Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are. If it's able to guess those 700 bits correctly, then they aren't true semantic information, and you really only have 300 bits you want to transfer. You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.
> Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are.
Sure you can. LLM doesn't know what those 700 bits are, but you do. You may not realize it, and may not even know it at the time of prompting, but you do by the time you're sending.
Typical case is like this: you have 500 bits of semantic information to transfer. You give 300 of them to LLM, and get back the 500 bits you knew you have, and extra 500 you can quickly confirm are correct and relevant. Some of them are just dereferences of your input - where you recalled a pointer, but not what it pointed to. Some of it is information you never had before, but are able to easily validate.
You send that to me. I likely immediately realize the message was AI-assisted, but I trust you to be a decent human being, and not an asshole that lobs unverified LLM vomit over the fence for others to deal with. End result: you communicate 1000 bits of information to me, instead of planned 500, and you yourself learn extra 500 bits.
This is the optimistic scenario, but it does happen when LLM operator is not an asshole.
(Excuse the strong language, but I spent a lot of effort every day on both dealing with inconsiderate people lobbing LLM output at me, and making sure never to act like one myself, so it's a topic close to my heart.)
For a long time after the internet arrived on the scene, a lot of online news stories would reference websites, papers, polls, etc. without linking to them. There are still news sources doing this today. Sometimes such articles interpret or place context around their hidden references, but a lot of the time they just summarize.
Giving someone the text output of a LLM is very similar to publishing a summary without links to the referenced material. When you were querying your LLM, you could have asked specific questions or asked for a custom focus or point of view. Your intended audience might have questions or different concerns, but they're unable to interact with your LLM. What you have delivered is static and unresponsive. It has all the disadvantages of being machine output without the advantage of being interactive, the way your LLM was for you.
It may have to wait until compute is cheap enough that tokens are essentially free, but we need a system to pass "hyperlinks" to LLM's primed with context, ready to be interactively queried on a chosen context. It's being overly generous to assume that people are putting even 300 bits into a LLM for every 1000 bits of regurgitated writing they try to pass off as their own. When people post LLM output as if it were their own, I have no choice but to assume they had zero knowledge of the subject, but this query taught them what they wanted to learn, and now they're sharing that. That's fine, but please pass an interactive LLM link rather than static text.
Once we have "hyperlinks" for LLM sessions, perhaps we can share LLM output a little more usefully and honestly.
I've seen professional journal pieces refer to science journal articles only to go and read the original article and find that it draws a different conclusion than what is implied by the journalist.
My father used to complain about that 40 years ago, though in his case he was reading newspapers rather than professional journals. But he'd point it out to me often enough that I started to see the pattern. Scientist publishes paper saying "We may have found evidence of X, which suggests the possibility that Y may also be occurring". Journalist: "Scientists find X which proves Y".
This has been happening for decades; I still see it happening today*. My cynical suspicion is that words like "maybe" and "suggests the possibility" don't sell enough papers.
* Worst offender I can remember was actually from the summary of a paper published on the research institution's own website, so I couldn't blame it on "Oh, the journalist misunderstood what the scientist wrote". Summary said "Exposure to X can, on average, cause a 40% higher chance of Y" (where Y was a negative health outcome). I clicked through to the study and read it. Turned out the confidence interval on that chance of Y was so wide, all you could say with 95% confidence was that exposure to X could do anything from reduce your chance of Y by 5 percent, or increase it by 85 percent, or somewhere in between. They had averaged -5 and +85 to get the scarier-sounding 40% number that they published in the summary, but the truth would have been far closer to "this confidence interval is so wide that we really can't conclude anything from this data". But that wouldn't be nearly as likely to get them grants, so they tortured the data in their summary so that it would look better.
There is also incentives to adapt the message to the outlet. If you send that data to peer review and say we found 40% increase, the reviewers would reject it, so they have to moderate themselves. But if you send a summary to the university’s outreach outlet saying that we found something or nothing we don’t know, then they would also reject putting it out. I think the truth is that a 95% confidence interval is just the wrong statistic to look at given that data, and that they could probably have analyzed it better.
I have recently reviewed a paper that referenced my own article… but the conclusion was so off, I actually went back and re-read that entire article just to be sure there was no hint to the conclusion that the author derived. There was none. Uncanny experience.
Link rot has accelerated in the past decade. Links are great, when they work. If they are a few years old, they often don't. You certainly can't count on it. If you're referencing a paper or an article, it's probably better to cite the title of the article, the name of the journal or magazine or newspaper it was published in, the author, and the date. Then someone might have a chance of finding it again.
> For a long time...a lot of online news stories would reference websites, papers, polls, etc. without linking to them.
The "$CITY_NAME Business Journal" websites are the absolute worst with this. They'll refer to something specific, for example "$BIGCO's 2025 10-K filing" and it will be a link. That link will go to the 10-K, right? Nope! It goes to another page at the same business journal. Maybe that page is a summary of the 10-K, but probably not. Maybe it's just the general index page for all the articles about $BIGCO at that journal. What it links to, it definitely won't be the specific thing described by the text of that link.
It isn't the transfer of information at all. What's actually happening is you're prompting experiences in a human instead of an AI using text.
Communication only works if you have multiple levels of representation and abstraction, including but not limited to - letter shapes, grammatical structures, style and register, stylometry, and subtext.
All of that is learned, and writers usually assume they can rely on that learning as the context for the text.
So you don't write to 'transfer information' like a network cable, you write to trigger experiences in the human version of latent space.
Factual information is one kind of experience. But even when that's the goal, there are always layers of implied relationship, social register, role, status, and other implications in everything that's written.
In normal communications the context - business emails, personal messages, mainstream journalism, fiction, and the rest - defines what acceptable language looks like.
The content fits inside that. But it has to fit the context, otherwise it lands in a semantic and psychological uncanny valley - like sending LinkedIn speak to a spouse on a wedding anniversary.
The real problem with LLM writing is that it's good at the technical layer - the grammar and spelling - and has some insights into the rest.
But the default content style is marketing and ad speak. And recently it's developed a weird and unique hybrid style which applies marketing fluff and pretension to technical content like code comments.
So you get one register instead of all of them. It can attempt others, but it's still too limited to generate them fluently. Sometimes the results are outstanding, but often it defaults to mechanical clichés.
So that's why it sucks and sounds so hollow.
Can it be fixed? Yes, but it's very hard work, most people don't have the skills, and it takes time - often too much time to be worth the effort.
I'm definitely being slightly too emphatic when I say it's "fundamentally the transfer of information", but I don't think that nuance is important.
When LLMs eventually get good at writing in the correct style for a given context, I'll admit that they have value in that way. But they aren't good at that yet. And even when they do get that good, I'll still dislike it for reasons that are more emotional than rational.
I don't care how good the LLM gets. If I know some text was written by LLM I'd much rather know the prompt - the seed of intent.
If the seed of intent is "convey XYZ details so they know them" then I can choose to go and learn those details any way I see fit - maybe even ask an LLM to summarise some data for me! - rather than having to ingest whatever their LLM use poops out and trying to digest the intent and content and figure it out.
When sharing summaries from calls that I attended but my team has not but I think they would find interesting and I simply don't have time to handcraft it myself I use LLM and attach my prompt and the transcript.
Attaching the prompt initially threw some people..."wait, your admitting to using AI..."..."err yea, unlike you with that PowerPoint you sent me last week". I sense this is the right way to go imho.
That "marketing style" isn't a style, it's the lack of content itself. You just restated their original point despite trying to object to it, because it was correct and there is no way around that.
Sometimes Claude's problem, such as when I ask it to summarize a long, complex session back to me, is it's too information dense. It uses weird invented terms to gloss over complex parts of the architecture instead of explaining them.
But no matter what - too dense or too sparse - it always sounds like Claude.
It misses the forest for the trees. It feels the need to highlight details not understanding what details are most relevant to a human reader and how to survey the larger problems in a cohesive way that emphasizes the right parts without cliche and undue emphasis.
I’m not sure the 300-bit → 1,000-bit framing applies in all instances. The 300 bits may be a compressed cue to a much fuller idea. The AI can combine that cue with its prior knowledge to help reconstruct what the prompter was trying to express, with the prompter then verifying whether it’s right. Without the relevant prior knowledge for reconstruction, or the prompter for verification, it becomes much harder to know whether you’ve reconstructed the intended idea.
Yup, it helps when the prompter reads and edits the AI-generated text, but usually they just skim it and send it unedited. Worse, it's usually not 300 bits -> 1000 bits (= a concise message covering all the relevant topics), it's more like 300 bits -> 3000 bits (= typical LLM verbal diarrhea hiding the relevant points in a wall of text).
Unless that 700 bit was transferred on a separate occasion the inferred 700 bits is not true information, anyone could have reconstructed it from the 300 bits.
This is true - but also, models are absolutely terrible at writing articles and I don’t want to read them.
The issue isn’t that a 300 bit idea is padded with 15 KB of content. You can take any human-written article and reduce it by 90% with next to no information loss. What you lose is what makes the article a compelling read instead of a fact table.
I think the reality is that we will see quality long form AI-written content at some point. It doesn’t even feel like labs are particularly interested in chasing that now; code sells way more tokens. Right now the trend is that subsequent models degrade in writing quality as long as that pulls them up on coding benchmarks.
What already happens:
- People give an LLM a bulleted list of points that they want expanded into a professional sounding document.
- The receiver doesn't wanna read all that. They put the full document into an LLM and ask it to summarize it into succinct bullet points.
I’ve been saying the same thing. I’m not saying all, but a significant amount of comms could be bullet points to the benefit of both sender and receiver.
I first noticed this a year ago when I read a gmail AI summary, then glanced at the main text and saw it was AI generated. I feel like there's good fodder in there for a dystopian sci-fi story about a future where nobody communicates directly with one another, it's all AIs translating, but the AIs slowly start to drift.
It's essentially the tower of babel. Each person will devolve to speak their own internal language only they understand. Each language will need to be encoded down to its meaning to be reinterpreted. None of us will know if the transformers are accurately decoding, or if the other person is accurately interpreting the decoding (which is arguably already a feature of human language without the computers in-between.)
It was in a gmail promotional video years ago already. Person A would use AI to turn their summary into email and person B would ask the AI to go back to the summary.
They knew it was going to be like that from the beginning.
I feel like a lot of this is a problem when someone technical is attempting to communicate a complicated technical subject to a less-technical audience.
I can only dumb a thing down so much before the description is useless (when you zoom out too much you lose the details). Even technical people who could understand it but are lazy / "in a hurry" use the summary, without thinking about what detail they are losing.
Even more infuriating is when they then reply to my email, having only read the AI summary, and ask a question that was already answered by my message.
This is the exact same thing that happened pre-AI, with the added step of wasting energy/resources on the AI summary in the middle.
It's worse than that. If it takes others longer for others to consume and understand what you're producing than it does for you to produce it, you'll never be able to communicate with someone efficiently. Communication breaks down at a fundamental level if if you can't keep up with the other side if outputting and they won't slow to allow you do do so.
I see this all the time now with LLM generated output. It's easy to have an LLM generate a chunk of content that can be dropped into a chat or comment, and when it took you 20 seconds to have something written up based on the shared understanding you and an LLM have about the context of the situation, but it takes other people 3-5 minutes to read and understand that content, that fundamentally doesn't scale. It's bad enough when one or two people are doing it, but if the whole team is doing it, the only way to keep up with the stream of information is to also consume it through an LLM. At that point you're likely to be missing much of the nuance, and the amount of errors will explode.
This can be alleviated by people reviewing the output of an LLM and making sure it both includes fundamental information that might be assumed by context and reducing it to the parts that are essential for the new context it's in. This takes time, but is extremely important.
Having an LLM write gobs of text to send to other people instead of doing it yourself is the equivalent of a low yield cognitive zip-bomb. Don't do it.
I don’t quite think this tracks. Perhaps you want to communicate 1000 bits that are well known and can be referenced with a 300 bit key. Then the LLM can easily retrieve the remaining information. It’s like sending someone a link to the Wikipedia page instead of explaining something yourself.
No, I don’t want to read LLM writing because it is BAD at it. It doesn’t really understand how humans think (because it thinks differently), and doesn’t seem to understand core principles very well (presumably due to the lack of world model), so it can’t write something humans enjoy yet.
That's kind of my point. If the 1000 bits are well known, then their inclusion isn't new semantic information. By pasting an LLM's output, you're deciding for your reader that they don't already know that information, and deciding that your LLM prompt is better than whatever they would to to obtain that information if they lack it. IMO, it's much better to give your readers the 300-bit key, and let them decide for themselves if they want/need to get more information, and if so, how.
Why not do both though? I often now write my human summary, then paste in also the content you could get by asking a bot (with markers for which is which). You’re perfectly welcome to ignore the bot text, or ask your own bot, but bots are pretty slow, so I also don’t want to wait for it to “decompress” that 300 bit blob to get the detailed page back. But my human summaries are also only intended to be 50 bits — compressed again to just pass the signal I mean to convey, and not the whole prompt needed to fetch the right info from the right place to substantiate the claim.
TLDR the length was the same curtesy of tl;dr before, just now with a different name.
> You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.
I like this example and it made me think, there is an analogy here to spec-driven development and vibe coding.
Rather than send 300+700 bits, like you said, send 300 (or less!) and let the human intelligence on the other side generate the result. Which supports the even older perspective: “If I had more time, I would have written a shorter letter.”
I’m not sure if this lands on anything very profound, but what about a pattern where, instead of codifying agent output at all, the only artifacts we share are the prompts. And the rewards (respect) accrue to those who generate the most generative among people and AI
I agree. But there's still a valid use here for LLMs: If I have 1 Million bits of information and I want to communicate a synthesis of 500 bits to you, LLMs can be a viable helper. It is rarely done that way - I agree, but the way I use LLMs: Drop in all the relevant context information (PDFs, HTML, Markdown, Pictures etc. - up to usually 200-300k tokens), then compile a synthesis/summary prompt (usually 1-2 A4 pages of handwritten text. Then copy the output (1-2 A4 Pages), manually edit and send off (often, including an archive of the full original conversation, so the human on the other side can consult the unfiltered original prompt + inferencing, if wanted).
