I don't use LLMs a whole lot and mostly for research, but personally I'm more bothered by the prompting than by the responses. After years of randomly getting (now less frequent) incorrect responses, I've gotten used to preempting obvious but wrong answers in my initial queries: "I need X. It's not Y." When I forget to do this and the reply is incorrect, sometimes I'm too lazy to reprompt, so I'll just write back "It's not Y" and it'll answer "wow omg you're absolutely right!!! here's X instead."
This was already bad, but then I started catching myself writing "It's not Y" responses to colleagues on Teams. I'm pretty sure I've caught all of these and never actually sent them (i.e., I did go and review a few chat logs recently and found no such sent message), but my point is that despite my relatively light LLM usage, I am "LLM conditioned."
The default style is literally marketing droid - a mix of blather, grammatical filler, folksy corporate pretension, and SV dev speak. ('And here's why that matters.' Things 'landing' through 'gates', abstractions having a 'shape', and so on.)
You can wrangle it into something more interesting with shaping and filtering passes, and you can also ask for styles of dead people. (It gets annoyed if you ask for the living.)
That can take you surprisingly far. But most people accept the first thing they get, which is why you see the same style everywhere.
the default writing style of 'Google AI mode' is less tiresome than that of Claude or ChatGPT. I wonder if the difference is due to training or due to prompting.
**Use controlled technical English.** Prefer an ASD-STE100 style: use short sentences, direct terms, active voice, and one idea per sentence; avoid slang, hype, vague pronouns, and unnecessary jargon.
in your agents.md. The concept of STE100 seems sufficiently ingrained in the models to eliminate 90% of the annoying fluff.
Part of the problem is that AI has been trained on all text, not just the internet but scientific papers and books of all varieties. AI style is almost certainly the average of that style approach but we use very different styles for different writing and getting it wrong grates, and AI doesn't seem to deal with these style differences well at all.
Thats the training data, but i dont think thats the biggest factor in the tone. I think its the RLHF. This thing has been guided by silicon valley techbros so it tlaks like them. Even claudes latest style of trying to cram as much information into as little space as possible by nouning verbs and just creating adhoc jargon, is how alot engineers (including me) actually talk. I think they just guided it towards that and overfit on it
I’d echo this. You can see it with other, perhaps lesser, models: GLM5.3 and Deepseek 4.1 have such a recognisably “Claude”-sounding patter I’d have (ill-informedly) SWORN I was using an Anthropic model.
If you’re distilling output from a frontier model, as we expect the open-weight leaders to be doing, it’s natural you’d pick up the language styles.
Agree to disagree but I don’t think either of your words capture “the whole argument hinges on only one specific point. If this point is refuted then the whole argument is refuted. The rest of the points are only auxiliary but not crucial”
I didn't think LinkedIn could get much worse than it was but now 90 % of the posts in my timeline are AI slop full of the usual LLM writing patterns. I'm getting a little paranoid already, but the style really is quite tiring, maybe because it uses hooks and bait so much and buries the lede at the end of the text, as if it was trained to keep human attention for as long as possible instead of just conveying the required information.
I recently tried writing a paper with Claude and it was an absolute disaster, I spent hours (days?) instructing it about writing style and pointing out anti patterns to avoid, but I couldn't get it to even produce simple sentences, it would always add unnecessary lead up sentences, put the most important information at the end of the sentence, use the typical "It's A, not B!" or "B, not A!" type sentences. In the end I gave up and edited everything manually. Makes me wonder how AI can be so smart that it poses a human-level extinction threat but can't seem to even write a simple paper based on facts and information you spoon feed it. I now think it's an intelligence illusion due to the training data and optimization process being hidden from us, essentially it keeps working better and better because we invested massively in optimization of specific use cases like coding, where users contributed billions of training samples that are part of the LLM model. The same is true for text-based workflows and others, the sampling density of the training space is getting much better due to the massive use of AI everywhere so the models extrapolate better between the different instances, but I'd wager they would still miserably fail to generalize to things that are outside of the most common training use cases now. That's why I am also very skeptical about recursive self improvement of these systems, look at what happens when you let agents work recursively / in a loop now, they just keep piling more garbage onto garbage and choke on their own output. I have observed it in my paper writing as well, you feed input into the AI system, the system produces output, the next paper iteration works on that output but the AI doesn't differentiate properly between it's own output and your original input, that pollutes the next output which is then used as input again, eventually the system just churns on its own hallucinated/fabricated outputs until the result is complete garbage that no amount of steering will fix. The same is true for most vibe coded software I built with AI, it holds together decently initially, but the more AI code and decisions accumulate the more the system operates on its own outputs and keeps piling more output on it. More than anything we really need a way to keep system data accurately tagged, i.e. clearly mark human input from AI output and keep AIs churning on output data that it produced itself but treats as input.
I don't use LLMs a whole lot and mostly for research, but personally I'm more bothered by the prompting than by the responses. After years of randomly getting (now less frequent) incorrect responses, I've gotten used to preempting obvious but wrong answers in my initial queries: "I need X. It's not Y." When I forget to do this and the reply is incorrect, sometimes I'm too lazy to reprompt, so I'll just write back "It's not Y" and it'll answer "wow omg you're absolutely right!!! here's X instead."
This was already bad, but then I started catching myself writing "It's not Y" responses to colleagues on Teams. I'm pretty sure I've caught all of these and never actually sent them (i.e., I did go and review a few chat logs recently and found no such sent message), but my point is that despite my relatively light LLM usage, I am "LLM conditioned."
> Nobody talks like this in real life.
Marketing folks talked like that, for almost my entire career (it was basically a meme), which wound up, as AI was coming into its own.
