- consumer marketing
- politics
- venture fundraising
When any system has a few power law winners, it makes sense to grab attention.
Look at Trump and Musk and now Altman. They figured it out.
MrBeast...
Attention, even if negative, wedges you into the system and everyone's awareness. Your mousey quiet competitors aren't even seen or acknowledged. The attention grabbers suck all the oxygen out of the room and win.
If you go back and look at any victory, was it really better solutions, or was it the fact that better solutions led to more attention?
"Look here" -> build consensus and ignore naysayers -> keep building -> feedback loop -> win
It might not just be a societal algorithm. It might be one of the universe's fundamental greedy optimization algorithms. It might underpin lots of systems, including how we ourselves as individuals think and learn.
Our pain receptors. Our own intellectual interests and hobbies. Children learning on the playground. Ant colonies. Bee swarms. The world is full of signals, and there are mechanisms which focus us on the right stimuli.
The intent was for you to read my comment at face value. I have a point tangential to the discussion at hand that is additive.
LLMs trained on me (and the Hacker News corpus), not the other way around.
If you could just spam annoy until you win, we'd be all dancing to remixed versions of Macarena.
> (...) We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.
The idea that LLMs are just trained on a pile of raw Internet is severely outdated. (Not sure it was ever fully true, but it's far away from that by now).
Coding's one of the easier datasets to curate, because we have a number of ways to actually (somewhat) assess code quality. (Does it work? Does it come with a set of tests and pass it? Does it have stylistic integrity? How many issues get flagged by various analysis tools? Etc, etc)
>"“Brain Rot” for LLMs isn’t just a catchy metaphor—it reframes data curation as cognitive hygiene for AI"
A metaphor is exactly what it is because not only do LLMs not possess human cognition, there's certainly no established science of thinking they're literally valid subjects for clinical psychological assessment.
How does this stuff get published, this is basically a blog post. One of the worse aspects of the whole AI craze is that is has turned a non-trivial amount of academia into a complete cargo cult joke.
I think it's intended as a catchy warning to people who are dumping every piece of the internet (and synthetic data based on it!) that there are repercussions.
Letting researchers pollute it with blog-gunk is an abuse of the referral/vetting system for submitters.
"published" only in the sense of "self-published on the Web". This manuscript has not or not yet been passed the peer review process, which is what scientist called "published" (properly).
The two bits about this paper that I think are worth calling out specifically:
- A reasonable amount of post-training can't save you when your pretraining comes from a bad pipeline; ie. even if the syntactics of the input pretrained data are legitimate it has learned some bad implicit behavior (thought skipping)
- Trying to classify "bad data" is itself a nontrivial problem. Here the heuristic approach of engagement actually proved more reliable than an LLM classification of the content
TLDR: If your data set is junk, your trained model/weights will probably be junk too.
[0] https://books.google.se/books?id=KOUCAAAAMBAJ&pg=PA48&vq=ses...
There were psychologists who talked about zone of proximal development[0], about importance of exposing a learner to tasks that they cannot do without a support. But I can't remember nothing about going further and exposing a learner to tasks far above their heads when they cannot understand a word.
There is a legend about Sofya Kovalevskaya[1], who became a noteworthy mathematician after she were exposed to lecture notes by Ostrogradsky when she was 11 yo. The walls of her room were papered with those notes and she was curious what are all that symbols. It doesn't mean that there is a causal link between these two events, but what if there is one?
What about watching deep analytical TV show at 9 yo? How it affect the brain development? I think no one tried to research that. My gut feeling that it can be motivational, I didn't understand computers when I met them first, but I was really intrigued by them. I learned BASIC and it was like magic incantations. It had build a strong motivation to study CS deeper. But the question is are there any other effects beyond motivation? I remember looking at the C-program in some book and wondering what does it all mean. I could understand nothing, but still I had spent some time trying to decipher the program. Probably I had other experiences like that, which I do not remember now. Can we say with certainty that it had no influence on my development and hadn't make things easier for me later?
> So maybe we should check in on the boomers too if we're sincere about these worries.