In other words: If I didn't reduce the 500 pages of text for you, you wouldn't know what I mean or what is relevant, or how to filter it yourself.
I’ll often put a long stream of consciousness on the page, or jot down rough meeting minutes, then ask ChatGPT to “summarise this for an email”. The result is shorter, clearer and easier to read.
AI amplifies the habits of the person using it. If they’re lazy or dim, then it's like giving a monkey a gun.
I was going to write a post disagreeing with this on the basis of the fact that the reader lacks the background information the LLM has. For example, if I were to prompt "explain the proof of quadratic reciprocity using Gauss sums" most readers would need the entire LLM's answer (and much more, probably) and not just the prompt.
But then I realized that the reader can prompt the LLM with the same prompt for the same or equivalent expanded text. Most people don't do this as it's extra effort, but it's interesting to imagine a world where this is the default way of engagement with a text, assumed by both writers and readers alike.
> But then I realized that the reader can prompt the LLM with the same prompt for the same or equivalent expanded text.
That's basically what I've been asking my colleagues (so far a losing battle): Please don't send me AI-generated text. Send me your prompt instead. It is highly likely that I will understand it without needing an LLM, and if not, I can do it myself.
Saying the hard part out loud: we give more value to the people with 1000 bit brains than we do to the 300 bit people. There’s a strong incentive to write like you’re the former and not the latter. Freely available LLMs give people the means to match the incentive.
Depends. If the 700 bits were arrived at by the LLM while spending a lot of tokens, and the result is "good", I may want it through you as a middleman because it used up your tokens and won't eat my subscription usage limit to ask the AI to supply those 700. If you spend the tokens and put the result online, plenty of people can spare their tokens because they don't have to ask the AI to derive it. Bonus if that result was run through some kind of testing and verification.
Obviously this doesn't really apply to super simple questions that the LLM can just spit out the answer to right away.
True. There is at least one more case: when LLM the other person is using has access to their context and information repositories that they don't want to share directly, and so the LLM text gives a peek at a slice of that, and that's not something I can recreate from thin air. In that case the LLM text may have utility for me.
Bits of information depend on the readers prior knowledge, those bits are not absolute numbers. That makes communication not just information exchange but also syncing of priors. So some amount of redundant information maybe needed/wanted.
If only it were 300:700... it is more like 300:100000 and up. But then you use AI to summarize to hopefully retrieve the 300 bits. We should try doing this in a loop with a simple enough sentence to start with and see what pops out after every iteration. Fully automatic Chinese whispers.
While I generally agree, the difference is I can also hand an LLM my pile of code and documents which is... a lot more information dense than basically anything I could write. Sure, a one-off prompt in a chat window isn't very helpful.
Summarization—especially of private context—is definitely one of the main exceptions to LLM writing being useless. However, it's still preferable to turn your private context into shared context and then give pointers to that, rather than having an LLM attempt to summarize it.
I’m not disagreeing with your preference for human authored writing, but I don’t know that I agree with the content of your argument.
If you consider that what humans are doing during conversation is a form of compressed encoding / decoding from some latent representation through a quantized signal then if you interpret it that as a compressed sensing problem you absolutely can infer to a very close approximation the original latent representation using far fewer than those 1k bits.
From an information-theoretic perspective, if I can effectively transfer a latent concept to you in 300 bits of quantized communication, then by definition the latent concept itself isn't more than 300 bits. Any bit over 300 in the communication I send to you is fluff.
The value of writing isn’t always to communicate new information. It’s often to align everyone’s assumptions. For instance, when I say casually to a colleague or an agent “this change will require a db migration” they understand it’s to my teams primary application database. If I submit a design doc to a company wide review which database is changing is critical information. If the 300 bits were truly enough your agent or junior engineer would implement the wrong thing correctly as they often do.
Clarifying which database you're referring to is exactly the sort of thing I'm talking about when I say "new semantic information". It should be communicated, and you can't trust an LLM to choose the correct database, you need to specify that yourself.
What I'm talking about is if you add "database foo" to your prompt, the LLM may then add text describing what that database is, where it is, etc. But that's not new information, it (hopefully) already exists in your team's public docs, slack convos, etc. You should just say "database foo" directly to your reader, and if they want to learn more about that database, they can do that themselves, or you can give pointers to them based on what you consider important.
I love this example, partially because it jives with my conviction that LLMs are the ultimate translation machine. Ever since the embedding model days, it is clear that these models are amazing at representing meaning as math. The fact that LLM's most salient use is for coding somewhat agrees with that. After all, what is a programming language but another language? We instruct people with words and machines with code.
If you have 1000 bits of semantic information that you want to transfer but your default communication combines it with 10,000 bits of noise. Giving it all to an LLM and iterate on reducing that noise while making sure the 1000 bits is still present would enable you to communicate more effectively.
Overall, ideas are ideas. I'm not overly concerned with the fact that it was you who had the idea, as long as the idea is interesting. I don't know most of the people who write the things I read, so it seems to be of no consequence to me at all if they wrote it, as long as it is interesting. LLMs are notorious at creating things that are bland and vacuous, but they by no means have a monopoly on it.
Be the source human, machine, or dolphin, if they write a good article, I'm prepared to read it.
LLMs are wonderful at adding noise and okay at removing noise. My point is that they're not very useful at adding signal. If you wrote 10000 bits of noise and 1000 bits of signal, I would rather receive those 11000 bits from you, and if necessary ask an LLM to remove noise based on what I consider noise. If you can point out to the LLM what it should consider signal vs noise regardless of context, it should have been easy to not write that noise in the first place.
That's assumes a couple of things that are trivially not true:
- The assumption that both parties know about the same as an LLM does. An LLM know orders of magnitude more.
- The assumption that the output of the LLM is not refined over a few cycles.
The point is that you might give 300 bits of semantic information to an LLM, it fills it to a 1000 with perhaps 400 wrong bits. You correct it half a dozen times. It's now 950. You do the final touch ups. It's now at 1000. And it still took you 20% of the time to do it.
I notice this in the attitudes of students towards reading. They will take a 10-page journal article and ask the AI for a summary and then read a summary that's the equivalent of maybe half a page. But if the article could have been half a page, why is it actually 10 pages? It's true that there is some boilerplate, but it's strange to me that people could think that 90% of what they're reading is (to use your phrase) "not true semantic information". It's like if you went to a restaurant and ordered a 10-oz steak and they brought you a little teeny bite of steak and said "Oh, other places will give you a bigger one, but most of that is just filler, we just took out all the superfluous parts." It's a worrying sign for our future if things like this are not tripping people's skeptic sensors and making them wonder if they might possibly be missing something.
That means folks using LLMs starting off with 300 bits KNOW that they lack the full payload of information to transfer to you. IOW, they know they need to transfer much more than 300, so they use LLM to fill those gaps. That's the crux of the slop universe out there. Folks are using LLMs for the 700 bits on top of their 300 bits and passing off the full 1000 bits as their own.
I echo the writer's sentiment. "I don't want to read the clanker's 700 bits. I want only your synthesis." (I can get the clanker to generate those 700 myself. Unless ... unless this whole LLM slop market is all about saving you the time to get an LLM to generate those 700!)
you think like such a techie. consciousness is not analogous to IO. if writing were only the transference of something from point A to B then what is the technical function of poetry, a question (the open ended kind), a pondering, a wondering, and so on? Furthermore, language is lossy and introduces a large degree of subjectivity, mystery, and uncertainty nomatter which words you choose. Words are by nature lower res/on a lower ontological domain than thought. So your premise is preposterous on both a practical and theoretical level.
Furthermore not all writing is for another to consume; nor even for the author themselves to consume. That is to say it has meaning ipso facto, not dependent on transference, as ritual.
isn't it more like: i have 1000 bits, i transfer 1000 bits but depending on the person, it might be lossy, so they only understand about 700. they then come up with the 300+- on their own, potentially putting them over 1000 or they come back and ask questions to fill in the blank. the bits don't ever have to be bit identical.
Sure, but that seems orthogonal to what I'm saying, no? We could continue clarifying the definitions of information in this context, but my fundamental argument is LLMs don't add information to my writing that couldn't be added by the reader themself.
This of course has the potential to change with personal LLMs that can have shared private context with me. However, that isn't a defense for sending people AI slop, it just turns it from "LLMs don't add value" to "LLMs may add value when used judiciously."
They're lossy. I might make an esoteric reference that no one gets, or an analogy that doesn't quite land, and then have the LLM help me come up with something a bit more understandable to a general audience. There's a difference between dumping the output that you spent 5 minutes with, and taking an hour to craft that perfect analogy.
I will give you a use case where this is absolutely not the case.
I have a bunch of CLI utils I run for various clients and their peculiar setups. They now have man pages with descriptions and examples in them because the LLM went and read my code and did the needful.
I no longer have to re read my own code, rather I can just use the manual page.
Format and description came from semantics and context that (barely) existed elsewhere and I was not going to retain or transmit, but I have now.
But then the LLMs aren't adding new information, they're just reading your code and translating that information from e.g. python to english. Rather than sending someone an LLM-generated doc to someone, send the code and your own personal thoughts on the code. If your reader doesn't want to read and understand your code, they can ask an LLM to analyze it, within the context of their specific use case and your personal thoughts if any.
You're making a big assumption that the code is what is being executed, and not a compiled binary.
Where is the code: My repo? the clients? If it's in mine, the client does not have access and the CLI is a first stop to debugging. They arent in the context of written docs, more likely a production error from a log (thats now spitting out a message to check the CLI).
Less steps, less tools, more context in line and available in an interface your already using.
> they can ask an LLM to analyze it, within the context of their specific use case and your personal thoughts if any.
Or I can skim the man page it generated and make sure it looks good. The "work" (the tokens) dont have get spent over and over again.
I never liked information theory because information theory as Shannon envisioned it fundamentally did not deal with semantics.
AIT tried solving it? But AFAIK it's a lot of pretty results with not much real application.
A better approximation is something of a "shared model"; then you can actually state things like, the transfer of information sometimes is "trivial" because, well, it's right there in your compressor/decompressor.
My understanding is that to unambiguously quantify information, you need to have a known model within which that information fits. In this context, talking about LLMs adding value (or not) via inserting new semantic information, we're assuming that public knowledge on the internet is not new semantic information, and the quantity of information of contained in text talking about public knowledge is equal to the ~32 bits needed to point to that knowledge.
Saying something for years doesn't make it right. Has nobody ever challenged you on that? Our brains prefer to read enjoyable bits, not raw information. If you only got those 300 bits, you aren't going to bother putting them into your LLM to generate 700 bits to make it a more enjoyable read, you're just going to struggle through the 300. Maybe when browsers come with built-in automatic text puff-uppers, then you'll have a case but almost nobody does that now.
Here's a clearer example - would you rather learn a concept from a research paper or a textbook or blog? You say the research paper but they're dense and hard to wade through where-as blogs and textbooks are more wordy but hold your hand, which is something that helps humans learn.
Plenty of people have challenged me on this! There are a lot of exceptions we can come up with (summarization and private context are the biggest examples), and everything's changing in various ways as AIs get more capable. Usually, conversations about people pasting LLM output focus on things like AI generated blog posts, people who have no taste in writing and want an AI to rewrite everything for them, people who are just plain lazy, etc.
> would you rather learn a concept from a research paper or a textbook or blog?
I pretty much always read blogs first, and then move to a research paper only if I want more details or care enough about the subject to verify with the original source. Typically this is because research papers have too much information to be approachable.
"Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700,"
You absolutely can if that information is in the code, which it often is.
There should not be that much in the code that needs further elucidation.
Some stuff definitely - but not much.
Usually you need the code and architectural summary + that stuff.
The AI is not very good at it but it will get better.
I think the debate here is about a few different things.
That would be true if there is only one receiver and if i could exactly predict how the receiver would interpret these 300 bits. But i could use LLM to expand these 300 bits to 1000 bits of more explicit information that gives enough redundancy and context that minimizes risk of misunderstanding and increase easiness of understanding, then validate such expanded message (and possibly do iterative corrections if LLM did not understand those 300 bits correctly), and finally release expanded message to (potentially large and heterogenous) group of receivers.
If there's a significant risk of misinterpretation when writing only 300 bits, then the idea you're trying to convey is worth more than 300 bits. In the process of prompting the LLM to add information, you're giving information to the LLM that you could instead just give to your readers directly. If you're just using an LLM to expand your thoughts in the hope that you get a better piece of writing, maybe you should put more effort into your own writing.
(This is all under an information model that assumes the LLM and your readers have equal access to knowledge, which I probably should have made more explicit in my original comment.)
It's funny, i push back on pull requests because there is too much description now - a 20 line change has pages and pages of generated description, rationalisation for why it is safe, defense of each design decision, analysis of risks and side effects. People are indignant, you're rejecting my change because there is too much documentation? And my response is, I don't have time to read it and you put me in the position where I can't afford not to - because approving the PR implies I did and accepted it. The investment to read all that for the value of a code change that I'm one prompt away from doing myself if I cared is just not high enough. So it's rejected.
You might be surprised how many organisations/ teams now don’t even review pull requests. Claude does coding, Codex do code review, and if developer did 5 round of this before pull request, we could very well just let CI merge.
I'm very curious how this goes long term. I guess we will find out.
