As someone who is aggressively pro AI, I've also noticed how awful everything is to read lately. It's always so hard to put my finger on the exact thing, but it's a mixture of everything sounding identical and everything being a rambling unconcise mess. I find I routinely start reading interesting posts and just .. stop part way, abandoning it. A site I read stories on has been flooded with AI generated content, all of it terrible, and it just makes me retreat to things created before 2023 as a way of filtering it all out.
A model trained to minimize statistical prediction errors is going to sound ... predictable (aka boring). A (good) human author tries to keep their writing interesting, while an LLM strives for predictable instead.
It's not just predicting content but also the form/shape of the response, highly shaped by RLHF preferences, which seems to encourage longer form responses which need padding with filler to generate the "preferred" amount of verbiage.
It seems most of these models have been trained to interpret questions as wanting a long-form authoritative/comprehensive-sounding response (even when midwit filler is all it has to offer), rather than initiating a short-turn conversation which would better allow you to steer the conversation. Apparently Muse is much better in this regard, but so far I've resisted signing up for any Meta/FaceBook service, and not inclined to break this winning streak!
The part I find grating is the attempts to punch everything up. ("It's not X. It's Y"). Like, its a react badge component, it doesn't need punching up. You didn't find the smoking gun in a murder mystery, just a misplaced CSS rule.
Don't these sort of conflict with being "aggressively pro AI"?
> how awful everything is to read lately
> flooded with AI generated content, all of it terrible
> everything sounding identical and everything being a rambling unconcise mess.
I can see the benefits of AI and enjoy using the technology - but, like many have said, it's wreaking havoc on the "content" that's out there nowadays. Gotta take some bad with the good, I suppose.
I suspect it’s partially an average / regression to mean.
If you built a model only based on long form journalism and The New Yorker voice, including both the template structure of the article and the grammar and syntax, the model would replicate the patterns it is built upon. You could do the same for high vocabulary rap.
When you stuff all of English or all of human thought into it with somewhat equal weight, including very technical writing the sum is a lack of style and elegance and personality.
The second problem is that writing often doesn’t happen linearly. You write bookends and fill in, edit and revise. You sort of know where you’re going and can then make decisions how to get there. Language models may benefit from being redesigned to perform passes. Select a template of some sort, fill in the mad lib, then run a detail pass, then run a persuasion / emotion pass, then run a style pass. Current model writing very much sounds like I would expect autocomplete to.
It’s heavily RL’d and it’s gotten worse because they’re using LLM judges, that’s why it’s gotten worse. The last good writing model was GPT 4.5.
I suspect they’re leaning heavily on some kind of auto verifier, and that’s why the writing sucks, because good writing is so subjective.
And they’re probably nerfing what it can get from training as they’ve lost lawsuits for scraping copyrighted work, so they’ll purposefully avoid anything that references a specific writers style, etc.
Isn't this easy for frontier 'labs' to fix? If the user requests lots of writing and prose, then use an auxiliary model which has been trained on 'award-winning' writing instead of all the crap on the internet?
Award-winning writing has all kinds of different styles. The average of them will probably get you some boring median soup once again, though maybe a bit less bland than the LLMs trained on oceans of LinkedIn and Reddit slop.
Gentle reminder that most AI token spend is used for TPS report type writing not Cutting edge fiction writing. Creative writing, or general human-to-human communication was never really the target. It's not surprising the first majority use of this was code.
Endless headlines like, "aArON SORkIn SAYs hE WiLL Never UsE ai to WRite" doesn't change that base fact. No one asked him to, other than clickbait journalists.
If you find this hard to believe, just think about relative market sizes.
The voice is one thing, but I have encountered multiple 'tech support' teams and platforms that will respond to me in pure, unedited LLM output.
I know this because the topic is usually an obscure issue, and I have already gone back-and-forth with multiple LLMs to see where their boundary of knowledge is on it, and it will often include hallucinations along the way (e.g. pointing to old/nonexistent settings pathways)
So when the first reply from the official 'support' channel contains the exact same nonexistent settings pathways, it is easy to conclude they are sending me the LLM output without doing the verification steps that I have already done on my end. When I point out what doesn't exist, the reply is always a similar flavor of the usual LLM "you're absolutely right" response after being corrected.
I just can't seem to stay interested and read LLM text. It just seems to lack going somewhere, its information density is often really low and its full of odd isms that I don't find very interesting to read. I don't have a good radar for detecting the various LLM particular speech patterns but everytime I have an issue with an article there is always people talking about how its clearly AI written.
I find the same thing with written results out of AI "research", it is a slog to read through it and most of it can be skipped even if it can sometimes be useful its an unenjoyable read.
One of my favourite rules: perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away. This is invaluable to writing. So much text from an LLM feels superfluous, like I could've said the same wall of text with just a few sentences. Writing is tightly coupled with thinking: refining thoughts, arguments, ideas, discarding them, mulling over them. LLM text always raises this nagging question in my mind: am I reading what you think, or what your agent parroted back and you accepted?
I can imagine that one of the aspects of being social creatures is that we are drawn to the entropy/surprise factor of natural language produced by a brain that isn't our own. Deriving a shared understanding without being able to precisely model the language output of someone else is like, the fundamental substance of conversation.
Maybe that's the key ingredient that's missing from LLM-generated text. Even when there is actual content there, there's this lack of surprise / uncertainty in its delivery that we require to stay engaged. We may not know what will be communicated, but knowing exactly how it will be communicated just kills our innate interest.
This presumes an LLM has something to say. The reason the text doesnt make much sense, is because it doesnt. It isnt "on topic" because it has no topic to be on. Sentences go on, are joined irrelevantly, and so on because theyre sampled from a distribution of "possibly relevant sentences". They aren't being addressed to a reader (whose mind is being modelled), on a topic the writer wishes to express.
Communication expresses an understanding. LLMs arent communicating.
LLMs generate code well enough. Code has no natural language semantics. Programming's condition of satisfaction is that the machine executes correctly. The condition of satisfaction of communication is that the communication expresses what there is to be communicated.
In the limit where an LLM is trained to repoduce an exmplar of communication, it seems as if it does so. In that limit, LLMs can reporduce anything.
If frontier labs wanted to improve "LLMs for communication" they would pay for reasoning traces to be produced which reproduced the thought process of writers/speakers thinking about their audiences. Rarely do such traces exist in prior datasets .
I know what you mean. The latest models are _better_, but there's still something off about the prose they generate. Obvious tells are mostly gone now, but some internal spider-sense still makes me narrow my eyes.
