alt Hacker NewsIt's Not Just X. It's Y
Comments
It's easy to focus in on particular linguistic tics, which will probably get smoothed away in future training. The underlying issue is that the LLM is trying to ape meaningful writing - which takes the reader from A to a surprising Z - without generally basing it on a meaningful insight.
Most of the common tells stem from that desire to signal the gap between what it's writing about now and how you previously thought things worked. And they really fall flat when it's writing about some milquetoast truism with all the edges sanded off.
Rather than have "Z" be self-evidently interesting, the LLM need to tell us that it's not "A". Except no one thought anything was "A" in the first place, and the "Z" is barely a "B" let alone a "Z".
Or things are "quietly X," implying that there is some secret knowledge that other people have.
Barring that, the LLM will signpost arguments, telling you how interesting things are. "This is the critical part..." before launching into another banal non-observation. Why have only "Section Header" when you can have "Section Header (another idea I had) - omg I just have so much to say about this". Maybe a list of weak ideas, presented in multiple ways. Bulleted with emojis. As a rule of three. As choppy sentences in a paragraph.
All of these follow the same pattern - trying to follow the FORM of having something interesting to say that differs from consensus understanding, without having the intelligence or boldness to actually midwife a new concept into the world.
I like that these AI idioms exist. They're like watermarks for text. It's worth the cost of humans avoiding them. Companies will eventually train their models to be undetectable, but society would be better if they didn't.
I remember someone posting about the most human of all traits being reassuring to see: Typos. I'm pretty sure people are not as averse to leaving or finding typos in text as they were 5 years ago, as these days it's a signal of humanity.
Same has been applying to art for a while. Several artists who have an "AI-ish" style have been wrongfully crucified for using AI. And been forced to post videos of their process end to end, in order to prove that they aren't using AI. It's a thing for artists to post their new stuff with: "AI could never do this."
> There is danger in evaluating for language patterns over its content
I agree, but it’s worth noting that that has been done since long before LLMs. Fifteen years ago, I used to teach a graduate course on academic writing pedagogy. The students and I would read research papers on the teaching of academic writing; we also analyzed textbooks and course syllabuses to get an idea about what was actually being done in classrooms. While phrases like “critical thinking” did come up, the overall focus was clearly on language patterns: sentence and paragraph structure, the use of transition words, vocabulary for hedging and boosting (i.e., making assertions seem weaker or stronger), etc.
In a university context, it can be very difficult to evaluate student writing based on its content. In humanities-focused and creative writing, what the student decides to say can be seen as an extension of the student’s personality, identity, and individual experience; if a teacher evaluates the content, including the reasoning, it can seem that the teacher is evaluating the student as a person. And if the students are in the sciences, especially at the graduate level, the writing teacher often won’t even understand what the students write because it is too technical. Teaching and evaluating language patterns, not content, is often the only option.
I think people overestimate the radius of avoidant behavior against AI idioms, and underestimate how the trove of AI generated text actually influence people's writing. It's not a one way street. If you mostly read AI generated content, your writing will inevitably resemble it.
In one of the essays posted here, which was, ironically, about AI in education, a sentence, that an AI could not possibly write, that I could possibly write, because of its length and unusual structure, before finally reaching the verb, went on for 25 words.
I don't know if it was written that way to show trust in the reader's intelligence, show disregard for reaching a wide audience, show a demonstration of skill, or was artifact of someone just thinking at that level.
People stopped actually reading when we dropped classical liberal education, right after WWII.
This is merely the end-state of industrialization, which is efficient and soulless.
I liked everything in this post, with one exception. I'm less sure that avoiding speaking like an AI is robbing us of language useful in critical thinking. I'm far more worried about people offloading their critical thinking to AI systems and losing the habit.
Also, the Greeks were worried about rhetoric and, in my opinion, rightly so. The skill to argue a point well is different than those that are needed to be correct. To become a skilled rhetoritician was viewed as dangerous (and right now AIs are only moderately good... though they are improving fast).
> RLVR is weirder, and I suspect it's why we see "It's not X, it's Y" so often.
This feels like an easy enough hypothesis to verify, for anyone in the business of training LLMs - does the not-X-but-Y rate increase after RLVR?
Surely these leading tells will be trained out of models pretty soon, given how well known and overused they are. And it might make the writing slightly worse in a way. But it is quite annoying how often this type of construction is used in everything at the moment.
I think that the current models are still like over-achieving savants rather than true human level because the largest model is only 1/10th the complexity of the human brain. I've recently become fairly convinced that new hardware paradigms (like types of CIM) are about to move from research into real-world development and scaling. So I believe within a few years, the model sizes will increase by another 10 times.
