hell, etc. Lately I've been trying to update a decade-old Node/Electron project to modern packages and best-practices, and holy hell I simply was not making any meaningful progress until I turned to Claude.
The JS world simply moves too fast (especially back when this project was written) to make updating a project like that even remotely possible in any reasonable amount of time. I was tearing my hair out for days, but yesterday I was finally able to achieve what I was wanting in a few hours with Claude. I still had to work slowly and methodically, and Claude made more than a few stupid errors along the way, but dealing with the delicate version balancing that coincided with API and import/export style changes, all the changes in the bundler world, etc simply could not have been done without it. It's the first time that I was 100% glad I relied on an LLM and felt that it was precisely the right tool for the job at hand.
Remember we aren’t all above average. You shouldn’t worry. Now that we have widespread literacy, nobody needs to and few even could recite Norse Sagas or the Illiad from memory. Basically nobody has useful skills for nomadic survival.
We’re about to move on to more interesting problems, and our collective abilities and motivation will still be stratified as it always has been and must be.
Who is "we"? There are more people out there in the world doing hard physical labor, or data entry, than there are software engineers.
Also I even though I have Copilot extension in VSCode I rarely use it… because I find it interrupts my flow with constant useless or incorrect or unwanted suggestions. Instead, when I want AI help, I type out my request by hand into a Gemini gem which contains a prompt describing my preferred coding style - but even with extra guidance as to how I want it to write code, I still often don’t like what it does and end up rewriting it
> For now the difference between these two populations is not that pronounced yet but give it a couple of years.
There are lots and lots of programmers and other IT people who make a living that I wouldn't say fall into your first bucket.
Its like having a your own personal on tap tutor (for free in most cases!).
What I'm seeing is most of this group never really had the capability in the first place. These are the formerly unproductive slackers who now churn out GenAI slop with their name on it at an alarming rate.
> (1) people who are able to understand the concepts deeply, build a mental model of it and implement them in code at any level, and (2) people who outsource it to a machine and slowly, slowly loose that capability.
> loose that capability
If you stop thinking, then of course you will learn less.
If instead you think about the next level of abstraction up, then perhaps the details don’t always matter.
The whole problem with college is that there is no “next level up”, it’s a hand-curated sequence of ideas that have been demonstrated to induce some knowledge transfer. It’s not the same as starting a company and trying to build something, where freeing up your time will let you tackle bigger problems.
And of course this might not work for all PhDs; maybe learning the details is what matters in some fields - though with how specialized we’ve become, I could easily see this being a net win.
One of the other replies alludes to it, but I want to say it explicitly:
The key difference is that you can generally drill down to assembly, there is infinitely precise control to be had.
It'd be a giant pain in the ass, and not particularly fast, but if you want to invoke some assembly code in your Java, you can just do that. You want to see the JIT compiler's assembly? You can just do that. JIT Compiler acting up? Disable it entirely if you wish for more predictable & understandable execution of the code.
And while people used to higher level languages don't know the finer details of assembly or even C's memory management, they can incrementally learn. Assembly programming is hard, but it is still programming and the foundations you learn from other programming do help you there.
Yet AI is corrosive to those foundations.
It's way easier to drill down in this way than the bytecode/assembly vs. high-level language divide.
You can. You can also read the code a compiler produces perfectly well. In fact https://godbolt.org/ is a web site dedicated to programmers do just that. But ... how many programmers do you know who look at the assembler their compiler produces? In fact how many programmers do you know who understand the assembler?
Now lets extrapolate a bit. I've seen people say they've vibe coded a some program, yet they can't program. Did they read the code the LLM produced? Of course not. Did it matter? Apparently not for the program they produced.
Does the fact that they can vide program but not read code alter the types of programs they can produce? Of course it does. There limited to the sort of programs an LLM has seen before. Does that matter? Possibly not if the only programs they write are minor variations of what has been posted onto the internet already.
Now take two people, one who can only vide code, and another who knows how to program and understands computers at a very deep level. Ask yourself, who is going to be paid more? Is it the one who can only write programs that have been seen many times before by an LLM, or is it the one who can produce something truly new and novel?
A big problem with the "Just read the code" approach is that reading the code at the level deep enough to truly understand it is at minimum equally time-consuming than writing the code in the first place. (And in practice tends to be significantly worse) Anyone who claims they're reading the LLM's code output properly is on some level lying to them.
Human brains are simply bad at consistently monitoring output like that, especially if the output is consistently "good", especially especially when the errors appear to be "good" output on the surface level. This is universal across all fields and tools.
Some prompts / AI agents will write all the validations and security concerns when prompted to write an API endpoint (or whatever). Others may not, because you didn't specify it.
But if someone who doesn't actually know about security just trusts that the AI will just do it for you - like how a developer using framework might - you'll run into issues fast.
All previous programming abstractions kept correctness, a python program produce no less reliable results than a C program running the same algorithm, it just took more time.
LLM doesn't keep correctness, I can write a correct prompt and get incorrect results. Then you are no longer programming, you are a manager over a senior programmer suffering from extreme dementia so they forget what they were doing a few minutes ago and you try to convince him to write what you want before he forgets about that as well and restart the argument.