My instinct says that these systems will expand their complexity to fully fit the cognitive budget of the agents that coded them and then atrophy the same way human-built systems do at lower cognitive budget. Only this time, because of the larger up front budget, the complexity ceiling will be higher, and the potential depth of the problem may be much much larger. It may mostly manifest as increasing cost over time - the agents grind for longer and longer, iterating over and over to fix all the failing tests, and the breaking point will be where it never converges and you come back to millions of dollars in budget spent and still tests are failing and effective gridlock on system changes.
But this may be all my human-biased fantasy that justifies still taking a role in software development.
> ... these systems will expand their complexity to fully fit the cognitive budget of the agents that coded them and then atrophy the same way human-built systems do at lower cognitive budget. Only this time, because of the larger up front budget, the complexity ceiling will be higher ...
and this is such a liability imo. I recently got fired for what I think was apparently merging a change to Kubeconfig that both the CTO and DevOps person approved. I made as damn sure as I could that the proposed change was good, but I needed their eyes on it and they clearly weren't, possibly be cause every other repo had a review bot and no special automatic deployment automations. The DevOps person literally commented and said "It's good to merge", who to trust
I also have ADHD-driven blah blah blah. I find achieving brevity laborious, but find LLM results to be unacceptable. A journalism class taught by a well-known professional food critic helped most, but consistently using the free “Hemingway App” editor was almost as beneficial. Blindly following every suggestion pares your writing down to a nub, but it’s great at highlighting sentences you need to rethink without blurring your voice and ideas with bland suggestions.
That's a solid recommendation, thanks. I also did journalism. So I can write succinctly, I just have to put effort in like you. Which is much more than I'm willing to put in after I just wrote a treatise on the human condition to send to the lead developer at 07:30 on Monday.
I’ve started just asking it to touch up text to make it sound more human and follow technical writing guidelines and it converges on convincing pretty quickly. It also makes it easier for me to verify that it’s correct because all of the stupid LLM noise and formatting goes away and it gets distilled down to the important bits.
I'm going to be a bit contrarian here. In general, I 100% agree, just send me the prompt; however sometimes I feel an LLM can help structure thoughts. It can be a good sparring partner. If you proofread your LLM content, re-prompt until it sounds right and no longer like AI content, and feel that it actually conveys the information incredibly well, then I'm happy.
One of the instructions I've hammered into Claude is "Write like a human. I don't want this to sound like AI content. Your standard style of writing would fail miserably if it was reviewed by an English teacher. I want you to write prose that is nice to read. For example, write full sentences instead of bullet points."
It works wonders. Suddenly, my plan documents are something I actually understand, and something I'd be happy to share as an RFC. They still need iterating every time before they're ready to share, but I no longer have "the honest truth is" and other BS in my output. It's so refreshing for my brain to be able to actually focus on the content.
I've noticed this with my partner, too. She used Claude to draft a strategy document and felt completely overwhelmed. A classic moment of "AI did all the thinking for me, but now I don't know what I'm presenting". Once I helped her prompt Claude into writing the strategy in proper English, she understood what the AI was proposing, rejected large parts of it, iterated several times and ended up with a draft document where she edited the finishing touches herself and felt that it was truly hers. The AI was still incredibly useful: it helped her with the blank slate problem, and tremendously sped up her workflow.
So yeah, if you proofread and iterate on your AI's output until you feel you'd be proud if you had written it yourself, I'm happy to read it, too.
I appreciate the direction of this article, and commend the author on publishing work, but the first sentence in the first paragraph is exactly what they lament.
> A pattern I see is that people use AI to build something new, then they use AI to retrospectively summarize what they have already built into a design document. Reading a document like this isn’t just difficult—it is punishing.
FWIW Pangram [0] "We believe that this entire text is human-written."
I've only just discovered pangram, but I've seen it referred to a few times in HN recently wit nothing obviously pejorative about it. Take it with all required grains of salt, though
That's a classic calling card of LLM prose but that doesn't mean they actually used an AI. Though sure, it would be nice if we gave this style a name and then abolished it.
I call it the “it’s not delivery—it’s DiGiorno” construct. I generally tune out when I see this construct, which is so prevalent on LinkedIn these days, which is inundated with clichéd writing.
No, usually humans write more than just a short unsubstantiated punchline in their "it's not x, it's y's", unless they're trying to sell you something.
Well, not quite the classic calling card - LLMs generally use a contrast of opposites. (e.g. "it wasn't a bug -- it was a feature", although that predates LLMs :)
TFA's use is more common in "normal" language: "it's not just [minor], it's [major]". (But, as others have pointed out, it was probably deliberately parodic anyway.)
> People who rarely produced original writing are suddenly producing
these posts are beginning to make me wince. ai is giving voice to a lot of folks we probably wouldn't even be hearing from otherwise - because not everyone does their thinking in articulate prose, and extracting their realizations as shareable language takes effort, or long, embarassing iterations with ai "workshopping" to get to something they can read back and go yes this is what I am trying to say.
now the sentiment is that whatever these people had to say they could have just come out and said it - and that would be more passionate and less disjointed - no, not necessarily, and the more it gets repeated the more it's sounding elite and pompous to me. for example this:
> I had someone write me a personal message about a sensitive topic that was clearly workshopped with AI in an effort to nuance the conversation and not offend me. But the message became impersonal, dispassionate, and disjointed. It had all of the parts, but it didn’t make sense as a whole. I wasn’t interested in reading it, or responding.
this reads as someone choosing to ignore a personal message about a sensitive topic - because of an assumption they made about their writing: that they workshopped it when they should not have - because whatever it was they were struggling to say they should have isntead trusted the author to be able to understand the precise meaning of their raw, inelegant thoughts no matter how embarrassed they might be to share them in that form. they're complaining about a message they probably weren't otherwise going to see.
But how is that a positive that people that cannot give a fuck about writing coherent thoughts down to share them can now hog your attention with something that has no clever meaning whatsoever? Is talking just for the sake of talking a good thing in your book?
> people that cannot give a fuck about writing coherent thoughts down
That's clearly not the type of people they are talking about. For some people expressing their thoughts in words is really, really hard. It doesn't mean they don't give a damn. Just like being in a wheelchair doesn't necessarily make you lazy.
How is ai giving them voice? All that generated slop does that you stop to want to read anything. And it is not their voice anyway. It is not their words, their ideas are mixed with ai generated points.
As for second paragraph, the writing "had no overall meaning". There is nothing to respond to if there is no overall meaning.
What we are seeing here is a Luddite reaction from artisans. A skill has been automated and those that have the skill are not happy about it.
For many writing is painful and the machines ease the pain. And like all machines it takes a while to use them skilfully. In particular remembering that like code it is write once and read many. What the reader needs should be at the forefront of a writers mind - both for code and prose.
Nobody gets on a motorbike and wins the TT in the first weekend.
There's a lot of misunderstanding here. The quality of LLM writing has not plateaued, it has dropped significantly. They've cut it to the point where people start noticing.
Do a search on "Claude Sonnet 4.5" on Reddit and you'll see lots of disappointed users [1]
Have you seen the episode "Darmok" of Star Trek The New Generation series? It is about a people with an impenetrable language aimed at packing as much information as possible by using references and metaphors, not descriptions. I guess this is where LLM language output is heading.
I'm curious if there have been any formal studies on this. I suspect that people have become better over time at identifying LLM writing styles, and are also combining that with selection/confirmation bias. Dunno what fraction of the perceived drop in quality comes from that.
A good code or commit comment should succinctly explain the subtle details or motivation not obvious from the content itself. AI comments tend to wax poetic about surface details.
The same goes for articles. I want the author to distill the material using their own voice and hard earned expertise. I don't want a generic text which sort of tells the tale.
> I would much rather someone be themselves and write with their own voice, or with some passion
I recently had a fairly successful article here on HN. The common thread among comments was appreciation for the passion and it being "hand-crafted". I am thrilled my passion shone through despite the dry subject. Still, it's sad that something simply being written by a human has become such a rare compliment.
On the note of imperfections... My preference is now strongly towards reading someone's stream of thought thrown down at speed over than that same stream of thought shat through the digestive track of an AI agent. At least that feels human.
To me, it also seems like they AI digestion is getting actively worse? As best as I can tell, all the agentic nature and reasoning for code is now making writing actively worse, as the agent pulls across your whole knowledge base and will take that one thought and eagerly join and context it thinks is relevant, with the reasoning spread throughout the page.
I've always wanted to write this essay, but now I no longer need to. My title would have been pretty similar: If You Want Me to Read It, Please Write It.
One minor addition: when a (conscious) human writes a proposal, a question or a request, they'd instinctively choose words with which they can express their uncertainty in some of the details. I as a reader can detect these uncertainties and I can choose to do more research assert or correct them, if it feels appropriate. AI-generated texts miss those cues, therefore they are much harder to read and connect to.
To me it is just a bittersweet irony: people who don’t know what a book is for 30-40 years or in earlier days got the RTFM (gif smiley not included) treatment dump one liners to produce a giant load of text corpus that needs to be processed by an AI to get the gist out of it with sometimes quite some refinement by asking many questions.
So there it is: cruel people who don’t even read their own prompts while happily believing others read their text load and pat the genius on the shoulder for being such a great guy in writing, while in essence it turned the RTFM movement on its head: UUOAI - “Use your own AI.”
People cannot communicate better by using the digital equivalent of logorrhea.
AI-generated writing does not bother me, unless it is intended as prose writing, where the flow communicates as much as the specific facts that are described. For technical writing, I usually just scan for important information anyway, and in that sense the only thing that bothers me about AI-generated writing is if it's too verbose and obscures the important information by leaning ineffectively on jargon and high-minded verbiage. I find that Claude Opus is very guilty of this, whereas GPT 5.6 variants are not.
>the only thing that bothers me about AI-generated writing is if it's too verbose and obscures the important information by leaning ineffectively on jargon and high-minded verbiage
But so many READMEs and design docs are degraded to this level by uncritical/unmonitored use of Claude. You make it sound like it’s an exception, not a rule.
In my experience, that brain damage inducing Claude-style technical writing is everywhere. It sucks not only because of its verbosity, but because it includes completely pointless tidbits extracted from random extended LLM sessions, with no attempt to prune redundant or gratuitous details.
It’s funny, this pattern is very similar to how I write code with AI these days.
I write almost every line but I have one or more agents always running against the changes alongside me evaluating correctness, suggesting approaches, auditing test coverage, etc.
I’ll also use it for grunt work refactors, scaffolding, resolving merge/rebase conflicts, etc.
These last few days, there has been a small stream of blogposts on HN expressing similar things, and I have enjoyed them all.
My question recently has been how to broach this subject with colleagues who really enjoy producing prose with AI. There is not yet a better cultural shorthand for this sort of thing than "slop" which is a harsh-sounding word and itself sort of a thought-terminating cliché. "I don't want to read what you didn't write" is maybe closer — but it needs a pithier and somewhat more encouraging encapsulation, like "I want to hear it from you".
Has anyone had good experiences setting up professional boundaries or team norms around AI-written docs?
Ask questions about how the audience is taking the blog posts. Say that many people seem to really not like LLM writing and ask how are they listening to their readers on this point.
You may find that brings a better and more human focused discussion. Who is the audience and what is best for them? Don't frame it about your preferences but about the real persons that your colleagues seek to talk to.
It's something that my company has been struggling with. Developers are generating mountains of code, documentation, and Jira ticket comments. It's incomprehensible and overwhelming.
For the team I lead, my guideline is AI generated is fine but it needs to be human-edited and/or summarized. You want me to read what you're offering? Put some effort into it and meet me halfway. I don't want AI generated gibberish with made-up terms. You'd better also understand what you are presenting as your work. It's been fairly well-received though we're still working on it.
I have some co-workers on other teams who use AI to generate responses to literally everything. Ask a simple question? Get pages of AI generated nonsense in response. They are proving a tougher nut to crack.
In my current position(DevOps) we are getting this kind of messages or issues a lot recently. While forwarding these issues to my team lead, i don't have to deal with them which feels great but once its assigned to me... Its another long process to understand what issue actually means and what i need to do.
Thats said, i don't know any professional way for this but currenlty trying to communicate with peers in order to work without a problem.
There is one person who is so good at writing with AI assistance.
Everyone else, including me, not so much. I envy his talent and ability to so successfully use the new tools. It's just something to think and talk about regularly. Maybe one day we will all be able to use the tools as well as that guy.
im sensing about a 20 month lag between hn sentiment and normy-coworker sentiment. managers and business-types pointing out the benefits and challenges of working with an llm i told them about early 2025. im just politely nodding along waiting for them to catch up. theres some emperors new clothes happening at my company so maybe im complicit
At a broad level, the issue isn’t generated content, it is the ratio of verification capacity to generation capacity (V/G). Your pain is because generation capacity has increased significantly, while verification is laborious and capacity has not (and can not) catch up.
Unlike spam which is from external sources and can be ignored, messages from other employees have to be responded to. I guarantee this is creating bottlenecks all across the firm, outside of the individuals who are feeling productive.
For fixes, theres theoretical approaches that might work?
If you need leadership to help you, then this issue has to become something that is on their radar, which means that something needs to go wrong or costs need to be registered.
The shortest conversation for that is to make people aware that generation has improved individual productivity, while moving the costs of that production to the rest of the firm.
If leadership is not at the stage to listen, then you need to move the costs you are incurring to the people who are sending them to you. Maybe set time aside to sit down with whoever sent a PR and then read what they sent together, to understand it.
It also makes a difference if tokens are being subsidized or not. If the firm doesn’t care how many tokens are being used, then you are naturally going to have over production.
this is intended as a silly observation and not an actual accusation, but for an article about not writing with AI, the following is one AI-ass sentence:
“Reading a document like this isn’t just difficult—it is punishing.”
LLM didn't invent it themselves, they are plagiarism machines. Obviously these kind of patterns had to be popular in the first place to be overused by LLMs
Also we have reached the point where LLMs are pubkishing more contents than humans so we have probably reached a point where humans will inconciously start copying and using LLMs style too.
c'mon, it's literally the top 2 AI writing tropes in a single sentence. i googled "AI tropes", and here are the first 2 tropes listed in the first search result:
1) negative parallelism: "It's not bold. It's backwards."
