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.
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 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.
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 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.
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.
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.
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’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.
LLMs do inference or computation among other things, so the remaining 700 bits can be something like that. The hidden implication in your claim is that computation adds no information content, which leads to an interesting philosophical discussion.
So for example, if I ask an LLM to give a proof or derive a new theorem from a set of axioms, according to your assumption, if it answers correctly, then I haven't learned anything new.
I am not really sure how to resolve this paradox in information theory.
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.)
A math teacher only has "this class is about math" to transfer, the rest is known. ;-)
Jokes aside, I think we're in a weird transition now where AI is used to generate text that looks good but is bad. In a few years people will know that and be more critical.
I think we went through a similar phase when DTP had it's breakthrough. Suddenly school papers were laser printed 300ppi times new roman and got more attention than better papers written by hand. But eventually that became the baseline.
I think Ai will make it so that well-written texts with clarity, good layout, correct illustrations, callouts etc become the norm, and will no longer impress anyone unless the information itself is actually good.
And if the information is good, it won't matter if it's AI generated or not.
I like to think of this idea of transfer of information (via words, let's say) from speaker/writer to listener/reader, as actually a fairly normal signal processing situation. One where the constructor of the message during it's 'rasterisation' from continuous thoughts to discrete words, can do the job well (ie: band-limit and 'antialias' the message, fully considering the target sampling domain), or badly (ignore the target domain and just speak/write from the source context, leading to 'aliasing' in the listener's received message).
I know this isn't how many people think about words, but I think the idea of antialiasing is relevant and something that people should think more about (including with respect to AI-generated text).
Obviously this doesn't really apply to super simple questions that the LLM can just spit out the answer to right away.
All that being said, I acknowledge that people (me included) love to be sloppy in their comms (with or without AI) and then blame others for misunderstanding. It's also unlikely we'll change soon. What can you do.
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.
It doesn't actually follow, because maybe the LLM is smarter than the original writer (at least in the domain the writing is about) and hence really is able to complete the ideas in a way the writer can't. As an existing example, consider formulating a conjecture and having an LLM prove it. But I agree; if I wanted to read an LLM's output I'd simply ask it myself rather than read someone's supposedly-human writing.
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 bet no one would like to get direct rude "source" instead.
You bet wrongly. Rudeness carries information.
“Fucking hell, how many times have I asked you to XYZ” is different from “G’day gov’nor, terribly sorry to bother you. May I remind you to XYZ? Would you mind doing so at your earliest convenience? My deepest regards, toodeloo”.
The former conveys urgency and annoyance while the former conveys that you can keep ignoring it (and straining the relationship).
1. Transliteration - roughly keeping the number of characters or bits, but translating to a different lingo, language or mental model (e.g. metaphors). Roughly the safest mode, but can still yield catastrophic results - it's safest if the author still provides taste and editing.
2. Compression - taking out redundancy to make the text more dense and more salient. The LLM chooses what to take out - and might take out the wrong things. More dangerous - but if you're happy with the salience and you believe the reader won't have time to read the uncompressed - it's probably safer than having the reader LLM compress without the benefit of your editing process.
3. Decompression - using the salience of your idea to add detail to the reader who wants to understand it fully, by utilising knowledge that is common to you and not common to the reader. This can be very powerful when there's no time to fully write the thing by a human - but it's the easiest to get wrong and to create slop. As an example - you could try explaining concept X + illustrate it through 3 examples. You know the examples are in public memory and easily retrievable - so you write your explanation of concept X, list the examples you want - and the LLM can take all of them, synthesise and bring the full package from your 300 bits to 1000 bits.
You are right that those are not the exact 1000 bits from the original brain, but they could contain 900 of the 1000 - which is still better communication efficiency than transferring 300.
I am however, more and more in the camp of fleshy brains writing everything, as my slop allergy rises.
- 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.
Exactly this. Just send me the prompt! ;)
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.
The real benefit is that the use of an LLM allows me to convey to it 1747 bits of scrambled information in an order that fits what's actually inside my head, and then have it unscramble that and convert it down to the 1000 you need. It can do that better and faster than I can.
It massively reduces the time it takes to make a short letter.
Can you explain to a child what it means when "GitHub is down" with less characters?
It is often about persuasion and that sometimes benefits from framing effectively, which I think an LLM can help with given the key points you’ve got.
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.
- people writing in a non native language
- people insecure in their writing
- people not used to writing in industry terms
- people with the curse of knowledge that are aware that they can’t write for a general audience well
Surely others too. None of those mean you have to read it, but I have gotten immense value from reading some things people have had ai write (and I’ve seen a ton of junk as well)
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.
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!)
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.
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.
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.
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> 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.
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 can't tell if this essay was written in earnest or as a subtle troll.
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.
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.
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...
We do not experience the world as words. Words and language are already an hallucination on top of reality and LLMs are an hallucination on top of language.
There are many use cases for LLMs that will help humanity progress. Putting AI in between yourself and the people you want to build productive relationships with is not one of them.
[0] myself included https://www.google.com/goto?url=CAESbQHrOzAViiJizn8otPF3AAeE...
I'd always rather read an actual person's thoughts, clearly communicated and written down in their voice. But, there's clearly a subsection of people who don't want to (or aren't confident in / etc) writing their ideas fully.
If they have a bullet point list, and they want to use AI to neaten up the bullet point list and present it back, that's fine by me. I wouldn't have minded reading their thoughts in a messy list, but AI is serving to make it more readable.
What I think everyone hates, is AI being used to convert the list into pseudo-thought-out-prose. It just contains a bunch of bloat that wastes people's time.
So my advice if you use AI to write and don't want to change: keep going, just stop seeing the desired output as human prose. Just neaten up your bullet point list / promot / whatever, and send me that.
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.
“Reading a document like this isn’t just difficult—it is punishing.”
What I often notice in pull request descriptions isn't necessarily a problem with them being written by AI. It's the lack of nuance. They don't tell me exactly what the author expects the reviewer to pay attention to, what might be risky, or where they would particularly value another set of eyes. Being able to articulate those things is far more important to me than whether AI was involved in writing the text.
I would also push back a little on the author's point about imperfections. Being able to say that imperfections are fine somewhat ignores the elephant in the room: bias. Bias can have very real consequences. The author may personally be more tolerant of variations or imperfections in writing, but that's not something we can realistically expect from everyone. I'm also not sure it's fair to put the burden of dealing with those biases entirely on the person doing the writing.
That's also why I keep coming back to the expectation of having a distinctive "voice." Not everyone has the same attachment to their writing voice in the first place. For some people, getting their ideas across clearly and being understood matters much more. If voice isn't something they particularly value or identify with, then insisting that they preserve it feels somewhat moot.
