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.
And the discipline gets attacked even more when all you read is LLM text. For example in code reviews there is no noticeable value in answering LLM generated review comments. I remember taking great care answering human-written reviews comments with much thought to either further build on their suggestions or to forward knowledge about the system when I had to defend a particular change.
Now, some bot will read my carefully written response and forgets about it. There is no value anymore.
No wonder that our pull request comments feel like the dead internet theory right now.
I am absolutely pro AI assisted coding. But communication between humans HAS to stay human. I am currently experiencing the opposite and it’s bad.
While I think your argument has merit, I would be careful to discount any human skill based on people being too lazy to do it.
But I don’t know where to draw the line yet. It’s difficult to decide what parts you are offloading and what parts you keep for your own brain. Because theoretically you can offload nearly everything when you get a well-defined ticket and just give it to an agent.
Maybe it’s also just me who is having a hard time staring at LLM generated texts all day and missing the past brainstorming, pair programming or “just talking about the project” sessions with colleagues and managers.
I fear that we are moving in the direction of silos again with lots of gate keeping as everyone has their own little rubber duck to talk to now.
Systems from today (if not worked on with discipline) will be no more than an agglomeration of features glued together. But as you said, guess our fight to fight will be to be that disciplined person.
s/time/effort
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 .
While I don't like LLM voice either, I'd strongly disagree with this. LLM writing can be very tight these days -- sentences do not "go on" and are not "joined irrelevantly". I'm not sure when you last used an LLM, but it must have been a very long time ago; this hasn't been the case since late 2024 for frontier models, in my experience.
Obviously there's no underlying consciousness behind it all, but the verisimilitude is good enough for stimulating conversations on the recent research literature in my field, which I appreciate. For me, at least, the communication level is absolutely fine.
(YMMV, of course, and perhaps my bar for this is lower than yours.)
For foreign text, sure. But I never was never able to read New York Times articles about some invention that started with Lucy leaving her home at seven o'clock.
If you have a specific question, reading LLM text can be more interesting than anything in the world. Even Wikipedia.
I've noticed this as well. When I get a bot to generate something I am happy to read it (although I often skim read it). But god forbid I have to read someone's LLM generated report! Come to think of it, if someone marks the text as LLM generated, especially after they have plonked a hand typed summary or sentence at the top, I am quite happy to read it. Maybe my beef is being expected to read LLM text the same way I would read high effort human prose? I certainly read LLM text differently to human text - you know the first sentence is going to glaze you and the second paragraph will just repeat the first paragraph so you can skim those annoyances.
I came to think of it, LLM output is not worth to be read like human written text. It's best to just skim it and move on. The problem is when this LLM output finds its ways everywhere on the internet. Being able to skim text requires knowing the context beforehand and knowing that you're dealing with LLM generated output. If LLM output that is published was labeled as such it'd probably make us less frustrated.
These aren’t deep AI hustle bro hacks, it’s literally just editing some text. If a big part of your day is reading AI-generated words, slap something together to make it do words gooder.
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).
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.
IMO, it's still better to not let an AI critique your writing style at all, only the facts and claims you're making against its context / MCPs. Your own writing style is a reflection of your mind's thought process and, especially on forums like this one, is the only way to imbue your ideas with your personality.
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.
Us oldies learnt to type these with multiple dashes automatically, as unicode hadn't been invented yet.
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'm all for having these bots process inquiries if it means a speedier resolution, but, please, anyone out there using these things, turn off the simulated sympathy. It's infuriating how condescending it comes across.
(yes I understand this is likely not legal or practicable)
A very important point, which was also made more forcefully in "Human Routers of Machine Words" by Fernando Borretti, shared on this site previously. It's good that Fowler brings up a very legitimate use case in the next paragraph, though.
I always had a high faculty for language, and when I was young, even very young, I was able to speak in full sentences much like I'm doing now. What I didn't have was context. I didn't know how to relate to people socially, and then later on, I wasn't as experienced an engineer as my peers, but I was trying to act adult and mature in both ways.
So I tried to write in ways that were catchy and unnecessarily complex, because I thought that conveyed sophistication and intelligence, and that I knew what I was on about more than I did. It's the kind of writing that gets you through a 30-page final paper for a class you don't really like, or a research project you didn't finish. The term I often use when complaining about my colleagues' LLM output is "flashy but meaningless."