2) em-dash addiction: "The problem -- and this is the part nobody talks about -- is systemic."
again, i'm not saying the author wrote this sentence using AI. i was just tickled by how stereotypically AI it sounded. for better or for worse, those characteristics are just going to set off the AI detector in people's heads now, as evidenced by the fact that there are like 10 separate comments on this post that picked up on the exact same thing.
Nobody is going to read anything regardless of whether you used a LLM to write it or not (but if you use a LLM they'll use that as their excuse for the virtue signalling aura farm), so honestly why bother writing.
If it's written down in a form that can't be repudiated, a succinct message to any stakeholders covering any decision of substance is good practice. They might not read it, but they cannot pretend I did not make a good faith attempt to inform them.
(And I try to be judicious about what counts as "of substance" so they can't accuse me of flooding them with messages)
I don't disagree with the main points of the article. But I feel like soon with all the writing that's been hating on AI writing recently on HN, the LLMs are going to be really good at writing articles about how bad AI is for writing...
I'm finding that executives at work are generating AI slop and sending it at each other and teams that are then meant to digest it, without any critical thought. Often these are sloppy and contain broad mistakes that aren't relevant to our company's setup.
On Windows, use wincompose. Space followed by a hyphen gives you an emdash with the cute half-width spaces around it. On Android, I can get an emdash by long pressing hyphen. There's also a keyboard shortcut on the mac for it. No idea about Linux/BSD or iOS, unfortunately.
On Windows I also use QuickAccent, now part of PowerToys. I could never remember the Alt-code for the em dash. Apparently it's Alt-0151. With QuickAccent I hold a letter and right or left arrow and up comes a menu of related symbols. It's pretty neat.
On Mac, it's option-shift-hyphen. Option-hyphen gives the en dash.
On Linux, you may be able to type Ctrl-Shift-U to get unicode entry and type 2014 and Enter. 2013 is the en dash. Again, I will never remember that.
This sentiment gets echoed over and over. While I can certainly see the value in human-created creative works, if the subject is pure facts, there are only so many ways to state the facts without being caught in the weeds and missing your entire point.
Moreover, people are finding it hard to differentiate what is an is not AI-generated with newer models, often attributing original work with those of LLMs. It has just become an easy scapegoat for lazy comprehension and a desire to do less. You are jumping at AI boogeymen.
Just about the only thing here I can level with you on is, yes, AI is far from perfect and will continue to advance. Otherwise, so much of this reads as fruity prose to excuse apathy.
A lot of my problem with AI writing is that the facts aren’t checked. You get a big cheesy dramatic arc but the core story isn’t factually correct. There’s only so much time in a lifetime, too little to waste fact checking someone else’s work because they couldn’t be bothered to.
I have junior devs submitting huge PRs with multi paragraph descriptions about load baring root causes and "why this works". Then I ask them directly what are the steps to test this and they don't know, they have never run the code.
Rather than entertain the idea of reviewing the code I just send it back to them until they work out how to run it. And almost always they submit a new change because the last one didn't actually work, despite how confident and articulate claude was to them.
A change can pass CI without actually making the change required. The tests test that the code does what it does, not that it does what the user wanted.
AI is really good at making tests that pass even if the change itself is wrong.
It's curious that you're pinning the blame of "excusing apathy" on the reader and not the writer. Why doesn't it go both ways? Given that producing LLM text is so cheap, your proposal amounts to an asymmetric war which is ultimately a DDOS on my finite time and attention. Or in other words: if it wasn't worth your time to write it, it's not worth my time to read it.
Let us say someone had few good ideas, and seeded them into a prompt, and after few back and forth, web searches via agent, feedback from the author, a piece of work was produced and the author decided to share it as a blog.
This is not much different than how people are producing original work LLM in areas such as math.
Would you object to reading their work because it was a byproduct of collaboration between AI/Humans? What about songs? movies? math proves? and software produces as such?
In my mind the issue isn't that it was crafted by AI, but that it wasn't crafted with the intention of getting the point across in a way that respects the reader's time or energy. The result is often needlessly wordy with information buried in a sea of flourish. This might be forgivable if the author had a certain entertaining style, but LLMs are aggressively mediocre by their nature, making wading through it painful.
Its certainly possible to get an LLM to write concise prose but thats not how most people are using them. We can talk all day on HN about the ideal LLM user that is conscientious, reviews the output, puts time and effort into stylistic concerns, etc. But none of that actually matters because thats not what people are doing; the entire reason they're using an LLM in the first place is so that they don't have to do that.
On that we agree then. I also don't want to go through mountains of tangled text written by an AI.
But what if the AI models improve their writing style considerably to match that of the best authors? which I think is plausible soon.
I feel in the near future, what really matters the most is the seed and the dialog/back and forth more than then who wrote what. And that the best content would be a by product of human/machine interaction atleast given the current generation of tech.
Until perhaps, in the the not so distant future, the math can encode our taste, judgments and develop imagination, then maybe the best human wouldn't be able to create anything better than the machine. But then again, we want to communicate the inner state of human being, because we want to build relationship, so content for consumption might be dominated by machines, but communication as way to build relationship might stay genuinely human...but even that might change, what if machines eventually become better at relationships than humans? that could be how AI truly "kill us"..I think within few decades we will discover that our intellect was a short period of cosmic evolution, and we will accept our fate as animals with analog organic intelligence that helped to bootstrap what comes next, this might be scary but humbling, and I think it's inevitable.
The lazy ones are the people spending 60s prompting then hitting copy/paste. They dump it on me as is and call it a job well done, which leads to me doing their job for them or calling them out to stop the behavior.
I don’t like doing either of these things. I don't want to do your job and I don't want to lecture about how it isn’t “your work” if you don’t touch the content after an LLM spits it out. It is rude and selfish to put me in that position.
And I don't want to read the millionth iteration of some old man complaining about the kids these days, written by a human or not, yet here we are.
If you don't like AI slop. Don't read it. But wasting your time generating human slop to complain about AI slop is so obviously futile that it immediately identifies the writer as lacking the capacity for reason or emotional clarity, or merely seeking attention for their self-promotion with clickbait.
The problem is, some of us work with colleagues, or even bosses, who throw AI replies at us in nearly every conversation — so we don’t have a choice to NOT read it.
I ask coworkers point blank to send me a non-llm version of their slop sagas. The initial exchange can be a bit awkward, particularly with new hires, but consistently asking for human prose has worked for me.
As an educator in university I have found the project proposals of students have become longer, to some degree more well written and quite certainly have LLMs involved.
This becomes problematic when it turns out people haven't even read what they sent me.
To clarify: this is about voluntary projects that do not win them extra points.
> Reading a document like this isn’t just difficult—it is punishing
I stopped reading here. I’ve become so sensitive to LLMism, I don’t want to go on. Tip; omit the needless ”it’s not X, it’s Y” and just write ”it’s Y”.
I’m fine with reading something you didn’t write if it has the information I need. I’m not fine with reading something you didn’t read and will just waste my time.
> I don’t want to live in a world where you use AI to summarize something important into unreadable text, and then I use AI in an attempt to decipher it. I want to hear you, imperfections and all.
It doesn't fundamentally change the equation if I use AI to prepare and then write it myself. If I'm using AI effectively, it's likely that you won't be able to tell.
This sort of post is increasingly coming off as high and mighty, where the user thinks they are being exceptionally creative and other people who are using AI are using it mindlessly.
“Reading a document like this isn’t just difficult—it is punishing.”
I’d be curious whether the author composed this sentence himself or it was the output of AI. Personally I often find myself “it’s not X it’s Y” and then recoiling in disgust and rephrasing it simply because AI has made it so grating from overuse.
Same here. I also used to love writing with em dashes, but don't anymore out of fear people will call my writing AI and stop reading. It'd not hard to do in most word processors, typing two hyphens "--" does it in Word and Pages, so I don't get why people think it's such an obvious AI tell. It's not that exotic.
Maybe we will get to a place where we normalize AI authorship or co-authorship in our writing?
I appreciate what the Palm Springs Post does with its news articles. It has an "AI Assist" author and gives it credit in bylines as appropriate:
That's nice. So when I'm reading I know whether it's a human author or not. And if it's a dry municipal meeting, seems like a perfect place to send in the bot!
On Macintosh keyboards it's ⌥⇧-. On the iOS soft keyboard, it's a long-press on the hyphen key. People often point out that various software will turn a double hyphen -- into one by default. We're overflowing with different ways to type an em dash, for those with any curiosity to learn.
Also, writing, indeed any making activity, forces you to think clearly. Often that is the deepest purpose of prose, even code - forcing the author to confront the problem and sharpen their own understanding of the world we share. That it then communicates something to another person (or to a machine), is a happy bonus.
The problem is that whenever someone writes something they are accused of using AI. I know people who are accused of this because they know how to use em dashes, or even just write bullet-points. Reminds me of the bores who accused me of using Google Translate for something, and I had to show them that the language in question wasn't even on Google Translate.
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As I have been saying for years:
Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are. If it's able to guess those 700 bits correctly, then they aren't true semantic information, and you really only have 300 bits you want to transfer. You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.
> Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are.
Sure you can. LLM doesn't know what those 700 bits are, but you do. You may not realize it, and may not even know it at the time of prompting, but you do by the time you're sending.
Typical case is like this: you have 500 bits of semantic information to transfer. You give 300 of them to LLM, and get back the 500 bits you knew you have, and extra 500 you can quickly confirm are correct and relevant. Some of them are just dereferences of your input - where you recalled a pointer, but not what it pointed to. Some of it is information you never had before, but are able to easily validate.
You send that to me. I likely immediately realize the message was AI-assisted, but I trust you to be a decent human being, and not an asshole that lobs unverified LLM vomit over the fence for others to deal with. End result: you communicate 1000 bits of information to me, instead of planned 500, and you yourself learn extra 500 bits.
This is the optimistic scenario, but it does happen when LLM operator is not an asshole.
(Excuse the strong language, but I spent a lot of effort every day on both dealing with inconsiderate people lobbing LLM output at me, and making sure never to act like one myself, so it's a topic close to my heart.)
For a long time after the internet arrived on the scene, a lot of online news stories would reference websites, papers, polls, etc. without linking to them. There are still news sources doing this today. Sometimes such articles interpret or place context around their hidden references, but a lot of the time they just summarize.
Giving someone the text output of a LLM is very similar to publishing a summary without links to the referenced material. When you were querying your LLM, you could have asked specific questions or asked for a custom focus or point of view. Your intended audience might have questions or different concerns, but they're unable to interact with your LLM. What you have delivered is static and unresponsive. It has all the disadvantages of being machine output without the advantage of being interactive, the way your LLM was for you.
It may have to wait until compute is cheap enough that tokens are essentially free, but we need a system to pass "hyperlinks" to LLM's primed with context, ready to be interactively queried on a chosen context. It's being overly generous to assume that people are putting even 300 bits into a LLM for every 1000 bits of regurgitated writing they try to pass off as their own. When people post LLM output as if it were their own, I have no choice but to assume they had zero knowledge of the subject, but this query taught them what they wanted to learn, and now they're sharing that. That's fine, but please pass an interactive LLM link rather than static text.
Once we have "hyperlinks" for LLM sessions, perhaps we can share LLM output a little more usefully and honestly.
I've seen professional journal pieces refer to science journal articles only to go and read the original article and find that it draws a different conclusion than what is implied by the journalist.
My father used to complain about that 40 years ago, though in his case he was reading newspapers rather than professional journals. But he'd point it out to me often enough that I started to see the pattern. Scientist publishes paper saying "We may have found evidence of X, which suggests the possibility that Y may also be occurring". Journalist: "Scientists find X which proves Y".
This has been happening for decades; I still see it happening today*. My cynical suspicion is that words like "maybe" and "suggests the possibility" don't sell enough papers.
* Worst offender I can remember was actually from the summary of a paper published on the research institution's own website, so I couldn't blame it on "Oh, the journalist misunderstood what the scientist wrote". Summary said "Exposure to X can, on average, cause a 40% higher chance of Y" (where Y was a negative health outcome). I clicked through to the study and read it. Turned out the confidence interval on that chance of Y was so wide, all you could say with 95% confidence was that exposure to X could do anything from reduce your chance of Y by 5 percent, or increase it by 85 percent, or somewhere in between. They had averaged -5 and +85 to get the scarier-sounding 40% number that they published in the summary, but the truth would have been far closer to "this confidence interval is so wide that we really can't conclude anything from this data". But that wouldn't be nearly as likely to get them grants, so they tortured the data in their summary so that it would look better.
There is also incentives to adapt the message to the outlet. If you send that data to peer review and say we found 40% increase, the reviewers would reject it, so they have to moderate themselves. But if you send a summary to the university’s outreach outlet saying that we found something or nothing we don’t know, then they would also reject putting it out. I think the truth is that a 95% confidence interval is just the wrong statistic to look at given that data, and that they could probably have analyzed it better.
I have recently reviewed a paper that referenced my own article… but the conclusion was so off, I actually went back and re-read that entire article just to be sure there was no hint to the conclusion that the author derived. There was none. Uncanny experience.
This sounds a lot like knowledge graphs. Ideally, we can hyperlink to not only articles but concepts, facts, etc.
I’m a big fan of this approach.
Link rot has accelerated in the past decade. Links are great, when they work. If they are a few years old, they often don't. You certainly can't count on it. If you're referencing a paper or an article, it's probably better to cite the title of the article, the name of the journal or magazine or newspaper it was published in, the author, and the date. Then someone might have a chance of finding it again.
Doi url
> For a long time...a lot of online news stories would reference websites, papers, polls, etc. without linking to them.