I find that it often isn't just too low, but also too high at the same time. It has a lot of information that is irrelevant, burying far too little relevant information for the work needed to extract it. I wonder if this is a side effect of attention mechanism, where the AI's ability to track information in a document is far less hampered by other information, leading to it organizing information to be both dense per word but any specific concept to be sparsely spread throughout the writing. I also wonder if this is why the order feels off, because the AI doesn't need for the information to be well ordered to the same extent that a human does, so the order of storing information is often overlooked when storing it into a document (and even information that needs to be ordered might have the ordering information separate from how it is ordered in the document).
I think the core functionality of human cooperation boils down to getting ideas from one mind to another or many. In that, I think AI is a boon for people with limiting abilities such as learning disabilities, low language fluency, or generally poor writing skills. It evens the playing field for new and interesting ideas to spread. I can't help feeling, sometimes, this is about people who excelled at something demanding they still have their place at the top. High literacy such that you can discern LLM Voice is quite an accomplishment but blanket dismissal reeks of privilege.
That being said. Yes, I hate some LLM Voice moments too and still sometimes judge people for copy and pasting, expecting others to do the comprehension when it seems they haven't done it themselves.
Broken record: LLMs are exceptionally useful writing critics if you know how to hold them correctly, but you can't use a single word they suggest to you --- the better the LLM's word choices seem, the more likely the LLM is infecting your voice. Better, by a lot, to prompt the LLM not to suggest any words, but just offer objective critiques.
Now there's an idea. LLMs may have taken the em-dash from us, but the em-en dash is unknown to Strunk & White and might be human-exclusive for some time yet.
I wonder if over time the two will simply merge and become indistinguishable from one another.
The human writing is already changing, as the author has observed.
The LLMs will follow the change, and soon meet us in the middle!
Or maybe the humans will do the opposite and shift to Sardukar-style communication instead?
LLMs can easily match that as well.
That style I would very much prefer to all the fluff people write today and were writing even before AI.
I currently have a case open with Honda corporate customer support, and I have this suspicion that the email responses I receive are being generated with an LLM directed by a human. Either that, or I'm corresponding with a human who has poor written communication skills. The text grammatically correct, but overly verbose and repetitive. It reminds me of LLM text, but from a model that is several generations behind today's models.
Maybe it's like when you can just tell a picture is AI. You recognize the words you are reading, but to your brain, the way the words are put together feel wrong.
Incidentally, I was reading Harry Potter to my kids last night. While waiting for everyone to arrive, I started reading the previous chapter to those who were present. But I read the sentences backwards while using a vocal inflection of regular narration. My 6-year-old recognized that something was wrong and asked me to "stop reading funny". My 11-year-old recognized that I was reading backwards. The same 6-year-old thinks every silly photo that I show him is AI, while my older children can actually spot the AI.
All human communication is based on internally simulating the presumed mind that was/will-be at the other end. Even now, some part of your brain--however subconscious--is building a tiny sketch of me with assumed beliefs and motivations, and you're using it to predict and interpret the next words.
But what happens when everything is persistently, subtly wrong? When we cannot smoothly map the text to a simulation? An inner-sim keeps throwing errors and mis-predicting what comes next is unnerving and draining. Like talking to a human who is not currently sane.
Sometimes I imagine LLM text like a plastic animal model put out to confuse local wildlife, where it hits just enough instinctive triggers that they try to mate with it before leaving in frustrated bafflement.
______
Half-Life 2, Dr. Kleiner: "Never fear, Gordon, she's de-beaked and completely harmless. The worst she might do is attempt to couple with your head. Fruitlessly!"
> All human communication is based on internally simulating the presumed mind that was/will-be at the other end
Which requires a bunch of things an LLM lacks, such as theory of mind, knowing who it is talking to, and willingness/ability to engage in short turn conversation to assess and steer the conversation according to the feedback it is getting from the person it is talking to.
It's hard to define and pin point what a "voice" is. Think about how you would describe the difference in voice of Tolstoy vs Dostoevsky, or William Gibson compared to Thomas Pynchon. You might have to read a lot of a writer to be able to notice their voice. You might even notice how a short story by an unknown writer resembles the voice of another.
Or think about reading something that might be stuff you could say in your own words but not actually in your own voice. Or maybe the opposite is a better example for you? That's what some poetry is about. The gap between voice and word.
Stylometry is the study and measurement of text, so that can go some distance to quantifying the difference between voices and can help to programmatically identify a human voice but I feel that because a voice comes from a person, the recognition is also mainly a human response.
At another level its about communication, person to person with the intention and desire to communicate something. LLM text output may seem uncanny as it's hard to emulate the intention and desire and empathy present in genuine human communication.
I would be interested in reading what poets say about LLM voices. I wonder if many LLM labs employ them?
Has anyone done any work on what it is that makes LLM Voice hard? Something I've noticed, and have seen other people comment in passing is that reading LLM output is "exhausting". It's often attributed to trying to detect the machine, or filter out BS or word density or some other explanation, but it feels like something more than just that. Like - there is something about LLM output that just doesn't "fit" our minds correctly, even though words are in the right places.
Surely someone with a neuro/psych background has picked this up and developed an evidence-backed explanation.
I read a lot of old material from 1-1900AD where I can pick upon the author's 'voice'. There are subtle clues, like if they had a regional accent, where they use a specific word or type of cadence.
AI does the same with responses where the cadence is some type of formula that becomes identifiable.
Each of the big models has their own but an example of the cadence is
A) Agreement or Acknowledgement with the User
B) Specific detail gets a paragraph of detail related to the previous response
C) Overview of the conversation's current state
D) Prompting or Direction for this conversation to go
E) Landing on a beat. Wrap up the vibe. Recap with a zinger.
I cannot unsee it and a few of the models are annoying. I have attempted to build out the Agents and context to avoid the 'last beat one liner' without success because after 2-3 more turns, I get "That challenge was accepted and you stepped up. You're the winner this round".
I started writing a novel recently - in french. I have a clear view of each scene by this I mean not exactly what happens but the general motion, its objective and where it should land. But only when I start writing does it come to life and so many things I did not anticipate pop up and give depth to the characters and sense to the overall plot. Never would the AI give me that, however good the result may be
I never use LLM to write anything, but unfortunately now whenever I am writing something, I am worried, am I sounding like an LLM? Using certain words, makes me worry, it will make my writing like an LLM, so I have started to avoid some words in my writing.