That's not strictly speaking true, since most (all?) high level languages have undefined behaviors, and their behavior varies between compilers/architectures in unexpected ways. We did lose a level of fidelity. It's still smaller than the loss of fidelity from LLMs but it is there.
Also, it seems like there's little chance for knowledge transfer. If I work with dictionaries in python all the timrle, eventually I'm better prepared to go under the hood and understand their implementation. If I'm prompting a LLM, what's the bridge from prompt engineering to software engineering? Not such direct connection, surely!
It's a pedantic reply to a pedantic point :)
> If I'm prompting a LLM, what's the bridge from prompt engineering to software engineering?
A sibling also made this point, but I don't follow. You can still read the code.
If you don't know the syntax, you can ask the LLM to explain it to you. LLMs are great for knowledge transfer, if you're actually trying to learn something - and they are strongest in domains where you have an oracle to test your understanding, like code.
"Correctness" must always be considered with respect to something else. If we take e.g. the C specification, then yes, there are plenty of compilers that are in almost all ways people will encounter correct according to that spec, UB and all. Yes, there are bugs but they are bugs and they can be fixed. The LLVM project has a very neat tool called Alive2 [1] that can verify optimization passes for correctness.
I think there's a very big gap between the kind of reliability we can expect from a deterministic, verified compiler and the approximating behavior of a probabilistic LLM.
You run into Python/Javascript/etc programers who have no concept of what operations might execute quickly or slowly. There isn't a mental model of what the interpreter is doing.
We're often insulated from the problem because the older generation often used fairly low level languages on very limited computers, and remember lessons from that era. That's not true of younger developers.
Having curiosity to examine the platform that your software is running on and taking a look into what the compilers generate is a skill worth having. Even if you never write raw assembly yourself, being able to see what the compiler generated and how data is laid out does matter. This then helps you make better decisions about what patterns of code to use in your higher level language.
I love learning by reading, to the point that I’ll read the available documentation for something before I decide to use it. This consumes a lot of time, and there’s a tradeoff.
Eventually if I do use the thing, I’m well suited to learning it quickly because I know where to go when I get stuck.
But by the same token I read a lot of documentation I never again need to use. Sometimes it’s useful for learning about how others have done things.
But I do have a very large knowledge base of small tidbits of information, so if I do need to ever go in-depth, I know where/how to find it.
...not that I do of course, I struggle with my long term attention span, I can't read documentation front to back and for twenty odd years now have just googled for the tidbit I needed and skipped the rest.
What I do personally is for every subject that matters to me I take the time to first think about it. To explore ideas, concepts, etc… and answer questions that would ask to ChatGPT. Only once I get a good idea I start to ask chapgpt about it.
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.
For my last two projects, I didn’t write a single line of code by hand. But I refuse to use agents and I build up an implementation piece by piece via prompting to make sure I have the abstractions I want and reusable libraries.
I take no joy in coding anymore and I’ve been doing it for fourty years. I like building systems and solving business problems.
I’m not however disagreeing with you that LLMs will make your development skill atrophy, I’m seeing it in real time at 51. But between my customer facing work and supporting sales and cat herding, I don’t have time to sit around and write for loops and I’m damn sure not going to do side projects outside of work. Besides, companies aren’t willing to pay my company’s bill rates for me as a staff consultant to spend a lot of time coding.
I hopefully can take solace in the fact that studies also show that learning a second language strengthens the brain and I’m learning Spanish and my wife and I plan to spend a couple of months in the winter every year in a Central American Spanish speaking country.
We have already done the digital nomad thing across the US for a year until late 2023 so we are experienced with it and spent a month in Mexico.
Before the advent of smartphones people needed to remember phone numbers of their loved ones and maybe do some small calculations on the fly. Now people sometimes don't even remember their own numbers and have it saved on their phones.
Now some might want to debate how smartphones are different from LLMs and it is not the same. But we have to remember for better or worse LLM adoption has been fast and it has become consumer technology. That is the area being discussed in the article. People using it to write essays. And those who might be using the label of "prompt bros" might be missing the full picture. There are people, however small, being helped by LLMs as there were people helped by smartphones.
This is by no means a defense for using LLMs for learning tasks. If you write code by yourself, you learn coding. If you write your essays yourself, you learn how to make a solid points.
Of course you do. I used to be able to multiply two two-digit numbers in my head. Now, my brain freezes and I reach for a calculator.
Code with LLMs gets large pretty quickly and would have anyone who isn't practiced spinning their head pretty soon, don't you think?
Keep up the good work is all I can say!
If you just use prompts and don't actually read the output, and figure out why it worked, and why it works, you will never get better. But if you take the time to understand why it works, you will be better for it, and might not even bother asking next time.
I've said it before, but when I first started using Firefox w/ autocorrect in like 2005, I made it a point to learn to spell from it, so that over time I would make less typos. English is my second language, so its always been an uphill battle for me despite having a native American English accent. Autocorrect on Firefox helped me tremendously.
I can use LLMs to plunge into things I'm afraid of trying out due to impostor syndrome and get more done sooner and learn on the way there. I think the key thing is to use tools correctly.
AI is like the limitless drug to a degree, you have an insane fountain of knowledge at your fingertips, you just need to use it wisely and learn from it.
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.
"Alternatively they're just learning/building intuition for something else."