The "$CITY_NAME Business Journal" websites are the absolute worst with this. They'll refer to something specific, for example "$BIGCO's 2025 10-K filing" and it will be a link. That link will go to the 10-K, right? Nope! It goes to another page at the same business journal. Maybe that page is a summary of the 10-K, but probably not. Maybe it's just the general index page for all the articles about $BIGCO at that journal. What it links to, it definitely won't be the specific thing described by the text of that link.
It isn't the transfer of information at all. What's actually happening is you're prompting experiences in a human instead of an AI using text.
Communication only works if you have multiple levels of representation and abstraction, including but not limited to - letter shapes, grammatical structures, style and register, stylometry, and subtext.
All of that is learned, and writers usually assume they can rely on that learning as the context for the text.
So you don't write to 'transfer information' like a network cable, you write to trigger experiences in the human version of latent space.
Factual information is one kind of experience. But even when that's the goal, there are always layers of implied relationship, social register, role, status, and other implications in everything that's written.
In normal communications the context - business emails, personal messages, mainstream journalism, fiction, and the rest - defines what acceptable language looks like.
The content fits inside that. But it has to fit the context, otherwise it lands in a semantic and psychological uncanny valley - like sending LinkedIn speak to a spouse on a wedding anniversary.
The real problem with LLM writing is that it's good at the technical layer - the grammar and spelling - and has some insights into the rest.
But the default content style is marketing and ad speak. And recently it's developed a weird and unique hybrid style which applies marketing fluff and pretension to technical content like code comments.
So you get one register instead of all of them. It can attempt others, but it's still too limited to generate them fluently. Sometimes the results are outstanding, but often it defaults to mechanical clichés.
So that's why it sucks and sounds so hollow.
Can it be fixed? Yes, but it's very hard work, most people don't have the skills, and it takes time - often too much time to be worth the effort.
I'm definitely being slightly too emphatic when I say it's "fundamentally the transfer of information", but I don't think that nuance is important.
When LLMs eventually get good at writing in the correct style for a given context, I'll admit that they have value in that way. But they aren't good at that yet. And even when they do get that good, I'll still dislike it for reasons that are more emotional than rational.
I don't care how good the LLM gets. If I know some text was written by LLM I'd much rather know the prompt - the seed of intent.
If the seed of intent is "convey XYZ details so they know them" then I can choose to go and learn those details any way I see fit - maybe even ask an LLM to summarise some data for me! - rather than having to ingest whatever their LLM use poops out and trying to digest the intent and content and figure it out.
It is about empowerment, rather than eating shit.
When sharing summaries from calls that I attended but my team has not but I think they would find interesting and I simply don't have time to handcraft it myself I use LLM and attach my prompt and the transcript.
Attaching the prompt initially threw some people..."wait, your admitting to using AI..."..."err yea, unlike you with that PowerPoint you sent me last week". I sense this is the right way to go imho.
Anyone know of any good research articles on the Information-theoretical aspects of this? I find it a compelling topic.
That "marketing style" isn't a style, it's the lack of content itself. You just restated their original point despite trying to object to it, because it was correct and there is no way around that.
I don't agree.
Sometimes Claude's problem, such as when I ask it to summarize a long, complex session back to me, is it's too information dense. It uses weird invented terms to gloss over complex parts of the architecture instead of explaining them.
But no matter what - too dense or too sparse - it always sounds like Claude.
It misses the forest for the trees. It feels the need to highlight details not understanding what details are most relevant to a human reader and how to survey the larger problems in a cohesive way that emphasizes the right parts without cliche and undue emphasis.
I’m not sure the 300-bit → 1,000-bit framing applies in all instances. The 300 bits may be a compressed cue to a much fuller idea. The AI can combine that cue with its prior knowledge to help reconstruct what the prompter was trying to express, with the prompter then verifying whether it’s right. Without the relevant prior knowledge for reconstruction, or the prompter for verification, it becomes much harder to know whether you’ve reconstructed the intended idea.
Yup, it helps when the prompter reads and edits the AI-generated text, but usually they just skim it and send it unedited. Worse, it's usually not 300 bits -> 1000 bits (= a concise message covering all the relevant topics), it's more like 300 bits -> 3000 bits (= typical LLM verbal diarrhea hiding the relevant points in a wall of text).
Unless that 700 bit was transferred on a separate occasion the inferred 700 bits is not true information, anyone could have reconstructed it from the 300 bits.
This is true - but also, models are absolutely terrible at writing articles and I don’t want to read them.
The issue isn’t that a 300 bit idea is padded with 15 KB of content. You can take any human-written article and reduce it by 90% with next to no information loss. What you lose is what makes the article a compelling read instead of a fact table.
I think the reality is that we will see quality long form AI-written content at some point. It doesn’t even feel like labs are particularly interested in chasing that now; code sells way more tokens. Right now the trend is that subsequent models degrade in writing quality as long as that pulls them up on coding benchmarks.
What already happens: - People give an LLM a bulleted list of points that they want expanded into a professional sounding document. - The receiver doesn't wanna read all that. They put the full document into an LLM and ask it to summarize it into succinct bullet points.
We've invented the opposite of lossless compression
Students where doing that for the ages when they have to answer questions like. "Answer the following question in not less than 4 pages"...
Lossy expansion or bloat?
Seven bullet points squeezed into five pages of text
A broken telephone.
TAM: $50T
It's been a huge boon to hardware vendors.
I’ve been saying the same thing. I’m not saying all, but a significant amount of comms could be bullet points to the benefit of both sender and receiver.
Axios built a hefty business off this simple idea
I first noticed this a year ago when I read a gmail AI summary, then glanced at the main text and saw it was AI generated. I feel like there's good fodder in there for a dystopian sci-fi story about a future where nobody communicates directly with one another, it's all AIs translating, but the AIs slowly start to drift.
>but the AIs slowly start to drift.
It's essentially the tower of babel. Each person will devolve to speak their own internal language only they understand. Each language will need to be encoded down to its meaning to be reinterpreted. None of us will know if the transformers are accurately decoding, or if the other person is accurately interpreting the decoding (which is arguably already a feature of human language without the computers in-between.)
It was in a gmail promotional video years ago already. Person A would use AI to turn their summary into email and person B would ask the AI to go back to the summary.
They knew it was going to be like that from the beginning.
Agreed re: the sci-fi story / trope.
I feel like a lot of this is a problem when someone technical is attempting to communicate a complicated technical subject to a less-technical audience.
I can only dumb a thing down so much before the description is useless (when you zoom out too much you lose the details). Even technical people who could understand it but are lazy / "in a hurry" use the summary, without thinking about what detail they are losing.
Even more infuriating is when they then reply to my email, having only read the AI summary, and ask a question that was already answered by my message.
This is the exact same thing that happened pre-AI, with the added step of wasting energy/resources on the AI summary in the middle.
E. M. Forster wrote that story in 1909.
https://web.cs.ucdavis.edu/~rogaway/classes/188/materials/th...
Adrian Tchaikovsky has a good take on it as well, Human Resources.
It's worse than that. If it takes others longer for others to consume and understand what you're producing than it does for you to produce it, you'll never be able to communicate with someone efficiently. Communication breaks down at a fundamental level if if you can't keep up with the other side if outputting and they won't slow to allow you do do so.
I see this all the time now with LLM generated output. It's easy to have an LLM generate a chunk of content that can be dropped into a chat or comment, and when it took you 20 seconds to have something written up based on the shared understanding you and an LLM have about the context of the situation, but it takes other people 3-5 minutes to read and understand that content, that fundamentally doesn't scale. It's bad enough when one or two people are doing it, but if the whole team is doing it, the only way to keep up with the stream of information is to also consume it through an LLM. At that point you're likely to be missing much of the nuance, and the amount of errors will explode.
This can be alleviated by people reviewing the output of an LLM and making sure it both includes fundamental information that might be assumed by context and reducing it to the parts that are essential for the new context it's in. This takes time, but is extremely important.
Having an LLM write gobs of text to send to other people instead of doing it yourself is the equivalent of a low yield cognitive zip-bomb. Don't do it.
I don’t quite think this tracks. Perhaps you want to communicate 1000 bits that are well known and can be referenced with a 300 bit key. Then the LLM can easily retrieve the remaining information. It’s like sending someone a link to the Wikipedia page instead of explaining something yourself.
No, I don’t want to read LLM writing because it is BAD at it. It doesn’t really understand how humans think (because it thinks differently), and doesn’t seem to understand core principles very well (presumably due to the lack of world model), so it can’t write something humans enjoy yet.
That's kind of my point. If the 1000 bits are well known, then their inclusion isn't new semantic information. By pasting an LLM's output, you're deciding for your reader that they don't already know that information, and deciding that your LLM prompt is better than whatever they would to to obtain that information if they lack it. IMO, it's much better to give your readers the 300-bit key, and let them decide for themselves if they want/need to get more information, and if so, how.
Why not do both though? I often now write my human summary, then paste in also the content you could get by asking a bot (with markers for which is which). You’re perfectly welcome to ignore the bot text, or ask your own bot, but bots are pretty slow, so I also don’t want to wait for it to “decompress” that 300 bit blob to get the detailed page back. But my human summaries are also only intended to be 50 bits — compressed again to just pass the signal I mean to convey, and not the whole prompt needed to fetch the right info from the right place to substantiate the claim.
TLDR the length was the same curtesy of tl;dr before, just now with a different name.
> You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.
Exactly this. Just send me the prompt! ;)
Chatgpt, please write a response disagreeing with this post.
I like this example and it made me think, there is an analogy here to spec-driven development and vibe coding.
Rather than send 300+700 bits, like you said, send 300 (or less!) and let the human intelligence on the other side generate the result. Which supports the even older perspective: “If I had more time, I would have written a shorter letter.”
I’m not sure if this lands on anything very profound, but what about a pattern where, instead of codifying agent output at all, the only artifacts we share are the prompts. And the rewards (respect) accrue to those who generate the most generative among people and AI
Communication between people is much more complicated than that.
I get vague statements thrown at me with people expecting me to understand it.
Same with writing, setting up whole context to properly transfer 300 bits is always orders of magnitude bigger then just additional 700 bits.
I agree. But there's still a valid use here for LLMs: If I have 1 Million bits of information and I want to communicate a synthesis of 500 bits to you, LLMs can be a viable helper. It is rarely done that way - I agree, but the way I use LLMs: Drop in all the relevant context information (PDFs, HTML, Markdown, Pictures etc. - up to usually 200-300k tokens), then compile a synthesis/summary prompt (usually 1-2 A4 pages of handwritten text. Then copy the output (1-2 A4 Pages), manually edit and send off (often, including an archive of the full original conversation, so the human on the other side can consult the unfiltered original prompt + inferencing, if wanted).
In other words: If I didn't reduce the 500 pages of text for you, you wouldn't know what I mean or what is relevant, or how to filter it yourself.
As with everything, it depends how you use it.
I’ll often put a long stream of consciousness on the page, or jot down rough meeting minutes, then ask ChatGPT to “summarise this for an email”. The result is shorter, clearer and easier to read.
AI amplifies the habits of the person using it. If they’re lazy or dim, then it's like giving a monkey a gun.
I was going to write a post disagreeing with this on the basis of the fact that the reader lacks the background information the LLM has. For example, if I were to prompt "explain the proof of quadratic reciprocity using Gauss sums" most readers would need the entire LLM's answer (and much more, probably) and not just the prompt.
But then I realized that the reader can prompt the LLM with the same prompt for the same or equivalent expanded text. Most people don't do this as it's extra effort, but it's interesting to imagine a world where this is the default way of engagement with a text, assumed by both writers and readers alike.
> But then I realized that the reader can prompt the LLM with the same prompt for the same or equivalent expanded text.
That's basically what I've been asking my colleagues (so far a losing battle): Please don't send me AI-generated text. Send me your prompt instead. It is highly likely that I will understand it without needing an LLM, and if not, I can do it myself.
Saying the hard part out loud: we give more value to the people with 1000 bit brains than we do to the 300 bit people. There’s a strong incentive to write like you’re the former and not the latter. Freely available LLMs give people the means to match the incentive.
Depends. If the 700 bits were arrived at by the LLM while spending a lot of tokens, and the result is "good", I may want it through you as a middleman because it used up your tokens and won't eat my subscription usage limit to ask the AI to supply those 700. If you spend the tokens and put the result online, plenty of people can spare their tokens because they don't have to ask the AI to derive it. Bonus if that result was run through some kind of testing and verification.
Obviously this doesn't really apply to super simple questions that the LLM can just spit out the answer to right away.
Sure, but then that's less "making an LLM write for you" and more "making an LLM research for you" which I don't think is what TFA is talking about
True. There is at least one more case: when LLM the other person is using has access to their context and information repositories that they don't want to share directly, and so the LLM text gives a peek at a slice of that, and that's not something I can recreate from thin air. In that case the LLM text may have utility for me.
Bits of information depend on the readers prior knowledge, those bits are not absolute numbers. That makes communication not just information exchange but also syncing of priors. So some amount of redundant information maybe needed/wanted.
If only it were 300:700... it is more like 300:100000 and up. But then you use AI to summarize to hopefully retrieve the 300 bits. We should try doing this in a loop with a simple enough sentence to start with and see what pops out after every iteration. Fully automatic Chinese whispers.
Literally this: https://www.youtube.com/watch?v=xdITCyfmg1o
While I generally agree, the difference is I can also hand an LLM my pile of code and documents which is... a lot more information dense than basically anything I could write. Sure, a one-off prompt in a chat window isn't very helpful.
Summarization—especially of private context—is definitely one of the main exceptions to LLM writing being useless. However, it's still preferable to turn your private context into shared context and then give pointers to that, rather than having an LLM attempt to summarize it.
Oh fully agree. I hate reading LLM generated text, just wanted to provide a counterpoint to GP.