I’ve started embracing my grammar flaws and typos. Emails and chats may get a proof, but only from me or another human, then I send it. I’ve deleted Grammarly because I think the flaws make the comms more authentic in the age of AI.
And of course I’ve dropped my use of the em-dash after being accused of being AI myself.
I wrote down a few of my thoughts a few weeks ago at https://notes.pault.ag/why-write/ -- I find myself scanning text to see if it's LLM before I actually engage with the content.
It is getting harder to do by the day, and has slowly moved from a mental exercise (look for specific markers) into a "gut feel" based on how the pieces fit together.
> As more people delegate writing to LLMs they will also delegate thinking and comprehension to LLMs. I don't want to do that, where's the fun of writing then?
I enjoy cooking from scratch, and I really don't like frozen foods, microwave/TV dinners, or even DoorDashing
but we're past complaining/arguing/shaming folks who don't cook every meal from scratch
you know what, I don't prefer eating it, but if my next door neighbor loves microwaving frozen $3 burritos, then go ahead
to some people, food is art, creativity, life
to others, food is fuel
Something like 90% of food in all US restaurants is from Sysco, US Foods, or similar.
Some of them purchase ingredients from those companies, and then make the food using those ingredients.
A great deal of restaurants, though, simply purchase pre-made frozen foods from those distributors, re-heat, and sell it to you at a premium without disclosing while you think it was freshly made. Mac & cheese, stuffed peppers, lasagna, burritos, most 'bar snacks' you can think of, etc.
The analogy here is that a lot of consumers recognize those restaurants that are simply reselling pre-made food without much value-add, and choose not to eat there.
I think the idea is if you are going to share LLM writing then why not just share the prompt which is shorter and has the message you want to convey anyway.
That is not to say that I don't think there is a place for LLM assisted writing. Writing is an exercise for the author and translates to oral argumentation so I don't think that fades away. But LLMs could do some "audience normalization". What if I could make a blogpost that is dynamic based on experience of the reader? There are lots of opportunities, but versus other fields where optimizing for laziness can be beneficial I don't think lazy writing currently has much value add.
This analogy looks quite good. Heavily processed food is not heathy for your body and heavy use of AI and other dopamine shortcuts like social media are not healthy for your brain. You have to approach both with causion.
To stick with that analogy, you also wouldn't serve a tv dinner to a guest unless you were somehow incapacitated. You certainly wouldn't do it if you were running a restaurant.
Add to that the fact that AI is significantly worse for you than TV dinners, and has an outsized harm to humanity, and the conclusion is plain:
Largely speaking eating is an activity that only involves yourself. Whereas, writing and thinking are largely activities to communicate ideas between yourself and others. Basically it matters more how this is done.
But isn't one problem here that it's not just personal consumption? People bemoan the proliferation of food-service (Sysco et al) "slop" at restaurants. It seems like LLM writing for others is serving that $3 burrito for $15 without even trying to doctor it up. If you're not willing to put in the effort to think about and produce your writing it's probably best to keep it for personal consumption.
There’s also an entire cohort who didn’t have the time to write that now do. They have some incredible, unrelated skill and can’t afford to hire a writer but need some writing done.
When I come across their slop I need to remind myself to let it go, else I’ll miss out enjoying the food from that Abuela who’s been cooking longer than I’ve been alive
I’m fine with my neighbour eating TV dinners, but if they invite me for a meal I expect to not be served a TV dinner, or to at a minimum be informed beforehand that that’s what going to be served.
Same thing with LLM-assisted writing. It’s one thing to do it for yourself, quite another to share it with other folks without warning.
I agree. I honestly think convenient food is perfectly fine for people most of the time. Recently I have been playing with "eternal stews" which I guess are logically the opposite of fast foods, but at the same time are focused on availability and convenience over intentionality and mastery (at least the way I make them).
Most food is literally just fuel. I think that is fine. And at some level, eating 3$ burritos for a quick lunch on the daily really makes you appreciate the depths of a home made risotto even more. :]
Even more importantly: it's not an either/or situation.
You can love to cook and enjoy every second of it, but sometimes you just need fuel and that $3 microwave burrito is sufficient. Just the same, 99% of your means could be frozen TV dinners, but that doesn't mean cooking a romantic meal for Valentine's Day is off the table.
There's a weird level of fear mongering occurring around AI that it will make you incapable of independent thought if you use it. People will throw around very light research that kind of points in that direction, but they'll never highlight any of the nuances in such an argument. You can use AI and still "think" if you choose to, just like how you can avoid AI and still opt out of "thinking" if you choose to. It has less to do with AI and more to do with the context of the individual using AI.
I think what Martin is hitting on in the article is though is that as more and more of the world’s communication is generated by LLMs we’re naturally going to adopt LLM-Voice into our own communication style. It is just as much about consumption, which is less in your control.
> I'm wary of them, partly due skeptism about their accuracy, but also because it rather misses the point.
Here, this reader wondered whether "to" was omitted intentionally to emphasize lack of LLM involvement. Then, on a second read through to find the sentence again, I noticed the first paragraph is also missing a period on its final sentence. Which just goes to show: we can all use editors and proof readers. (According to the acknowledgements section, nine humans read drafts.) Now, I don't want to intentionally leave typos in my writing, but for me it's preferable to LLM-voice no doubt.
> But I also would prefer rather a genuine human's clunky prose to the smooth but soulless LLM.
Amen. This reminds me of a bit from Kitchen Confidential about how Anthony Bourdain would prefer a simple and plain home cooked meal over an ornate restaurant meal any day of the week. I read because I want to hear what a human has to say. It's the errors and malapropisms and style that give a unique and human voice. Doesn't this contradict my earlier statement about not leaving errors in my own writing? It turns out I'm perfectly tolerant of errors in anyone's writing but my own. (I should probably bring that up with a therapist.)
There's a fine line between using an llm to write a personal blog vs using it to help you come up with an account plan on why you want to sell to a company that will be used internally.
> 3: Michael Nygard commented: “There you really aren't trying to communicate thoughts from one person to other people. You're communicating with a process rather than a human. A human may open the envelope but that person is acting as a component in a cybernetic organism.”