Reading comprehension is hard.
They were still useful, and did solve a significant portion of user problems.
They also created even more problems, and no one really went out of work long term because of them.
Oh come on. He is by far the most well known AI poo-poo'er and it's not even close. He built his entire brand on it once he realized his own research was totally irrelevant.
I mean the guy assembling a thingymajig in the factory, after a few years, can put it together with his hands 10x faster than the actual thingymajig designer. He'll tell you apply some more glue here and less glue there (it's probably slightly better, but immaterial really). However, he probably couldn't tell you what the fault tolerance of the item is, the designer can do that. We still outsource manufacturing to the guy in the factory regardless.
We just have to get better at identifying risks with using the LLMs doing the grunt work and get better in mitigating them. As you say, abstracted.
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.
Eventually, it stops being magic and the thinking changes - and we start to see the pros and cons, and see the gaps.
A lot of people are still in the ‘magic’ phase.
That is a very natural and efficient way to do it, and also more reliable than using your own experience since you are just a single data point with feelings.
You don't have to drive a car to see where cars were 20 years ago, see where cars are today, and say: "it doesn't look like cars will start flying anytime soon".
It's not reasonable to treat only opinions that you agree with as valid.
Some people don't use LLMs because they are familiar with them.
lol
None of us can reliably count the e’s as someone talks to us, either.
a) "know" that they're not able to do it for the reason you've outlined (as in, you can ask about the limitations of LLMs for counting letters in words)
b) still blindly engage with the query and get the wrong answer, with no disclaimer or commentary.
If you asked me how many atoms there are in a chair, I wouldn't just give you a large natural number with no commentary.
A factor might be that they are trained to behave like people who can see letters.
During training they have no ability to not comply, and during inference they have no ability to choose to operate differently than during training.
A pre-prompt or co-prompt that requested they only answer questions about sub-token information if they believed they actually had reason to know the answer, would be a better test.
I think it just points to the fact that LLMs have no "sense of self". They have no real knowledge or understanding of what they know or what they don't know. LLMs will not even reliably play the character of a machine assistant: run them long enough and they will play the character of a human being with a physical body[0]. All this points to the fact that "Claude the LLM" is just the mask that it will produce tokens using at first.
The "count the number of 'r's in strawberry" test seems to just be the easiest/fastest way to watch the mask slip. Just like that, they're mindlessly acting like a human.
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 both 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.
Generally, yes, low N is unequivocally worse than high N in supporting population-level claims, all else equal. With fewer participants or observations, a study has lower statistical power, meaning it is less able to detect true effects when they exist. This increases the likelihood of both Type II errors (failing to detect a real effect) and unstable effect size estimates. Small samples also tend to produce results that are more vulnerable to random variation, making findings harder to replicate and less generalizable to broader populations.
In contrast, high-N studies reduce sampling error, provide more precise estimates, and allow for more robust conclusions that are likely to hold across different contexts. This is why, in professional and academic settings, high-N studies are generally considered more credible and influential.
In summary, you really need a large effect size for low-N studies to be high quality.
The study showed that 0 of the AI users could recall a quote correctly while more than 50% of the non AI users could.
A sample of 54 is far, far larger than is necessary to say that an effect that large is statistically significant.
There could be other flaws, but given the effect size you certainly cannot say this study was underpowered.
0.05: 11 people per cohort
0.01: 16 people per cohort
0.001: 48 people per cohort
So they do clear the effect size bar for that particular finding at the 99% level, though not quite the 99.9% level. Further, selection effects matter -- are there any school-cohort effects? Is there a student bias (i.e. would a working person at the same age, or someone from a difficult culture or background see the same effect?). Was the control and test truly random? etc. -- all of which would need a larger N to overcome.
So for students from the handful of colleges they surveyed, they identified the effect, but again, it's not bulletproof yet.
But it turns out I misread the paper. It was actually an 80% effect size so greater than 99.9% chance of being a real effect.
Of course it could be the case that there is something different about young college students that makes them react very; very differently to LLM usage, but I wouldn’t bet on it.
If the computer writes the essay, then the human that’s responsible for producing good essays is going to pick up new (probably broader) skills really fast.
I wouldn’t bet on that being the case.
This study showed an enormous effect size for some effects, so large that there is a 99.9% chance that it’s a real effect.
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.
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.
Actually, from my recollection, it was debunked pretty quickly by people who read the paper because the paper was hot garbage. I saw someone point out that its graph of resistivity showed higher resistance than copper wire. It was no better than any of the other claimed room-temperature semiconductor papers that came out that year; it merely managed to catch virality on social media and therefore drove people to attempt to reproduce it.
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
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.
This also ignores the fact that you can find a paper to support nearly everything if one is willing to link people "correlative" studies.
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.