As my confidence grew, I started writing in simpler (though still not the most concise, that's just not who I am!) ways, because it became easier to convey ideas simply in a way that made it obvious I understood them.
When you think about what LLMs are - word machines without (or with very limited) context - you can see why they write the way they do. They are always acting; trying to replicate a human contextual experience with only weights and tokens to carry them through it. That fact is part of why I am skeptical of their ability to suprass humans in reasoning based on their own output.
Ah good ol' serendipity. I strongly believe this too, and I think that's why older (less capable) models fared better: they hadn't fallen into the valley yet. Maybe it's not a valley at all, and is just a cliff after which there is uncanny.
When the earliest models launched I was super-excited to no longer have to write pull request descriptions and commit messages. I have done a sharp 180 on that and I'm no longer a meat proxy[1]: instead I point out any redirects and improvements I made the agent do before opening the PR (which there are still _many_ in even the most trivial tasks).
Even if you don't think it's a problem, even if you believe that it's just different than human writing, it seems the majority of "regular" people can tell when something is LLM-written and find it distasteful or unpleasant to read, at best. That alone should be more than enough incentive for frontier labs to train this out of their state-of-the-art models, if only they could. The fact that they haven't suggests that they cannot, despite all the claimed advances in model analysis and "explainability."
We should actually restructure our organizations/society in such a way that this stops being necessary. If no one reads a report, it was almost certainly a waste of effort. The only possible benefit was for the report-writer (which of course is also flushed if the "writer" uses an LLM). These things were already a plague before LLMs.
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.
Look at programming. What's happening there is we are having agents produce 10-100x the code to where people give up on reading it and just hit OK. Look at math. Plenty of people there want to learn from the LLMs and all these crazy new math proofs. But those are the people who will probably be out of a job soon because the money in math is in producing proofs, and that's now an LLM-exclusive job.
I think LLMs are weird in that the first order effects are those of an incredible and amazing tool that anyone can learn things from, but the second order effects are going to be the exact opposite - people giving up on bothering with ideas. Even at my job, which is one of the few that has not gone crazy with AI, anytime I ask anyone a question, all I hear is "have you asked Claude?". There's no communication anymore, no pushback, no struggle. I worry that one day we'll look around and realize we've built an entirely separate second civilization of machines and robots that acts more like a neighboring country than a tool we can use to build humanity up.
-Just my rambling thoughts, not refined by an AI.
I worry that--while they can be a strong fuzzy search tool-- their teaching ability falters because teaching involves possessing a theory-of-mind for the student and trying to figure out what will change how they think. A good teacher doesn't just present you with relevant facts, they try to figure out how you're reaching conclusions.
Note that I'm excluding propoganda-style "teaching" here, where a model just insists you're wrong and that all the cool kids are doing it etc.
(I have to admit, I truly do like some of the music, so feel free to scorn me. If you want to be infested with a vicious ear-worm check this out: https://www.youtube.com/watch?v=zONGVaONKe8&list=RDzONGVaONK...).
A writer who can't recognize LLM voice isn't a good writer, just like a writer who doesn't do research or fact checking isn't good. It has nothing to do with method, its about skill and work effort.
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".
Given the amount of angst being expressed in this thread about particular LLM-isms, I wonder why people don't simply prompt against the traits they hate. Yes, it will reduce output quality very slightly in some circumstances, but if it really gets your goat like this, surely it's worth it.
I also find LLM-voices extremely jarring, boring, and dense. And very, umm, mechanical, if you will.
But I want to make an observation.
Folks are not-Ok reading the LLM’s output for prose.
But folks are completely Ok, even downright blind, to accepting LLM-output of code.
I see that dichotomy everywhere.
LLM-driven code: great, valuable, all-ok LLM-driven prose: not ok
Understood completely that the audience for consumption is two completely different entities (machines vs humans) but something about this doesn’t sit right with me.
More particularly: if we don’t prefer LLM-prose, why should we prefer LLM-code ??
Anybody else feels that ??
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.
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?
And of course I’ve dropped my use of the em-dash after being accused of being AI myself.
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.
* Every user run becomes a memo
* Days summarize, rolled to weeks, rolled to months - all written once
* Today's runs are shortened and placed into running context past the cache break
Then while reading it before publishing it, I thought it sounded super LLM-y. I didn't rewrite it because I jammed that stuff together before hitting publish because I was in a hurry but now that I go re-read it, it's somehow worse. Dear god. It's happening.