But of course: umanufactuting itself creates " productive mechanism whose parts are human beings, as Adam Ferguson’s describes the industrial workshop: "an engine, the parts of which are men".
i noticed something similar yet different. because, sadly, most of what i read today is llm output (the chain of thoughts, or the summary at the end of a big chunk or work), i tend to "adopt" this style in my writing, hence, writing as an llm without using one. humans are an amazing creatures at copying stuff and adapting to our environment
A billion humans use AI daily, they write about 0.5T tok and read about 2T tok (estimated). In a week they amount to more than the whole training set of GPT-4. Human language is being shaped back by AI language.
Thank you for posting this.
I don't have a lot to add on the subject, but I found it interesting and thought-provoking given the age we live in.
I find it really hard to tell real human and LLM text apart, even though some people claim they can, calling it "AI slop".
> In time it may be that LLMs will be so good that we can't tell the difference.
I have to believe that we are within a couple of years if not months of this. This problem is too well-known to not be a major focus of the frontier labs' efforts.
My own feeling is that this signal will perhaps never fully go away. Not in months; most likely not in a couple of years.
Not least because it’s getting worse rather than better. But also because as AI gets better at writing like humans, humans will change how they communicate to avoid being confused with it.
Which will get a lot easier once human writing is nearly extinct. Now that could happen in two years.
Yeah, I swear AI writing is worse than it was. Part of my big model release tests has always been asking it to write a few chapters in different styles, and I swear they have slowly gone from really bad, to boring and plain (I remember Gemini 3 being peak of this), to now over-complicated and quite bad. My guess is that they were initially being trained on writing quality but that has taken a backseat since it's not really a profit generator.
I think the signals also show up in different ways to different people.
For example, I don't dislike the way Gemma 4 writes so much.
But I am British and Deepmind's output somehow seems more compatible with the way British people communicate (it's a very multinational team, I suspect that is an influence).
Other people pick up on Gemma's AI writing tells a little more readily than I do. I have a greater tolerance for Gemma (and Muse Glimmer) than ChatGPT or Claude (or Qwen).
They're still heavily RL'd on response formatting/clarity, and against common LLM-isms. Long form prose quality is expensive to RL so I expect this to languish for a while until other low hanging fruit has been picked.
> But if I want to communicate with humans, my humanity has to shine (or at least ooze) out of my prose.
Yeah. I want to be able to touch a human being (or at least have one touch me) through the words. LLMs poison that, even if a human prompted the LLM.
Translation... I think I object to translation less than Martin does. There's a human back there, and they're writing all the words, and then an LLM is translating them. The LLM may not translate them perfectly, but they aren't going to introduce "load-bearing" when it wasn't there.
The bit about using LLM when you're writing for a bureaucratic process rather than a human was interesting, at least to me. I hadn't thought of it that way before, but I think it makes sense.
People have been talking about the importance of curation for more than a decade.
Because many good ideas are not immediately obvious good ideas, in an environment at risk of information overload, we need some way to quickly vet ideas to see whether they are worth investing the time to consider them. (It is often the case that the best ideas require considerable investment of time and effort to be judged as good, and, it's also often the case that this determination can be made only at the very end of such a process.)
“Don't judge a book by its cover,” sure, but, historically, there have been superficial markers of care and attention that we've used to these ends. These are obviously very flawed signals, but, you know, maybe if you took a lot of time to craft a message, I don't feel that bad if I waste a bunch of time trying to decode it. These markers—attention to detail (e.g., spell and grammar checking,) clear-structure (e.g., “bottom line up front,”) and typographical flourishes (e.g., smart quotes and em-dashes and extended unicode like diæreses…)—were an indication of some modicum of quality control and care. I, myself, used to brag about or show-off having set up .Xcompose keybindings (https://wiki.archlinux.org/title/Xorg/Keyboard_configuration...) which was just rare enough across the general population to be noteworthy.
All of this that was once somewhat expensive is now cheap or basically free.
Even if I make a point of going back to a pre-UTF-8 Usenet style (or even intentionally insert run-on sentences or misspellings or grammatical errors,) the cost of an LLM to style-transfer its output to match this is barely much of anything. All it means is I wrote one prompt for the first draft, and then one more prompt for the refinement.
Additionally, some LLM habits are actually good habits. Putting the bottom-line up front (in bold, even!) is a great habit in a business e-mail! Decision-makers genuinely struggle with reading through long paragraphs of detail!
Unfortunately, the problem with the LLM output is that it takes good, valuable, superficial or structural habits that it can cheaply apply, and it drapes those around inane, banal, cloying, muddy, meandering meaninglessness.
Today, we can maybe still judge a book by its cover, because even one more prompt worth of refinement is still too much effort, but I don't think that's amounts to much, since the frequency and loudness of complaints of LLM writing means that model trainers are going to train their models to adapt to whatever signals we use for quick vetting. I am skeptical whether this training will concurrently improve the novelty, innovativeness, or other intangible qualities of the actual message. (With the models, as they exist today, and as we use them today, at least in the field of software development, I see extremely poor capability at generating innovation… Personally, I don't even like the quality of the Python code they spit out, but they sure do type real fast, and, like, pejoratives aside, unexciting, mediocre, corporate-y code does have a real place in the world.)
So what happens when the economics of production in the medium significantly outpace the capabilities of production in the message? The LLM can write a comment nearly indistinguishable from any given human… except when you actually take the time to read through it, it's just inane drivel. Thoughtless and a waste-of-time. And there's basically no way to manage your investment of time and effort to tell one way or the other.
You know, maybe it was never the case that superficial markers of effort ever meant anything at all. Maybe there never was a way to tell from the envelop whether the letter is worth reading… and it was confirmation bias all along. Maybe there really are no shortcuts to judge whether an idea has any merit…
Because, for sure, some of the real bangers of comments I've posted on HN and similar forums—all my XCompose-configuration aside—have gotten relatively few upvotes!
personally, i'm for LLM and AI usage in writing if it helps the writer communicate ideas, their stories, and triumphs in ways they may never have been able to before. I just had one of my posts removed from the front page here which was disheartening because it feels as though what was judged was prose rather than the technical insight in to a niche problem, which seems counterintuitive considering i've always thought of Hacker News being an inclusive place for the technical and engineering skills rather than their level of the english language.
> I just had one of my posts removed from the front page here which was disheartening because it feels as though what was judged was prose rather than the technical insight in to a niche problem, which seems counterintuitive considering i've always thought of Hacker News being an inclusive place for the technical and engineering skills rather than their level of the english language.
The problem with LLM voice is that it sends the signal that it's a likely waste of effort to try to figure out if there's any "technical insight" there, because more often than not, there isn't.
