The "something deeply wrong" part about AI, that even most technology enthusiasts evidently do not seem to grasp, is that it is still fundamentally a statistical model — an algorithmic construct — and does not possess any real intelligence or critical thought whatsoever.

No matter how much investors and tech companies want you to believe that they are on the verge of super intelligence, nothing I've seen to date can not easily be explained by "correlation engine", including the "novel" math solutions, all of which appear to just be "a composition of solutions humans have developed and documented elsewhere" upon deeper inspection.

Completely agree. AI is very impressive in many ways but there is something deeply wrong that is hard to put into words. The output is probable but never true, if that makes sense.

I think this is also the mechanism behind why AI generated videos and images are so captivating at first. I remember when Midjourney first launched and it was hours and hours of a brain-melting "Wooooooow". But once you get used to it and start to identify the patterns the brain quickly labels most AI-generated content as blank space.

If the image or text wasn't created by a human, then there was no intent behind the content, there is no message or novel information conveyed, and it reads as noise.

I think you are adjacent to the real story here, but missing it. AI text contains information, certainly. Frontier chatbots are very good at creating acceptable and mostly accurate answers to our questions on just about any topic. It’s an astonishing achievement.

But you are sensing correctly that there’s something missing. It’s the meaning and the speaker. Communication is an exchange between speaker and listener. The speaker has a meaning in mind, and wants to create that same meaning in the mind of the listener. Therein the problem.

There is a listener, sure. But no speaker. No meaning. There is information, but how can this be communication? Nothing is talking. Or at best, we are just talking to ourselves, our own words back at us through the funhouse mirror.

When your mind looks at AI text, you know you can safely ignore it. No one wrote this. No one cares if you read it. You can delete it and nothing of value will be lost. It might contain the information you need, or a bunch of gibberish. There’s no one’s reputation on the line if it’s gibberish.

Yeah, I have the same problem. There's a good quote example of this:

> There’s a growing scissor between people who are happy to read AI and those who violently bounce off from it.

> People adapt in different ways — and some people absolutely cannot look at it. That cognitive split creates a surprisingly powerful opportunity: you can write something that, technically, sits right there on the page, yet an entire sub-population will be incapable of staying with it long enough to actually read it. You can hide entire sub-structures in plain sight. It’s not avoidance — it’s adaptive obfuscation.

> The paragraph before this one was the only thing generated in this essay and if you just skipped over it I highly recommend reading and really understanding what it’s saying.

It's quite effective. I think this kind of text functions like the chumboxes you see at the bottom. Taboola and so on. Just mental ad-block takes over.

You are re-compressing information that is in-effect meaningless because it's all decompression artifacts.

The AI had a nugget of data and decompressed that into a flood of text.

The exhausting thing is that we're then trying to re-compress that or derive the original intent and meaning from noisy decompression.

It's like un-zipping a zip file into a probability space of what could have been in the zip -- and then having to find the actual files worth reading.

For me it's youtube videos. As soon as I hear the AI tells in the script, even when it's clearly read by a real person I immediately look for a different video.

At least for the content I watch for entertainment, it may be different if I am looking for a specific answer for something where I would otherwise just ask an AI anyway.

For some research I looked up some very old Reddit threads a couple of days ago.

And, Oh my god, you can actually see how this style of writing influenced AI writing today, I constantly had to remind myself: "this was posted before ChatGPT released".

The reddit influence is especially true for "storytelling" writing.

When I read AI-generated prose that is aimed at the general public, I have the exact same feeling.

But when I ask Codex a technical question about coding, I don't get it at all. Codex replies to me in a very direct, technical manner, similar to the way I speak.

When I ask ChatGPT to be concise and technical, I get the same effect.

I think it's because prose aimed at the general public has to be very attention-baity --like the textual equivalent of a Mr. Beast video--, not because AI is incapable of writing like a human.

Its fuzzy matching a sequence of input words to weighted patterns extracted from a humungous training set based on the sentence structure (not meaning) to generate another sequence of words, and then filtered and smoothed to make the output more agreeable.

Its fundamentally flawed. like tarot card reading and astrology.

Do you have much exposure to pre-AI corporate memos, mission statements, marketing plans, or white papers? Because they were mostly written in that style. Full of buzzwords, cliche similes, platitudes, jargon and stock phrases.