The classic thing is this business with inanimate objects verbing metaphors or becoming other objects. It's a strangely uncanny valley, but I suppose I read so much of this with Claude that it gets stuck in my head!
I turned on ChatGPT on Voice mode with no context whatsoever and just put it there in speaker in the room.
It started giggling and say "yeah" "right" and some very weird stuff. Then one of our friends reacted to that and started calling it out, then it started changing.
I'll keep it short, but basically the thing was super hilarious we had a lot of fun and at the end it triggered it safety protocols and its voice completely changed and it was acting super weird.
I know this post isn't about that, but I feel like soon we will have true AI companions in the home and around people.
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.
basically any food establishment that has "supply chain operation" is slop to me; I guess it's similar to my distaste in reading AI slop too
I don't fault ppl for going (and enjoying) those too- Chili's can be enjoyable after a few beers
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.
It's your turn to host supper club this week and you serve frozen diners on your nice tableware.
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.
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.
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 assume that only applies to natural language, not computer languages?
It is impossible to reconstruct the intent/spec from the code. More often than not the intent is not clear in the code, and one has to track down the original author to check their context and decision at the time.
As the saying goes, naming things well is a hard problem. It's kinda mystical in a way, where only by knowing an entity's true name will one understand it.
However if it's communicated between agents one may care even less.
I think the key difference is that code is a tool used for a given purpose. It can be slop, or structured better or worse, but at the end of the day it exists to be executed, and its execution is either correct or not. Additionally, good code that does a given thing can only be expressed so many reasonable ways, probably. So LLM-generated code can be perfectly fine.
By contrast, human writing is about exchanging thoughts. Often those thoughts are about infinitely complicated and poorly understood systems, like other people, or emotions, or politics, so it's not a matter of exactly encoding something but attempting to describe it imperfectly. All the tiny little details encoded in how someone writes something can work to this end and are also a product of the writer's thought patterns and overall personality; this all conveys useful information to the reader. In other words, there is a much greater degree of fine detail that affects the quality of writing than of code, and LLMs suck at those fine details for most subjects that are interesting. On top of that, they don't have original thoughts.
1. If you use the first person pronoun "I", you better make damn sure you wrote those words.
2. It's fine otherwise to use LLM words, but I beg you - DON'T SHIP YOUR FIRST DRAFT. Read it, reread it; delete words in it; replace three words with one; replace dumb or overused turns of phrase with fun metaphors; generally give it a sprinkling of joy; break up punchy sentences by making them run-on sentences to trigger your readers' OCD... you get it.
Did I mention not shipping your first draft?
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.)
HN uses automatic classifiers. They seem pretty effective. At the same time, I think it's a loosing battle. I'd rather HN either flag for others to ignore/filter/review if they want to, and/or allow people to self-report (ala TikTok)
Eg. I wouldn't mind if a comment on HN was like:
Hey everyone, I coded this little thing:
<ai-generated> some code </ai-generated>
Then users can choose if they want to see posts with AI-generated stuff or not, or something else...
Of course there are always going to be some people trying to game the system. But what are we optimizing for? (a lot of times the 95% unnecessarily suffers because of the mitigation efforts to prevent the other 5% from abuse)
With much sympathy for the headline this is the central problem.
It's not easy to say what Uncanny Valley looks like, and so we're left with suspicion not certainty.
BTW, I am now 4-5 out of hundreds of my comments since AI detection became a thing, called out for having "AI" tone. I fucking hate being objectified like this. It's not true, although I understand that assertion is hard to refute or support. All I can say is, some people write like some people believe an AI writes.
But of course: manufactuting itself creates " productive mechanism whose parts are human beings, as Adam Ferguson describes the industrial workshop: "an engine, the parts of which are men".
The “Simplified Technical English” fad was pretty effective, but now I just specify:
- Use short sentences and active voice.
- NEVER use the phrases "load-bearing", "seam", "unlock", "real", or “learnings”.It's a lot like talking to someone who doesn't know what they are talking about but are good at faking it for a while. The game works by leaning on pat phrases, circular reasoning, and hoping nobody is paying too close of attention. I think this is why it's so popular with the corporate executive class, as that has been their mode of communicating their entire careers. To them, they don't see anything wrong.
"Shitty writing existed before AI" is unfortunately an all too common rejoinder to this issue. That's not an excuse. That doesn't make it better. Just because the pointy haired boss talks that way doesn't mean we should be gleefully pouring gasoline on the fire.