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?
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.
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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.
Opus 5 and Fable 5.1 commit/PR messages are incomprehensible garbage.
However, Astra messages are nearly perfect for a copy/paste to less-technical stakeholders. I maybe fix a line or two.
Give it a year, and I suspect that I won’t even need to make those fixes.
The only problem right now is I can’t get my team 20x OpenAI accounts due to supply constraints. We’re all stuck waiting, hoping Anthropic either ups their game or OpenAI gets more capacity.
We would easily pay $1000 a month per developer/PM for a business tier ~30x account or similar that let everyone use Astra all week without running out of tokens. And that is entirely because of the writing and communication improvements.
Only a privileged person can say this. Not everybody is a native speaker or good communicator. As an IT guy somewhere on the spectrum, I have damaged my career on multiple occasions by clumsy writing.
There was just something about them, like they were written by someone obsessed with detail but without a clear idea of the audience they were writing to.
The result always ended up being a document that was way too long, way too detailed, without context for those details, speaking over the head of the reader.
I hope this was an in-joke but my fingers still reflexively closed the tab as soon as I saw it and I had to go reopen it again…
> "The design document is no longer a proposal to build consensus, bring people along with you, and refine ideas through slow, deliberate thinking—it is a summary made by a machine, in exhausting detail, without context or perspective. "
Yeah I am skipping this one for sure.
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.
What should worry us is that AI may introduce a slant that the prompter assimilates during their conversation with the tool, which could introduce a subtle control lever over public opinion.
At this point I can't tell if it's a pun or the anti AI article is AI written as well. This sucks.
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What the humans would see would be what is filtered up to them after all of this evaluation is done. And content generation would be the reverse. People would provide a seed of an idea and then their agents would turn that into potentially useful work.
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.
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.
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...
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If you don't get to the point and continue to go on and on, I will use a LLM to summarize what you wrote and then decide whether to spend time listening/reading what you wrote.
My own solution for this (for now at least) is to add a preamble section “AI;DR” to PR descriptions, filling what a TL;DR section would have done in the past. I’ve socialized to my peers that no part of such a section will be written by AI, and it should contain exactly this missing high-level content described by the author.
And yet, every person high up in business that I talk to, including my wife, advises me to use Claude for this sort of thing. I don't get it. Claude text sucks, and it hurts my eyes when I'm forced to read it.
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One thing that has markedly helped is tweaking our AGENTS.md prompt and prose-heavy skills like `/pr` or `/ticket` to specify that all technical output needs to be written in ASD-STE100 Simplified Technical English. It has helped us so much. We still have to `/decomment` or minimize to remove unnecessary stuff that's been written in ASD-STE100, but the output is a lot easier to digest.
OTOH, we have a guy in marketing that has taken it upon himself to use Claude to draft these wildly complicated and dense implementation specs that he doesn't understand for various things that he just hands off to my team to implement. He has no context for how our product is built, the various layers and their interactions, etc. so they're often flat out wrong and overly complex, invent systems that do not exist, all of that. Then he gets mad that we're not moving as fast as he wants, because he gave us the implementation spec! I have been struggling to come up with a way to diplomatically tell him that his actions are slowing us down, and if he'd just describe what he needs instead of speccing it himself, we'd be twice as fast...
I have a large and complicated project at $DAYJOB where I am the solo dev with a large team of domain experts. They AI dump me with suggestions (rather rarely, thank god) for features with complicated implementation details despite having no insight into the codebase. What is the point of that? I can't discuss it with them. If I jump on a call, they'd have to say "oh sorry — I don't know, I'll get back to you".
When I need to send them a message about something domain specific I have no knowledge of, I'll spend hours understanding it as far as I can. And then wording it in a way that makes sense to me. I use AI A LOT in the understanding and proof reading of messages, but the wording and writing is mine.
I'd rather understand something partially, write a bit poorly, and send an imperfect message than to AI dump a perfectly formed message which takes everything into account but not really understand everything that I'm sending. It closes the door to actual communication between two humans.
"I don't like the way other people use AI, but the way I use it is okay."
I'm going to stop reading right there. I don't want to read that either.
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.
Agreed! This is a classic Slop Grenade[1]
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.
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’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.
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”.
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Media: "Scientists claim their discoveries are useless"
Btw, I also think a 95% confidence interval is just the wrong statistic to look at given that data, and that they could probably have analyzed it better.
It's a similar thing. We live in the "attention economy", and research institutions - particularly after the US President openly went and had his minions cut funding to research purely on ideological reasons, but it's been a problem for decades - are just as susceptible to blow stuff out of proportion to make headlines and thus increase the chance someone might throw some money over the fence.
And media does the same, just to manufacture artificial debate. And so do politicians.
And frankly, I'm fed up with that, we will drive ourselves into a wall.
It's never the case that someone misunderstood what scientist wrote. Much like the scientific papers, news articles, including those reporting specifically on the discovery, have their own goals, and the paper being cited is used as evidence or argument for article's own "study". Except for press, the standard is rhetorical, not scientific, it's the conclusions and not the methods that are "pre-registered" at the start, and claims are defended by "hey it's just a point of view", not by statistical significance.
In your own example of worst offender: the scientific study was trying to establish and quantify the connection between X and Y. The summary article was trying to push the angle that "this institution is doing important work". It started with that conclusion, and the paper cited was just the first thing the author found that could be easily massaged into supporting that conclusions by rhetorical standards.
Same paper might get cited by journalist trying to push for "X is bad for you", and they'll do roughly the same as the summary article. And, same paper may be cited by someone claiming they have a miracle cure for Y, and they'll make a honest observation that "absence of X reducing Y is a common bullshit claim based on misunderstanding the paper [citation], that actually shows there's no correlation there, I mean look at the confidence intervals, even the author says that in text nobody bothers to read"... - citation may be honest, but the article itself is still using it to prop up a different flavor of bullshit.
TL;DR: don't believe news. It's bad for your mental and physical health (p<00.05).
I’m a big fan of this approach.
I still remember a recent example where one of those trivia accounts on Twitter posted an interesting story about some guy whose life completely changed after an accident, but neither linked to a source or named the person in question.
The only way I was able to verify it was true was through someone in the comments asking the platform's AI chatbot, and the chatbot providing context that I could research and verify...
I agree, this drives me crazy. Ironically, one of my favorite uses for Claude is to ask, "What study is this news article talking about?"
It's pretty good at digging up the source and related sources. And most of the time, if you read the source, the article is nonsense and gets everything wrong.