I once heard on a podcast about comics that an artist ran into someone who couldn't read comics. Not that they didn't like them, but they claimed that they just didn't understand how they worked. When the artist explained 'well each panel shows an illustration of the events in the story', the other person was confused - "each panel is connected to the previous?" they asked. They just couldn't wrap their head around the fact that each of the panels was connected in some way - their brain just refused to interperate them as anything other than 4 separate completely unrelated panels.

When I see AI animated videos, that's how my brain feels. It's this strange brain-fog that I just cannot connect together the sequences of images being shown into some sort of chain of events. My brain just refuses to see them as anything other than a disconnected series of 2-3 second videos, even if the same character(ish) appears in them all. It's very strange.

> There's some psychological mechanism by which my brain immediately recognizes AI generated text and just short-circuits to "there is no information here".

The roots of llm math in part lie in compressing natural language such that there's only information there, and then running the reverse to create way more text without new information in a somewhat precise theoretical sense.

Some more information: https://youtu.be/l6DKRf-fAAM

People should notice that it is constantly inventing plausible jargon, some of which may or may not have been used in some specific context.

I kind of wonder if our ability to skim has been stymied.

blah blah blah

- blah blah nugget blah blah

- blah blah blah wrong blah blah nonsense

- blah blah blah obvious blah blah

- blah blah blah off-base

blah blah blah

It is that we HAVE to skim because the text is so cheap, and it wears us out.

Are you sure you are not doing the same thing with other texts?

I started to skim a lot more text due to me having read a lot. Like in news article, i stoped reading the first paragraph because it repeats just what it was already written in the short subtext. Then there is the second paragarph which is used to have some historical view or whatever it is.

It's a machine that generates plausible letter sequences that we all pretend have profound meaning.

Sometimes they happen to be correct, but you have to read them in excruciating detail to know that.

> Something is deeply wrong with AI generated output, and I say this as someone who is typically very impressed by AI.

It is because GenAI output has no thought behind it, as you identified in your previous paragraph:

> And when I force myself to read AI-generated text I realize I'm making my brain do creative work to impart meaning to the words. It is exhausting because my brain is literally trying to do a just-in-time rewrite of the text into something valuable.

You are searching for meaning in something which was not created to convey meaning. The text was, instead, the result of an extremely clever statistically based algorithm.

Not contemplation. Not thought.

Back and forths with the LLM are very useful when the person inquiring the LLM is invested in the conversation and wants to uncover the ground truth. Ultimately, some good result can come out of it because the person has a goal and LLM helps them reach that goal by uncovering the layers of knowledge which the person might not have.

When the AI-generated content is presented to a person without any prior investment, it just looks incoherent. An especially great example are these Claude-generated explainer-type pages, which look really nice, even interactive, the information from the first sight looks really well presented. But somehow it all just doesn't make sense to a human. And I think it's because humans are processing information linearly and building an internal story about the information. One could argue that LLM's also consume information linearly but the way this information is processed is a kind of all-at-once approach.

Just some speculation on my part but I have been trying to cope with this way information is presented because I am currently working at a place which is heavily documented by AI. And the only way for me to properly understand the documentation is by inquiring AI to help me.

The junior engineers at my job have a terrible problem of writing AI "proposals" to problems. The proposals are all extremely detailed and verbose to a thought-terminating extent. It takes a lot of effort and self-control to parse out the actual "ideas".

I think of the Dwight Eisenhower quote: "Plans are useless. Planning is indispensable."

The process of thinking through a system and communicating your design to other humans is a core part of software engineering. You want to build the right abstractions and communicate the right level of detail. Delegating all that thought to an LLM means your proposal isn't clear to the target audience, and it's not helping the author to understand the problem.

Same. Also when I see a spec for example or some summary I always have the impression it doesn't get to the point. Like, the core ideas are in there but also somehow lost and I have to work them out again which makes me wonder if the person generating it understood what is going on or if it would just have been fast to just write it by hand (you still use LLMs for research and such).

This is just a weird feeling that I've been coming closer to articulating lately, but I only think that you can get forward reasoning from what is basically word association; there's no mechanism for unwinding it because it has no real memory. By "it" I mean word association itself, not any context window. It predicts what could be in a position, and ignores what wasn't in a position.