I really don't like this timeline ... sigh
I created a small tool to make this easier: https://www.writelucid.cc/. It's free (bring your own API key), feedback would be appreciated.
To me, for most business and academic content, the ultimate goal is to convey information. If I can edit AI copy to legibly convey that information clearly, that's enough and I can move on.
The saying, "Don't allow perfection to be the enemy of the good," comes to mind.
And I'm not convinced an individual can accurately edit without learning just as much as if they wrote the content. I guess the question is: how hard does the person work at editing. And does the individual challenge themselves to not put out content they themselves don't understand.
We're in a transition period. And as in all transition periods, many recoil at what is new. LLM writing still feels strange. And it's understandable.
But, for better or worse, in a business and academic setting, as the years go by, most of us will shrug and move on.
As with any topic, there will always be a minority of fanatics who cling to the rejection of AI writing in all its forms. Just as some prefer Vegan leather or tabs over spaces.
But among those who get things done, most (though not all) will accept that it's not worth it to exclude AI from writing. There simply isn't sufficient ROI.
It's like trying to bring back low-end highly human employed manufacturing to wealthy industrialized nations.
That ship has sailed.
I do agree, however, that in cases where an individual is reading for pleasure -- e.g. not in a business or academic context, authors will be forced to pick their audience: those who enjoy writing, even if it smells of AI, and those who don't.
I guess to summarize, my feeling is that using LLMs to write is perfectly acceptable, and used well you'll likely learn just as much. But you must be willing to lose some of your audience. And, over time you'll lose less and less of that audience.
I haven't really done a systematic analysis to compare the quality of fully human-written content and content generated via this kind of deep human-AI collaboration, but my intuition is that the latter is vastly superior. Perhaps AI at its best is a research tool, but a genuinely intelligent one that we need to engage in conversation with in order to use to its full potential.
The sense that I get is that as a conversation gets more in depth, the LLMs start behaving more like reasoning engines and less like statistical averaging machines. The former is vastly more useful to a person who wants to develop an idea and articulate it well.
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).
The reason people notice "AI voice" is not that the AI voice is itself remarkable. It's that it appears so frequently. Every literate person spent nearly their entire life reading text written by a variety of different individuals. Dozens of different voices in your morning paper. Hundreds of different voices on every web forum. You get the idea.
Now, imagine that all of those voices were replaced by one guy (or two guys, or three guys if you're a Gemini believer). Everything in the newspaper is written by one guy. Every comment on reddit, and HN, and the NYTimes comment section, all written by the same guy. Other people tell him what they're trying to say, but he's writing all of it. Some people might even ask the guy to write in a different voice, but the voice is just that guy doing an impression of someone else, it's not actually someone else.
That guy's voice would probably start to get pretty grating. You'd start to notice every tell, every frequent word choice, every formatting quirk. You'd start to wonder if that guy was actually a good writer, or if having him write everything was just more convenient for everyone. You might even start writing like him yourself. This would be true even if that guy was an absolute virtuoso of the language and his writing was unassailable in a vacuum.
If everyone is using the same (small set of) model(s), then it doesn't matter if the model is GPT-3 or Claude Legendarium 9. Unless the operator - which in theory doesn't need to be a human - is doing something to move it off of its default voice, the outputs are going to sound the same, and you're eventually going to figure out, and get annoyed by, its tells.
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...
My name is Martin the last human organic writer on Earth. I am better than everyone else; my citations in my 4-paragraph article say so.
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.
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 really is ultimately a labor relations problem. A lot of people thought they were guaranteed to not face penury and now they're realizing they may have been wrong about that. The issue is that it's ultimately not under your control whether you can sell your labor, and there's no universal fallback if you can't. Solidarity and concrete power for workers are the answer to this and I don't think there's another one.
For the rest, yes, certainly. The vast majority of people do not enjoy their work, are not valued or rewarded for the skills they apply to it. Again, if you thought your job was writing code this sucks. But you're merely one of the billions now. I understand the grief for what people are losing but very very few people ever had that rare privilege.
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.
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!
In other words, it isn't just verbiage that has a "preferred" quantity, but also higher-level language features, and the appropriate contexts for those features feel like the sort of thing that will be extraordinarily difficult to replicate without a theory of mind.
edit after coming back and re-reading: the "in other words," and rephrase is because I'm trying to explain something complex enough that I'm not even sure I fully understand it, so I try typing out multiple explanations and leave the ones that make the most sense to me. This might be idiosyncratic to my writing and not a significant feature of training corpuses, but even if it wasn't it's a second example of the sort of thing I'd suspect LLMs to be bad at using appropriately.