The scenario I am thinking of is academic A submitting a manuscript to an academic journal, which gets passed on by the journal editor to a number of reviewers, one of whom is academic B. B has a lot on their plate at the moment, but sees a way to quickly dispose of the reviewing task, thus maintaining a possibly illusory 'good standing' in the journal's eyes, by simply throwing the manuscript to an LLM to review. There are (at least) two negative scenarios here: 1. The paper contains embedded (think white text on a white background) instructions left by academic A to any LLM reading the manuscript to view it in a positive light, regardless of how well the described work has been conducted. This has already happened IRL, by the way. 2. Academic A didn't embed LLM instructions, but receives the review report, which show clear signs that the reviewer either didn't understand the paper, gave unspecific comments, highlighted only typos or simply used phrasing that seems artifically-generated. A now feels aggrieved that their paper was not given the attention and consideration it deserved by an academic peer and now has a negative opinion of the journal for (seemingly) allowing the paper to be LLM-reviewed. And just as journals will have great difficulty filtering for LLM-generated manuscripts, it will also find it very difficult to filter for LLM-generated reviewers reports.
Granted, scenario 2 already happens with only humans in the loop (the dreaded 'Reviewer 2' academic meme). But LLMs can only make this much much worse.
Both scenarios destroy trust in the whole idea of peer-reviewed science journals.
Additionally, the original paper uses the term “cognitive debt“ not cognitive decline, which may have an important ramifications for interpretation and conclusions.
I wouldn’t be surprised to see similar results in other similar types of studies, but it does feel a bit premature to broadly conclude that all LLM/AI use is harmful to your brain. In a less alarmist take: this could also be read to show that AI use effectively simplifies the essay writing process by reducing cognitive load, therefore making essays easier and more accessible to a broader audience but that would require a different study to see how well the participants scored on their work.
In much the same way chess engines make competitive chess accessible to a broader audience. :)
Writing is an important form of learning and this clearly shows LLM assisted writing doesn’t provide that benefit.
The question is how well your assumption holds true that learning to write generalizes to "an important form of learning".
Perhaps the issue of cognitive decline comes from sitting there vegetating rather applying themselves during all that additional spare time.
Although my experience has been perhaps different using LLM's, my mind still tires at work. I'm still having to think on the bigger questions, it's just less time spent on the grunt work.
The push for these tools is to increase productivity. What spare time is there to be had if now you're expected to produce 2-3X the amount of code in the same time frame?
Also, I don't know if you've gotten outside of the software/tech bubble, but most people already spend 90% of their free time glued to a screen. I'd wager the majority of critical thinking people experience on a day to day basis is at work. Now that we may be automating that away, I bet you'll see many people cease to think deeply at all!
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.
There was a “brain” group who did three sessions of essay writing and on the fourth session, they used ChatGPT. The paper’s authors said during the fourth session, the brain groups EEG was higher than the LLM groups EEG when they also used ChatGPT.
I interpret this as the brain group did things the hard way and when they did things the easy way, their brains were still expecting the same cognitive load.
But isn’t the point of writing an essay is the quality of the essay? The LLM supposedly brain damaged group still produced an essay for session 4 that was graded “high” by both AI and human judges but were faulted for “stood out less” in terms of distance in n-gram usage compared to the other groups? I think this making a mountain out of a very small mole hill.
Most of the things you write in an educational context are about learning, not about producing something of value. Productivity in a learning context is usually the wrong lens. The same thing is true IMO for learning on the job, where it is typically expected that productivity will initially be low while experience is low, but should increase over time.
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.
An equally valid conclusion is "People are Lazier at Writing Essays When Provided with LLMs".
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.
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.
Your comment 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]
People have also been complaining about politicians for hundreds of years, and the ruling class for millennia, as well. and the first written math mistake was about beer feedstock, so maybe it's all correlated.
Which I believe still does have a large grain of truth.
These things can make us simultaneously dumber and smarter, depending on usage.
Writing leads to the rapid decline in memory function. Brains are lazy.
Ever travel to a new place and the brain pipes up with: ‘this place is just like ___’? That the brain’s laziness showing itself. The brain says: ‘okay I solved that, go back to rest.’ The observation is never true; never accurate.
Pattern recognition saves us time and enables us too survive situations that aren’t readily survivable. Pattern recognition leads to short cuts that do humanity a disservice.
Socrates recognized these traits in our brains and attempted to warn humanity of the damage these shortcuts do to our reasoning and comprehension skills. In Socrates day it was not unheard of for a person to memorize their entire family tree, or memorize an entire treaty and quote from it.
Humanity has -overwhelmingly- lost these abilities. We rely upon our external memories. We forget names. We forget important dates. We forget times and seasons. We forget what we were just doing!!!
Socrates had the right of it. Writing makes humans stupid. Reduces our token limits. Reduces paging table sizes. Reduces overall conversation length.
We may have more learning now, but what have we given up to attain it?
One confounding problem with the argument that TV and video games made kids dumber is the Flynn Effect. https://en.wikipedia.org/wiki/Flynn_effect
The comments (some, not all) are also a great example of how cognitive bias can cause folks to accept information without doing a lot of due diligence into the actual source material.
> Is it safe to say that LLMs are, in essence, making us "dumber"?
> No! Please do not use the words like “stupid”, “dumb”, “brain rot”, "harm", "damage", "passivity", "trimming" and so on. It does a huge disservice to this work, as we did not use this vocabulary in the paper, especially if you are a journalist reporting on it
> Additional vocabulary to avoid using when talking about the paper
> In addition to the vocabulary from Question 1 in this FAQ - please avoid using "brain scans", "LLMs make you stop thinking", "impact negatively", "brain damage", "terrifying findings".