It's the opposite, all big news websites do this. Fairly sure it's part of the policy.
I would say only small, niche websites link to sources.
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.
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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.
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.
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.
> 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
These are methods of encoding, there's no reason all of these can't be represented in an LLM from a technical point of view.
> and subtext.
This is the other half of the equation to me. Humans communicate by relating shared experiences, an LLM cannot have shared experiences. While it might be able to encode subtext that has been specifically called out and explained, it will never be able to encode the breadth of human subtext, especially that which is reliant on emotion.
I don't believe it is possible to change this until the point mankind truly develops a "wetware interface" to the digital world (and I personally don't want such a thing to exist).
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'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.)
https://web.cs.ucdavis.edu/~rogaway/classes/188/materials/th...
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.
I disagree. Unless you gave additional information to the LLM yourself, the LLM doesn't know more than your audience does about what the meaning of such a comment would be. An LLM could certainly come up with something plausible, but it wouldn't necessarily be what you intended.
TLDR the length was the same curtesy of tl;dr before, just now with a different name.
Beyond that people have different motivations and goals and only a limited time to achieve them. Basically I wouldn't be so quick to judge. Plenty of students have dropped out and gone on to do impressive things and that's a bit beyond reading only the abstract for a few assignments.
You don't learn what to discard if you just have an AI do it for you.
Alternatively those 700 bits are information the LLM added, but where is that information coming from? Is it noise? Random facts? Random lies? And who is the receiver even talking with if most of what they read is something the sender didn't know?
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.
You've unintentionally circled the error here. The "purpose" of an article extends beyond "convey this essential information".
By analogy, a textbook contains far more words than a spec sheet, but attempts to train the human to be able to easily interpret spec sheets. The so-called "information" content of both might be equivalent, yet one does a better job of teaching students.
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.
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The primary reason is that human language is becoming a proof of work, that speaking out loud or writing directly indicates that the idea is important enough for a human to express. This is more costly than llm output, which is often just botspam.
From a realistic perspective in the context of people copy-pasting LLM output, my thoughts are that asking an LLM to research for you is more defensible, but it's still better to read the LLM's research results and write the important parts in your own words (partly because the LLM probably used way more words than necessary for the context).
If the receiver wanted these 200 added bits, she could infer them either herself or even use an llm to do it.
My brain does not contain all the information that can be added by an LLM; a human brain could contain it, even the biggest LLMs are about 1% of the (if you approximate synaptic count ~= parameters) parameter count of a human brain, but none actually will.
What my brain may actually contain is the information necessary to verify the (in this example) more than 200 bits the LLM claims to have added and trim out the parts which are false, retaining the (in this example) 200 "new" bits of new information added by the LLM.*
Concrete example: I am a software developer by training, though not a web developer. If someone who does not have any developer experience asks me to make a web app, I am forced to use an LLM as I do not know enough JS etc syntax to get it done myself. But as we all know, LLMs are only "ok" but not "good" at making software, so there are a lot of rough edges and outright mistakes. My experience as a software developer extends to detecting such failures and I can usually correct them.
The original person, someone who has no developer experience, can also prompt the LLM. Right now, this would result in something that retains all the errors, because they didn't have someone like me intermediating between them and the LLM.
I add bits by removing noise, the LLM adds bits but they contain noise.
I do not know for how long this will remain true, but today it is true.
* Feels like P versus NP to me. The answers AI generate are at their best when they're easy to verify. Then again, when they're easy to verify, they can be RLed to get good at this quickly and the need to verify goes down, leaving them still pretty bad at things that are hard to verify.
No, they're not. Getting those bits takes energy.
SOTA LLMs know way more than any individual on approximately anything there is to know (and what they don't, they can look up faster than people can). It's very easy for them to make the "missing" 200 bits explicit, rather than implicit, which in practical terms is the same as adding 200 bits that weren't there before.
Theoretically, an idealized omnipotent mind / AGI could derive the unifying theory from reading your HN comment on a phone screen. There is enough information there, if you were able to extract every bit of evidence available from it. But you are not. Neither am I. It would take us practically infinite work to try, solving this most cruel mathematical riddle.
> This is the optimistic scenario
So is it typical or optimistic?
> 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 why are you so eager to defend your fantastical scenario? It doesn’t matter how considerate you are, truth is the overwhelming majority of people aren’t and won’t be. We’re discussing reality here, not “what could be if we lived in a utopia which will never come to pass”.
The optimistic case is "having 500 bits, giving LLM 300, getting back 1000, and learning extra 500 in the process". Real numbers are lower. People don't vouch thoroughly and don't catch all mistakes.
But reasonable people don't send every output from LLMs to others without giving it a cursory glance (obvious hallucinations or nonsense would paint the sender as incompetent or inconsiderate), and that alone eliminates the worst levels of noise. A cursory read and cutting out obvious bullshit before sending is enough to make the message carry more bits of information than the propmpt.
> the overwhelming majority of people aren’t and won’t be.
In my experience, the "overwhelming majority" are giving something between a cursory glance and cursory edit; whether the resulting message has more or less information than prompt then depends on how much noise LLM added on top. The inconsiderate people I deal with, they often send "net more bits than in prompt" outputs, but those outputs are also verbose and not fully filtered for bullshit, thus it's effortful to tease out the signal from noise.
Me specifically, I never send anyone LLM output I haven't give at least a quick read (not skim, read) to make sure it's reasonable and there is no obvious bullshit there. And then I still mention it's LLM-sourced.
> If it’s less than 50% of the time, it means that by not modifying it, you have added at most one bit of information to what you originally wrote. (...) Instead of sending the LLM response, you could send the prompt and one extra bit indicating whether the LLM response to the prompt should be modified, followed by the modifications.
It's not the case, though. Prompts are not interchangeable with output. There is no guarantee that if you send a prompt, and recipient passes it to their LLM, they'll receive anything similar to what you did. It may have mistakes - different mistakes - or just spend focus differently.
The extra bits I claim LLMs can add to the message hinge strictly on you vouching for the response. Of course, you can just prompt an LLM, learn from the response, and then write your message clean, containing both the bits you originally had, and the bits you gained. But at that point, the LLM already gave you text containing all those bits - if you can vouch for it, you may as well copy it over and save yourself the trouble.
I’m not saying that the response is interchangeable, but that due to the data processing inequality, it cannot convey strictly more information than the prompt.
> The extra bits I claim LLMs can add to the message hinge strictly on you vouching for the response.
My argument is that if you vouch at least 50% of the time, the vouching only adds one bit of useful information – either you vouch or not.