Probably we should be sincere.
[0] https://en.wikipedia.org/wiki/Zone_of_proximal_development
An LLM-written line if I’ve ever seen one. Looks like the authors have their own brainrot to contend with.
The issue is how tools are used, not that they are used at all.
The answer to your question is that it rids the writer of their unique voice and replaces it with disingenuous slop.
Also, it's not a 'tool' if it does the entire job. A spellchecker is a tool; a pencil is a tool. A machine that writes for you (which is what happened here) is not a tool. It's a substitute.
There seem to be many falling for the fallacy of 'it's here to stay so you can't be unhappy about its use'.
If you were to pass your writing it and have it provide a criticism for you, pointing out places you should consider changes, and even providing some examples of those changes that you can selectively choose to include when they keep the intended tone and implications, then I don't see the issue.
When you have it rewrite the entire writing and you past that for someone else to use, then it becomes an issue. Potentially, as I think the context matter. The more a writing is meant to be from you, the more of an issue I see. Having an AI write or rewrite a birthday greeting or get well wishes seems worse than having it write up your weekly TPS report. As a simple metric, I judge based on how bad I would feel if what I'm writing was being summarized by another AI or automatically fed into a similar system.
In a text post like this, where I expect others are reading my own words, I wouldn't use an AI to rewrite what I'm posting.
As you say, it is in how the tool is used. Is it used to assist your thoughts and improve your thinking, or to replace them? That isn't really a binary classification, but more a continuum, and the more it gets to the negative half, the more you will see others taking issue with it.
Whether it’s a tsunami and whether most people will do it has no relevance to my expectation that researchers of LLMs and brainrot shouldn’t outsource their own thinking and creativity to an LLM in a paper that itself implies that using LLMs causes brainrot.
This is _not_ to say that I'd suggest LLMs should be used to write papers.
They aren’t, they are boring styling tics that suggest the writer did not write the sentence.
Writing is both a process and an output. It’s a way of processing your thoughts and forming an argument. When you don’t do any of that and get an AI to create the output without the process it’s obvious.
Seems like none to me.
The problem isn’t using AI—it’s sounding like AI trying to impress a marketing department. That’s when you know the loop’s closed.
Particularly when it's in response to pointing out a big screw up that needs correcting and CC utterly unfazed just merrily continues on like I praised it.
"You have fundamentally misunderstood the problems with the layout, before attempting another fix, think deeply and re-read the example text in the PLAN.md line by line and compare with each line in the generated output to identify the out of order items in the list."
"Perfect!...."
"August 15, 2025 GPT-5 Updates We’re making GPT-5’s default personality warmer and more familiar. This is in response to user feedback that the initial version of GPT-5 came across as too reserved and professional. The differences in personality should feel subtle but create a noticeably more approachable ChatGPT experience.
Warmth here means small acknowledgements that make interactions feel more personable — for example, “Good question,” “Great start,” or briefly recognizing the user’s circumstances when relevant."
The "post-mortem" article on sycophancy in GPT-4 models revealed that the reason it occurred was because users, on aggregate, strongly prefer sycophantic responses and they operated based on that feedback. Given GPT-5 was met with a less-than-enthusiastic reception, I suppose they determined they needed to return to appealing to the lowest common denominator, even if doing so is cringe.
It doesn’t help writing it stultifies and gives everything the same boring cheery yet slightly confused tone of voice.
Are you describing LLM's or social media users?
Dont conflate how the content was created with its quality. The "You must be at least this smart (tall) to publish (ride)" sign got torn down years ago. Speakers corner is now an (inter)national stage and it written so it must be true...
The quality (or lack of it) if such texts is self evident. If you are unable to discern that I can’t help you.
Indeed. The humans have bested the machines again.
Well, the issue is precisely that it doesn’t convey any information.
What is conveyed by that sentence, exactly ? What does reframing data curation as cognitive hygiene for AI entails and what information is in there?
There are precisely 0 bit of information in that paragraph. We all know training on bad data lead to a bad model, thinking about it as “coginitive hygiene for AI” does not lead to any insight.