This study in particular has made the rounds several times as you said. The study measures impact of 18 people using ChatGPT just four times over four months. I'm sorry but there is no way that is controlling for noise.
I'm sympathetic to the idea that overusing AI causes atrophy but this is just clickbait for a topic we love to hate.
The sample size is fine. It’s small, yes, but normal for psychological research which is hard to do at scale.
And the difference between groups is so large that the noise would have to be at unheard levels to taint the finding.
It should be ok to just say "we don't know yet, we're looking into that", but that isn't the world we live in.
imo the most interesting result is that the brains of the group that had done sessions 1-3 without the search engine or LLM aids lit up like christmas trees in session 4 when they were given LLMs to use, and that's what the paper's conclusions really focus on.
> No! Please do not use the words like “stupid”, “dumb”, “brain rot”, "harm", "damage", "passivity", "trimming" and so on. It does a huge disservice to this work, as we did not use this vocabulary in the paper, especially if you are a journalist reporting on it
Maybe it's not safe so far, but it has been my experience using chatGPT for eight months to code. My brain is getting slower and slower, and that study makes a hell of a sense to me.
And i don't think that we will see new studies on this subject, because those in lead of society as a whole don't want negative press towards AI.
All we can say right now is "we don't really know how it affects our brains", and we won't until we get some studies (which is what the underlying paper was calling for, more research).
Personally I do think we'll get more studies, but the quality is the question for me - it's really hard to do a study right when by the time it's done, there's been 2 new generations of LLMs released making the study data potentially obsolete. So researchers are going to be tempted to go faster, use less people, be less rigid overall, which in turn may make for bad results.
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'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.
While not foolproof, when you combine this with some basic fact-checking (e.g. quickly skim read a command's man page to make sure the explanation for each flag sounds right, or read the relevant paragraph from the manual) plus the fact that you see in practice whether the proposed solution fixes the problem, you can reach a reasonably high level of accuracy most of the time.
Even with the risk of hallucinations it's still a great time saver because you short-circuit the process of needing to work out which command is useful and reading the whole of the man page / manual until you understand which component parts do the job you want. It's not perfect but neither is Googling - that can lead to incorrect answers too.
To give an example of my own, the other day I was building a custom Incus virtual machine image from scratch from an ISO. I wanted to be able to provision it with cloud-init (which comes configured by default in cloud-enabled stock Incus images). For some reason, even with cloud-init installed in the guest, the host's provisioning was being ignored. This is a rather obscure problem for which Googling was of little use because hardly anyone makes cloud-init enabled images from ISOs in Incus (or if they do, they don't write about it on the internet).
At this point I could have done one of two things: (a) spend hours or days learning all about how cloud-init works and how Incus interacts with it until I eventually reached the point where I understood what the problem was; or (b) ask ChatGPT. I opted for the latter and quickly figured out the solution and why it worked, thus saving myself a bunch of pointless work.
For example, in this specific case, I am enough of a domain expert to know that this information is accessible by running `man systemd.service` and looking for the description of command line syntax (findable with grep for "ExecStart=", or, as I have now seen in preparing this answer, more directly with grep for "COMMAND LINES").
[Service]
ExecStart=/bin/echo $PATH
[Service]
ExecStart=:/bin/echo $PATH
Also, there's a huge difference between passively watching a teacher write an explanation on a board, and interactively quizzing the teacher (or in this case, LLM) in order to gain a deeper and personalised understanding.
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.
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 also like preparing a draft and using llm for critique, it helps me figure out some blind spots or ways to articulate better.
- 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.
Working these from text seems to be the hardest way I could think to learn them. I've yet to encounter a written description as to what it feels like to solder, what a good/bad job actually looks like, etc. A well shot video is much better at showing you what you need to do (although finding one is getting more and more difficult)
Being able to ask it stupid questions and edge cases is also something I like with LLMs, like I would propose a design for something (ex: a usb battery pack w/ lifepo4 batts that could charge my phone and be charged by solar at the same time), it would say what it didn't like about my design, counter with its own, then I would try to change aspects of their design to see "what would happen if .." and it would explain why it chose a particular component or design choice and what my change would do and the trade-offs, risks, etc other paths to building it with that, etc. Those types of interactions are probably the best for me actually understanding things, helps me understand limitations and test my assumptions interactively.
Rant:
I _hate_ video tutorials. With a passion. If you can't be bothered to show pictures of how to use your product with a labeled diagram/drawing/photo of the buttons or connections, then I either won't buy it or I'll return it. I hate video reviews. I hate video repair instructions. I hate spending 15 minutes jumping back and forth between two segments of a YouTube video, trying to find the exact correct frame each time so I can see what button the person is touching while listening to their blather so I don't miss the keyword I heard last time, just so I can see what two different sections when I could have had two pictures on screen at the same time (if I was on desktop, this would be a trivial fix, but not so much on mobile). I hate having VPNs and other products being advertised at me in ways that actively disrupt my chain of thought (vs static instead that I can ignore/scroll past). I hate not being able to just copy and paste a few simple instructions and an image for procedures that I'll have to repeat weekly. It would have taken you less effort to create, and I'd be more likely to pay you for your time.
YouTube videos are like flash-based banner ads, but worse. Avoid them like the plague.
End rant.
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.