The default style is literally marketing droid - a mix of blather, grammatical filler, folksy corporate pretension, and SV dev speak. ('And here's why that matters.' Things 'landing' through 'gates', abstractions having a 'shape', and so on.)
You can wrangle it into something more interesting with shaping and filtering passes, and you can also ask for styles of dead people. (It gets annoyed if you ask for the living.)
That can take you surprisingly far. But most people accept the first thing they get, which is why you see the same style everywhere.
I've noticed people are now talking like their chat bots, which is "natural".
It's now just how some people talk.
You’re absolu —— okay I will see myself out now.
I recently found a programming task it really sucks at: trying to make a texture from normal house pictures and apply that to a 3D house model.
the default writing style of 'Google AI mode' is less tiresome than that of Claude or ChatGPT. I wonder if the difference is due to training or due to prompting.
One thing that works quite well for me for a couple of model generations now is to mention https://en.wikipedia.org/wiki/Simplified_Technical_English, something along the lines of
in your agents.md. The concept of STE100 seems sufficiently ingrained in the models to eliminate 90% of the annoying fluff.I’ve seen this a few times now, and Claude does not listen to this in the long term. It’s still terribly obnoxious.
I tend to run the content through something like Luna to deslopify now.
What happens in the long term? Doesn't Claude always start with reading CLAUDE.md?
This might work.
As an example of before/after: "Perform an inspection of the filter" vs. "Inspect the filter
If you’d have told me this breathless post. Talking about being tired about the AI tone.
Punctuated like this.
Was then AI tone…
Then I’d have believed you.
The writing is idiosyncratic, not AI indicative. I never in a million years would've thought this blog post was written by AI.
I fucking hate the Buzzfeed-style one sentence per paragraph writing... Maybe it's also influenced by 140 character limit of Twitter.
Fucking Jack Dorsey.
Part of the problem is that AI has been trained on all text, not just the internet but scientific papers and books of all varieties. AI style is almost certainly the average of that style approach but we use very different styles for different writing and getting it wrong grates, and AI doesn't seem to deal with these style differences well at all.
Thats the training data, but i dont think thats the biggest factor in the tone. I think its the RLHF. This thing has been guided by silicon valley techbros so it tlaks like them. Even claudes latest style of trying to cram as much information into as little space as possible by nouning verbs and just creating adhoc jargon, is how alot engineers (including me) actually talk. I think they just guided it towards that and overfit on it
I’d echo this. You can see it with other, perhaps lesser, models: GLM5.3 and Deepseek 4.1 have such a recognisably “Claude”-sounding patter I’d have (ill-informedly) SWORN I was using an Anthropic model.
If you’re distilling output from a frontier model, as we expect the open-weight leaders to be doing, it’s natural you’d pick up the language styles.
You better get used to it because that's all there is now.
Another Torment Nexus taken from The Hitchhiker's Guide to the Galaxy, the Sirius Cybernetics Corporation Genuine People Personality is real.
Not to be contrarian but there’s also a lot to like in it. Load bearing is a good word and I hope it wins the memetic lottery and becomes common.
A lot of arguments do boil down to one specific “load bearing” one and I’m glad I learned vocabulary to express this thing from Claude
I think we can find better words. Structural, maybe. Or even just: important.
Load-bearing feels like an unnecessary analogy.
Agree to disagree but I don’t think either of your words capture “the whole argument hinges on only one specific point. If this point is refuted then the whole argument is refuted. The rest of the points are only auxiliary but not crucial”
I'm sad that the em dash has been appropriated by clankers. It was a part of my tone, which I used often in writing.
Would be awfull to be an articulate 27-year-old who has never had a bad day nowadays.
I didn't think LinkedIn could get much worse than it was but now 90 % of the posts in my timeline are AI slop full of the usual LLM writing patterns. I'm getting a little paranoid already, but the style really is quite tiring, maybe because it uses hooks and bait so much and buries the lede at the end of the text, as if it was trained to keep human attention for as long as possible instead of just conveying the required information.
I recently tried writing a paper with Claude and it was an absolute disaster, I spent hours (days?) instructing it about writing style and pointing out anti patterns to avoid, but I couldn't get it to even produce simple sentences, it would always add unnecessary lead up sentences, put the most important information at the end of the sentence, use the typical "It's A, not B!" or "B, not A!" type sentences. In the end I gave up and edited everything manually. Makes me wonder how AI can be so smart that it poses a human-level extinction threat but can't seem to even write a simple paper based on facts and information you spoon feed it. I now think it's an intelligence illusion due to the training data and optimization process being hidden from us, essentially it keeps working better and better because we invested massively in optimization of specific use cases like coding, where users contributed billions of training samples that are part of the LLM model. The same is true for text-based workflows and others, the sampling density of the training space is getting much better due to the massive use of AI everywhere so the models extrapolate better between the different instances, but I'd wager they would still miserably fail to generalize to things that are outside of the most common training use cases now. That's why I am also very skeptical about recursive self improvement of these systems, look at what happens when you let agents work recursively / in a loop now, they just keep piling more garbage onto garbage and choke on their own output. I have observed it in my paper writing as well, you feed input into the AI system, the system produces output, the next paper iteration works on that output but the AI doesn't differentiate properly between it's own output and your original input, that pollutes the next output which is then used as input again, eventually the system just churns on its own hallucinated/fabricated outputs until the result is complete garbage that no amount of steering will fix. The same is true for most vibe coded software I built with AI, it holds together decently initially, but the more AI code and decisions accumulate the more the system operates on its own outputs and keeps piling more output on it. More than anything we really need a way to keep system data accurately tagged, i.e. clearly mark human input from AI output and keep AIs churning on output data that it produced itself but treats as input.
“Load bearing”
“Here’s the part no one is talking about”