I’m not disagreeing with your preference for human authored writing, but I don’t know that I agree with the content of your argument.
If you consider that what humans are doing during conversation is a form of compressed encoding / decoding from some latent representation through a quantized signal then if you interpret it that as a compressed sensing problem you absolutely can infer to a very close approximation the original latent representation using far fewer than those 1k bits.
From an information-theoretic perspective, if I can effectively transfer a latent concept to you in 300 bits of quantized communication, then by definition the latent concept itself isn't more than 300 bits. Any bit over 300 in the communication I send to you is fluff.
The value of writing isn’t always to communicate new information. It’s often to align everyone’s assumptions. For instance, when I say casually to a colleague or an agent “this change will require a db migration” they understand it’s to my teams primary application database. If I submit a design doc to a company wide review which database is changing is critical information. If the 300 bits were truly enough your agent or junior engineer would implement the wrong thing correctly as they often do.
Clarifying which database you're referring to is exactly the sort of thing I'm talking about when I say "new semantic information". It should be communicated, and you can't trust an LLM to choose the correct database, you need to specify that yourself.
What I'm talking about is if you add "database foo" to your prompt, the LLM may then add text describing what that database is, where it is, etc. But that's not new information, it (hopefully) already exists in your team's public docs, slack convos, etc. You should just say "database foo" directly to your reader, and if they want to learn more about that database, they can do that themselves, or you can give pointers to them based on what you consider important.
I love this example, partially because it jives with my conviction that LLMs are the ultimate translation machine. Ever since the embedding model days, it is clear that these models are amazing at representing meaning as math. The fact that LLM's most salient use is for coding somewhat agrees with that. After all, what is a programming language but another language? We instruct people with words and machines with code.
If you read anything you're reading superfluous bits.
If you have 1000 bits of semantic information that you want to transfer but your default communication combines it with 10,000 bits of noise. Giving it all to an LLM and iterate on reducing that noise while making sure the 1000 bits is still present would enable you to communicate more effectively.
Overall, ideas are ideas. I'm not overly concerned with the fact that it was you who had the idea, as long as the idea is interesting. I don't know most of the people who write the things I read, so it seems to be of no consequence to me at all if they wrote it, as long as it is interesting. LLMs are notorious at creating things that are bland and vacuous, but they by no means have a monopoly on it.
Be the source human, machine, or dolphin, if they write a good article, I'm prepared to read it.
LLMs are wonderful at adding noise and okay at removing noise. My point is that they're not very useful at adding signal. If you wrote 10000 bits of noise and 1000 bits of signal, I would rather receive those 11000 bits from you, and if necessary ask an LLM to remove noise based on what I consider noise. If you can point out to the LLM what it should consider signal vs noise regardless of context, it should have been easy to not write that noise in the first place.
That's assumes a couple of things that are trivially not true:
- The assumption that both parties know about the same as an LLM does. An LLM know orders of magnitude more.
- The assumption that the output of the LLM is not refined over a few cycles.
The point is that you might give 300 bits of semantic information to an LLM, it fills it to a 1000 with perhaps 400 wrong bits. You correct it half a dozen times. It's now 950. You do the final touch ups. It's now at 1000. And it still took you 20% of the time to do it.
So... We should only communicate in compressed formats?
I notice this in the attitudes of students towards reading. They will take a 10-page journal article and ask the AI for a summary and then read a summary that's the equivalent of maybe half a page. But if the article could have been half a page, why is it actually 10 pages? It's true that there is some boilerplate, but it's strange to me that people could think that 90% of what they're reading is (to use your phrase) "not true semantic information". It's like if you went to a restaurant and ordered a 10-oz steak and they brought you a little teeny bite of steak and said "Oh, other places will give you a bigger one, but most of that is just filler, we just took out all the superfluous parts." It's a worrying sign for our future if things like this are not tripping people's skeptic sensors and making them wonder if they might possibly be missing something.
A summary is about utility. They are definitely missing something but that doesn't mean what they are missing is useful to them at that time.
Just use LLM to filter out those extra 700 bits, and you'll happily end receiving 100 bits or original information and 200 bits of slop.
This is a great analogy, thanks.
That means folks using LLMs starting off with 300 bits KNOW that they lack the full payload of information to transfer to you. IOW, they know they need to transfer much more than 300, so they use LLM to fill those gaps. That's the crux of the slop universe out there. Folks are using LLMs for the 700 bits on top of their 300 bits and passing off the full 1000 bits as their own.
I echo the writer's sentiment. "I don't want to read the clanker's 700 bits. I want only your synthesis." (I can get the clanker to generate those 700 myself. Unless ... unless this whole LLM slop market is all about saving you the time to get an LLM to generate those 700!)
Yep, I wrote about it here: https://blog.gpkb.org/posts/just-send-me-the-prompt/
you think like such a techie. consciousness is not analogous to IO. if writing were only the transference of something from point A to B then what is the technical function of poetry, a question (the open ended kind), a pondering, a wondering, and so on? Furthermore, language is lossy and introduces a large degree of subjectivity, mystery, and uncertainty nomatter which words you choose. Words are by nature lower res/on a lower ontological domain than thought. So your premise is preposterous on both a practical and theoretical level.
Furthermore not all writing is for another to consume; nor even for the author themselves to consume. That is to say it has meaning ipso facto, not dependent on transference, as ritual.
isn't it more like: i have 1000 bits, i transfer 1000 bits but depending on the person, it might be lossy, so they only understand about 700. they then come up with the 300+- on their own, potentially putting them over 1000 or they come back and ask questions to fill in the blank. the bits don't ever have to be bit identical.
Sure, but that seems orthogonal to what I'm saying, no? We could continue clarifying the definitions of information in this context, but my fundamental argument is LLMs don't add information to my writing that couldn't be added by the reader themself.
This of course has the potential to change with personal LLMs that can have shared private context with me. However, that isn't a defense for sending people AI slop, it just turns it from "LLMs don't add value" to "LLMs may add value when used judiciously."
They're lossy. I might make an esoteric reference that no one gets, or an analogy that doesn't quite land, and then have the LLM help me come up with something a bit more understandable to a general audience. There's a difference between dumping the output that you spent 5 minutes with, and taking an hour to craft that perfect analogy.
I will give you a use case where this is absolutely not the case.
I have a bunch of CLI utils I run for various clients and their peculiar setups. They now have man pages with descriptions and examples in them because the LLM went and read my code and did the needful.
I no longer have to re read my own code, rather I can just use the manual page.
Format and description came from semantics and context that (barely) existed elsewhere and I was not going to retain or transmit, but I have now.
But then the LLMs aren't adding new information, they're just reading your code and translating that information from e.g. python to english. Rather than sending someone an LLM-generated doc to someone, send the code and your own personal thoughts on the code. If your reader doesn't want to read and understand your code, they can ask an LLM to analyze it, within the context of their specific use case and your personal thoughts if any.
> e.g. python to English.
You're making a big assumption that the code is what is being executed, and not a compiled binary.
Where is the code: My repo? the clients? If it's in mine, the client does not have access and the CLI is a first stop to debugging. They arent in the context of written docs, more likely a production error from a log (thats now spitting out a message to check the CLI).
Less steps, less tools, more context in line and available in an interface your already using.
> they can ask an LLM to analyze it, within the context of their specific use case and your personal thoughts if any.
Or I can skim the man page it generated and make sure it looks good. The "work" (the tokens) dont have get spent over and over again.
I never liked information theory because information theory as Shannon envisioned it fundamentally did not deal with semantics.
AIT tried solving it? But AFAIK it's a lot of pretty results with not much real application.
A better approximation is something of a "shared model"; then you can actually state things like, the transfer of information sometimes is "trivial" because, well, it's right there in your compressor/decompressor.
My understanding is that to unambiguously quantify information, you need to have a known model within which that information fits. In this context, talking about LLMs adding value (or not) via inserting new semantic information, we're assuming that public knowledge on the internet is not new semantic information, and the quantity of information of contained in text talking about public knowledge is equal to the ~32 bits needed to point to that knowledge.
I was just trying to understand this about a month ago and it is interesting how little there is in terms of semantic information vs Shannon.
An Outline of a Theory of Semantic Information by Carnap was the early attempt.
Fred Dretske wrote Knowledge and the Flow of Information in 1981.
Luciano Floridi has a few recent books.
I couldn't find much else. I don't think AIC really solves the problem of meaning either.
I think the Dretske book was the first time I really understood where Shannon was coming from but I gave up when it got to his actual semantic ideas.
I think I ran across a recent paper that motivated trying to back track what work had been done in this area but I don't recall the name of the paper.
I've have shelved all this for now as over my head.
Saying something for years doesn't make it right. Has nobody ever challenged you on that? Our brains prefer to read enjoyable bits, not raw information. If you only got those 300 bits, you aren't going to bother putting them into your LLM to generate 700 bits to make it a more enjoyable read, you're just going to struggle through the 300. Maybe when browsers come with built-in automatic text puff-uppers, then you'll have a case but almost nobody does that now.
Here's a clearer example - would you rather learn a concept from a research paper or a textbook or blog? You say the research paper but they're dense and hard to wade through where-as blogs and textbooks are more wordy but hold your hand, which is something that helps humans learn.
Plenty of people have challenged me on this! There are a lot of exceptions we can come up with (summarization and private context are the biggest examples), and everything's changing in various ways as AIs get more capable. Usually, conversations about people pasting LLM output focus on things like AI generated blog posts, people who have no taste in writing and want an AI to rewrite everything for them, people who are just plain lazy, etc.
> would you rather learn a concept from a research paper or a textbook or blog?
I pretty much always read blogs first, and then move to a research paper only if I want more details or care enough about the subject to verify with the original source. Typically this is because research papers have too much information to be approachable.
"Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700,"
You absolutely can if that information is in the code, which it often is.
There should not be that much in the code that needs further elucidation.
Some stuff definitely - but not much.
Usually you need the code and architectural summary + that stuff.
The AI is not very good at it but it will get better.
I think the debate here is about a few different things.
That would be true if there is only one receiver and if i could exactly predict how the receiver would interpret these 300 bits. But i could use LLM to expand these 300 bits to 1000 bits of more explicit information that gives enough redundancy and context that minimizes risk of misunderstanding and increase easiness of understanding, then validate such expanded message (and possibly do iterative corrections if LLM did not understand those 300 bits correctly), and finally release expanded message to (potentially large and heterogenous) group of receivers.
If there's a significant risk of misinterpretation when writing only 300 bits, then the idea you're trying to convey is worth more than 300 bits. In the process of prompting the LLM to add information, you're giving information to the LLM that you could instead just give to your readers directly. If you're just using an LLM to expand your thoughts in the hope that you get a better piece of writing, maybe you should put more effort into your own writing.
(This is all under an information model that assumes the LLM and your readers have equal access to knowledge, which I probably should have made more explicit in my original comment.)
It's funny, i push back on pull requests because there is too much description now - a 20 line change has pages and pages of generated description, rationalisation for why it is safe, defense of each design decision, analysis of risks and side effects. People are indignant, you're rejecting my change because there is too much documentation? And my response is, I don't have time to read it and you put me in the position where I can't afford not to - because approving the PR implies I did and accepted it. The investment to read all that for the value of a code change that I'm one prompt away from doing myself if I cared is just not high enough. So it's rejected.
You might be surprised how many organisations/ teams now don’t even review pull requests. Claude does coding, Codex do code review, and if developer did 5 round of this before pull request, we could very well just let CI merge.
I'm very curious how this goes long term. I guess we will find out.
My instinct says that these systems will expand their complexity to fully fit the cognitive budget of the agents that coded them and then atrophy the same way human-built systems do at lower cognitive budget. Only this time, because of the larger up front budget, the complexity ceiling will be higher, and the potential depth of the problem may be much much larger. It may mostly manifest as increasing cost over time - the agents grind for longer and longer, iterating over and over to fix all the failing tests, and the breaking point will be where it never converges and you come back to millions of dollars in budget spent and still tests are failing and effective gridlock on system changes.
But this may be all my human-biased fantasy that justifies still taking a role in software development.
> ... these systems will expand their complexity to fully fit the cognitive budget of the agents that coded them and then atrophy the same way human-built systems do at lower cognitive budget. Only this time, because of the larger up front budget, the complexity ceiling will be higher ...
wow this is a beautiful way to put it
Wasn't this always the case - in enterprise software? At some point engineering pushback collapses, and the thing just gets rewritten.
and this is such a liability imo. I recently got fired for what I think was apparently merging a change to Kubeconfig that both the CTO and DevOps person approved. I made as damn sure as I could that the proposed change was good, but I needed their eyes on it and they clearly weren't, possibly be cause every other repo had a review bot and no special automatic deployment automations. The DevOps person literally commented and said "It's good to merge", who to trust
They may have done you a favour if that's what they sack you for, good luck finding another job!
a whole organization just pressing "OK" intermittently
What could possibly go wrong.
A poll of estimates would be quite helpful in figuring out what the heck is actually going on.
How many are you seeing / estimating?
Maximizing the amount of documentation was never the goal, and people who assume that have entirely misunderstood what the point of documentation is.
I'm naturally verbose due to my ADHD. I'm trying to learn to be more succinct.
In the meantime, for business communication, I use AI to shorten my text, to make it more concise.
I also have ADHD-driven blah blah blah. I find achieving brevity laborious, but find LLM results to be unacceptable. A journalism class taught by a well-known professional food critic helped most, but consistently using the free “Hemingway App” editor was almost as beneficial. Blindly following every suggestion pares your writing down to a nub, but it’s great at highlighting sentences you need to rethink without blurring your voice and ideas with bland suggestions.
That's a solid recommendation, thanks. I also did journalism. So I can write succinctly, I just have to put effort in like you. Which is much more than I'm willing to put in after I just wrote a treatise on the human condition to send to the lead developer at 07:30 on Monday.