This is exactly the use case an LLM might (huge emphasis on might, depends on workflow, agentic vs. relying on contextual which can hallucinate) be good at and yet humans are notoriously bad at, because we are swayed by emotional responses and it is easy to have an emotional response to text that is programmed to look good for you and you alone.
> you communicate 1000 bits of information to me, instead of planned 500, and you yourself learn extra 500 bits.
Extremely optimistic. If this were the ideal scenario, you would USE the LLM to garner information ABOUT those 500 bits and then reframe them in a way that you yourself would put it. If there is insight, your "word" in your mental register now expands from the original 1000 bits to 1500 or 2000, and then are "processed" by your human brain that includes subconscious choices that are meaningful to the end result. There are tons of hidden semiotic data in your diction and wording (think resource forks in classic MacOS/HFS, only visible to the filesys) that is lost when you rely on another source to put together words for you; it's as if it is a game of Telephone. These are subtleties which you may intend for your recipient to receive and which are crucially important to your recipient and are irretrievable, it is intrinsically lossy. You have an alphabet soup of words, they cannot be put together by an LLM in exactly the way your brain did. We must rely on the fact that we ourselves put this together, the "aha" moment when an LLM does it for you is illusory and does not itself provide meaningfully important confirmation that you indeed say what you mean to say. Of course, humans say things and put things in way we do not intend to all the time. I still fundamentally believe this is more honest than relying on a third party that is not capable of understanding human emotional nuance to put together language for you, when language is and always has been a manner in which to dictate human emotional nuance.
> (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.)
Does not negate the fact that LLM output itself, at least when used to convey human emotions or thoughts, is lossy. A very highly compressed JPEG with added interpolation from an upscale algorithm might come up with cool details that were never present in the original, and may look cool to both you and the recipient but are not honest to the source material. You receiving 240p JPEGs on a day-to-day basis is irrelevant to this. For the purposes of communication, it is a massive error which has the potential to compound, regardless of whether or not you or the recipient believe this to be the case.
Yes, but at that point in practice we're getting into over-optimizing territory. In this optimistic case I presented, you could learn those 500 bits yourself and formulate a clean message yourself, with all 1000 bits in it, but since LLM already gave you the text, and you feel you vouch for, you may as well send it over and save yourself the effort.
In reality the numbers are probably lower, and writing the message yourself is IMO also a good way to be truly sure you vouch for the "extra" 500 bits, as it forces you to actually pay attention. There's a chance you'll find inconsistency in output, or in your own understanding. I don't begrudge people for eventually cutting the process off here, for practical reasons - it's the fuzzy line between accuracy and perfectionism.
> A very highly compressed JPEG with added interpolation from an upscale algorithm might come up with cool details that were never present in the original, and may look cool to both you and the recipient but are not honest to the source material.
Again, I think it's a wrong take. LLMs aren't pulling the information out of their asses, and you are also not able to express every information directly. LLM can "upscale" information and you can take a look and recognize, "yes, this is exactly as it was", even without being able to write out that "upscaled" version by yourself. Verification is often easier than direct recall.
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.
But I can most of the time, and correct it if it chooses the wrong thing. That's the whole reason LLMs are faster. Its the reason we can give a paragraph prompt and get a kLOC PR back but only need to correct about 5% of it.
It depends on the teacher. I’ve had good and bad math classes, and LLMs today are quite a bit better than the bad ones. The worst human teacher in my memory didn’t offer interactions. He walked in, turned his back to the class, wrote equations on the board for 45 minutes, and then left. The best math teachers, the ones better than LLMs, are the ones who share the joy and sense of discovery and history of math, and not just the mechanics. But there aren’t that many teachers of that sort.
Learning by using the internet without LLMs is rarely very good, but more often than not in my experience sucks much worse than using LLMs. If you include using public LLMs in internet usage, then it’s not very different from just using LLMs that search the internet. I have heard that a huge swath of today’s high school and college kids are reaching for chatGPT before Google (which is incidentally OpenAI’s goal), and that many of them would rather talk to chatGPT than talk to a teacher. I’m going to refrain from making any claims, but I believe there are a lot of people who disagree with your ranking of the options.
Foreign language learning is one case where I love using LLMs, because it’s not typically an option otherwise. You can practice non-stop and have conversations with someone fluent in a language who will be infinitely patient with your mistakes. This is true of math and other subjects too; using LLMs to practice, so that the human teacher isn’t the bottleneck, to supplement and reinforce the human interactions, is usually better than using the internet without LLMs. The other reason many people prefer talking to LLMs is the lack of judgement. If you aren’t getting it and ask the teacher one too many basic questions, they treat you differently. Sometimes it’s necessary and helpful, and sometimes it’s harmful and takes a long time to change. LLMs don’t do that, they just explain and explain. That lack of judgement is a big reason many people prefer LLM interaction to human interaction.
It's not an either-or though, LLM generated text already provides lots of value in many situations right now. It's also used for fluff, yes, but much of what humans write is fluff too, reporters often get paid by the word.
The point is that the value in a text has nothing to do with whether it was generated by an LLM or not. What matters is if it's useful or not.
You are stranded in a desert island. You start writing a message "Help, I am..." and pass at that point.
Somebody finds the message. They can no doubt come up with plausible continuations like "Help, I am Robinson Crusoe" or "Help, I am hungry" but they cannot create information. No matter how smart and how long you stare at the message, that is not going to tell you what the original person would have written.
Isn't it from Claude Shannon that information lowers uncertainty? Infinite regurgitation or massaging of data does not create new information. You will get the information form the LLM, not from that original person.
Consider, for example, that if somebody doesn't know English at all, then before receiving the message, their best guess at what it is is some probability distribution over all English characters (or sounds, depending on what we assume them to know), and after knowing the first part is "Help, I am" that distribution might not change much at all. Therefore, they derived very little information from this message.
Going in the opposite direction: keeping fixed the knowledge someone starts with, there is an upper limit to how sure they could be (even if they are logically omniscient) in completing the message (that is, a lower limit on the entropy of their probability distribution) - this is what you're talking about in your example. But this limit only becomes important under these constraints - for example, knowing more about the person who wrote the message can let you predict it better, and if predictor A isn't logically omniscient, predictor B can do better than it with the same prior knowledge, just by being smarter than A.
Also, "pi" is a unique constant name. You've conveyed far more than 10^[5|6] information in the subject phrase.
Then what's the point of the original writer?
I am not ready to use the Americanism, 'teh telephone game', since nobody knows what that means, yet everyone over a certain age knows what 'Chinese whispers' means.