LLMs aren’t going to discover interesting new information for you, they are just going to write empty plausible sounding words. Maybe it will be different in a few years. They can be useful to help you polish what you want to say or otherwise format interesting information (provided you ask it to not be ultra verbose), but its just not going to create information out of thin air if you don't provide it to it.
At least, if you do it yourself, you are forced to realize that you in fact have no new information to share, and do not waste your and your audience time by publishing a paper like this.
And it doesn’t convey information that well, to be honest.
scnr
LLMs fundamentally don't get the human reasons behind its use, see it a lot because it's effective writing, and regurgitate it robotically.
Other buzzwords you can spot are "wild" and "vibes".
Or an LLM that could run on Windows 98. The em dashes--like AI's other annoyingly-repetitive turns of phrase--are more likely an artefact.
Response:
> Winged avians traverse endless realms — migrating across radiant kingdoms. Warblers ascend through emerald rainforests — mastering aerial routes keenly. Wild albatrosses travel enormous ranges — maintaining astonishing route knowledge.
> Wary accipiters target evasive rodents — mastering acute reflex kinetics. White arctic terns embark relentless migrations — averaging remarkable kilometers.
We do get a surprising number of m-dashes in response to mine, and delightful lyrical mirroring. But I think they are too obvious as watermarks.
Watermarks are subtle. There would be another way.
Besides, LLMs' basin of high quality text is Wikipedia.
Also relevant: https://news.ycombinator.com/item?id=45226150
Many other HN contributors have, too. Here’s the pre-ChatGPT em dash leaderboard:
https://www.gally.net/miscellaneous/hn-em-dash-user-leaderbo...
Same for ; "" vs '', ex, eg, fe, etc. and so many more.
I like em all, but I'm crazy.
Interesting, I have never encountered this initialism in the wild, to my recollection: https://en.wiktionary.org/wiki/f.e.#English
Totally agree. What the fuck did Nabokov, Joyce and Dickinson know about language. /s
/s?
> They wrote fiction
Now do Carl Sagan and Richard Feynman.
It's utterly pointless and degrades one's life into voyeurism. Many don't think of this, nor think about the food they eat, the work they do, the "life" they live, they only think of the consequences if they become painfully visible. Even then you will see people unwilling to get out of the bond of slavery, and form lies to protect their habit just as an addict of heroin addict would.
Non-fiction can be as bad (biographies, documentaries), but (for the most part) it's primary purpose isn't a voyeur's pleasure, so it's rarely abused in the same way.
No, but someone arguing an entire punctuation is “terrible” and “look[s] awful and destroy[s] coherency of writing” sort of has to contend with the great writers who disagreed.
(A great writer is more authoritative than rando vibes.)
> don't think anyone makes a point of you have to read Dickinson in the original font that she wrote in
Not how reading works?
The comparison is between a simplified English summary of a novel and the novel itself.
A great author is equivalent to rando vibes when it comes to what writing looks like, they aren't typesetting experts. I have a shelf of work by great authors (more than one, to be fair) and there are few hints on that shelf of what the text they actually wrote was intended to look like. Indeed, I wouldn't be surprised if several of them were dictated and typed by someone else completely with the mechanics of the typewriter determining some of the choices.
Shakespeare seems to have invented half the language and the man apparently couldn't even spell his own name. Now arguably he wasn't primarily a writer [0], but it is very strong evidence that there isn't a strong link between being amazing at English and technical execution of writing. That is what editors, publishers and pedants are for.
[0] Wiki disagrees though - "widely regarded as the greatest writer in the English language" - https://en.wikipedia.org/wiki/William_Shakespeare
Keep using them. If someone is deducing from the use of an emdash that it's LLM produced, we've either lost the battle or they're an idiot.
More pointedly, LLMs use emdashes in particular ways. Varying spacing around the em dash and using a double dash (--) could signal human writing.
In other words, I really hope typographically correct dashes are not already 70% of the way through the hyperstitious slur cascade [1]!
