MIT Study Finds AI Use Reprograms the Brain, Leading to Cognitive Decline

We're just moving up the abstraction ladder, like we did with compilers. I don't care about the individual lines of code, I care about architecture, code structure, rigorous automated e2e tests, contracts with comprehensive validation, etc. Rather than waste a bunch of time pouring over agent PRs I just make them jump over extremely high static/analytic hurdles that guarantee functionality, then my only job is to identify places where the current spec and the intended functionality differ and create a new spec to mitigate.

A lot of the boosterism seems to come from those who never had the ability in the first place, and never really will, but can now hack a demo together a little faster than before. But I'm mostly concerned about those going through school who don't even realize they're undermining themselves by reaching for AI so quickly.

I've posted this Asimov short story before, but this comment inspires me to post it again.

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!

What about people who use the machines to augment their learning process? I find that being able to ask questions, particularly “dumb” questions that I don’t want to bother someone else with and niche questions that might not be answered well in the corpus, helps me better understand new concepts. If you just take the answers and move on, then sure, you’re going to have a bad time. But if you critically interrogate the answers and synthesize the information, I don’t see how this isn’t a _better_ era for people who want to develop a deep understanding of something.

I think this is a fun problem: Say LLMs really do allow people to be 2X or 3X more productive. Then those people that understand concepts and rely on LLMs should be more successful and productive. But if those people slowly decline in ability they will become less successful in the long term. But how much societal damage is done in the meantime? Such as creating insecure software or laying off/not hiring junior developers.

That reminds me of back when 12,500 years ago I could really shape a flint into a spear head in no time. Took me seasons to learn that skill from the Masters. Then Sky Gods came and taught people metal-fire. Nobody knows how to really chip and carve a point any more. They just cast them in moulds. We are seeing a literal split of skills in front of our eyes. People who know how to shape rocks deeply. And people who just know how to buy a metal tool with berries. I like berries as much as the next person, but give it a couple of years, they will be begging for flint tips back. I guarantee it. Those metal users will have no idea how to survive without a collective industry.

(3) people who never had that capability.

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.

The way I’ve been coding recently: I often ask the AI to write the function for me, because lazy. And then I don’t like the code it wrote, so I rewrite it. But still it is less mental effort to rewrite “mostly correct but slightly ugly code” than write the whole thing from scratch

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

> Just beware the “real programmers hand-write assembly” fallacy. It was said that compilers would produce a generation of programmers unable to understand the workings of their programs. In some sense, this is true! But, almost nobody thinks it really matters for the actual project of building things.

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.

> Just beware the “real programmers hand-write assembly” fallacy

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.

I'd caution that the people not familiar with working at the low level are often missing a bunch of associated knowledge which is useful in the day to day.

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.

I would agree with the statement that you don't need to know or write in assembly to build programs, but what you end up with is usually slow and inefficient.

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 have never needed to write assembly in a professional context because of the changes you describe, but this does not mean I don't have a need to understand what is going on at that level of abstraction. I _have_ had occasion to look at disassembly in the process of debugging before, and it was important that I was not completely lost when I had to do this. You don't have to do something all the time for the capacity to do something to be useful. At the end of the day engineering is about choosing the correct tradeoffs given constraints, and in a professional environment, cost is almost always one of the constraints.

I fall into this trap more than I’d care to admit.

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.

Assuming this observation applies generally, give one point to the embodiment crowd.

It's not the same with LLMs. What the study finds out is actually much more serious. When you use a phone or a calculator, you don't lose cognitive faculties. But when you delegate the thinking process to an LLM, your Brain gets phisically changed, witch leads to a cognitive damage. It's a completely different league.

Same here, i just finished my subscription at ChatGPT, i feel free again.

> I would describe the study size and composition as a limitation, and a reason to pursue a larger and more diverse study for confirmation, 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.

The experimental setup is hopelessly flawed. It assumes that people’s tasks will remain unchanged in the presence of an LLM.

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'd love to see much more diverse selection of schools. All of these schools are extremely selective so you are looking at extremely selective slice of the population.

Most studies don't replicate. Unless a study is exceptionally large and rigorous, your expectation should be that it won't replicate.

Please no. Remember that room temperature superconductor nonsense that went on for way too long? Let's please collectively try to avoid that..

I believe LLMs have the potential to (for good or ill, depending on your view) destroy academic journals.

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.