The latter (with its twisted mix of Australian, Cockney and Kings English) carries a calm sarcastic tone which indicates ones displeasure far more than the former, more vulgar statement, could ever hope to achieve.
One is however, reminded that Americans simply don't get our sarcasm, frequently leading to some amusing cultural clashes.
This is a great way of summarizing a lot of the TV we’re getting in the US that have British characters, and it’s annoying once you notice it.
See how the accent changes in Lie to Me between season 1 and season 3:
Regardless, in Blighty, such sarcasm is by no means twisted and indirect. It is as plain as the nose on ones face. As I said earlier, many Americans simply don't get such sarcasm, leading to comic clashes of culture.
But there is a reason no one is talking that way...
- If you're giving additional prompts to the LLM to refine its output, then you're the one adding real information, not the LLM. The LLM is just rephrasing the information and adding noise.
- You are adding real information. The LLM is also adding real information. That's the entire point. It happens very often that an LLM suggest something to me that I did not know or simply did not think about. An LLM solved Navier-Stokes recently. That was most certainly not just adding noise. That's real information purely generated by an LLM. Information that was worth a million dollar price. Information that man centuries of mathematicians were not able to do generate.
And I'd still rather read a human's interpretation of the solution to NS than read whatever the LLM wrote.
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What I was trying to say is that sometimes you need to provide additional context and information to those not using the same information model. Maybe it's not compression, but rather you have to include the 700kb of your information model with your message.
The LLMs don't have access to private information other than what you give it. If you give 300 bits of information to your LLM, the information it has is now "all public knowledge + 300 bits", and any text it generates is a subset of that information. By definition, it can't add any more information. If you give those 300 bits directly to me, I (and optionally my LLM if I want) now have "all public knowledge + 300 bits + my private thoughts", which is a superset of what your LLM has. Anything your LLM can infer can be inferred by me and my LLM. All your LLM can do is repackage that information into different text.
My opinion is that I don't get value out of that repackaging. I would rather read your packaging of those 300 bits rather than the LLM's packaging of those 300 bits.
For this current discussion, we could use an information model saying that your LLM has a knowledge base private to just you and it (e.g. local files or other conversations), and say that's separate from the prompt you give it. It sounds like maybe that's the information model you're thinking within.
In that frame of reference, maybe your shared knowledge base has 200 bits, and you type 100 bits into your LLM's prompt. My argument would still be that you're "giving" 300 bits to the LLM, and you should instead give them to me by sharing your knowledge base, or giving me the relevant information in your knowledge base in your own words. The latter option is definitely more work for you, and is the weakest point in my argument, but I'll still hold that preference.
I don't believe that it holds true in context of typical communication between humans. You need to include the additional text to save people time looking things up, make the story flow better, fit in within the expected style of writing for intended audience etc.
To me the problem with LLM writing is not that someone is sending me 700kb of information I can get elsewhere, but that they didn't invest the time to understand what would be useful for me as a reader. I don't need 700kb of generic slop, but I probably need additional 300kb of relevant publicly available info to help me understand the purpose of the new information being sent.
In my opinion, not understanding the reader is exactly why bad writers remain bad writers - LLM doesn't know who the document is written for exactly.
"No work today"
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."
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> people insecure in their writing These people can grow up, I don't care. Not a good enough reason to send a slop grenade.
>people not used to writing in industry terms Similar to the non-native speakers, but slightly less in magnitude. They can educate themselves though.
- people with the curse of knowledge that are aware that they can’t write for a general audience well These people probably can get some value out of it but they should take care
Still not really a good enough reason in the end
I disagree, but I think neither of us are going to benefit from continuing this discussion.
When you talk about something you're wondering about, you're saying that you're missing information. Your ponderings are dancing around the void in your knowledge, defining its boundaries, and maybe imagining what answers might be able to fill that void.
When you put your thoughts into words, they're insufficient. You have so many ideas swirling around in your head, and you can never put them all on a page in the fidelity at which they exist internally. But words are the best we have. Whatever words you write are your best attempt to convey your thoughts to me (barring other media). You're distilling your inner voice that speaks a language only you can understand, into an outer voice that others can understand.
I don't think I'm exactly refuting you here. I think what you've written makes sense, and caused me to think about many things, more so than any other reply to me today. But I also don't think your comment is refuting the point I was trying to make, mainly that LLMs rarely add value in human-to-human communication.
I could probably have pasted my comment and yours into an LLM, and it would have come up with a clearer thread connecting my words to yours. But that thread probably wouldn't have been any of the ones either of us saw, would it?
Thanks for adding a new perspective to the conversation :)
Surely the ability to do that is worth taking note of.
I am not advocating for letting LLM's write for you, to be clear. Sentiment wise, I largely agree with you. Just not with your total writing off of the possibility that it could serve.
It's easy to imagine an LLM aiding the communication between a mentally disabled person and their parent/caretaker.
Or, perhaps, some day, between animal and man. Who cares if the mediating component "hallucinates" some particulars of expression if it achieves the goals both want, which were previously impossible?
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.
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.
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.
(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.)
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> 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.
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.
wow this is a beautiful way to put it
This thing called attention economics has captured my attention quite hard. Digital opiates are everywhere since 10 to 20 years. And in my view, the part that they implicitly of explicitly strive to capture your attention is new.
To use the wooley term “quality”, the top 20% might stand a good chance of making huge strides. But the remaining 80% of projects (in particular the bottom 20%) will atrophy extremely quickly. Yet, these will be the project that many push LLM’s too as their domain/technology is complex and/or outdated. Digital transformations that can be done quickly will be tantalising but ultimately unsatisfactory long term (as you describe).
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I'm old enough to remember using CVS and then subversion in companies. People would commit straight to main (which was then called "trunk"), because making feature branches and merging them was cumbersome. And, on regular intervals, the person responsible for some corner of the codebase would do a show-and-tell presenting it to peers, but without the sharply defined boundaries of what the code looked like before vs. after some recent set of changes. People might remember some things from the previous show and tell or from first hand experience with that code, but that kind of memory is necessarily fuzzy, and diffs weren't an artefact that was typical to look at. So, these reviews didn't block people, and any comments that came from reviews defined a direction that things should go from here on out. If a corner of the codebase was deemed to be in a bad shape, the blame around that was equally fuzzy.
Everyone is fatigued by endless code review which you get no credit for and has become massively more of a burden.
All PRs are superficially fine now. There are no typos, there is unit test coverage, but there are deeper issues that require massive amounts of effort and time to spot.
Lots of review comments about various conditions that wouldn't feasibly happen (same shit with claude now).
But then I'd see these same reviewers approving PRs where the bigger design was just fundamentally broken. Oh, we're adding a blocking call on our hot path, but at least the method name makes it very clear that it is blocking.