Maybe it's not fair that you're paying the price for others using LLMs to abuse their readers, but it's the reality that you have to deal with. It's also not fair to expect readers to wade through all that abusive LLM prose in order to find your (assumed) non-abusive post.
Once LLM writing becomes indistinguishable from human writing, it will be really sad. There will just be a collapse of human engagement, and you might as well just talk to your machine because then at least you know what you're getting and aren't being deceived.
in some ways I agree, however I think that speaks more about the reader themselves. Just like when communicating with someone who's first language isn't english, I don't disregard the meaning and intent behind their words simply because the way they communicate isn't formally correct. Regarding my post that was removed, it was lovely to see the comments of people that had enjoyed the technical write up. Me personally, i'll always value an attempt at engaging and communicating than none at all.
Good lord, someone not being good at english does NOT mean that they put not effort or care into their attempted output. It is RACIST to make that association and thats why its an obvious faux pas.
An LLM prose article ABSOLUTELY can mean that. To attempt to conflate the two things is maliciously misleading.
This is an interesting corollary to a thing I'm seeing where "devs who thought their job was writing code" are in like existential danger.
Technical skills aren't worth shit and never were, except as far as they enable you to engage with other people in ways you and they find valuable. This used to be really far! It made it easy to believe that the technical skills on their own were what was valuable.
But the engagement was always the valuable thing, generating technical insight was a necessary prerequisite for it. Now anyone with a $20 claude sub can generate technical insight into niche problems, so what's left is everything else. Everything of value.
> This is an interesting corollary to a thing I'm seeing where "devs who thought their job was writing code" are in like existential danger.
I think you're misunderstanding that anxiety. I think this is what's going on:
1. For a lot of people, using technical skills like coding is the part of the job they enjoyed.
2. Closely related to the above point: for a lot of people, technical skills was the part they were good at.
> Technical skills aren't worth shit and never were, except as far as they enable you to engage with other people in ways you and they find valuable. This used to be really far! It made it easy to believe that the technical skills on their own were what was valuable.
> But the engagement was always the valuable thing, generating technical insight was a necessary prerequisite for it. Now anyone with a $20 claude sub can generate technical insight into niche problems, so what's left is everything else. Everything of value.
3. More broadly: if "they" don't find the "technical skills" you provided valuable anymore, why do they need to keep you around? Why not cut your 10-person team down to 3 people (the manager, architect, and technical lead) and fire you and the other 6? Not everyone can re-invent themselves as a product person, even if they had the skills. It's a game of musical chairs.
I find a lot of LLM writing exhausting to read. It is like I have to exert extra energy to attenuate my BS detector to cut through what is actually being said, and why.
AI doesn't help the writer communicate ideas. It helps them obfuscate those ideas behind bloated language where even the writer can't discern if it's good or not.
It's the modern equivalent of going to a thesaurus and replacing all your natural words with five-dollar ones.
If you're not confident in your English then just write your brilliant ideas in broken English. Or at most do a direct translation. But don't fluff it with AI's nonsense.
Imagine you cooked something great, a deliciously tender cake or something. I ask you if I can try some, you say sure, cut off a little piece for me, then you drop it and tell I can have it on the floor. That is what AI is doing for writing. Why should I care how the cake would taste, if I don't like eating on the floor?
i don't really agree with that view, but i respect your opinion. In my view, i see it more like someone who's never baked a cake before, offering you a slice, and whether or not it's good may be up to you to decide.
Assuming the author had to input good points for the LLM to expand it into a full length post, just give me the points. Give both formats if one must. Translate, spellcheck, fix mistakes with an LLM. Don’t inflate 3 bullet points to a 10 minute read.
I can’t distinguish between well meaning slop and blogspam slop. I might be able to if I’m well informed on the topic and can point out inaccuracies. Otherwise I just don’t know what is accurate and what is filler or outright wrong. LLM-isms are very distracting and exhausting to sort through. It just doesn’t make for a good reading experience.
Yes, this. I mean, if you're writing a novel meant to be read, maybe not. But if you're just trying to communicate 3 ideas, write them out in a way I can quickly tell if it's worth reading more.
There are few things (relatively speaking, I mean online activities, etc) more annoying than seeing a click-baity headline, and then having to read 17 paragraphs to actually get to the point that I realize I already knew and understood it, and it's just slop padding.
I'll repost a comment I made a while back describing the process I use, which I think balances AI in keeping your voice.
If you care about your voice, don't let LLMs write your words. But that doesn't mean you can't use AI to think, critique and draft lots of words for you. It depends on what purpose you're writing it for. If you're writing an impersonal document, like a design document, briefing, etc then who cares. In many cases (scientific papers, legal documents) you already have to write them in a voice that is not your own. Go ahead and write these in AI. But if you're trying to say something more personal then the words should be your own, AI will always try to 'smooth' out your voice, and if you care about it, you gotta write it yourself.
Now, how do you use AI effectively and still retain your voice? Here's one technique that works well: start with a voice memo, just record yourself maybe during a walk, and talk about a subject you want, free form, skip around jump sentences, just get it all out of your brain. Then open up a chat, add the recording or transcript, clearly state your intent in one sentence and ask the AI to consider your thoughts, your intent and ask clarifying questions. Like, what does the AI not understand about how your thoughts support the clearly stated intent of what you want to say. That'll produce a first draft, which will be bad. Then tell the AI all the things that don't make sense to you, that you don't like, just comment on the whole doc, get a second draft. Ask the AI if it has more questions for you, you can use live chat to make this conversation go smoother as well, when the AI is asking you questions, you can talk freely by voice. Repeat this one or two more times, and a much finer draft will take shape that is closer to what you want to say. During this drafting state, the AI will always try to smooth or average out your ideas, so it is important to keep pointing out all the ways in which it is wrong.
This process will help you with all the thinking involved being more up-front. Once you're read and critiqued several drafts, all your ideas will be much more clear. Then, sit down and write your own words from scratch, they will come much easier after all your thoughts have been exercised during the drafting process.
It's not just predicting content but also the form/shape of the response, highly shaped by RLHF preferences, which seems to encourage longer form responses which need padding with filler to generate the "preferred" amount of verbiage.
It seems most of these models have been trained to interpret questions as wanting a long-form authoritative/comprehensive-sounding response (even when midwit filler is all it has to offer), rather than initiating a short-turn conversation which would better allow you to steer the conversation. Apparently Muse is much better in this regard, but so far I've resisted signing up for any Meta/FaceBook service, and not inclined to break this winning streak!