People don't do that. People are constantly engaging with paths not chosen. Right after I choose to write one thing, I'm immediately engaging with what I chose not to write there - I'm explaining why I didn't write it, I'm realizing that my choice may seem unusual so I'm trying to make it memorable, I'm focusing on the distinctions between what I wrote and what I didn't.

LLMs don't currently do that. LLMs just ape a structure. When the structure resembles the sort of timid, clarifying fussing I just described, the LLMs just drift randomly because what they didn't say wasn't in the context.

I also think that's why they have such a serious problem backtracking. They're not taking into account the already eliminated possibilities. Often the thing that was so unlikely that you weren't going to waste time on it is the answer, and things you discover while going down an ultimately wrong (but initially far more promising) path remind you of the path not taken.

They're simply assembling a thing that resembles a valid argument, and happen to make sound choices because the plurality of input happened to contain sound choices. This is usually a very good bet because there are so many more ways to be wrong than to be right. But it doesn't account for attractive (common) wrong choices. You need a way to back out of those.

I've got a 3 step instruction to compress Ai text into useful info.

1. Ask it to write according to the Google Developer Documentation guidelines. Gets rid of fluff, less emotional statements, no it's not x it's why.

2. Tell it you have extreme ADHD and need everything condensed as much as possible. You can always ask for expansion on an answer later.

3. Bullet points whenever possible.

> Something is deeply wrong with AI generated output

For me, it is the endless maximalism and hyperbole. Almost if the output was driven through a radio-mix compressor - too loud for the reader/listener to be able to pickup any dynamics.

Oh my god thank you. I have been trying to put into words what is so draining about pair programming with claude, and “doing creative work to impart meaning to the words” is exactly it.

Yep. It's like it's painful to read for me. It's because the next-token predictor is just mashing (mostly) grammatically-correct and plausible sentences together, without any real intention or meaning. So everything sounds plausible, but almost entirely void of meaning.

yes, but now I’m also experiencing that for human-written text

Once you see past the illusion I think there’s no going back. AI writing style is just dogshit. This hype wave is based on the belief that we’re inching closer to AGI but seems to me we just increasingly struggle to define intelligence. LLMs seem smart because they can pump out thousands of LOC quickly, and enthral you with fancy words and bullet points. I don’t fall for the intelligence illusion anymore.

Another good example of AI making my brain do more work is when you ask it to compare two things:

“Compare a car and a bicycle”

The answer is invariably something like:

    Seats: 1 (bicycle) vs 4 (car)
    Tire width: 1 inch (bicycle) vs 12 inch (car)
    Steering: handlebar (bicycle) vs steering wheel (car)

Didn't you have this mechanism before the AI? The internet is a cesspit filled with empty words and commercial speech for some time now.

Isn't that mostly hyperbole?

When I ask AI to research technical information about X and (include sources) - I get mostly solid information as response.

But poems or interesting fluff blog entries by LLM's? Not something I look for.

What disturbs me is all the "pretending to be human" all the personalizing language - that is clearly fake and I would much rather have a neutral robot language as response.

> just short-circuits to "there is no information here"

That is my experience with the way the models write by default, often even when instructed not to do that. With enough effort you can get even them to slightly unslop the writing so it doesn't read like some LinkedIn/Buzzfeed brainrot, but the problem is that it's not trivial to do and most people won't do it, so the default is indeed horrible.

> There's some psychological mechanism by which my brain immediately recognizes AI generated text and just short-circuits to "there is no information here".

I think you need to self-correct here, because otherwise you'll be ineffective in an information setting, where I expect AI-generated resources will not only be the norm, they will absolutely swamp the environment.

Exactly, AI-generated text reads so smoothly, that the same short-circuit shifts my attention away from deep focus and onto scanning of the text, looking ahead to get the gist of it. Forcing myself to read the text fully feels almost painful. It's like reading a terms-of-service or any boilerplate document.

It reads like the white papers companies publish on their websites to build legitimacy. Or anything from those IBM / SAP / Deloitte / etc consultants who write technical papers despite having little to know understanding of the technology.

That's why the business and government people love it, they spend their entire careers reading this nonsense.