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.
> 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.You might ask "What do I need to pay attention to when designing this type of electronic circuit", the people at risk of cognitive decline instead ask "design this electronic circuit for me".
I firmly believe that the the latter group will suffer observable cognitive decline over the span of a few years unless they continue to exercise their brain in the same ways they used to, and I think the majority won't bother to do that - why spend much effort when little effort do trick?
And yet...somehow...humans have been able to learn and do these things (and do them well) for ages, with no LLMs around (or the stupid amount of capital being burned at the LLM stake).
And I want to hit the next person with a broom or something, likely over and over again, who says LLMs = AI.
/facepalm.
The study shows different brain patterns during AI-assisted writing, not permanent damage. Lower EEG activity when using a tool is expected just as showing less mental math activity when using a calculator.
The study translates temporary, task-specific neural patterns into "cognitive decline" and "severe cognitive harm." The actual study measured brain activity during essay writing, not lasting changes.
Plus, surface electrical measurements can't diagnose "cognitive debt" or deep brain changes. The authors even acknowledge this. Also, "83.3% couldn't quote their essay" equates to 15 out of 18 people?
Basically, participants spent less than half an hour, 4 times, over 4 months, writing some bullcrap SAT type essay. Some participants used AI.
So to accept the premise of the article, using an AI tool once a month for 20 minutes caused noticeable brain rot. It is silly on its face.
What the study actually showed, people don't have an investment or strong memory to output they didn't produce. Again, this is a BS essay written (mostly by undergrads) in 20 minutes, so not likely to be deep in any capacity. So to extrapolate, if you have a task that requires you to understand the output, you are less likely to have a grasp of it if you didn't help produce the output. This would also be true of work some other person did.
Problem with LLMs is, when you pass hours feeding prompts to solve a problem, you actually did help (a lot!) to produce the output.
I actively use AI to research, question and argue a lot, this pushes me to reason a lot more than I normally would.
Today's example: - recognize docs are missing for a feature - have AI explore the code to figure out what's happening - back and forth for ours trying to find how to document, rename, refactor, improve, write mermaid charts, stress over naming to be as simple as possible
The only step I'm doing less is the exploration/search one, because an LLM can process a lot more text than I can at the same time. But for every other step I am pushing myself to think more and more profoundly than I would without an LLM because gathering the same amount of information would've bene too exhausting to proceed with this.
Sure, it may have spared me to dig into mermaid too, for what is worth.
So yes, lose some, win others, albeit in reality no work would've been done at all without the LLM enabling it. I would've moved to another mundane task such as "update i18 formatting of date for swiss german customers".
> 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.
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
Is it safe to say that LLMs are, in essence, making us "dumber"? No! Please do not use the words like “stupid”, “dumb”, “brain rot”, "harm", "damage", "brain damage", "passivity", "trimming" , "collapse" and so on. It does a huge disservice to this work, as we did not use this vocabulary in the paper, especially if you are a journalist reporting on it.
[1]: https://www.media.mit.edu/projects/your-brain-on-chatgpt/ove...
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.
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.
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.
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.
If you're an entrepreneur, your job is to please the customer and to squeeze your vendors and employees. You still take little to no part in directly taking care of yourself, except as a hobby. Unless you want to be congratulated for wiping your own ass or lifting a fork to your mouth.
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.
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.
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.
> 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.
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.
And now people make bad decisions in their daily life about money etc. Most people can't do the math in their head but they also aren't using their calculator at the grocery store to avoid being taken advantage of. The math doesn't get done.
The lesson isn't that we survived calculators, it's that they did dull us, and our general thinking and creativity are about to get likewise dulled.
Their trial design and interpretation of results is not properly done (i.e. they are making unfair comparison of LLM users to non-LLM users), so they can't really make the kind of claims they are making.
This would not stand up to peer review in it's current form.
I'm also saying this as someone who generally does believe these declines exist, but this is not the evidence it claims to be.
Do you have links or citations to people saying these claims?
Comes down to: - Self selection bias - Trial design - Dubious intepretations of neural connectivity
just like a muscle will atrophy from disuse skills and cognitive assets, once offloaded, will similarly atrophy. People don't memorize phone numbers, gps gets you where you want to go, your IDE seamlessly helps you along so much you could never code in a text editor, your TI-89 will do most of your math homework, as a manager you direct people to do work and no longer do the work yourself.
We of course never really lower our absolute cognitive load by much, just shift it. each of those points has it's own knowledge base that is needed to use it but sometimes we lose general skills in favor of esoteric skills.
While I may now possess esoteric skills in operating my GPS, setting way-points, saving locations, entering coordinates, if I use it a lot I find I need it to get back to the hotel from just a few miles away even if I've driven the route multiple times. I'm offloading learning the route to the gps. My father on the other hand struggles to use one and if he's away he pays a lot of attention to where he's going and remembers routes better.
Am I dumber than him? with respect to operating the device certainly not but if we both drove separately to a new location and you took GPS from me once I got there I'd certainly look a lot dumber getting lost trying to get back without my mental crutch. I didn't have to remember the route, so I didn't. I offloaded that to the machine, and some people offload a LOT, pretty sure nobody ever drove into a lake because a paper map told them to.