> how awful everything is to read lately > flooded with AI generated content, all of it terrible > everything sounding identical and everything being a rambling unconcise mess.
I can see the benefits of AI and enjoy using the technology - but, like many have said, it's wreaking havoc on the "content" that's out there nowadays. Gotta take some bad with the good, I suppose.
If you built a model only based on long form journalism and The New Yorker voice, including both the template structure of the article and the grammar and syntax, the model would replicate the patterns it is built upon. You could do the same for high vocabulary rap.
When you stuff all of English or all of human thought into it with somewhat equal weight, including very technical writing the sum is a lack of style and elegance and personality.
The second problem is that writing often doesn’t happen linearly. You write bookends and fill in, edit and revise. You sort of know where you’re going and can then make decisions how to get there. Language models may benefit from being redesigned to perform passes. Select a template of some sort, fill in the mad lib, then run a detail pass, then run a persuasion / emotion pass, then run a style pass. Current model writing very much sounds like I would expect autocomplete to.
I suspect they’re leaning heavily on some kind of auto verifier, and that’s why the writing sucks, because good writing is so subjective.
And they’re probably nerfing what it can get from training as they’ve lost lawsuits for scraping copyrighted work, so they’ll purposefully avoid anything that references a specific writers style, etc.
It makes no sense on being "aggressively pro AI" without any payment. It is like being an unpaid shill.
Endless headlines like, "aArON SORkIn SAYs hE WiLL Never UsE ai to WRite" doesn't change that base fact. No one asked him to, other than clickbait journalists.
If you find this hard to believe, just think about relative market sizes.
I know this because the topic is usually an obscure issue, and I have already gone back-and-forth with multiple LLMs to see where their boundary of knowledge is on it, and it will often include hallucinations along the way (e.g. pointing to old/nonexistent settings pathways)
So when the first reply from the official 'support' channel contains the exact same nonexistent settings pathways, it is easy to conclude they are sending me the LLM output without doing the verification steps that I have already done on my end. When I point out what doesn't exist, the reply is always a similar flavor of the usual LLM "you're absolutely right" response after being corrected.
I find the same thing with written results out of AI "research", it is a slog to read through it and most of it can be skipped even if it can sometimes be useful its an unenjoyable read.
Maybe that's the key ingredient that's missing from LLM-generated text. Even when there is actual content there, there's this lack of surprise / uncertainty in its delivery that we require to stay engaged. We may not know what will be communicated, but knowing exactly how it will be communicated just kills our innate interest.
Communication expresses an understanding. LLMs arent communicating.
LLMs generate code well enough. Code has no natural language semantics. Programming's condition of satisfaction is that the machine executes correctly. The condition of satisfaction of communication is that the communication expresses what there is to be communicated.
In the limit where an LLM is trained to repoduce an exmplar of communication, it seems as if it does so. In that limit, LLMs can reporduce anything.
If frontier labs wanted to improve "LLMs for communication" they would pay for reasoning traces to be produced which reproduced the thought process of writers/speakers thinking about their audiences. Rarely do such traces exist in prior datasets .
I find that it often isn't just too low, but also too high at the same time. It has a lot of information that is irrelevant, burying far too little relevant information for the work needed to extract it. I wonder if this is a side effect of attention mechanism, where the AI's ability to track information in a document is far less hampered by other information, leading to it organizing information to be both dense per word but any specific concept to be sparsely spread throughout the writing. I also wonder if this is why the order feels off, because the AI doesn't need for the information to be well ordered to the same extent that a human does, so the order of storing information is often overlooked when storing it into a document (and even information that needs to be ordered might have the ordering information separate from how it is ordered in the document).
That being said. Yes, I hate some LLM Voice moments too and still sometimes judge people for copy and pasting, expecting others to do the comprehension when it seems they haven't done it themselves.
Duality of man.
Now there's an idea. LLMs may have taken the em-dash from us, but the em-en dash is unknown to Strunk & White and might be human-exclusive for some time yet.
-- - --
Or maybe the humans will do the opposite and shift to Sardukar-style communication instead? LLMs can easily match that as well.
That style I would very much prefer to all the fluff people write today and were writing even before AI.
Maybe it's like when you can just tell a picture is AI. You recognize the words you are reading, but to your brain, the way the words are put together feel wrong.
Incidentally, I was reading Harry Potter to my kids last night. While waiting for everyone to arrive, I started reading the previous chapter to those who were present. But I read the sentences backwards while using a vocal inflection of regular narration. My 6-year-old recognized that something was wrong and asked me to "stop reading funny". My 11-year-old recognized that I was reading backwards. The same 6-year-old thinks every silly photo that I show him is AI, while my older children can actually spot the AI.
But what happens when everything is persistently, subtly wrong? When we cannot smoothly map the text to a simulation? An inner-sim keeps throwing errors and mis-predicting what comes next is unnerving and draining. Like talking to a human who is not currently sane.
Sometimes I imagine LLM text like a plastic animal model put out to confuse local wildlife, where it hits just enough instinctive triggers that they try to mate with it before leaving in frustrated bafflement.
______
Half-Life 2, Dr. Kleiner: "Never fear, Gordon, she's de-beaked and completely harmless. The worst she might do is attempt to couple with your head. Fruitlessly!"
Which requires a bunch of things an LLM lacks, such as theory of mind, knowing who it is talking to, and willingness/ability to engage in short turn conversation to assess and steer the conversation according to the feedback it is getting from the person it is talking to.
Or think about reading something that might be stuff you could say in your own words but not actually in your own voice. Or maybe the opposite is a better example for you? That's what some poetry is about. The gap between voice and word.
Stylometry is the study and measurement of text, so that can go some distance to quantifying the difference between voices and can help to programmatically identify a human voice but I feel that because a voice comes from a person, the recognition is also mainly a human response.
At another level its about communication, person to person with the intention and desire to communicate something. LLM text output may seem uncanny as it's hard to emulate the intention and desire and empathy present in genuine human communication.
I would be interested in reading what poets say about LLM voices. I wonder if many LLM labs employ them?
Surely someone with a neuro/psych background has picked this up and developed an evidence-backed explanation.
AI does the same with responses where the cadence is some type of formula that becomes identifiable.
Each of the big models has their own but an example of the cadence is
A) Agreement or Acknowledgement with the User
B) Specific detail gets a paragraph of detail related to the previous response
C) Overview of the conversation's current state
D) Prompting or Direction for this conversation to go
E) Landing on a beat. Wrap up the vibe. Recap with a zinger.