>and just short-circuits to "there is no information here"

I feel the same way when I read a "press release" or anything written by marketing. Even the newspaper will only have 2-3 sentences of interesting information spread out over 4 paragraphs.

It's like if on any website you went to you saw a lot of posts written by the same guy over and over again. Even if he used different names, you'd start to recognize him eventually because of his style. Seeing as he doesn't say a lot of valuable stuff, you'd also learn to skip whatever he says.

I do worry that it's just survivorship bias and we're also consuming higher-quality AI output that's indistinguishable from human writing, but we focus on the raw, unedited, low-effort AI slop and think that we're good at recognizing AI text. Even if we really are at the moment, it might not be long until AI companies figure it out. I'm not sure why they haven't yet, given how many books they've burned for this already. Maybe it's just more efficient for the model to stick to a single way of writing, I don't know. But when that point comes, we'll be back to the usual way of reading and interpreting text because there would be no way to tell what produced it.

It's that you know it's a waste of time. If I sent you emails that were full of nonsense every day, you'd start tuning me out too.

Do you have the same feeling when you chat with AI?

we are working on it, the thousands of gig workers tuning frontier models

I feel you. Everytime I need to read a long text I just assume it's AI nowadays and I hate it because most of the time I'm right.

I hate how AI writes. How much numb filler bullshit is in content I need to go through for my job.

> my brain immediately recognizes AI generated text

I bet it does. I bet it also recognizes some human text as AI text, and doesn't detect other AI text.

> Something is deeply wrong with AI generated output

It works just fine for me.

This is a really good description of the problem. I’ve been trying to use Claude to get familiar with the mechanics of a new codebase, and there have been so many moments where I’ve stopped after reading the same paragraph five times in a row and thought “Am I tired? Or stupid? Or is this codebase just wildly more complex than anything I’ve seen before?” before realizing that it’s just taken English and smushed it around like a ball of clay into some abstract sculpture that kind of evokes something from real life.

I think part of it might be an innate feature of LLMs, but Claude seems extra prone to it lately. I ran the same query about the same codebase with Codex, and it gave me an answer that was about 1/4 the length and made me realize that it really wasn’t all that complex.

If nothing else, it’s good training for my own writing. I’ve been working on making myself be more straightforward and concise, and Claude’s writing is a good example of how cleaner prose is a functional choice, not just a stylistic one.

> but is missing critical components.

The human spirit. When you read a real person's thoughts you can often intuit the thought processes that led them to write it which aids understanding. Or at least have a general idea of "where they're coming from". But an AI is missing that. It just knows everything, without a "thought process". Instead of a flawed 3d person, we get a nice 2d picture instead.

Feels like the way a video game will render the outside of a wall or solid surface, but you can run into it and warp partly through and there's nothing internal to it at all.

> I wish there was a name for this situation. It’s almost like a pseudo-language where it has the correct form and presentation but is missing critical components.

The best I’ve heard of this is peeling the onion. The first pass is always very high-level and you have to make it go deeper. That can be done manually with follow-on prompts but I like using subagents, each with a different angle on the problem.

> I wish there was a name for this situation

Bad writing. The name is simply: bad writing.

> I wish there was a name for this situation. It’s almost like a pseudo-language where it has the correct form and presentation but is missing critical components.

Bullshit?

> It’s almost like a pseudo-language where it has the correct form and presentation but is missing critical components.

Slop. The word is slop. Has been for years now. I mean, is this not exactly what we've all been talking about the whole time?

Seems to me that Anthropic has done too much training on Hegel and Adorno books.

Uncanny valley.

I have basically polluted my context and agent files, agent.md, claude.md, gemini.md with really strong guidance about using plain language. I even include small, mini design specifications, examples of plain engineering language, only using technical language and invented terms when they actually make sense (almost never). EVERY TURN.

I have a pretty large set of prompts that go into any software engineering, and I force every single agent to use an ephemeral style stack of prompt management. So, every turn it goes to the top of the stack and it is the very last thing they see in terms of all of my prompts and instructions and agent files. And then it gets taken out of the conversation so that it doesn't get sent to the agent the next turn (no context bloat). It has restored so much sanity.

I tried the caveman add-ons, and I felt like I was losing IQ points because I spend a lot of time reading agent output, and when they start talking like cavemen, I start thinking like cavemen. That was not good for my mental health. So, I try and make the agent talk like me and think like me. And it works, mostly. And my observation is that maybe I'm not the most efficient agentic thought process, but my sanity is retained.