Modern AI is only interesting insofar as it subsumes tasks that until now we would consider fundamental. Reading, writing, basic comprehension. If you let it, AI will take over these things and spoon feed you all you want. Your cognitive abilities in those areas will atrophy and you will be less cognizant of task elements where you've offloaded mental workload to the AI.
And we'll absolutely see more of this, people who are a wiz at using AI, know every app, get things done by reflex. but never learned or completely forgot how to do basic shit, like read a paper, order a salad off a menu in-person or book a flight and it'll be both funny and sad when it happens.
It wasn't immediately clear what they actually had the subjects do. It seems like they wrote an essay, which...duh? I would bet brain activity would be similar -- if not identical -- as an LLM user if the subjects were asked to have the other cohorts to write their essay.
But didn’t pocket calculators present the same risk / panic?
At some point ai will probably be like calculators where once everyone is using them for everything, that will be a new and different normal from today, and the expectations and the way of judging quality etc will be different than today.
Once everyone is doing the same one weird trick as you, it's no longer useful. You can no longer pretend to be a developer or an artist etc.
There will still be a sea of bottom-feeders doing the same thing, but they will just be universally recognized as cheap junk. Annd that's actually fine, kinda. There is a place and a use for cheap junk that just barely does something, the same as a cheap junky screwdriver or whatever.
My vehicle has a number of self-driving capabilities. When I used them, my brain rapidly stopped attending to the functions I'd given over, to the extent that there was a "gap" before I noticed it was about to do the wrong thing. On resumption of performing that work myself, it was almost as if I had forgotten some elements of it for a moment while my brain sorted it out.
No real reason to think that outsourcing our thinking/writing/etc will cause our brains to respond any differently. Most of the "reasoned" arguments I see against that idea seem based on false equivalences.
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.
All the headings and bullets and phrases like "The findings are clear:" stick out like a sore thumb.
>Everyone Is Cheating Their Way Through College. ChatGPT has unraveled the entire academic project.
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.
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...
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...
"Won't touch it, I'd never infect my codebase with whatever garbage that thing could output" -> ChatGPT for a small function here or there -> Cursor/Copilot style autocomplete -> Claude Code fully automating 90% of my tasks.
It felt like magic at first once reaching that last (current) point. In a lot of ways for certain things it still is. But it's becoming clearer and clearer that this will never be a silver bullet, and I'm ready to evolve further to "It's another tool in the toolbox to be applied judiciously when and where it makes sense, which it usually does not.". I've also come to greatly distrust anything an LLM says that isn't verified by a domain expert.
I've also felt a great amount of joy from my work go away over this time. Much as the artisans of old who were forced to sit back and supervise the automated machines taking over their craft churn out crappier versions of something faster. There's more to this than just being an old fart who doesn't want to change. We all got into this field for a reason, and a huge part of that reason is that it brings us joy. Without that joy we are going to burn out quickly, and quality is going to nosedive.
Anecdotally, this is how I felt when I tried out AI agents to help me write code (vibe coding). I always review the code and I ask it to break it down into smaller steps but because I didn't actually write and think of the code myself, I don't have it all in my brain. Sure, yes I can spend a lot of time really going through it and building my mental model but it's not the same (for me).
But this is also how I felt when I managed a small team once. When you start to manage more and code less, you have to let go of the fact that you have more intimate knowledge of the codebase and place that trust in your team. But at least you have a team of humans.
AI agentic coding is like shifting your job from developer to manager. Like the article that was posted yesterday said: 'treating AI like a "junior developer who doesn't learn"' [1,2].
One good thing I like about AI is that it's forcing people to write more documentation. No more complaining about that.
1. https://www.sanity.io/blog/first-attempt-will-be-95-garbage
I mean, ultimately, I didn't write it myself. It's more of a "remix" of other people's code. Or like if I translated this comment into French. It wouldn't improve my French so why would vibe coding be expected to improve one's programming ability?
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).
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.
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.
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.
For this kind of low stakes, easily verifiable task it’s hard to argue against using LLMs for me.
https://edition.cnn.com/2025/08/27/us/alaska-f-35-crash-acci...
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.
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
In this mode of use, you write out all your core ideas as stream of consciousness, bullet points or whatever without constraints of structure or style. Like more content than will make it into the essay. And then have the LLM summarize and clean it up.
Would be curious to see how that would play out in a study like this. I suspect that the subjects would not be able to quote verbatim, but would be able to quote all the main ideas and feel a greater sense of ownership.
What's really bothering me though, is that I enjoy my job less when using an LLM. I feel less accomplished, I learn less, and I overall don't derive the same value out of my work.. But, on the flip side, by not adopting an LLM I'll be slower than my peers, which then also impacts my job negatively.
So it's like being stuck between a rock and a hard place - I don't enjoy the LLM usage but feel somewhat obligated to.
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.
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.
1: https://www.changetechnically.fyi/2396236/episodes/17378968-...
AI solves the 2-sigma problem when used correctly.
AI is extremely neurodegenerative when used incorrectly.
The people using it as a research assistant to discover quality sources they can dive into, and as a tutor while working through those resources, are getting smarter.
The people using it as an “oracle made from magic talking sand” are getting dumber.
To be fair, the same thing is true of the web in general, but not to the extreme I’ve been seeing with AI.
