I recently worked on something very complex I don't think I would have been able to tackle as quickly without AI; a hierarchical graph layout algorithm based on the Sugiyama framework, using Brandes-Köpf for node positioning. I had no prior experience with it (and I went in clearly underestimating how complex it was), and AI was a tremendous help in getting a basic understanding of the algorithm, its many steps and sub-algorithms, the subtle interactions and unspoken assumptions in it. But letting it write the actual code was a mistake. That's what kept me from understanding the intricacies, from truly engaging with the problem, which led me to keep relying on the AI to fix issues, but at that point the AI clearly also had no real idea what it was doing, and just made things worse.

So instead of letting the AI see the real code, I switched from the Copilot IDE plugin to the standalone Copilot 365 app, where it could explain the principles behind every step, and I would debug and fix the code and develop actual understanding of what was going on. And I finally got back into that coding flow again.

So don't let the AI take over your actual job, but use it as an interactive encyclopedia. That works much better for this kind of complex problem.

Where I'm skeptical of this study:

- 54 participants, only 18 in the critical 4th session

- 4 months is barely enough time to adapt to a fundamentally new tool

- "Reduced brain connectivity" is framed as bad - but couldn't efficient resource allocation also be a feature, not a bug?

- Essay writing is one specific task; extrapolating to "cognition in general" seems like a stretch

Where the study might have a point:

Previous tools outsourced partial processes - calculators do arithmetic, Google stores facts. LLMs can potentially take over the entire cognitive process from thinking to formulating. That's qualitatively different.

So am I ideologically inclined to dismiss this? Maybe. But I also think the honest answer is: we don't know yet. The historical pattern suggests cognitive abilities shift rather than disappear. Whether this shift is net positive or negative - ask me again in 20 years.

[Edit]: Formatting

1: https://www.catharsisinsight.com 2: https://ashleyjuavinett.com

this actually does include a crazy amount of long form latex expositions on a bunch of projects im having a blast iterating on. i must be experiencing what its almost like not having adhd

The kids are using ChatGPT for simple maths...

Earlier, I had to only keep my phone away and not open Instagram while studying. Now, even thinking can be partially offloaded to an automated system.

https://arxiv.org/abs/2506.08872

I'm so grateful for AI and always use it to help get stuff done while also documenting the rational it takes to go from point A to B.

Although it has failed many times, I've had ZERO problems backtracking, debugging its thinking, understand what it has done and where it has failed.

We definitely need to bring back courses on "theory of knowledge" and the "Art of problem" solving etc.

This is very different from, say, writing an essay I'm gonna publish on my blog under my own name. I would be MUCH more interested in an experiment that isolates people working on highly cognitively demanding work that MATTERS to them, and seeing what impact LLMs do (or don't) have on cognitive function. Otherwise, this seems like a study designed to confirm a narrative.

What am I missing

We're heading toward AI-first systems whether we like it or not. The interesting question isn't "does AI reduce brain connectivity for essay writing" - it's how we redesign education, work, and products around the assumption that everyone has access to powerful AI. The people who figure out how to leverage AI for higher-order thinking will massively outperform those still doing everything manually.

Cognitive debt is real if you're using AI to avoid thinking. But it's cognitive leverage if you're using AI to think faster and about bigger problems.

I don't know that the same makes as much sense to evaluate in an essay context, because it's not really the same. I guess the equivalent would be having an existing essay (maybe written by yourself, maybe not) and using AI to make small edits to it like "instead of arguing X, argue Y then X" or something.

Interestingly I find myself doing a mix of both "vibing" and more careful work, like the other day I used it to update some code that I cared about and wanted to understand better that I was more engaged in, but also simultaneously to make a dashboard that I used to look at the output from the code that I didn't care about at all so long as it worked.

I suspect that the vibe coding would be more like drafting an essay from the mental engagement POV.

I'm very curious to see if we start to see things like this as a new skill, requiring a different cognitive style that's not measured in studies like this.

Thinking everything ML produces is just shorting the brain.