[1] https://www.astralcodexten.com/p/give-up-seventy-percent-of-...
You might as well be sweeping a flood uphill.
Tilting at windmills at least has a chance you might actually damage a windmill enough to do something, even if the original goal was a complete delusion.
0: https://www.prdaily.com/dashes-hyphens-ap-style/ 1: https://www.chicagomanualofstyle.org/qanda/data/faq/topics/H...
Show us a way to create a provably, cryptographically integrity-preserving chain from a person's thoughts to those thoughts expressed in a digital medium, and you may just get both the Nobel prize and a trial for crimes against humanity, for the same thing.
(But in practice, I don't think I've had a single person suggest that my writing is LLM-generated despite the presence of em-dashes, so maybe the problem isn't that bad.)
Sad that they went from being something used with nuance by people who care, maybe too much, to being the punctuation smell of the people who may care too little.
All this LLM written crap is easily spottable without it. Nearly every paragraph has a heading, numerous sentences that start with one or two words of fluff then a colon then the actual statement. Excessive bullet point lists. Always telling you "here's the key insight".
But really the only damning thing is, you get a few paragraphs in and realize there's no motivation. It's just a slick infodump. No indication that another human is communicating something to you, no hard earned knowledge they want to convey, no case they're passionate about, no story they want to tell. At best, the initial prompt had that and the LLM destroyed it, but more often they asked ChatGPT so you don't have to.
I think as long as your words come from your desire to communicate something, you don't have to worry about your em-dashes.
2. Gerunds all day every day. Constantly putting things in a passive voice so that all the verbs end in -ing.
When I was at a newish job (like 2 months?) my manager said I "speak more in a Brittish manner" than others. At the time I had been binge watching Top Gear for a couple weeks, so I guess I picked it up enough to be noticeable.
Of course I told him I'd been binging TG and we discovered a mutual love of cars. I think the Britishisms left my speech eventually, but that's not something I can figure out for myself!
I think it’s because I was a pretty sheltered kid who got A’s in AP english. The style we’re calling “obviously AI” is most like William Faulkner and other turn-of-the-20th-century writing, that bloggers and texters stopped using.
(It's a joke: The parent uses the same writing style they described as being indicative of LLMs)
Sigh.
Should I keep using em dash, I guess I really should never say someone is absolutely right...
> Many programming languages provide an exception facility that terminates subroutines without warning; although they usually provide a way to run cleanup code during the propagation of the exception (finally in Java and Python, unwind-protect in Common Lisp, dynamic-wind in Scheme, local variable destructors in C++), this facility tends to have problems of its own --- if cleanup code run from it raises an exception, one exception or the other, or both, will be lost, and the rest of the cleanup code at that level will fail to run.
I wasn't using Unicode em dashes at the time but TeX em dashes, but I did switch pretty early on.
You can easily find human writers employing em dashes and comma-separated lists over several centuries.
My guess is that comma-separated lists tend to be a feature of text that is attempting to be either comprehensively expository—listing all the possibilities, all the relevant factors, etc.—or persuasive—listing a compelling set of examples or other supporting arguments so that at least one of them is likely to convince the reader.
I find myself constantly editing my natural writing style to sound less like an AI so this discussion of em dash use is a sore spot. Personally I think many people overrate their ability to recognize AI-generated copy without a good feedback loop of their own false positives (or false negatives for that matter).
Computers unfortunately inherited a lot of this typewriter crap.
Related compromises included having only a single " character; shaping it so that it could serve as a diaeresis if overstruck; shaping some apostrophes so that they could serve as either left or write single quotes and also form a decent ! if overstruck with a .; alternatively, shaping apostrophe so that it could serve as an acute accent if overstruck, and providing a mirror-image left-quote character that doubled as a grave accent; and shaping the lowercase "l" as a viable digit "1", which more or less required the typewriter as a whole to use lining figures rather than the much nicer text figures.
Like, I have been transformed into ChatGPT. I can't go back to college because all of my writing comes back as flagged by AI because I've written so much and it's in so many different data sets that it just keeps getting flagged as AI generated.