In general I agree that the current AI reviews are creating too much noise and it is masking these bigger design issues.
How many are you seeing / estimating?
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.
In the meantime, for business communication, I use AI to shorten my text, to make it more concise.
So far I haven't had a reason to go back through commits to isolate any issues but if I do hoping the 'why' messages may come in handy for my LLM lol
Until I fully understand what's going on, the PR doesn't move and my interrogation of the LLM doesn't end. My interaction is littered with "Explain X" and "How does this square with Y?" and "What if Z happens?"
The interrogation is the point, without me having to wade through hundreds of lines of irrelevant code to get at the meat of the matter.
We have this at work : fully AI-generated code and description. People will give review comments generated by AI which the "author" replies with an AI-generated response, all with LLM wording full of jargons no one understands not even the person who sent it. When you ask them what they meant, yeah idk Claude said so
Every time the LLM throws jargon around, you call it. "What do you mean by gated wedge?" You call its bullshit, check what it's saying against your understanding of the overall system, and keep it on the straight and narrow.
It's a lot like supervising a junior dev who happens to be very quick at absorbing lots of info, but not so great at the big picture.
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All of the posts in 2026 on that blog area about AI, by the way.
We've had AI output all these decades, but never recognized it. Is this evidence of time travel?
\s
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'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
Of course Claudeish or GPTish ("unusually") will easily push me away. Just like any repetitive or obnoxious tendencies that might appear in human writing.
If this actually works, it's absurd. The implication is that Anthropic could trivially make Claude sound less obnoxious, but chooses not to. I don't see a way this could be justified as a safety feature or anything, so.
I agree, using LLMs is disproportionately frowned upon, but what matters is if you invested your own attention in the process. I use LLMs a lot for sparing, usually ask it to assume some opposing persona or use web search, not relying on its defaults.
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.
My attention is a finite resource.
Everyone is an elitist, when you've enplaned do you assume the pilot is qualified to fly it or do you hope everyone gets a turn? Admittance to the canon is no less a responsibility, would you trust the yoke of human culture to the artless, craftless, and naive?
not everyone does their thinking, if you dont I dont want you to have voice
I still want the imperfect human.
Because while I dislike AI writing, it matters in context. If someone is trying to formulate a coherent thought of work tasks required of me then I don't really care if it reads like AI as long as the point they're trying to get to me is clear.
If there's an attempt at something human, or emotionally important I will quickly balk at anything spat out through an LLM. If you're trying to communicate at the human level with me I want your input, flaws warts and all.
A card with just...nothing personal on it has as much value as machine text. I understand that you took the effort to think of me, but that's the bare minimum. If trying to express yourself isn't worth the time to make it personal then I'd almost appreciate if you didn't try at all because it hurts more to believe I'm not worth the effort.
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.
IMO it is a costly/goodhart-resistant way to “show your work” and help other people understand or challenge your mental model. (IE a justification for something you believe to be true). Overly polished writing is performative, it’s hard to take seriously once you’ve read The Economist/LW enough to see how poorly “well written” correlates with truth
To a certain extent are all wrong or ignorant about almost everything because our knowledge/time are very limited. But it is really important to understand what other people think in order to coordinate with them/align human goals and understanding.
It’s good that the average person taking the lowest-friction path to using an LLM in bad faith is easy to identify now. The more obvious and disliked it becomes, the more they’ll be hit with the stick to actually know things and not bother people. It’s so much worse to be “bad and stupid and not care” than “possibly cringe or wrong”
I feel the same. I've actually started to appreciate things I used to dislike. Like typos, or grammatical errors. I used to see it as a lack of attention to detail. But more and more it now feels like "hey, something written by a fellow human!".
Same thing for video voice overs. Things like a bad quality microphone, or someone who doesn't pronounce things very clearly. Now I go: for sure a human!
There's a reason ASD-STE100 is mentioned everywhere now. They're at the point where people can't understand what they're saying.
(And I try to be judicious about what counts as "of substance" so they can't accuse me of flooding them with messages)
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.
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.
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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.
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.
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.
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.
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.
As much as you want to say "actually do your job, jackass." Being goal oriented has had the best results for me.
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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.
You’re giving them too much credit.
Sure, I use precise and technical language, but I'm still a human and I can understand what people are getting at when they use imprecise terms. But if the text passes through an LLM I can't tell whether the result actually matches the original problem or whether the LLM has made guesses or filled in blanks.
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.
— (em dash)
Option+- gives you an "en dash": – which is longer than a normal hyphen.
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.
AI is really good at making tests that pass even if the change itself is wrong.
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.
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.
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?
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This is wrong. Reporters frequently don’t understand the science or the nuance in the science.
Reporting and science are two very different disciplines. Reporters rarely have a deep background in science and almost never have a background in the specific area that they’re reporting on.
Hell, even scientists have trouble accurately describing the work of a different scientific discipline.
Don’t invent bad faith motivations; they exist but most of the time it’s just two people slightly talking past each other.
I'm not inventing them, but maybe conflating two sources:
1. Malice directly intending to hurt or defraud people. Probably not as common as how I make it seem.
2. Not caring. Well, I subscribe to the view that not expending effort to be accurate when talking to other person is as bad as slashing their tires (paraphrasing an old quip), so I very much consider bullshitting and picking a conclusion and then massaging facts to fit it, to be acting in bad faith too.
Honestly, it's plain weird to say that people never just make mistakes.
PS - worth adding that "I misunderstood" and "I didn't care enough" are not mutually exclusive. You can do both, so saying "they didn't misunderstand, they just didn't care" isn't a reasonable rebuttal. But even setting that aside, there'll be plenty of folks who care but still don't understand.
If you have found a prompt that gives a good result, then by sharing it with me I now have a tool that gives a good result.
The problem is not the use of LLMs. It is the "passing it off as your own work".
Output is not interchangeable with the prompt. In many cases, the prompt does not have the information the sender wanted to give you, and there is no guarantee that your LLM will give those information - or do it correctly - if you use the prompt yourself.
The entire value of here is that sender read the output and is vouching for it. This is where "bits of information" come from. If the sender cannot be trusted to verify and vouch for the LLM text they're sending to you, well, they're an asshole and you should rebuke them or find someone more considerate of others to talk with. Them giving you their prompt doesn't help you with anything.
But my point is that the prompt is the nearest encapsulation of the intent of the originator. Give me the data and the prompt. Vouch for the output if you like, but I want the source.
Here's the proximal prompt "Okay, take everything we've been talking about for 2 hours and apply those edits to the the final draft for publication."