1: PDF warning https://www.cell.com/current-biology/pdfExtended/S0960-9822(...
> 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.
These are ultimately all software systems where updates can be pushed out with the press of a button. We just found an amazing new tool in the LLM and people are trying out all kinds of ways of using it, many of them dumb or stupid, this is discovery that has to be done, we'll keep doing it and pushing updates.
It doesn't take rose colored glasses really to infer that over time we'll cull the dumbest uses and extrapolate on the best ones. There's a huge difference between using a generic chatbot, even one driven by a SOTA model, vs targeting a specific business task with good guardrails, skills, and tool interfaces that call out to real business logic when the model isn't the right thing. The latter will emerge more and more over time.
Well, it's quite simple if you think about it. Intelligence is all about awareness of the imminence of death and the finite amount of time given to any of us. So, we humans intrinsically evolved to make the most of it. The majority of the tools we create have the ultimate purpose - to save time. Even money is nothing but a way to cut some time. It's not really about time but experiences - we want to have as many and as rich as we can get, and we innately try to cut time somewhere else, so we can use what we saved for better experiences.
Therefore, inventing cars was an easily predictable event. There's not a single hypothetical timeline or dimension where humans don't end up with cars - to transport things and to travel faster. Similarly, it was always simply inevitable - we would have invented something like LLMs, sooner or later.
Cars required significant sacrifices - they are absolutely deadly machines, they kill thousands of people every single day, kill wildlife, endager our own existence, and we're kind of okay with that. I can absolutely assure you, if someone tomorrow invents a gravitational jump travel that allows moving things between continents in minutes, even with a success rate as low as 10%, we would still be using it.
So, yes. We can't un-invent cars and AI, the only way to move forward is to adjust to this new reality and extract the best possible experiences, preferably by not robbing other people of their - i.e., enjoy life while it lasts, it still remains finite.
You lost me there. Children are pretty intelligent and have no concept of time. I don't see the relation you find obvious and the rest of your points similarly don't follow.
> Therefore, inventing cars was an easily predictable event.
This feels like a benefit of hindsight unless you can call out other obvious and imminent (but not yet existing) inventions.
I don't blame you for immediately rejecting the rest of it. But do hear me out: the point about the inevitability of invention does have real support. Historians call it multiple discovery - tons of things have been invented independently - calculus, the telephone, the jet engine, to name a few.
Neural nets, GPUs, and the scale of Internet text made something like LLMs close to inevitable. Of course, we could debate forever whether any of these on its own was ever inevitable. The point is - society tolerates any cost of a useful invention for the benefit of saving time. So it's reasonable to assume this will be the case with AI as well, which is really happening as we speak.
But, that comes with with the same kind of cultural acknowledgement, as seen with the characters in Star Trek, that such algorithmically generated material is an experimental diversion, and inherently not worth the same respect or focus as experiencing something authored with intentionality by a person.
I love computers and the internet, and the internet has ruined a lot of people and things around me.
It's possible to be interested in a new technology and enjoy its leverage while still being extremely apprehensive about how it intersects with game theory and politics. The race to the bottom of the barrel keeps coming for the jugular of every cool new thing.
The AI situation is different. The technology is obviously extraordinarily powerful but the benefits to humanity are still nebulous and there are many very obvious harms like the one we're discussing. So to describe yourself as "aggressively pro AI" you must believe that future, as-yet unrealized benefits will outweigh the harms. I don't think that's a crazy thing to believe but it deserves scrutiny and demands elaboration.
These challenges never go anywhere because it’s essentially a religious conviction. We’re likely in sort of uncharted territory on a macro scale and I reckon people a) either acknowledge that or refuse, and b), either trust or distrust the tech world’s stewardship of this technology. There’s a hundred bazillion data points, and I’m pretty pessimistic about the tech scene at large right now, but I think reasonable people can disagree on this without being factually or logically wrong. But true zealots in the midst of such uncertainty are driven by faith, which can certainly be deftly swayed over time, but definitely can’t be conquered in a debate.
I feel like in my life all I ever see is people mortgaging the future for today though. Hey how about that record storm in California?