I see AI wars as creating coherent stories. Company X starts using ML and they believe what was produced is valid and can grow their stock. Reality is that Company Y poised the ML and the product or solution will fail, not right away but over time.

The study seems interesting, and my confirmation bias also does support it, though the sample size seems quite small. It definitely is a little worrisome, though framing it as being a step further than search engine use makes it at least a little less concerning.

We probably need more studies like this, across more topics with more sample size, but if we're all forced to use LLMs at work, I'm not sure how much good it will do in the end.

If you give up your hands-on interaction with a system, you will lose your insight about it.

When you build an application yourself, you know every part of it. When you vibe code, trying to debug something in there is a black box of code you've never seen before.

That is one of the concerns I have when people suggest that LLMs are great for learning. I think the opposite, they're great for skipping 'learning' and just get the results. Learning comes from doing the grunt work.

I use LLMs to find stuff often, when I'm researching or I need to write an ADR, but I do the writing myself, because otherwise it's easy to fall into the trap of thinking that you know what the 'LLM' is talking about, when in fact you are clueless about it. I find it harder to write about something I'm not familiar with, and then I know I have to look more into it.

It's a tool, and this study at most indicates that we don't use as much brain power for the specific tasks of coding but do they look into for instance maintenance or management of code?

As that is what you'll be relegated to when vibe coding.

this doesn't seem like a clear problem. perhaps people can accomplish more difficult tasks with LLM assistance, and in those more difficult tasks still see full brain engagement?

using less brain power for a better result doesn't seem like a clear problem. it might reveal shortcomings in our education system, since these were SAT style questions. I'm sure calculator users experience the same effects vs mental mathematics

If you are feeling over reliant on these tools then I quickfix that's worked me is to have real conversations with real people. Organise a coffee date if you must.

Seems to focus only on the first part and not on the other end of it.

Similar mess with can be found in `Figure 34.`, with an added bonus of "DO NOT MAKE MISTAKES!" and "If you make a mistake you'll be fined $100".

Also, why are all of these research papers always using such weak LLMs to do anything? All of this makes their results very questionable, even if they mostly agree with "common intuition".

The last one I saw was about smartphone users who do a test and then quit their phone for a month and do the test again and surprisingly do better the second time. Can anyone tell me why they might have paid more attention, been more invested, and done better on the test the second time round right after a month of quitting their phone?

If that’s true, then maybe we could leverage what we know about good management of human subordinates and apply it to AI interaction, and vice versa.

I have actually been improving in other fields instead like design and general cleanliness of the code, future extensability and bug prediction.

My brain is not 'normal' either so your mileage might vary.

So, is it ok for coding? :-)

An interesting visual exercise to see latent information structure in language is to pixelize a large corpus as bit map by translating the characters to binary then run various transforms on it and what emerges is not a picture of random noise but a fractal like chaos of "worms" or "waves." This is what LLMs are navigating in their high dimensional latent space. Words are not just arbitrary symbols but objects on a connected graph.

We find that people having to perform mental arithmetics as opposed to people using calculators exhibited more neural activities. They were also able to recall the specific numbers in the equations more.

... So what?

The consequence of making anything easier is of course that the person and the brain is less engaged in the work, and remembers less.

This debate about using technology for thinking has been ongoing for literally millennia. It is at least as old as Socrates, who criticized writing as harming the ability to think and remember.

>>And now, since you are the father of writing, your affection for it has made you describe its effects as the opposite of what they really are. In fact, it will introduce forgetfulness into the soul of those who learn it: they will not practice using their memory because they will put their trust in writing, which is external and depends on signs that belong to others, instead of trying to remember from the inside, completely on their own. You have not discovered a potion for remembering, but for reminding; you provide your students with the appearance of wisdom, not with its reality. Your invention will enable them to hear many things without being properly taught, and they will imagine that they have come to know much while for the most part they will know nothing. And they will be difficult to get along with, since they will merely appear to be wise instead of really being so.”[0]

To emphasize: 'instead of trying to remember from the inside, completely on their own ... not a potion for remembering, but for reminding ... the appearance of wisdom, not its reality.'