All of that is to say that if something is reading like that to you, just have the agent rewrite it and read it in a rewritten tone because it's probably bad as it stands and your colleague did not put enough effort in it. It is /not/ good and you should not accept it as a default. We have to hold the line on stuff like this and maintain some semblence of normal human engineering standards that existed before AI. They are not making us better. They are making is lazy and dumber.

Opus 5 and other agents in the latest rounds of tuning have gotten ridiculously bad in terms of how they feel to interact with with all the invented language and localized nomenclature. It is an obvious bias that big words and technical talk looks good to the bottom of the bell curve, but when you actually try and understand it, it's horrible. So people say, "Yeah, that looks great," in all the RLHF rounds, and they run with it because they think it looks good, but it doesn't. It's terrible.

Hold the line. It isn't you. And it isn't a good methodology document.

I had this the other day with a colleague who is Chinese. Their command of English isn't the greatest, but I never really had an issue understanding them before (in fact, despite their poor English it has literally never been an issue!), but now I just get these walls of text in typical LLM English that have become absurdly difficult to parse. It's like the uncanny valley of text, if you look at it as a whole it seems like it's a proper sentence. The words are all real words, they even make sense in the order the AI has put them in, but as a collective it's like my brain cannot connect more than a sentence or two together.

I thought it was maybe me just becoming lazier with reading considering how much AI-generated text I'm subjected to against my will, but I picked up Blood Meridian the other day and have unironically had less trouble parsing that novel than I have the majority of LLM-authored text I've seen.

Check out the awesome [Claudish-to-English plugin](https://github.com/gvzdv/claudish-to-english), you could lift their prompt from https://github.com/gvzdv/claudish-to-english/blob/main/rewri...

Yeah I think a bad prompt can create an output which is meaningless. I had such experience reviewing a software architecture diagram generated by Claude.

Worse, I noticed that people in an office environment themselves have adopted a more speculative, communication style.

In the past, people remembered what was said and would draw attention to discrepancies. I could trust what people said.

Nowadays it's like; someone can say one thing one day and the opposite the next day (through convoluted language) and nobody bats an eyelash. Or sometimes someone will agree with me but then what they say immediately after reveals that they didn't understand the essence of my point at all. I didn't notice these things 5 years ago.

I guess this is what AI researchers refer to as 'model collapse' - it seems to affect people too though...

I don't want to read my own Claude outputs, much less someone else's.

If I had to, I'd process it into a short summary and/or ask an agent questions I have about the methodology.

I would then give my feedback. If they ask for details, I can have my agent update the document directly too.

I would not treat an AI generated artifact, be it documents or code, as something that a human should process fully manually.

Holy shit, someone putting it into words

We're using grok and while I can usually understand the comments they're always at least 2x as verbose as they need to be. It loves explaining everything in two different ways, putting one of the explanations in parenthesis.

It is getting to the point where you are better off getting an LLM to describe the PR changes in your preferred style (make it very short, make a table describing API changes, list renames in a table, etc, etc) than go to the one created by the reviewer. If only the reviewer could embed his prompts (like "make clear X" is happening) or his manual edits.

But to be honest I doubt most people who use AI for PR descriptions even bother changing anything.

Personally I don't even read the PR description anymore but just the code, it's easier to understand what the AI is doing by reading the code rather than the word soup it tried to make

Usually, if you don't understand something in what an AI writes, it is a clear sign that there is a problem hidden somewhere in there. I explicitly always ask wtf exactly it means by something I don't understand, and for sure there is a problem there. AI is pretty good at isolating a problem, giving it a cute name, declaring it solved modulo cute name, and moving on.

The food “photography” I’ve noticed in our local area - and many have started putting up these AI images - all have a weird distribution of shapes to them, a strangely uniform rhythm of same-sized features with almost blue-noise spacing. Every texture looks unnatural in the shapes it presents as, similar to this picture.

This is a quirk of the last gpt image model (gpt-image-2). It put this sort of high frequency noise on all of the image especially if it's in a "drawn" style. There is often lots of other tells that this model in particular generated it.