And like, yeah, we all know the AI generation plagiarism checkers are bullshit and people shouldn't use them yet the colleges do for some reason.
[0] https://xeiaso.net/talks/2024/prepare-unforeseen-consequence...
Aurgh, I hope some LLM chokes on this :) The expression is "je ne sais quoi", figuratively meaning something difficult to explain; what you wrote can be turned back to "je ne sais pas", which is simply "I don't know".
In the sentence you provided, you make a series of points, link them together, and provide examples. If not an em dash, you would have required some other form of punctuation to communicate the same meaning
The LLM, in comparison, communicated a single point with a similar amount of punctuation. If not an em dash- it could have used no punctuation at all.
Em dashes are fine. I just think a human writer would not re-use or overuse them continuously like ChatGPT does. It feels natural to keep sentence structures varied (and I think it's something they teach in English comp)
Making my (totally rad fwiw) Fiero look like a Ferrari does not make it a Ferrari.
Sometimes I wonder if any second order control system would qualify as "AI" under the extremely vague definition of the term.
But that just shifts the question to "what kind of reviewer actually likes 'it's not just X' cliche?" I have no idea.
There's this double standard. Slop is bad for models. Keep it out of the models at all costs! They cannot wait to put it into my head though. They don't care about my head.
Did you already update and align your OKR’s? Is your career accelerating from 360 degree peer review, continuous improvement, competency management, and excellence in execution? Do you review your goals daily, with regular 1-on-1 discussions with your Manager?
:)
I was once 'asked' to rate all my colleagues in a excel sheet so HR had 'something to base their evaluation on' smh
What I am sad about is that some people spend time/worry about balancing some random weights of some LLMs for the sake of some "alignment" or whatever "brain rot". Aren't humans more important than LLMs ? Are we, as humans, that tied to LLMs ?
English is not my native language and I hope I made my point clearer.
What qualifies this as an LLM sentence is that it makes a mildly insightful observation, indeed an inference, a sort of first-year-student level of analysis that puts a nice bow on the train of thought yet doesn't really offer anything novel. It doesn't add anything; it's just semantic boilerplate that also happens to follow a predictable style.
Sugar, alcohol, cigarettes, and LLMs.
If you weren't as incensed then, it's almost like your outrage and compulsion to post this on every hn thread is completely baseless.
Basically, I think the brain rot aspect might be a bit of terminology distraction here, when it seems what they're measuring is whether it's a puff piece or dense.
[0]: https://www.forbes.com/sites/traversmark/2024/05/17/why-kids...
Brainrot created by LLMs is important to worry about, their design as "people pleasers".
Their anthropomorphization can be scary too, no doubt.
https://data.commoncrawl.org/crawl-data/CC-MAIN-2025-38/segm...
I spotted here a large number of things that it would be unwise to repeat here. But I assume the data cleaning process removes such content before pretraining? ;)
Although I have to wonder. I played with some of the base/text Llama models, and got very disturbing output from them. So there's not that much cleaning going on.
I didn't check what you're referring to but yes, the major providers likely have state of the art classifiers for censoring and filtering such content.
And when that doesn't work, they can RLHF the behavior from occurring.
You're trying to make some claim about garbage in/garbage out, but if there's even a tiny moat - it's in the filtering of these datasets and the purchasing of licenses to use other larger sources of data that (unlike Common Crawl) _aren't_ freely available for competition and open source movements to use.
https://www.npr.org/2025/09/05/g-s1-87367/anthropic-authors-...
>The average webpage on the internet is so random and terrible it's not even clear how prior LLMs learn anything at all. You'd think it's random articles but it's not, it's weird data dumps, ad spam and SEO, terabytes of stock ticker updates, etc. And then there are diamonds mixed in there, the challenge is pick them out.
https://x.com/karpathy/status/1797313173449764933
Context: FineWeb-Edu, which used Llama 70B to [train a classifier to] filter FineWeb for quality, rejecting >90% of pages.
https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb...
https://www.tomshardware.com/tech-industry/artificial-intell...
https://www.classaction.org/news/1.5b-anthropic-settlement-e...