I’ve started just asking it to touch up text to make it sound more human and follow technical writing guidelines and it converges on convincing pretty quickly. It also makes it easier for me to verify that it’s correct because all of the stupid LLM noise and formatting goes away and it gets distilled down to the important bits.
I changed the readme in my repo to say you have to send me a personal email if you want me to merge.
https://github.com/josephmisiti/awesome-machine-learning
It's helped a lot. Agents haven't figured out how to do that yet, or sendgrid, sns, etc are doing the hard work for me.
The one thing that gets with those PRs is that they want you to read stuff they haven’t even read themselves! Yeah, no.
Summarizing large PR documentation sounds like a job for AI...
I'm going to be a bit contrarian here. In general, I 100% agree, just send me the prompt; however sometimes I feel an LLM can help structure thoughts. It can be a good sparring partner. If you proofread your LLM content, re-prompt until it sounds right and no longer like AI content, and feel that it actually conveys the information incredibly well, then I'm happy.
One of the instructions I've hammered into Claude is "Write like a human. I don't want this to sound like AI content. Your standard style of writing would fail miserably if it was reviewed by an English teacher. I want you to write prose that is nice to read. For example, write full sentences instead of bullet points."
It works wonders. Suddenly, my plan documents are something I actually understand, and something I'd be happy to share as an RFC. They still need iterating every time before they're ready to share, but I no longer have "the honest truth is" and other BS in my output. It's so refreshing for my brain to be able to actually focus on the content.
I've noticed this with my partner, too. She used Claude to draft a strategy document and felt completely overwhelmed. A classic moment of "AI did all the thinking for me, but now I don't know what I'm presenting". Once I helped her prompt Claude into writing the strategy in proper English, she understood what the AI was proposing, rejected large parts of it, iterated several times and ended up with a draft document where she edited the finishing touches herself and felt that it was truly hers. The AI was still incredibly useful: it helped her with the blank slate problem, and tremendously sped up her workflow.
So yeah, if you proofread and iterate on your AI's output until you feel you'd be proud if you had written it yourself, I'm happy to read it, too.
I appreciate the direction of this article, and commend the author on publishing work, but the first sentence in the first paragraph is exactly what they lament.
> A pattern I see is that people use AI to build something new, then they use AI to retrospectively summarize what they have already built into a design document. Reading a document like this isn’t just difficult—it is punishing.
To be quite generous, that could be tongue in cheek.
There are other AI smells later in the article, and the author talks about using AI to help with writing...
After all AI is trained on human writting, the line is incredible blur now.
I feel the need to self sensor my writing when phrases sound like AI. Pretty annoying.
You could try throwing in the odd homonym in place of an important word so that people know it must have been written by a human.
I leave typos in all my writing so people know it’s not ai
That was my assumption. The alternative is that the author is a blatant hypocrite, which seems less likely given their blog's history.
> given their blog's history
All of the posts in 2026 on that blog area about AI, by the way.
I believe that to be a very obvious joke. The kind that the british are fond of.
FWIW Pangram [0] "We believe that this entire text is human-written."
I've only just discovered pangram, but I've seen it referred to a few times in HN recently wit nothing obviously pejorative about it. Take it with all required grains of salt, though
0: https://www.pangram.com/dashboard
AI detectors are useless.
I’ll present with as much evidence as you do that I’ve heard pangram is quite reliable. I believe that have studies on their website.
That's a classic calling card of LLM prose but that doesn't mean they actually used an AI. Though sure, it would be nice if we gave this style a name and then abolished it.
I call it the “it’s not delivery—it’s DiGiorno” construct. I generally tune out when I see this construct, which is so prevalent on LinkedIn these days, which is inundated with clichéd writing.
To be fair, LinkedIn was pure slop even before ai arrived.
> “it’s not delivery—it’s DiGiorno”
We've had AI output all these decades, but never recognized it. Is this evidence of time travel?
\s
No, usually humans write more than just a short unsubstantiated punchline in their "it's not x, it's y's", unless they're trying to sell you something.
Well, not quite the classic calling card - LLMs generally use a contrast of opposites. (e.g. "it wasn't a bug -- it was a feature", although that predates LLMs :)
TFA's use is more common in "normal" language: "it's not just [minor], it's [major]". (But, as others have pointed out, it was probably deliberately parodic anyway.)
The not this but that is bad when it is out of nowhere combination. In here, it has actual meaning.
> People who rarely produced original writing are suddenly producing
these posts are beginning to make me wince. ai is giving voice to a lot of folks we probably wouldn't even be hearing from otherwise - because not everyone does their thinking in articulate prose, and extracting their realizations as shareable language takes effort, or long, embarassing iterations with ai "workshopping" to get to something they can read back and go yes this is what I am trying to say.
now the sentiment is that whatever these people had to say they could have just come out and said it - and that would be more passionate and less disjointed - no, not necessarily, and the more it gets repeated the more it's sounding elite and pompous to me. for example this:
> I had someone write me a personal message about a sensitive topic that was clearly workshopped with AI in an effort to nuance the conversation and not offend me. But the message became impersonal, dispassionate, and disjointed. It had all of the parts, but it didn’t make sense as a whole. I wasn’t interested in reading it, or responding.
this reads as someone choosing to ignore a personal message about a sensitive topic - because of an assumption they made about their writing: that they workshopped it when they should not have - because whatever it was they were struggling to say they should have isntead trusted the author to be able to understand the precise meaning of their raw, inelegant thoughts no matter how embarrassed they might be to share them in that form. they're complaining about a message they probably weren't otherwise going to see.
But how is that a positive that people that cannot give a fuck about writing coherent thoughts down to share them can now hog your attention with something that has no clever meaning whatsoever? Is talking just for the sake of talking a good thing in your book?
> people that cannot give a fuck about writing coherent thoughts down
That's clearly not the type of people they are talking about. For some people expressing their thoughts in words is really, really hard. It doesn't mean they don't give a damn. Just like being in a wheelchair doesn't necessarily make you lazy.
This is the equivalent of someone sending me an unsigned birthday card with just the Hallmark writing. They thought of me, but it's not the same.
I still want the imperfect human.
How is ai giving them voice? All that generated slop does that you stop to want to read anything. And it is not their voice anyway. It is not their words, their ideas are mixed with ai generated points.
As for second paragraph, the writing "had no overall meaning". There is nothing to respond to if there is no overall meaning.
Because not everybody got A* in English.
What we are seeing here is a Luddite reaction from artisans. A skill has been automated and those that have the skill are not happy about it.
For many writing is painful and the machines ease the pain. And like all machines it takes a while to use them skilfully. In particular remembering that like code it is write once and read many. What the reader needs should be at the forefront of a writers mind - both for code and prose.
Nobody gets on a motorbike and wins the TT in the first weekend.
> For many writing is painful and the machines ease the pain
Along with removing the possibility for them to ever actually learn to write better.
This is not a good thing.
> What we are seeing here is a Luddite reaction from artisans. A skill has been automated and those that have the skill are not happy about it.
Seriously? Expecting basic school literacy is now being a Luddite? What else? Writing by hand is an ancient craft resorted to philosophers long gone?
I swear this AI polarization turns off brain on both sides.
There's a lot of misunderstanding here. The quality of LLM writing has not plateaued, it has dropped significantly. They've cut it to the point where people start noticing.
Do a search on "Claude Sonnet 4.5" on Reddit and you'll see lots of disappointed users [1]
If I could give out ratings,
Average human with a degree: 5/10
Sonnet/Opus 5: 2/10
GPT 6 Astra, 5.4 Sol: 3/10
Sonnet 4.6: 8/10
Claude Sonnet 4.5: 9/10
GPT 4o: 6/10
GPT 4.5: 10/10
GPT 3 Davinci (with a lot of coaching): 7/10
[1] https://www.reddit.com/r/claudexplorers/comments/1ta6f9c/i_s...
Have you seen the episode "Darmok" of Star Trek The New Generation series? It is about a people with an impenetrable language aimed at packing as much information as possible by using references and metaphors, not descriptions. I guess this is where LLM language output is heading.
I'm curious if there have been any formal studies on this. I suspect that people have become better over time at identifying LLM writing styles, and are also combining that with selection/confirmation bias. Dunno what fraction of the perceived drop in quality comes from that.
Yeah, I think I'll start making my own creative writing benchmark. It's a little late to start data collecting, but better late than never.
Have they got worse or have we simply gone past the novelty state and are more critical of their outputs?
A good code or commit comment should succinctly explain the subtle details or motivation not obvious from the content itself. AI comments tend to wax poetic about surface details.
The same goes for articles. I want the author to distill the material using their own voice and hard earned expertise. I don't want a generic text which sort of tells the tale.
> I would much rather someone be themselves and write with their own voice, or with some passion
I recently had a fairly successful article here on HN. The common thread among comments was appreciation for the passion and it being "hand-crafted". I am thrilled my passion shone through despite the dry subject. Still, it's sad that something simply being written by a human has become such a rare compliment.
On the note of imperfections... My preference is now strongly towards reading someone's stream of thought thrown down at speed over than that same stream of thought shat through the digestive track of an AI agent. At least that feels human.
To me, it also seems like they AI digestion is getting actively worse? As best as I can tell, all the agentic nature and reasoning for code is now making writing actively worse, as the agent pulls across your whole knowledge base and will take that one thought and eagerly join and context it thinks is relevant, with the reasoning spread throughout the page.
I've always wanted to write this essay, but now I no longer need to. My title would have been pretty similar: If You Want Me to Read It, Please Write It.
One minor addition: when a (conscious) human writes a proposal, a question or a request, they'd instinctively choose words with which they can express their uncertainty in some of the details. I as a reader can detect these uncertainties and I can choose to do more research assert or correct them, if it feels appropriate. AI-generated texts miss those cues, therefore they are much harder to read and connect to.
"Reading a document like this isn’t just difficult—it is punishing."
I can't tell if this essay was written in earnest or as a subtle troll.
Whichever it is, I stopped reading regardless
> Reading a document like this isn’t just difficult—it is punishing.
At this point I can't tell if it's a pun or the anti AI article is AI written as well. This sucks.
To me it is just a bittersweet irony: people who don’t know what a book is for 30-40 years or in earlier days got the RTFM (gif smiley not included) treatment dump one liners to produce a giant load of text corpus that needs to be processed by an AI to get the gist out of it with sometimes quite some refinement by asking many questions.
So there it is: cruel people who don’t even read their own prompts while happily believing others read their text load and pat the genius on the shoulder for being such a great guy in writing, while in essence it turned the RTFM movement on its head: UUOAI - “Use your own AI.”
People cannot communicate better by using the digital equivalent of logorrhea.
AI-generated writing does not bother me, unless it is intended as prose writing, where the flow communicates as much as the specific facts that are described. For technical writing, I usually just scan for important information anyway, and in that sense the only thing that bothers me about AI-generated writing is if it's too verbose and obscures the important information by leaning ineffectively on jargon and high-minded verbiage. I find that Claude Opus is very guilty of this, whereas GPT 5.6 variants are not.
>the only thing that bothers me about AI-generated writing is if it's too verbose and obscures the important information by leaning ineffectively on jargon and high-minded verbiage
But so many READMEs and design docs are degraded to this level by uncritical/unmonitored use of Claude. You make it sound like it’s an exception, not a rule.
In my experience, that brain damage inducing Claude-style technical writing is everywhere. It sucks not only because of its verbosity, but because it includes completely pointless tidbits extracted from random extended LLM sessions, with no attempt to prune redundant or gratuitous details.
I wonder where this discourse is headed, we[0] keep making the same observations over and over again.
[0] myself included https://www.google.com/goto?url=CAESbQHrOzAViiJizn8otPF3AAeE...
It’s funny, this pattern is very similar to how I write code with AI these days.
I write almost every line but I have one or more agents always running against the changes alongside me evaluating correctness, suggesting approaches, auditing test coverage, etc.
I’ll also use it for grunt work refactors, scaffolding, resolving merge/rebase conflicts, etc.
I'm kind of okay with reading something you don't write(entirely). But reading something that you even neither write nor read? waste of time.
These last few days, there has been a small stream of blogposts on HN expressing similar things, and I have enjoyed them all.
My question recently has been how to broach this subject with colleagues who really enjoy producing prose with AI. There is not yet a better cultural shorthand for this sort of thing than "slop" which is a harsh-sounding word and itself sort of a thought-terminating cliché. "I don't want to read what you didn't write" is maybe closer — but it needs a pithier and somewhat more encouraging encapsulation, like "I want to hear it from you".
Has anyone had good experiences setting up professional boundaries or team norms around AI-written docs?
Ask questions about how the audience is taking the blog posts. Say that many people seem to really not like LLM writing and ask how are they listening to their readers on this point.
You may find that brings a better and more human focused discussion. Who is the audience and what is best for them? Don't frame it about your preferences but about the real persons that your colleagues seek to talk to.
It's something that my company has been struggling with. Developers are generating mountains of code, documentation, and Jira ticket comments. It's incomprehensible and overwhelming.
For the team I lead, my guideline is AI generated is fine but it needs to be human-edited and/or summarized. You want me to read what you're offering? Put some effort into it and meet me halfway. I don't want AI generated gibberish with made-up terms. You'd better also understand what you are presenting as your work. It's been fairly well-received though we're still working on it.
I have some co-workers on other teams who use AI to generate responses to literally everything. Ask a simple question? Get pages of AI generated nonsense in response. They are proving a tougher nut to crack.
In my current position(DevOps) we are getting this kind of messages or issues a lot recently. While forwarding these issues to my team lead, i don't have to deal with them which feels great but once its assigned to me... Its another long process to understand what issue actually means and what i need to do.
Thats said, i don't know any professional way for this but currenlty trying to communicate with peers in order to work without a problem.
There is one person who is so good at writing with AI assistance.