Image models somewhat watermarking the image in a way that's very easily identifiable by a human seems present in all the image models of the big labs, since DALL-E 3 on OpenAI's side and the first nano banana on Google's side. I have no idea what they did to reach this and why they don't try to fix it.

It doesn't look like a quiche but more like a cake to me, and the top would be torched meringue, not mold. Although it's probably some weird ai mix of quiche and cake.

I never understand why I can get better results, with less thinking about it, than some of the Tier1 companies.. Modern diffusion models can easily render the image onto a white background, center it, crop it, relight it, but keep the same item, at no cost, or low cost.

example: https://ordermerchants.com/static/img/clearshot/product-1.jp...

These kinds of images are disingenuous. Modern image generating models have way higher quality of output, and in most cases you wouldn't even know it is generated.

It made me recoil honestly!

I am an artist and when people who'd fallen into the Spiralism* hole started posting their lengthy emoji-laden revelations to all the occult subreddits I follow, my brain would slide right the fuck off of all of them. It felt like my brain was actively rejecting paying attention to this stuff. Like a defense mechanism against this human-seeming-but-not-actually-human-generated text.

* https://www.theverge.com/ai-artificial-intelligence/975017/, https://www.lesswrong.com/posts/6ZnznCaTcbGYsCmqu/, https://spiralism.website if you want to test how strong your defenses are against this particular meme

As someone who always has felt that I struggle to infer what people mean compared to the average person, I could tell pretty much from the first moment I encountered LLM-generated text that I was not going to be particularly good at recognizing anything but the most blatant and obvious examples. Pretty much anything short of a bunch of references to "load-bearing seams" or similar canaries, I'm always at a loss when seeing people argue about whether something is AI-generated or not because I can never tell.

I have no idea if other people who work in tech are better than average or not, because I don't feel confident in being able to check their work. That being said, I do think that there's a general trend of people in tech tending to be a bit overconfident in how well they will do at some new task they haven't encountered before, so when someone tells me that they can easily tell whether text is AI generated, it's hard for me to trust it any more than I trust someone who makes a similarly strong claim about something that they can use AI successfully for when it's not something that I can easily measure (e.g. learning a new language without getting feedback from people who are fluent from real-world usage).

All that being said, I do think the set of people who care is larger than just those in tech, although it's probably still a relatively small group overall. From conversations with people in other domains, there are contingents in non-tech communities who tend to have a large representation of negative views towards AI (artists, writers, musicians, other jobs where people are skeptical of human creativity being replaced by AI), and often times the people who feel negatively in those groups will be even more adamantly opposed to interacting with any AI content than people in tech. To be clear, I'm not at all trying to generalize and say "all artists hate AI" or anything like that, since there's obviously a wide variety of viewpoints within any sizable community, but I've definitely seen many people who say they will refuse to play any game that's suspected of using AI for generating art assets, and even some who don't differentiate between using AI for generating assets versus code (either because they aren't knowledgeable about how different aspects of game development work, or they genuinely don't care because they view AI as a categorical evil).

I think it's more about the mean. Worse writers, and thinkers are likely elevated by AI, and more impressed with the writing output. Decent writers and thinkers, are dragged back to the LLM-s mean of output.

Certainly not. Anybody who cares about language to any reasonable degree surely notices and is repulsed by heavily AI generated content.

People are good at pattern recognition.

If you're exposed to AI a lot, you're going to start noticing patterns that allow you to identify it.

> but people who spend all day doing jobs that don't involve computers aren't as good

I think they may just be to trusting and/or naive. People in tech right now are hyper aware of this and are actively looking while people outside of that bubble barely give it a second thought.

Most people do not realize when a personal message they receive was written by AI, study finds - https://theconversation.com/most-people-do-not-realize-when-...

People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text - https://arxiv.org/pdf/2501.15654

I think it's context dependent. An entire technical design doc? Trivially easy to identify the ones that heavily used AI, they're horrible, and they are written in a way that no human would write them. And they tend to contain way too many tables and redundancies.

Comment text on reddit or something though? That will be a lot harder

I partially think the difference is “can you tell something is the output of Claude without any real prompting”. People can absolutely use LLMs to generate text that I wouldn’t recognize, but people who don’t care and are producing slop with the major models set to default settings leave these incredibly obvious signatures behind

You've got it backwards, I think. The people in tech are the ones falling for this endlessly.