I cannot unsee it and a few of the models are annoying. I have attempted to build out the Agents and context to avoid the 'last beat one liner' without success because after 2-3 more turns, I get "That challenge was accepted and you stepped up. You're the winner this round".
I really don't like this timeline ... sigh
And of course I’ve dropped my use of the em-dash after being accused of being AI myself.
I wrote down a few of my thoughts a few weeks ago at https://notes.pault.ag/why-write/ -- I find myself scanning text to see if it's LLM before I actually engage with the content.
It is getting harder to do by the day, and has slowly moved from a mental exercise (look for specific markers) into a "gut feel" based on how the pieces fit together.
I enjoy cooking from scratch, and I really don't like frozen foods, microwave/TV dinners, or even DoorDashing
but we're past complaining/arguing/shaming folks who don't cook every meal from scratch
you know what, I don't prefer eating it, but if my next door neighbor loves microwaving frozen $3 burritos, then go ahead
to some people, food is art, creativity, life to others, food is fuel
also applies to writing and thinking
It’s not ok to write stories with AI and post them for others to read claiming “I did that”
Get the difference?
Some of them purchase ingredients from those companies, and then make the food using those ingredients.
A great deal of restaurants, though, simply purchase pre-made frozen foods from those distributors, re-heat, and sell it to you at a premium without disclosing while you think it was freshly made. Mac & cheese, stuffed peppers, lasagna, burritos, most 'bar snacks' you can think of, etc.
That is not to say that I don't think there is a place for LLM assisted writing. Writing is an exercise for the author and translates to oral argumentation so I don't think that fades away. But LLMs could do some "audience normalization". What if I could make a blogpost that is dynamic based on experience of the reader? There are lots of opportunities, but versus other fields where optimizing for laziness can be beneficial I don't think lazy writing currently has much value add.
Add to that the fact that AI is significantly worse for you than TV dinners, and has an outsized harm to humanity, and the conclusion is plain:
AI should not be imposed on others.
It's your turn to host supper club this week and you serve frozen diners on your nice tableware.
When I come across their slop I need to remind myself to let it go, else I’ll miss out enjoying the food from that Abuela who’s been cooking longer than I’ve been alive
Same thing with LLM-assisted writing. It’s one thing to do it for yourself, quite another to share it with other folks without warning.
right. he is talking about the first group. where did you get the 'shame' part?
I like your analogy. Microwaved empty calories perfectly describes llm output. You are going to pay the price sooner or later.
Most food is literally just fuel. I think that is fine. And at some level, eating 3$ burritos for a quick lunch on the daily really makes you appreciate the depths of a home made risotto even more. :]
You can love to cook and enjoy every second of it, but sometimes you just need fuel and that $3 microwave burrito is sufficient. Just the same, 99% of your means could be frozen TV dinners, but that doesn't mean cooking a romantic meal for Valentine's Day is off the table.
There's a weird level of fear mongering occurring around AI that it will make you incapable of independent thought if you use it. People will throw around very light research that kind of points in that direction, but they'll never highlight any of the nuances in such an argument. You can use AI and still "think" if you choose to, just like how you can avoid AI and still opt out of "thinking" if you choose to. It has less to do with AI and more to do with the context of the individual using AI.
Here, this reader wondered whether "to" was omitted intentionally to emphasize lack of LLM involvement. Then, on a second read through to find the sentence again, I noticed the first paragraph is also missing a period on its final sentence. Which just goes to show: we can all use editors and proof readers. (According to the acknowledgements section, nine humans read drafts.) Now, I don't want to intentionally leave typos in my writing, but for me it's preferable to LLM-voice no doubt.
> But I also would prefer rather a genuine human's clunky prose to the smooth but soulless LLM.
Amen. This reminds me of a bit from Kitchen Confidential about how Anthony Bourdain would prefer a simple and plain home cooked meal over an ornate restaurant meal any day of the week. I read because I want to hear what a human has to say. It's the errors and malapropisms and style that give a unique and human voice. Doesn't this contradict my earlier statement about not leaving errors in my own writing? It turns out I'm perfectly tolerant of errors in anyone's writing but my own. (I should probably bring that up with a therapist.)
But of course: umanufactuting itself creates " productive mechanism whose parts are human beings, as Adam Ferguson’s describes the industrial workshop: "an engine, the parts of which are men".
I have to believe that we are within a couple of years if not months of this. This problem is too well-known to not be a major focus of the frontier labs' efforts.
Not least because it’s getting worse rather than better. But also because as AI gets better at writing like humans, humans will change how they communicate to avoid being confused with it.
Which will get a lot easier once human writing is nearly extinct. Now that could happen in two years.
For example, I don't dislike the way Gemma 4 writes so much.
But I am British and Deepmind's output somehow seems more compatible with the way British people communicate (it's a very multinational team, I suspect that is an influence).
Other people pick up on Gemma's AI writing tells a little more readily than I do. I have a greater tolerance for Gemma (and Muse Glimmer) than ChatGPT or Claude (or Qwen).
Yeah. I want to be able to touch a human being (or at least have one touch me) through the words. LLMs poison that, even if a human prompted the LLM.
Translation... I think I object to translation less than Martin does. There's a human back there, and they're writing all the words, and then an LLM is translating them. The LLM may not translate them perfectly, but they aren't going to introduce "load-bearing" when it wasn't there.
The bit about using LLM when you're writing for a bureaucratic process rather than a human was interesting, at least to me. I hadn't thought of it that way before, but I think it makes sense.
Because many good ideas are not immediately obvious good ideas, in an environment at risk of information overload, we need some way to quickly vet ideas to see whether they are worth investing the time to consider them. (It is often the case that the best ideas require considerable investment of time and effort to be judged as good, and, it's also often the case that this determination can be made only at the very end of such a process.)
“Don't judge a book by its cover,” sure, but, historically, there have been superficial markers of care and attention that we've used to these ends. These are obviously very flawed signals, but, you know, maybe if you took a lot of time to craft a message, I don't feel that bad if I waste a bunch of time trying to decode it. These markers—attention to detail (e.g., spell and grammar checking,) clear-structure (e.g., “bottom line up front,”) and typographical flourishes (e.g., smart quotes and em-dashes and extended unicode like diæreses…)—were an indication of some modicum of quality control and care. I, myself, used to brag about or show-off having set up .Xcompose keybindings (https://wiki.archlinux.org/title/Xorg/Keyboard_configuration...) which was just rare enough across the general population to be noteworthy.