What exactly does that give you?
If I describe all of the individual muscle contractions and joint motions required to walk across a room, it's hundreds of pages of data to say almost nothing. That is the exact opposite of dense.
By contrast a poet can deliver many concepts and many layers and even practically a fractal choose-your-own-adventure in only a dozen words. That is what dense is.
Even Markov chain autocorrect tools do better than 50% odds*, and even GPT-2 was significantly better than that kind of autocorrect.
* at the word level; IDK how redundant/efficient language is when it comes to bits-worth-of-fact-claims-per-word. But "your cat is sitting on my" -> [mat, laundry, roof, head, belly, laptop, microwave, …] clearly has many bits of information, and a Markov chain will encode the most likely next word even if the user doesn't know what the most likely next word is. Verifying where the cat is sitting is also very easy, as is correction.
Some specific conclusions would be far less than 1 bit.
The average will depend on both the question and the AI.
A LLM adds noise, not information. At least in this framing.
I will try harder. Consider entropy.
The first sentence in this comment contains 32 characters; from the point of view of a naïve channel with no compression, that's 256 bits (given none require breaking out of the first bytes of UTF-8).
It did not take 2^256 attempts to construct the first sentence in this comment, because the generation process was not flipping coins per bit.
LLMs also do not emit bits chosen with a [0: 0.5, 1: 0.5] probability distribution.
From a compression point of view, the bits-transmitted-per-bits-in-message ratio can be reduced such that more likely messages use fewer bits than less likely messages. However, this requires the receiver to agree with the sender what the probability distribution over tokens is.
Intelligence is, amongst other things, a compression algorithm. If I can predict your next token, and we both know this, we can agree in advance that you don't need to actually send it.
No single human brain is able to predict the output of an LLM anything like well enough to do that.
In entropy terms: LLMs are noisy sources, their output does contain false statements, yet they add more bits of signal than of noise relative to a human alone.
Or at least, they can add more add more bits of signal than of noise relative to a human alone, but humans who blindly copy-paste the output of an LLM without checking are a pain and add zero value to whatever situation they happen to be in.
For some hypothetical scenario, writing software because I know they can do that, asking an LLM to write some code for you may easily give you 10 kilobits of positive information (code that mostly works), and -30 bits of noise (each bit being one binary decision's worth of incorrect choice by the LLM in what to write, i.e. bugs); if you as a user don't know how to handle the -30 noise that could easily be a totally useless app, but if you can filter out 30 bits of noise, either manually because those 30 bits happen to be your skill set, or even in some cases by prompting it again with the failure mode, then you get to benefit from the 10 kilobits of good stuff that you didn't have before.
In many (but not all) cases, LLMs can fix more than 1 bit of mistakes per follow-up prompt.
Only in the case where the LLM message is not reviewed before sending, and only if we assume reliable LLM (so that the receiver could recreate the same output if given the original prompt). This is not a realistic scenario.
> My argument is that if you vouch at least 50% of the time, the vouching only adds one bit of useful information – either you vouch or not.
The alternative to vouching isn't "not vouching", but "correcting and cutting out wrong bits and vouching for the rest", which means the single "vouched for it" adds all the bits that are in final message but weren't there in the prompt.
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One question to ask is why is your contrived LLM scenario any different than a math teacher using a textbook, or district/state worksheets? Math teachers typically do not write the course text or exercises, and never have. Very few math teachers do their own writing.
I can read the room. Coding is going to go the way of some other disciplines where machines do most of the detail work and and we know stuff works by verification. There are other fields like this.
Do I like it or not? That doesn't really matter. I need a job, so I'm going to get good at the new way to ensure I continue to have a job. I consider this to be a smart thing to do for myself and my family.
Unfortunately I see a lot of (senior as well) engineers who think that just a vanilla LLM reviewing another LLM is sufficient, and my comment was directed towards those. If however you see the LLM era as needing more test support and systems than ever before in the form E2E tests and so forth, where "code review" as such becomes mostly irrelevant as you have such a strong test system in place that if that passes you can be sure it doesn't break anything for users, then yes, that's good.
Code reviews may not even happen, or if they do, it'll be all automated, and the verification will be the key.
Ask anyone in the semiconductor industry when last they understood the design of those things.
It's very easy to argue with stawman arguments.
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I suspect it is for everybody, but I might be blowing smoke up my own ass.
What do you mean? Are you a "Write-Not"? https://www.paulgraham.com/writes.html
Bollocks. We managed to communicate before LLMs, but now suddenly everyone needs to write like a lobotomised Hemingway?
> What we are seeing here is a Luddite reaction from artisans.
I suggest reading up on and understanding the history of the Luddites, instead of repeating the same tired thoughtless meme.
> For many writing is painful
Then do something else! Draw, dance, sing, sculpt. Figure out what isn’t painful to you and express yourself that way.
> Nobody gets on a motorbike and wins the TT in the first weekend.
And no one wins ever by getting others to run for them.
Are you from West Yorkshire? I am.
I went to school in Batley and I've been past the steeple at Cooper Bridge many times.
Given much of my family were mill workers, the Luddite tale is very much part of who I am.
Along with removing the possibility for them to ever actually learn to write better.
This is not a good thing.
When I read AI-enhanced writing, I always feel like I have to peer through the blinds to see the writer's true intention. This feels very dishonest and partly offensive, because it can sometimes take several paragraphs before I realize that I'm reading AI output, rather than someone's processed thoughts.
Writing has always been incredibly difficult for me, both due to incredible difficulties with handwriting and finding my own voice. That difficulty I find in writing is the friction that identifies what my voice is, the words I choose and the meaning I attempt to convey through my own understanding.
I cannot read AI-generated crap for the life of me because all of it simply has no meaning. I will go back and read through paragraphs, trying to grasp at the meaning of something that does not exist. If your argument is that AI-generated writing eases the pain then I will make the opposite argument: reading AI-generated slop increases the pain 10x. I would sooner read through an essay with endless punctuation issues and spelling mistakes than one passed through an LLM because one is an earnest attempt to communicate while the other is offloading all of the mental burden to me. Which gets to the root issue of a lot of the complaints around people slinging slop: you make it marginally easier for you, and then far worse for everyone around you.
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.
Nobody cares what your English grade is. Everyone would prefer to just hear you speak in your own voice. This isn't Luddism, this is the equivalent of people pushing their selfie through five Instagram filters until they look like an Oblivion character because they have no confidence in their own appearance.
This is not done in the service of readers, nobody wants the slop, it's writing dysphoria
Objectively false
They are hardly artisans. They are simply the vocal few who repeatedly conflate communication with art simply because the written word can be used for both.