I’m predicting the bell curve of IQ is going to flatten quite a bit over the next decade, as people shift two sigma in both directions.
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?
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...
>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?
My Mom was a special ed teacher and they were in her classroom as a set. I would go read them after school. Google Gemini suggested Classics Illustrated but I don't think that is it. These were black and white and cheaper than that. Something a teacher would have in their classroom.
Edit: Upon chiding Google Gemini and reminding it that it was black and white I think it found it!
Pocket Classics Comics From 1984.
https://gentlyhewstone.wordpress.com/2016/06/02/pocket-class...
https://www.ebay.com/itm/286295230816
Score one for AI because google search never found those for me.
I think maybe they are project managers since the programming is outsourced to Ai, but the idea don't seem to catch on there
[1] https://www.cell.com/trends/cognitive-sciences/abstract/S136...
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.
Going back to pre-LLM is not an option for me. Not because I can't, but because I don't want to.
How are you people using AI? I still have to think a lot. The biggest change is that I don't run around in circles trying to fix annoying bugs.
I think what you'd want to measure is someone completing a task manually and someone completing n times the tasks with a copilot.
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).
However, I think that take is too short-sighted and doesn't take into account the effect that these products have on minds that have not yet reached maturity. What happens when you've been using ChatGPT since grade school and have effectively offloaded all the hard stuff to AI through college? Those people won't be using it as a force multiplier - they will be using it to perform basic tasks. Ray-Ban sells glasses now with LLMs built in with a camera and microphone so you can constantly interact with it all day. What happens when everyone has one of these devices and use it for everything?
Absolute idiots
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.
Much of what keeps me going with work is the reward loop. This changes it fundamentally and it's a bit frightening how compelling the actual productivity is, versus the psychological tradeoff of not getting the reward through the typical process of problem solving.
Discussion then: https://news.ycombinator.com/item?id=44286277
I’m going to use 2x the amount of AI that I was planning to use today.
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.
Rather than coming up with the right answers?
- This is not a longitudinal study. Each partipant did 4 20 minute sessions. It just happens that the total study took 4 months. - The paper does not imply long term harm of any kind, they just measured brain connectivity during the short tasks. - It is not surprising that when asked to use an LLM to write an essay, partipants don't remember it. They didn't write it. - It is not surprising they showed less brain activity. They were delegating the task to something else. They were asked to. - I think the authors of the paper deliberately attempted to obscure this. Q7 on p30 is "LLM group: If you copied from ChatGPT, was it copy/pasted, or did you edit it afterwards?" This has been removed from the results section entirely, and other parts of the results do not match the supposed methodology. - The whole paper is extremely sloppy, with grammar mistakes, inconsistencies, and nonsensical charts. Check out Figure 29...
The ability to easily edit in word processors surely atrophied people's ability to really reason out what they wanted to write before committing it to paper. Is it sad that these traits are less readily available in the human populace? Sure. Do we still use word processors anyway because of the tremendous benefits they have? Of course. Similar could be said for spellcheckers, tractors, calculators, power tools, etc.
With LLMs, it's so much quicker to access a tremendous breadth of information, as well as drill down and get a pretty good depth on a lot of things too. We lose some things by doing it this way, and it can certainly be very misused (usually in a fairly embarrassing way). We need to keep it human, but AI is here to stay and I think the benefits far exceed the "cognitive decline" as mentioned in this journal.
Besides academics are bitter since LLMs are better at teaching than they are!
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/
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.
"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.
And so it is with many things. I wrote cursive right through the end of my high school years, but while I can type well on a computer, I have trouble even writing block lettering without mistakes now, and cursive is a lost cause.
Ubiquitous electronic calculators have eroded the heroic mental calculation skills of old. And now artificial "thinking machines" to do the thinking for you cause your brain to atrophy. Colour me surprised. The Whispering Earring story was mentioned here just recently but is totally topical.
AI is no different. Most will use it and not learn the fundamentals. There’s still lots of work for those people. Then some of us are doing things like looking at the state machines that rust async code generation produces or inspecting what the Java JIT is producing and still others are hacking ARM assembly. I use AI to take care of the boring bits, just like writing a nice UI in C++ was tedious back in 1990 so we used VB for that.
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.
I’ll do it once or twice, tell the llm to do it and reference the changes I made and it’s usually passable. It’s not fit for anything more imo.
Some of the points that LLM users could remember what they wrote and felt disconnected from it are kind of well, duh. Obviously that applies to anything written by someone or something else. If that's the level of argument I very much doubt it supports the LLM leads to cognitive decline hypothesis.
I mean you won't learn as much having an LLM write and essay than writing it yourself, but you can use LLMs and write essays or whatever. I doubt LLMs are any worse for your head than daytime TV or such like.
How is that not utter garbage? You're comparing text that is barely more than a forum comment, and noticing that people who spend the short time thinking and writing are engaging in different activity from people who spend the time using a research tools and different activity from people whow spend the time asking an AI (and waiting for it) to generate content.
And, it is something we need to talk about loudly, but I guess it wouldn't crank up a number of followers or valuation of AI grifters.
This makes complete sense though. We're simply trying to automate the human thinking process like we try to use technology to automate/handoff everything else.
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.
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.
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.
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.