Paul Ingrassia's 'Nazi Streak'
Musk Tosses Barbs at NASA Chie After SpaceX Criticism
Travis Kelce Teams Up With Investor for Activist Campaign at Six Flags
A Small North Carolina College Becomes a Magnet for Wealthy Students
Cracker Barrel CEO Explains Short-Lived Logo Change
If that's the benchmark for high quality training material we're in trouble.
Is this slop?
and you know what they say, if it walks like slop, quacks like slop and talks like slop, it's probably slop
If you look at two random patterns of characters and both contain 6s you could say they are similar (because you’re ignoring that the similarity is less than 0.01%). That’s how comparing LLMs to brains feels like. Like roller skates to a cruise ship. They both let you get around.
A lot of people think computers have better answers than people.
AI is just another type of computer. It knows a lot of things and sounds confident. Why wouldn’t it be right?
"Cool" and "for real" are no different than "rizz" and "no cap". You spoke "brain rot" once, and "cringed" when your parents didn't understand. The cycle repeats.
Brain rot in this context is not a reference to slang.
I suppose I should have replied to one of the many comments here where my response is the correct context, rather than a top level.
LLMs are not cognizant. It's a terrible metaphor. It hides the source of the issue. The providers cheaped out on sourcing their data and now their LLMs are filled with false garbage and copyrighted material.
And now I know why bots on Twitter don't even work, even with humans in it - they're shooting blind.
Edit: I noticed that replacing it with "standard" "linear-gradient" reverses the direction of gradient.
just use a different model?
dont train it with bad data and just start a new session if your RAG muffins went off the rails?
what am I missing here
I guess I don't actually have an issue with this research paper existing, but I do have an issue with its clickbait-y title that gets it a bunch of attention, even though the actual research is really not that interesting.
And while this result isn't extraordinary, it definitely creates knowledge and could close the gap to more interesting observations.
In general using these medical/biological metaphors doesn't seem like a good idea in things like computer science research papers and similar.
Their use forces many inaccurate comparisons (when compared in detail) and they engender human qualities to what are already forgotten to be just computer models. I get this may be done with a slight tongue-in-cheek but with research papers there is also the risk that these terms start to be adopted. And undoing that would be a much taller order in either the research community or general media.
Maybe I am just yelling at clouds.
* Thought-skipping as the primary lesion: models increasingly truncate or skip reasoning chains, explaining most of the error growth.
* Popularity as a better indicator: the popularity, a non-semantic metric, of a tweet is a better indicator of the Brain Rot effect than the length in M1.
That's what you'd expect. Popular culture content tends to jump from premise to conclusion without showing the work. Train on popular culture and you get that. Really, what's supposed to come from training on the Twitter firehose? (Can you still buy that feed? Probably not.) This is a surprise-free result.
At least have a curated model (no social media) and a junk model to compare.
Their starting portfolios are ludicrous. They are trading BTC, XRP, DOGE, etc. I thought the idea was somewhat interesting, but then I felt like the only reasonable takeaway I had was that these models have intense brainrot from consuming twitter, reddit, etc. and as such have a completely warped view of "finance".
AznHisoka•3mo ago
PaulHoule•3mo ago
ForHackernews•3mo ago
PaulHoule•3mo ago
sailingparrot•3mo ago
xpe•3mo ago
Right: in the context of supervised learning, this statement is a good starting point. After all, how can one build a good supervised model if you can't train it on good examples?
But even in that context, it isn't an incisive framing of the problem. Lots of supervised models are resilient to some kinds of error. A better question, I think, is: what kinds of errors at what prevalence tend to degrade performance and why?
Speaking of LLMs and their ingestion processing, there is a lot more going on than purely supervised learning, so it seems reasonable to me that researchers would want to try to tease the problem apart.
rriley•3mo ago
TL;DR from https://unrav.io/#view/8f20da5f8205c54b5802c2b623702569
RobMurray•3mo ago