Compared to upcoming 100 trillion parameter models, humans will obviously be _much_ dumber/slower than AI in all fields. Already with the 10T models, some LLMs beat 99.9% of humans in competitive programming.
The AI hatred from many may actually continue to increase, but in cases where the bottom line matters, we are rapidly approaching the point where writing or work product that looks like it is human-authored will be suspect just on that basis. In other words, for some people it will be the reverse -- "this work looks like it was created by a human" could be devastating for your businesses credibility at that point.
For everyone claiming that this is a trope of LLM text because it is a trope in the training data: how do you know this trope doesn't emerge during RLHF?
This is how early forms of "reasoning" in LLMs worked: just literally inserting words like "Wait...", "Hmm...", "Let me reconsider...", "But is it really..." into the token stream.
It's bigger than that, it's large
TLDR - it's not just AI detection. It's policing of human thought.
Anyway, yeah, people trusting AI to do a better job in reasoning than fellow humans, without justification, worries me. We have no formal theory of informal reasoning (that LLMs mimic), so we cannot verify it any better than with humans.
You have to trust someone, to ground your beliefs. Trusting AI is just trusting some other people (who trained it) by proxy. Once you realize it, you might as well try to trust people you know.
nice article, but i think as a non native english speaker, i always use the model in english for reasoning and then translate the output to my language. most of these considerations do not apply. because the translation step is taking out alot of these language artifacts
Clearly humans always type "it's not merely X, but also Y"
this article is great. we need to protect our ways of thinking, and it's going to be -- already is -- extremely difficult
> In the end, shaming people for writing that gets flagged as AI can lead people to sidestep structures the model has learned from us
It's interesting why LLMs generate constructions like this more frequently than they presumably exist in the training set. I wonder if this is some sort of mode collapse caused by post training, and/or maybe because they are training on synthetic data so these things become self-perpetuating and self-amplifying (a feedback loop)?
The lesson for humans worried about being falsely identified as AI is just learn to write better! It doesn't matter where your repertoire of phrasing comes from (copying AI or not), but one of the basic rules of writing is not to repeat yourself unless you are doing so deliberately for a purpose. Go ahead and use "It's not just X. It's Y" if you want to, but if you use it multiple times in the same short piece of writing, then you may deserve to be called out for poor style, if not for being an AI.
An old xkcd comic that is somewhat related to the current witch hunt that some text that the author claims he wrote himself was actually written by an AI:
Turing Test
https://xkcd.com/329/I'm not dropping emdashes -- though you can always tell mine by their two-hyphen form lol
I've also never used an AI detector, and probably never will.
In my experience:
1. The people who rely the most on AI writing don't like to admit it. I catch obvious AI hallucinations in my boss's "documentation," and he always insists it was his own human oversight, despite it being very obviously a mistake I've caught Claude (and importantly, no human coworker) making repeatedly
2. I don't trust a machine more than myself to judge writing
3. Obvious AI "tells" just make it clear i don't need to keep reading, not that i need some kind of validation. In some sense, i guess that might save me time? But i still have to have read enough to know what it is...
In general, i think the author makes great points about how _LLM "thinking" is just the reproduction of the language of reasoning_, that is not necessarily a replacement for actual reasoning. It'll take a lot more than that for me to believe an AI is "thinking" and not just giving statistically reasonable answers (reasonable or actionable though they may be)
An excellent article that perfectly articulates the absurdity of focusing on style of writing over content.
You’re absolutely right to push back on this.
Sometimes it’s not just about the Ys but also the Qs.
We solved this problem already with the antislop sampler: https://arxiv.org/abs/2510.15061
> Because if Pangram's AI system found me guilty, that's the end of my career. That's literally extortion.
How is this different from humans? When I went to high school, my teachers extorted me too. Especially subjects like English and unlike Math, where evaluation is 100% subjective.
Another bunch of dead give aways in code bases with READMEs is the repetitive:
- "No X, No Y, No Z." pattern
- "Here is X - it makes Y"
The worst and most obvious one is the constant over use of emoji ticks and crosses.
You’re absolutely right. This is the smoking gun. This changes everything.
>Recent overuse by language models has led many to declare it bad writing. I'm not so sure.
It is bad writing.
"So, if we publicly shame people whose text looks like it might have been written by a machine – because it mimics the language used for human reasoning – and people stop writing in ways that they internalize as "AI writing" out of fear of false detection, it sends a signal that your language for reasoning must be policed, or you too could be held up to public scrutiny."
This is honestly both terrifying and well articulated.
High praise to the blog author.