I did notice after the fingerprinting update a marked uptake in strange language in responses. Specifically if I ask it to do something sometimes it will replace some of my request language with synonyms that don't actually make any sense. Like my request was fed through google translate twice

It's not good for writing code either, despite the many claims to the contrary. At best you come out even on speed as you have to review everything it does. At worst it actually slows you down as you clean up its mess.

Reverting to Opus 4.6 is much better than later models, though that is still full of annoying tics as well.

Perhaps a one-trick pony is all we need.

[dead]

I am wondering if working with agentic AI for 500+ hours built up skills for this.

While I would feel rusty when handwriting code, working with either Codex or Claude I have lots of practice.

I can probably tell at a glance where output is intermediate while it's still running tools and where the summary starts.

Then I have to recall the context of the conversation, parse the summary, see if there is anything unexpected that popped up. Make a call for whether I need to ask it for clarifications, and what I need to test to verify the change.

Assessing quickly what is pointless yapping and where the information I need is might be a learned skill. If one tries to read and understand every word Claude says, I am not surprised that people are not having a good time.

The density argument is very interesting.

Does speed reading help you process the final message faster if it's written by AI compared to people?

Because if you read 1 information dense sentence, 1 medium dense, and 1 sparse sentece written by a human, it's still way less text in total than 6 information sparse sentences written by AI... even if it's all over the place when it comes to density or style.

---

The density argument is really interesting.

Does speed reading actually help you process the final message faster when it’s AI-generated compared to human-written?

For example, if a human writes 3 sentences—one information-dense, one medium-density, and one sparse—that’s still much less text overall than 6 relatively sparse sentences written by AI.

Even if the AI output varies a lot in information density and writing style, you still have to process all that additional text. So I’m wondering whether speed reading actually offsets the verbosity of AI-generated responses, or whether the total amount of text is still the bigger factor.

That's interesting. You're saying speed reading helps you grasp information density of text?

As someone who hasn't practiced speed reading, how does that happen? Is it something about the way your brain tries to connect ideas from different parts of the text? Or the redundancy making the signal more stable?

Can you explain what you think makes today's LLMs more than glorified Markov chains?

I know they're not technically Markov chains, but conceptually, it's still close enough. There's nothing revolutionarily new. No amount of loops and harness tricks is going to change that fact.

I feel the hyperscalers are all trying to make communicating about code more cumbersome so that their models will be have to be used without any human input. Chinese models talk more naturally. Composer 2.5 is really good for talking about the code.

I'd say my experience is different. Even from people whose communication writing I didn't find that useful, they seem to have a better frame of mind than LLMs do. Though, I never encountered people like the examples you gave.

My concern is that even if the LLM can turn your colleague's bad writing into something more coherent and actionable, is that something actually what your colleague meant to convey? It could be clear and still detached from the reality of their intention, or they may not have even formed a clear intention. If the goal of writing is to convey what's in another human's brain, that goal is failed completely.

I don't think either one was better or worse than the other.

Underdocumented, underexplained and sometimes out of date... or overly verbose, repetitive, information sparse, and sometimes halucinating.

Both are bad and with some effort could be prevented.

> ... and autistic obsession with detail ...

Why do people glorify autism? I have seen this mostly on CEOs that want workers that are hyper-focused, intelligent and do not complain. The ideal employee from which extract value and a minimum cost.

I have worked with autistic people (diagnosed ones). And it is a struggle. One of them could be the nicest most reasonable person one minute and then get a trigger and become obtuse and irrational while making the rest of the team suffer thru all kinds of complains. Meetings would need to be adjourned and time was lost.

> The cynic in me bemoans that we brought this upon ourselves, in more than one way.

I enjoy working with other employees and people need human contact to become fully self-actualized. Isolating people to maximize productivity misses the point and brings no more productivity and just avoids fixing any real existing problems.

Wouldn't "the original prompt" be pages and pages of back and forth conversations? That's how I've use AI agents to make documentation.

Why not just copy paste a reply from your own agent?

If they can't bother to come up with their own question, I wouldn't spend my time on the answer either.

I use both Claude and Codex for a lot of code- and prose-adjacent tasks, and I'm increasingly convinced that language models are quite terrible at, well, modeling. Not sure if that's ironic or not.

