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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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
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.
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
A broken telephone.
TAM: $50T
It's been a huge boon to hardware vendors.
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.
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...
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.
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.
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.
Anyone know of any good research articles on the Information-theoretical aspects of this? I find it a compelling topic.
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.
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.
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.
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
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.
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.
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.
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)
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.
To be quite generous, that could be tongue in cheek.
That was my assumption. The alternative is that the author is a blatant hypocrite, which seems less likely given their blog's history.
All of the posts in 2026 on that blog area about AI, by the way.
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.
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.
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.)
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.
We've had AI output all these decades, but never recognized it. Is this evidence of time travel?
\s
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.
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?
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
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.
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.
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.
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.
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.
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 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.
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?
“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:
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!
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.
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.
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.
Maximizing the amount of documentation was never the goal, and people who assume that have entirely misunderstood what the point of documentation is.
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.
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.
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.”
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.
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.
on a Mac, it's Option+Shift+-
— (em dash)
Option+- gives you an "en dash": – which is longer than a normal hyphen.
How do I type this character on your English keyboard? — also not reading what you didn't write.
I'd like to know how to type it with voice dictation.
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.
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.
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...
"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
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...
If it isn't worth the time for someone to write something themselves, it isn't worth my time to read it.
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’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.
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.
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...
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.
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 irony is that this article reads a lot like it was written by AI.
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...
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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.