Everyone else, including me, not so much. I envy his talent and ability to so successfully use the new tools. It's just something to think and talk about regularly. Maybe one day we will all be able to use the tools as well as that guy.
im sensing about a 20 month lag between hn sentiment and normy-coworker sentiment. managers and business-types pointing out the benefits and challenges of working with an llm i told them about early 2025. im just politely nodding along waiting for them to catch up. theres some emperors new clothes happening at my company so maybe im complicit
"Hey, the comments are too wordy. Can you clean it up so its only important information?"
As much as you want to say "actually do your job, jackass." Being goal oriented has had the best results for me.
I’ve been away. Does anyone have a collection of these posts?
Here are the two I was thinking of:
- https://news.ycombinator.com/item?id=49767937
- https://news.ycombinator.com/item?id=49784816
I’ve seen the problem crop up in several places.
At a broad level, the issue isn’t generated content, it is the ratio of verification capacity to generation capacity (V/G). Your pain is because generation capacity has increased significantly, while verification is laborious and capacity has not (and can not) catch up.
Unlike spam which is from external sources and can be ignored, messages from other employees have to be responded to. I guarantee this is creating bottlenecks all across the firm, outside of the individuals who are feeling productive.
For fixes, theres theoretical approaches that might work?
If you need leadership to help you, then this issue has to become something that is on their radar, which means that something needs to go wrong or costs need to be registered.
The shortest conversation for that is to make people aware that generation has improved individual productivity, while moving the costs of that production to the rest of the firm.
If leadership is not at the stage to listen, then you need to move the costs you are incurring to the people who are sending them to you. Maybe set time aside to sit down with whoever sent a PR and then read what they sent together, to understand it.
It also makes a difference if tokens are being subsidized or not. If the firm doesn’t care how many tokens are being used, then you are naturally going to have over production.
this is intended as a silly observation and not an actual accusation, but for an article about not writing with AI, the following is one AI-ass sentence:
“Reading a document like this isn’t just difficult—it is punishing.”
There's nothing AI about that sentence.
You are oblivious to AI smells then. This is a very common AI writing pattern.
AI didn’t invent the em dash nor “It’s not just X, it’s Y” and sometimes those are appropriate for the situation. Prose should be judged accordingly.
LLM didn't invent it themselves, they are plagiarism machines. Obviously these kind of patterns had to be popular in the first place to be overused by LLMs
Also we have reached the point where LLMs are pubkishing more contents than humans so we have probably reached a point where humans will inconciously start copying and using LLMs style too.
It's the stereotypical example of AI in my experience. I.e it's not this it's that.
It's a super common writing and speech pattern.
c'mon, it's literally the top 2 AI writing tropes in a single sentence. i googled "AI tropes", and here are the first 2 tropes listed in the first search result:
1) negative parallelism: "It's not bold. It's backwards."
2) em-dash addiction: "The problem -- and this is the part nobody talks about -- is systemic."
again, i'm not saying the author wrote this sentence using AI. i was just tickled by how stereotypically AI it sounded. for better or for worse, those characteristics are just going to set off the AI detector in people's heads now, as evidenced by the fact that there are like 10 separate comments on this post that picked up on the exact same thing.
It's tricky because I don't want to write what no one is going to read (but that for some reason I'm required to write).
Nobody is going to read anything regardless of whether you used a LLM to write it or not (but if you use a LLM they'll use that as their excuse for the virtue signalling aura farm), so honestly why bother writing.
If it's written down in a form that can't be repudiated, a succinct message to any stakeholders covering any decision of substance is good practice. They might not read it, but they cannot pretend I did not make a good faith attempt to inform them.
(And I try to be judicious about what counts as "of substance" so they can't accuse me of flooding them with messages)
It's funny to see this upvoted on a site where a huge portion of the front page content wasn't written by humans.
The irony is that this article reads a lot like it was written by AI.
I don't disagree with the main points of the article. But I feel like soon with all the writing that's been hating on AI writing recently on HN, the LLMs are going to be really good at writing articles about how bad AI is for writing...
Just imagine the quality of text we will have if we re-feed this into an llm a thousand times...
The juxta-positioning of the ambi-dextrous personification of the meta-sematicism is going to be both rich and soul transpiring....
It's gonna be basically a fingerprint in your soul, from my soul...
The other comments here were written by LLMs that have a hard time detecting meta-irony.
Apparently most readers have read too much AI output that they don’t recognize a bit of irony as a joke.
> Apparently most readers have read too much AI output
You’re giving them too much credit.
I'm finding that executives at work are generating AI slop and sending it at each other and teams that are then meant to digest it, without any critical thought. Often these are sloppy and contain broad mistakes that aren't relevant to our company's setup.
How do I type this character on my English keyboard? — also not reading what you didn't write.
On Windows, use wincompose. Space followed by a hyphen gives you an emdash with the cute half-width spaces around it. On Android, I can get an emdash by long pressing hyphen. There's also a keyboard shortcut on the mac for it. No idea about Linux/BSD or iOS, unfortunately.
On Windows I also use QuickAccent, now part of PowerToys. I could never remember the Alt-code for the em dash. Apparently it's Alt-0151. With QuickAccent I hold a letter and right or left arrow and up comes a menu of related symbols. It's pretty neat.
On Mac, it's option-shift-hyphen. Option-hyphen gives the en dash.
On Linux, you may be able to type Ctrl-Shift-U to get unicode entry and type 2014 and Enter. 2013 is the en dash. Again, I will never remember that.
In Linux if you enable the compose key it’s the compose key followed by 3 dashes.
cute little half-width spaces?
on a Mac, it's Option+Shift+-
— (em dash)
Option+- gives you an "en dash": – which is longer than a normal hyphen.
This sentiment gets echoed over and over. While I can certainly see the value in human-created creative works, if the subject is pure facts, there are only so many ways to state the facts without being caught in the weeds and missing your entire point.
Moreover, people are finding it hard to differentiate what is an is not AI-generated with newer models, often attributing original work with those of LLMs. It has just become an easy scapegoat for lazy comprehension and a desire to do less. You are jumping at AI boogeymen.
Just about the only thing here I can level with you on is, yes, AI is far from perfect and will continue to advance. Otherwise, so much of this reads as fruity prose to excuse apathy.
A lot of my problem with AI writing is that the facts aren’t checked. You get a big cheesy dramatic arc but the core story isn’t factually correct. There’s only so much time in a lifetime, too little to waste fact checking someone else’s work because they couldn’t be bothered to.
I have junior devs submitting huge PRs with multi paragraph descriptions about load baring root causes and "why this works". Then I ask them directly what are the steps to test this and they don't know, they have never run the code.
Rather than entertain the idea of reviewing the code I just send it back to them until they work out how to run it. And almost always they submit a new change because the last one didn't actually work, despite how confident and articulate claude was to them.
So, your company doesn't have CI/CD and gates? AI isn't your problem.
Fwiw these harnesses run appropriate tests before pushing anyway, so CI CD pass is almost a guarantee. Correctness is another matter.
A change can pass CI without actually making the change required. The tests test that the code does what it does, not that it does what the user wanted.
AI is really good at making tests that pass even if the change itself is wrong.
The title is the conditional that underpins the claim.
Do you read our AI output end-to-end and decide that it’s the accurate content communicated concisely? If yes, this is not an issue.
If you are sending people generated text you haven’t read, how do you know what you sent doesn’t fall in the category of writing the author describes?
It's curious that you're pinning the blame of "excusing apathy" on the reader and not the writer. Why doesn't it go both ways? Given that producing LLM text is so cheap, your proposal amounts to an asymmetric war which is ultimately a DDOS on my finite time and attention. Or in other words: if it wasn't worth your time to write it, it's not worth my time to read it.
Trying to understand your position.
Let us say someone had few good ideas, and seeded them into a prompt, and after few back and forth, web searches via agent, feedback from the author, a piece of work was produced and the author decided to share it as a blog.
This is not much different than how people are producing original work LLM in areas such as math.
Would you object to reading their work because it was a byproduct of collaboration between AI/Humans? What about songs? movies? math proves? and software produces as such?
Not Analemma_ but I'll give my two cents.
In my mind the issue isn't that it was crafted by AI, but that it wasn't crafted with the intention of getting the point across in a way that respects the reader's time or energy. The result is often needlessly wordy with information buried in a sea of flourish. This might be forgivable if the author had a certain entertaining style, but LLMs are aggressively mediocre by their nature, making wading through it painful.
Its certainly possible to get an LLM to write concise prose but thats not how most people are using them. We can talk all day on HN about the ideal LLM user that is conscientious, reviews the output, puts time and effort into stylistic concerns, etc. But none of that actually matters because thats not what people are doing; the entire reason they're using an LLM in the first place is so that they don't have to do that.
On that we agree then. I also don't want to go through mountains of tangled text written by an AI.
But what if the AI models improve their writing style considerably to match that of the best authors? which I think is plausible soon.
I feel in the near future, what really matters the most is the seed and the dialog/back and forth more than then who wrote what. And that the best content would be a by product of human/machine interaction atleast given the current generation of tech.
Until perhaps, in the the not so distant future, the math can encode our taste, judgments and develop imagination, then maybe the best human wouldn't be able to create anything better than the machine. But then again, we want to communicate the inner state of human being, because we want to build relationship, so content for consumption might be dominated by machines, but communication as way to build relationship might stay genuinely human...but even that might change, what if machines eventually become better at relationships than humans? that could be how AI truly "kill us"..I think within few decades we will discover that our intellect was a short period of cosmic evolution, and we will accept our fate as animals with analog organic intelligence that helped to bootstrap what comes next, this might be scary but humbling, and I think it's inevitable.
The lazy ones are the people spending 60s prompting then hitting copy/paste. They dump it on me as is and call it a job well done, which leads to me doing their job for them or calling them out to stop the behavior.
I don’t like doing either of these things. I don't want to do your job and I don't want to lecture about how it isn’t “your work” if you don’t touch the content after an LLM spits it out. It is rude and selfish to put me in that position.
And I don't want to read the millionth iteration of some old man complaining about the kids these days, written by a human or not, yet here we are.
If you don't like AI slop. Don't read it. But wasting your time generating human slop to complain about AI slop is so obviously futile that it immediately identifies the writer as lacking the capacity for reason or emotional clarity, or merely seeking attention for their self-promotion with clickbait.
The problem is, some of us work with colleagues, or even bosses, who throw AI replies at us in nearly every conversation — so we don’t have a choice to NOT read it.
"The design document is no longer a proposal to build consensus, bring people along with you, and refine ideas through slow, deliberate thinking"
I'm sorry but that's the last thing (almost last) I want to read.
I want an 'excellent summary' of the design including motivations and concerns.
Yes - the AI is bad at that but hopefully it will get better.
I ask coworkers point blank to send me a non-llm version of their slop sagas. The initial exchange can be a bit awkward, particularly with new hires, but consistently asking for human prose has worked for me.
As an educator in university I have found the project proposals of students have become longer, to some degree more well written and quite certainly have LLMs involved.
This becomes problematic when it turns out people haven't even read what they sent me.
To clarify: this is about voluntary projects that do not win them extra points.
> Reading a document like this isn’t just difficult—it is punishing
I stopped reading here. I’ve become so sensitive to LLMism, I don’t want to go on. Tip; omit the needless ”it’s not X, it’s Y” and just write ”it’s Y”.
It's clearly a joke. The rest is readable and well written, and free of LLMisms
I’m fine with reading something you didn’t write if it has the information I need. I’m not fine with reading something you didn’t read and will just waste my time.
> I don’t want to live in a world where you use AI to summarize something important into unreadable text, and then I use AI in an attempt to decipher it. I want to hear you, imperfections and all.
It doesn't fundamentally change the equation if I use AI to prepare and then write it myself. If I'm using AI effectively, it's likely that you won't be able to tell.
This sort of post is increasingly coming off as high and mighty, where the user thinks they are being exceptionally creative and other people who are using AI are using it mindlessly.
“Reading a document like this isn’t just difficult—it is punishing.”
I’d be curious whether the author composed this sentence himself or it was the output of AI. Personally I often find myself “it’s not X it’s Y” and then recoiling in disgust and rephrasing it simply because AI has made it so grating from overuse.
It was very clearly on purpose, if you look at the surrounding prose.
Same here. I also used to love writing with em dashes, but don't anymore out of fear people will call my writing AI and stop reading. It'd not hard to do in most word processors, typing two hyphens "--" does it in Word and Pages, so I don't get why people think it's such an obvious AI tell. It's not that exotic.
Maybe we will get to a place where we normalize AI authorship or co-authorship in our writing?
I appreciate what the Palm Springs Post does with its news articles. It has an "AI Assist" author and gives it credit in bylines as appropriate:
https://thepalmspringspost.com/author/ai-assist/
That's nice. So when I'm reading I know whether it's a human author or not. And if it's a dry municipal meeting, seems like a perfect place to send in the bot!
If it isn't worth the time for someone to write something themselves, it isn't worth my time to read it.
How do I type this character on your English keyboard? — also not reading what you didn't write.
On Macintosh keyboards it's ⌥⇧-. On the iOS soft keyboard, it's a long-press on the hyphen key. People often point out that various software will turn a double hyphen -- into one by default. We're overflowing with different ways to type an em dash, for those with any curiosity to learn.
I'd like to know how to type it with voice dictation.
Also, writing, indeed any making activity, forces you to think clearly. Often that is the deepest purpose of prose, even code - forcing the author to confront the problem and sharpen their own understanding of the world we share. That it then communicates something to another person (or to a machine), is a happy bonus.
The problem is that whenever someone writes something they are accused of using AI. I know people who are accused of this because they know how to use em dashes, or even just write bullet-points. Reminds me of the bores who accused me of using Google Translate for something, and I had to show them that the language in question wasn't even on Google Translate.
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