There is no question this is a true dichotomy and trade-off.

The question is where on the spectrum we should put ourselves.

That answer is likely different for each task or goal.

For learning, we should obviously be working at a lower level, but should we go all the way to banning reading and writing and using only oral inquiry and recitation?

OTOH, a peer software engineer manager with many Indians in his group said he was constantly trying to get them to write down more of their plans and documentation, because they all wanted to emulate the great mathematician Ramanujan who did much of his work all in his head, and it was slowing down the SE's work.

When I have an issue with curing a particular polymer for a project, should I just get the answer from the manufacturer or search engine, or take the sufficient chemistry courses and obtain the proprietary formulas necessary to derive all the relevant reactions in my head? If it is just to deliver one project, obviously just get the answer and move on, but if I'm in the business of designing and manufacturing competing polymers, I should definitely go the long route.

As always, it depends.

[0] https://newlearningonline.com/literacies/chapter-1/socrates-...

A general education should focus on structure, all mental models built shall reinforce one another. For specific recommendations, completely replace the current Euler inspired curricula with one based on category theory. Strive to make all home and class work multimedia, multi-discipline presentations. Seriously teach one constructed meta-language from kindergarten. And stop passing along students who fail, clearly communicate the requirements.

I believe this is vital for students. Think about Student-AI interaction. Does this thing the AI is telling me fit with my understanding of the world, if it does they will accept it. If the student can think structurally the mismatch will be as obvious as a square peg in a round hole. A simple check for an isomorphism. Essentially expediting a proof certificate of the model output.

That said, I also think it is important to not get an overly negative takeaway from the study. Many of the findings are exactly what you would expect if AI is functioning as a form of cognitive augmentation. Over time, you externalize more of the work to the tool. That is not automatically a bad thing. Externalization is precisely why tools increase productivity. When you use AI, you can often get more done because you are spending less cognitive effort per unit of output.

And this gets to what I see as the study's main limitation. It compares different groups on a fixed unit of output, which implicitly assumes that AI users will produce the same amount of work as non-AI users. But that is not how AI is actually used in the real world. In practice, people often use AI to produce much more output, not the same output with less effort. If you hold output constant, of course the AI group will show lower cognitive engagement. A more realistic scenario is that AI users increase their output until their cognitive load is similar to before, just spread across more work. That dimension is not captured by the experimental design.

Back when it came out, it was all the rage at my company and we were all trying it for different things. After a while, I realized, if people were willing to accept the bullshit that LLMs put out, then I had been worrying about nothing all along.

That, plus getting an LLM to write anything with meaning takes putting the meaning in the prompt, pushed me to finally stop agonizing over emails and just write the damn things as simply and concisely as possible. I don't need a bullshit engine inflating my own words to say what I already know, just to have someone on the other end use the same bullshit engine to remove all that extra fluff to summarize. I can just write the point straight away and send it immediately.

You can literally just say anything in an email and nobody is going to say it's right or wrong, because they themselves don't know. Hell, they probably aren't even reading it. Most of the time I'm replying just to let someone know I read their email so they don't have to come to my office later and ask me if I read the email.

Every time someone says the latest release is a "game changer", I check back out of morbid curiosity. Still don't see what games have changed.

- Socrates on Writing.

for me, it's purely a research tool that I can ask infinite questions to

A door has been opened that cant be closed and will trap those who stay too long. Good luck!

/s

This is a non-study.

There's a famous satirical study that "proved" parachutes don't work by having people jump from grounded planes. This study proves AI rots your brain by measuring people using it the dumbest way possible.

I want a life of leisure. I don’t want to do hard things anymore.

Cognitive atrophy of people using these systems is very good as it makes it easier to beat them in the market, and it’s easier to convince them that whatever slop work you submitted after 0.1 seconds of effort “isn’t bad, it’s certainly great at delving into the topic!”

Also, monkey see, monkey speak: https://arxiv.org/abs/2409.01754