All of this that was once somewhat expensive is now cheap or basically free.
Even if I make a point of going back to a pre-UTF-8 Usenet style (or even intentionally insert run-on sentences or misspellings or grammatical errors,) the cost of an LLM to style-transfer its output to match this is barely much of anything. All it means is I wrote one prompt for the first draft, and then one more prompt for the refinement.
Additionally, some LLM habits are actually good habits. Putting the bottom-line up front (in bold, even!) is a great habit in a business e-mail! Decision-makers genuinely struggle with reading through long paragraphs of detail!
Unfortunately, the problem with the LLM output is that it takes good, valuable, superficial or structural habits that it can cheaply apply, and it drapes those around inane, banal, cloying, muddy, meandering meaninglessness.
Today, we can maybe still judge a book by its cover, because even one more prompt worth of refinement is still too much effort, but I don't think that's amounts to much, since the frequency and loudness of complaints of LLM writing means that model trainers are going to train their models to adapt to whatever signals we use for quick vetting. I am skeptical whether this training will concurrently improve the novelty, innovativeness, or other intangible qualities of the actual message. (With the models, as they exist today, and as we use them today, at least in the field of software development, I see extremely poor capability at generating innovation… Personally, I don't even like the quality of the Python code they spit out, but they sure do type real fast, and, like, pejoratives aside, unexciting, mediocre, corporate-y code does have a real place in the world.)
So what happens when the economics of production in the medium significantly outpace the capabilities of production in the message? The LLM can write a comment nearly indistinguishable from any given human… except when you actually take the time to read through it, it's just inane drivel. Thoughtless and a waste-of-time. And there's basically no way to manage your investment of time and effort to tell one way or the other.
Because, for sure, some of the real bangers of comments I've posted on HN and similar forums—all my XCompose-configuration aside—have gotten relatively few upvotes!
Same thing with conference talks. The ones with half a million or a million views on YouTube… the real magical ones, I think, are the ones no one seemed to pay any attention to… e.g., https://www.youtube.com/@PyDataTV/search?query=james%20powel...
The problem with LLM voice is that it sends the signal that it's a likely waste of effort to try to figure out if there's any "technical insight" there, because more often than not, there isn't.
Maybe it's not fair that you're paying the price for others using LLMs to abuse their readers, but it's the reality that you have to deal with. It's also not fair to expect readers to wade through all that abusive LLM prose in order to find your (assumed) non-abusive post.
Once LLM writing becomes indistinguishable from human writing, it will be really sad. There will just be a collapse of human engagement, and you might as well just talk to your machine because then at least you know what you're getting and aren't being deceived.
An LLM prose article ABSOLUTELY can mean that. To attempt to conflate the two things is maliciously misleading.
Technical skills aren't worth shit and never were, except as far as they enable you to engage with other people in ways you and they find valuable. This used to be really far! It made it easy to believe that the technical skills on their own were what was valuable.
But the engagement was always the valuable thing, generating technical insight was a necessary prerequisite for it. Now anyone with a $20 claude sub can generate technical insight into niche problems, so what's left is everything else. Everything of value.
I think you're misunderstanding that anxiety. I think this is what's going on:
1. For a lot of people, using technical skills like coding is the part of the job they enjoyed.
2. Closely related to the above point: for a lot of people, technical skills was the part they were good at.
> Technical skills aren't worth shit and never were, except as far as they enable you to engage with other people in ways you and they find valuable. This used to be really far! It made it easy to believe that the technical skills on their own were what was valuable.
> But the engagement was always the valuable thing, generating technical insight was a necessary prerequisite for it. Now anyone with a $20 claude sub can generate technical insight into niche problems, so what's left is everything else. Everything of value.
3. More broadly: if "they" don't find the "technical skills" you provided valuable anymore, why do they need to keep you around? Why not cut your 10-person team down to 3 people (the manager, architect, and technical lead) and fire you and the other 6? Not everyone can re-invent themselves as a product person, even if they had the skills. It's a game of musical chairs.
It's the modern equivalent of going to a thesaurus and replacing all your natural words with five-dollar ones.
If you're not confident in your English then just write your brilliant ideas in broken English. Or at most do a direct translation. But don't fluff it with AI's nonsense.
If the ideas are good they will be recognized.
I can’t distinguish between well meaning slop and blogspam slop. I might be able to if I’m well informed on the topic and can point out inaccuracies. Otherwise I just don’t know what is accurate and what is filler or outright wrong. LLM-isms are very distracting and exhausting to sort through. It just doesn’t make for a good reading experience.
There are few things (relatively speaking, I mean online activities, etc) more annoying than seeing a click-baity headline, and then having to read 17 paragraphs to actually get to the point that I realize I already knew and understood it, and it's just slop padding.
No more LLM voice! Every now and then you might get a nonsensical artifact or two, but that's the price you pay for better groceries.
If you care about your voice, don't let LLMs write your words. But that doesn't mean you can't use AI to think, critique and draft lots of words for you. It depends on what purpose you're writing it for. If you're writing an impersonal document, like a design document, briefing, etc then who cares. In many cases (scientific papers, legal documents) you already have to write them in a voice that is not your own. Go ahead and write these in AI. But if you're trying to say something more personal then the words should be your own, AI will always try to 'smooth' out your voice, and if you care about it, you gotta write it yourself.
Now, how do you use AI effectively and still retain your voice? Here's one technique that works well: start with a voice memo, just record yourself maybe during a walk, and talk about a subject you want, free form, skip around jump sentences, just get it all out of your brain. Then open up a chat, add the recording or transcript, clearly state your intent in one sentence and ask the AI to consider your thoughts, your intent and ask clarifying questions. Like, what does the AI not understand about how your thoughts support the clearly stated intent of what you want to say. That'll produce a first draft, which will be bad. Then tell the AI all the things that don't make sense to you, that you don't like, just comment on the whole doc, get a second draft. Ask the AI if it has more questions for you, you can use live chat to make this conversation go smoother as well, when the AI is asking you questions, you can talk freely by voice. Repeat this one or two more times, and a much finer draft will take shape that is closer to what you want to say. During this drafting state, the AI will always try to smooth or average out your ideas, so it is important to keep pointing out all the ways in which it is wrong.
This process will help you with all the thinking involved being more up-front. Once you're read and critiqued several drafts, all your ideas will be much more clear. Then, sit down and write your own words from scratch, they will come much easier after all your thoughts have been exercised during the drafting process.