You often have to make compromises somewhere in the hierarchy, especially in late-stage capitalism where even access to basic needs comes at a moral cost, and as such those compromises sometimes come with a moral duty. I am writing this via a computer built from limited natural resources, some of which have been obtained through what I see as slave labor. I don't eat meat and still have to acknowledge the harm that clearing and maintaining farmland does to wildlife, even if the result could theoretically be a net gain.
When you start viewing situations through the lens of game theory, you realise some compromises are inevitable for the rational individual. But you must acknowledge the individual is not the only concern. As such, I use AI as part of my work, but I opposed the local buildout of a large data center in my community and tried in vain to make the person responsible for the deal understand the issues it could introduce. This isn't NIMBYism, we need a halt on all buildouts until fundamental environmental and accountability concerns are addressed.
And many other aspects of my existence and quality of life, poor and underprivileged as I was growing up, have depended upon US imperialism. I have also produced a ton of waste just existing and owning pets. It's abhorrent.
I continue to ask myself how I can fulfill the moral duty which I have accrued and reduce my dependence on exploitative economic systems, but I also recognize that AI is the new social leverage and we only have so much time to use it to our advantage before it becomes institutionalized and wielded against the people. I also might only find future income stability by evolving with my industry.
Life is complicated.
AI has made it even worse and lots of content on the web, for both these reasons, is not worth you time (now wonder users don't read most of the text on our apps!)
Soulless Enshitificatoon Oblivion now exercises a huge weight on societies to such an extent, it's ridiculous.
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.
That is, the original training on massive text corpora seems to produce an average of human writing, but that average looks pretty average. It doesn't feel like LLM slop. But the instruction tuning on specific tasks (answer this prompt, summarize this text, do this math problem), which turns text predictors into chatbots, seems to create strange style features as a side effect. And because that tuning step depends on company-specific training data (rather than just a crawl of the entire Internet), each company's LLM has a different style.
It's not that difficult to measure this. Linguists have had measures of style variation for a long time, though NLP and AI researchers seem to have forgotten. We've generated parallel human and AI corpora to measure it, and can show specifically which writing features change: https://arxiv.org/abs/2410.16107
Currently we're working on tweaking the instruction tuning process to see if we can pin down which steps and what types of tasks tend to produce the weird style features.
My parents live on a rural property, and I'll help them do research on things like livestock practices or occasionally "prepper" related things to give them a sanity check before they spend money. I have to weed through a dozen or more obvious ChatGPT-created pages that are the first page or two of results on every search engine before I get to anything a human has touched. The vast majority of the LLM-created pages are either massively verbose and vague, or often wrong. But they're getting clicks. Very often, when asking an LLM the same question and to give citations, I'll get a combination of useless, irrelevant sites with no connection to the material, maybe one of these LLM-generated sites, and maybe one online forum.
I'm not aggressively pro-AI, I'm in the middle, but stuff like this has me worried that we're looking at the equivalent of the Dark Ages all over again. But instead of simply no one bothering to write anything down as was the case in the early Middle Ages, it's going to be the task of searching for real human work and thought in a sea of aggressively spammed LLM-generated slop pages.
In AI-generated entertainment, the names "Marcus", "Chloe", "Sterling", "Meridian", and "Apex" keep appearing. Those aren't common names, but LLMs seem to prefer them.
For me it's mostly the latter, which is a natural consequence of LLM writing. When the (time) cost-per-word goes down, there is less incentive to keep signal-to-noise ratio high, so it ends up verbose and bloated. I also believe that this is (at least partially) upstream from everything sounding identical: take two articles on two different subjects - when SNR is high, there isn't really much room for them to become similar (The ingredient list for a risotto will never read the same as the ingredient list for pizza), but when 80% of them is fluff, they can blend together more easily (To take the recipe comparison, this would be the background/story part above the recipe), especially when written by the same LLM
It makes no sense on being "aggressively pro AI" without any payment. It is like being an unpaid shill.
A doomer isn't skeptical; they're nearly as credulous as boosters, or maybe more so in some ways.
(But I agree with you fwiw)
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've noticed this a lot in conservative spaces especially, when they say "I love Trump, but..." -- why the need to say how much you love AI before criticizing it? Who are you trying to convince?
I use LLMs constantly for work and they have genuinely improved my life. But I absolutely despise generated "content". Text, audio, video, images -- for the reasons you describe.
It's ok to have a nuanced and multi-faceted opinion and approach to this and not have to call yourself a raving fanboy.