Grammar check is one thing, but AI mangles your thoughts.
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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.
I normally assume people make mistakes. I don't believe this is a good explanation for news publications, university press releases, politicians, etc. because those are organizations with agenda, and commit "misunderstanding" of this type pretty much in every thing they publish. The pattern here is pretty conclusive, IMO.
The pattern of personal motives fits misunderstanding. You need to show that there is an organizational pattern of "malice" (your word, not mine), rather than an organizational pattern of "we are trying to publish quickly, and quality accidentally falls to the wayside". I.e., negligence, not malice.
You haven't provided even a shred of evidence suggesting there's malice at the journalist level. Every science journalist I have met genuinely cared about the science (which is why they were writing on it), but they didn't have time to learn enough about the subjects to understand they were oversimplifying things.
The 911 call went like this, according to its transcript. Caller: "This guy looks suspicious, like he's on drugs or something. It's raining and he's walking around looking into windows." 911 operator: "Can you describe him? What race is he?" Caller: "He looks black."
How the TV news reported it on the air was: Caller: "This guy looks suspicious ... He looks black."
Omitting excess verbiage is one thing. Omitting words that entirely change the context of the statement, making it look like the caller was racially prejudiced rather than responding to a specific question, is something else entirely. That was the last time I trusted reporting from that particular source (it was NBC, by the way).
My principle is that when someone lies to me, I stop trusting them. By lying I mean not just omitting details, or having an obvious bias, but deliberately telling me A when they clearly know that the truth is not-A. I could not see that report any other way but a deliberate lie, knowing the truth and attempting to make people believe the opposite.
LLM is not a random symbol generator (hint: training data is not random), and no reasonable person is going to just prompt an LLM and send its output without giving it at least cursory check (at the very least so that blatantly stupid hallucinations don't paint the sender as inconsiderate or incompetent).
That check alone can add bits to the final signal.
So there are 2^300 possible ideas, only 2 outcomes from the cursory check, how do you get 2^700 outcomes? Most of those are just random variations the LLM added which is not a transfer of information. You would be lucky to even identify which of the 2^300 ideas was being conferred.
LLMs are not even odds on all possible outputs, they are biased towards patterns which are upvoted by the training mechanism (at a minimum: the source material, RLHF, and synthetic data).
The information any trained model transfers to output, is information it gained during its training.
No single human is capable of having consumed all that training data.
I'm reminded of an old quote:
The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man.I wouldn't read a maths book produced by a calculator either. There would be nothing of value in it - if I know my times tables and plus tables.
Then you didn't bother to read, or understand, my argument.
In particular this bit: "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."
Are you sure you haven't got to the stage where you don't read anything properly at all?
[1] "There's actually some very important things going on during the embodied experience of writing by hand," says Ramesh Balasubramaniam, a neuroscientist at the University of California, Merced. "It has important cognitive benefits."
https://www.npr.org/sections/health-shots/2024/05/11/1250529...
"Cynthia Dunlop recently shared survey results on reading AI-written articles entitled Report: How developers react to AI-scented blog posts. For the majority of readers, if they think an article is AI-assisted or AI-authored, they will stop reading (78%) and avoid the author in the future (71%)[...] the strongest result in the survey was people overwhelmingly preferred the author’s own writing (98%), with all its flaws and idiosyncrasies,[...]*
when it comes to ai people love to claim to hate it outright - this thread is full of descriptions of ai writing being impossible to read, totally incoherent, and superlatively bad/worse than average human writing. meanwhile evidence suggests that we might actually kind of like ai writing - with a growing portion of samples making hn's frontpage (https://www.salahadawi.com/hacker-news-ai-detector/state-of-...)
I've seen my share of ai slop and I get where the rage is coming from - I think folks could do a lot better than shouting down every use of ai as slop, especially while hypocritically using ai in their own writing. the pompous elitism on display in this thread alone is gross even for hn
There is no elitism in demanding that people use their own words, or if you want to call it elitism then elitism is healthy and good. Imagine ten years ago you talk to someone online and they told you they outsourced their posting to a call center guy in the third world, that's what this is. Literacy and agency aren't gross, it's what we ought to expect of an educated adult. It's fundamentally a sign of honesty that if I talk to you or read what you write I want to hear words out of your mouth, not some meat puppet version of you hooked up to a chatbot.
It's actually hard to define the difference between information that comes from the model and just random variation, maybe something to do with the cross entropy between the sender and the model?
Indeed.
The best case is a P vs NP situation: can the claims from the AI be easily verified, or not?
This does not excuse people too lazy (or overly impressed*) who fail to attempt the verification.
> It's actually hard to define the difference between information that comes from the model and just random variation, maybe something to do with the cross entropy between the sender and the model?
Mm.
Thanks to a philosophy course I did half a lifetime ago, I think there's a fundamental problem defining "information" in this context. It feels like it should mean "knowledge" because the discussions about Shannon entropy and transmission channels assumes there is an actual source-of-truth, but my conclusion from discussions about why "knowledge" can't just mean a "justified true belief" is thay I now don't believe we can do better than "belief"; an LLM can generate tokens that change your beliefs, but ultimately neither you nor I nor some annoying colleage who has made themselves redundant to the LLM, can be an oracle with definitely-true knowledge.
(I have of course tried asking an LLM about this thread; I don't feel it illuminated anything new for me, none of what it suggested made it into this comment).
* In the early days of LLMs, I was overly-impressed. Then I realised we were doing the same thing with LLMs today that we did with 3D graphics in the 90s, where every new engine was hailed as "photorealistic" only to be dismissed 6 months later when something better came along: https://archive.org/details/nextgen-issue-26
Only now it's every 11 weeks rather than 6 months.
demanding that people not use a technology that makes expression accessible to them, and insisting that they can only produce the sort of slop that you're sick of seeing, is not just elitist - it's indignantly pompous. it's neither healthy nor good - it's hateful and hurtful
> ..they outsourced their posting to a call center guy in the third world
nobody actually does this though - this exaggerated caricature dwells only in the hateful mind. to be fair there are content farms as there always have been and ai slop is bellowing from their stacks, but the article we're commenting on and your sentiment are not aimed at them - they're targeting the people we're not used to hearing from who might have interesting things to share now that technology is helping them organize their thoughts. you want to say "if you needed ai to organize your thoughts, your thoughts aren't worth organizing" there's nothing good or healthy about that sentiment - you think you're "demanding literacy of educated adults" but actually you're demanding a particular kind of linguistic fluency you happen to value - that's not a defense of literacy it's hallmark elitist gatekeeping