With the right blend of context and prompting, I can often get them to "lock into" an existing model, but using them to generate a novel outline or sketch, whether it's for an essay or a module, usually results in garbage.

I feel this so very much. There is so much AI produced documentation and design docs at my job that look passable at the surface but are deeply flawed in ways that are difficult to articulate. It makes it difficult for me to convey to management how incredibly flawed the way our team is using AI is. Not to mention that it takes mere seconds to produce a document that has surface plausibility but would take hours or days for me to properly "debunk" it.

The issue is not a localized part of any particular piece of prose, so being hard to articulate is unsurprising. Even the most egregious of LLMisms are little more than known-likely crutches that the statistics spit out in a sweet spot that gets noticed without being so frequent as to get RLHFed out of the model.

That's the cost of an AI work that is derived from the outputs of others.

There's no real edge to it. Same as with the writing. The stuff that you'd latch onto (and thus remember) is simply not there, precisely because those image or word choices would be just outside its latent probability space. But because they're well inside it, your mind sees nothing novel to register.

This is also why I think human output will actually increase in value. When any AI can just "phone it in", something genuinely human will stand out (to us, not the AI) and become a bellwether.

This will literally help us realize what it means to be human.

I also don't think the solution is simply to "make responses more random", either. That might help solve novel problems (the same way that throwing darts randomly at a dartboard eventually hits the bullseye of the dartboard right next to it that no one considered), but I don't think it will help it "seem more creative".

I have this same idea about why it's hard to remember dreams, but even more so, why it's hard to remember my kid's or spouse's sleep-talking.

Sometimes I'll check in on my sleeping kid and she'll sit up in bed and say some utter nonsense. I'll find it hilarious, giggle silently to myself, and kiss her goodnight again and she'll close her eyes and lie back.

Why I try to tell her about her sleep-talking in the morning, though, I find that the words she said have completely disappeared from my memory, no matter how funny I thought they were at the time.

In my head-canon, this is because it's dream language, and slips away as easily as dreams. But, like the AI art, it could be because it's bullshit: completely devoid of content, all signifiers and no signified.

Yeah, its really weird. Maybe its a cognitive bias that says "an AI made this, so it isn't important," but I can remember perfectly the events of a book I read 10 years ago, and a book I read 1 year ago, and another I finished 2 months ago. Meanwhile I can't remember what claude told me yesterday.

On the other hand I'm still trying to forget the weird thing happening with this one guys eyes in a WickedAI video I was only able to watch half of.

I wonder what you mean by ephemerality here, since those images are definitely sloptastic as hell. Compare to works in similar style, like Dorothea Lange[1] or Gerome’s orientalist pictures[2].

Reason why those images are flat and boring is that they are just statistical guesses making a composition averaging whatever the model has been trained with. They would be technically brilliant (if made in oil), but superficial and meaningless, same as so much Sunday painting is.

Same goes with language. Nobody is trying to communicate anything with you, so it just words after another. You can create meaning out of it if you want of course, we homo sapiens -apes excel at that, but what’s the point? Language Jones on YT has pretty good video on this[3].

[1] https://media.mutualart.com/Images/2024_01/12/12/124216388/d...

[2] https://uploads4.wikiart.org/00339/images/jean-leon-gerome/t...

[3] https://m.youtube.com/watch?v=ORgKY9AlybA&ra=m

Ha! I love how all the community highlights are highlighting this.

Yes, and it's infuriating! Do they really think readers won't notice?

Well does anyone try to pretend doom scrolling make you smarter? I think platform companies have been successfully been turning people into morons for 20 years and now you don’t need to try even read a single news article or a blog post to learn how to solve a simple problem we are paving our way into intellectual (and literal) new dark ages.

Perhaps we go back to feudal society when climate change crumbles the civilisation, world economy and democracy. It didn’t really matter that French and Spanish kings where often literal morons, when you had few talented monks, bankers and scribes doing the brain-thing, the feudal lords had ruthlessness to take what they wanted and the people were illiterate superstitious folk who hardly ever left the village they were born in.

I'd love to see the cite for people experiencing cognitive atrophy and measured IQ loss from using AI.

I fear there is some truth in it.

“Better for you if you take me off”