> I feel like saying papers pre peer review should be taken with a grain of salt should be stopped.

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

Rather given the reproducibility crisis, how much salt does peer review nock off that grain? How often does peer review catch fraud or just bad science?

There's two questions at play. First, does the research pass the most rigorous criteria to become widely-accepted scientific fact? Second, does the research present enough evidence to tip your priors and change your personal decisions?

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.

> 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. :)

It also showed that people couldn’t successfully recall information about what they’d just written when they used LLM assistance.

Writing is an important form of learning and this clearly shows LLM assisted writing doesn’t provide that benefit.

I'd say the framing is wrong. Do we call delivery drivers lazy because they take the highway rather than the backroads? Or because they drive the goods there rather than walk? They're missing out on all that traffic intersection experience.

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.

I would argue that intellectual laziness can and will lead to cognitive decline as much as physical laziness can and will lead to muscle atrophy. It’s akin to using a maps app to get from point a to b but not ever remembering the route, even though someone has done it 100 times.

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.

> But isn’t the point of writing an essay is the quality of the essay

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.

Ever read books in the Bobiverse? They provide an pretty functional cognitive model for how human interfaces with tooling like AI will probably work (even though it is fiction) -- lower level actions are pushed into autonomous regions until a certain deviancy threshold is achieved. Much like breathing -- you don't typically think about breathing until it becomes a problem (choking, underwater, etc.) and then it very much hits the high level of the brain.

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.

Because some of the effect sizes were so large that the probability of the effect being real is greater than 99.9%.

Ironically you’re now replicating the reddified response to this paper by attacking the sample size.

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.

Yup, I even found myself a bit hopeful that maybe it was a follow-up or new study and we'd get either more or at least different information. But that bit of hope is also an example of my bias/sympathy to that idea that it might be harmful.

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.

Ironically there should be another study of how not using AI is also leading to cognitive decline on Reddit. On programming subreddits people have lost all sense of engineering and have simply become religious about being against a tool.

>I feel like HN is being Reddit-ified

It's september and september never ends

You are referencing your own personal experience, and while that is an entirely valid opinion for you to have personally about your usage, it's not possible to extrapolate that across an entire population of people. Whether or not you're doing that, part of the point I was making was how people who "think it makes sense" will often then not critically analyze something because it already agrees with their preconceived notion. Super common, I'm just calling it out cause we can all do better.

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.

> I ask follow up questions to make sure I understand why the AI's answer works.

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.

I think the school experience proves that doesn’t work. Reminds me of a teacher carefully breaking down the problem on the board and you nodding along when it is unfolding in front of you in a directed manner. The question is if you can do it yourself come the exam. If all you did to prepare is watch the teacher solve it, with no attempt to solve it from scratch yourself during practice, you will fail the exam.

I just process text information better. Videos are kind of overstimulating and often have unrelated content, and I hate having to rewind back to a part I need while I'm in the middle of something. With LLMs I can get a broad overview of what I'm doing, tell it what materials I already have on hand and get specific ideas for how to practice. Soldering is probably one of the harder ones to learn by text, but the description of the techniques to use were actually really understandable (use flux, be sure the tip is tinned, touch the pad with the tip to warm it up a little, touch again with the iron on one side of the pad and insert the solder in on the other side and it gets drawn in, pull away (timing was trial and error). And then I'd upload a picture of what I did for review and it would point out the ones that had issues and what likely went wrong to cause it (ex: solder sticking to the top of the iron and not the pad), and I would keep practicing and test that it worked and looked like what was described. It may not be the ideal technique or outcome, but it unblocked me relatively quick so I could continue my project.

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.

[dead]

The asking people part was the hard thing for me, always has been. That honestly was the missing piece for me. I absolutely agree that written docs and online content are sufficient for some people, that's how I learned Linux and sysadmin stuff, but I tried on and off to get into electronics for years that way and never got anywhere.

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.

I agree, the study didn't do that or have any thoughts on that.

The irony. It isn't even a new study. Way too much has been written about this flawed study when we should just be doing more studies.

We can at least change the link to the actual paper instead of a vaccine denier's AI generated summary.

Instead of trying to balance dreck can we just... not upvote any dreck

Being anti-AI drivel is completely fine though.

This is sad but true.

And the pipeline is cooked now with some universities now allowing for AI use. It’s like what cliffnotes did for reading comprehension but over all aspects of life and all domains. What a coming tsunami.