I’ve thought this for a while, but why hasn’t it happened yet? At this point, OpenAI and Anthropic and friends could definitely remove the AI “smell” from writing output, or give users a first class way to specify a writing style.
So why haven’t they? My theory is they see this as a sort of fingerprint, useful to not train on later. Or something. Maybe they just don’t care. Certainly feels either intentional or a result of ambivalence.
It’s certainly true today that I probably wouldn’t know an AI written article if the author went out of their way to use one of the many prompts available to tone down the AI-isms.
You're overthinking it. LLM's are massive scale regressors. Rhetorical techniques, cliches and memes (in the original Dawkins definition) spike the dataset and skew the data such that certain phrasings or rhetorical techniques will appear in the output in a similar ratio to what exists in the training set; however, the reality is that humans are able to change writing techniques depending on the nature of the conversation and the medium. LLM's don't have that, all of the scholastic papers, buzzfeed articles, and reddit posts factor into the output regardless of what it's writing. Meanwhile your average human can switch tone, style, and register depending on the situation and what they're writing.
I asked claude to take the above and rewrite it in it's own words and it came up with:
"LLMs are essentially large-scale statistical regressors — they're built on huge volumes of text, so the rhetorical patterns, clichés, and memes (in the Dawkins sense of self-replicating cultural units) that saturate the training data show up in outputs at roughly the frequency they occur in that data.
The key limitation: a human writer naturally shifts register, tone, and style based on context — what they're writing, who it's for, the medium. An LLM doesn't really do that in the same way. Every academic paper, Buzzfeed listicle, and Reddit thread it was trained on bleeds into its output regardless of the actual writing task at hand, whereas a person adapts fluidly to the situation."
Even with the context, it still included an emdash, the rhetorical technique of threes, and "the key limitation". It's an inherent weakness of LLM's. There is no fixing it.
If the underlying prompt of the model stays the same, it seems to me that LLMs will always have common tells unless overridden with a thorough prompt from the end user. It's like if you had 1 person write half of the content on the internet. You'd probably get pretty good at noticing their writing style.
Maybe my understanding of LLMs is wrong, but it seems obvious to me that when you have a large corpus of LLM output you will eventually notice common tells when everyone is using the same models, weights, and base prompt.
Because it's doing what is does best: making predictions, which works great when you're not being judged on aesthetics, such as coding or math, but the human thought process is messy or erratic. It just falls apart when you do the next token process to it. If the goal is to "convey information in readable chunks," AI does great at this.
It’s natural to the fact it’s the same model. Everyone has tics, and when a given model is asked to write millions of texts, they become visible. But! Some portion of the audience and user base can’t see it, so there is no benefit to fixing it. Case in point, yet another hustler felt very clever posting slop, and the likely actual audience (Google’s ranking system) probably does like it.
So why haven’t they? My theory is they see this as a sort of fingerprint, useful to not train on later. Or something. Maybe they just don’t care. Certainly feels either intentional or a result of ambivalence.
It’s certainly true today that I probably wouldn’t know an AI written article if the author went out of their way to use one of the many prompts available to tone down the AI-isms.