Wikipedia:Signs of AI writing

This is a list of writing and formatting conventions typical of AI chatbots such as ChatGPT, with real examples taken from Wikipedia articles, drafts, comments, and other content. It is a field guide to help detect undisclosed AI-generated content on Wikipedia: while some of the signs may be broadly applicable, some may not apply in a non-Wikipedia context.[a] Not all text featuring these indicators is AI-generated, as the large language models that power AI chatbots are trained on human writing, including Wikipedia. Many elements of AI writing can be found in editorials, blogs, or fan fiction.

The Most Famous AI Writing Tic Is Also the Most Mysterious

Why chatbots love “it’s not X, it’s Y”

Human Intelligence, the Secret of Artificial Intelligence

Artificial intelligence is mysterious: we speak to it and it seems to understand what we say. Proof that it understands is that it responds with text or speech that makes sense, and sometimes more …

Ethan Mollick on Twitter / X

There is a lot being written about the stylistic tells of AI writing (em-dashes, etc.) but this paper looks at AI narrative tellsFascinating differences between AI & human narrative, and asking AI to write in different styles doesn't do much to change it https://t.co/azkRHz34NQ pic.twitter.com/oTxSGBNYYE— Ethan Mollick (@emollick) May 28, 2026

I pulled ~90,000 Reddit posts about what makes writing "sound like AI" to determine the biggest AI-slop giveaways (Part 2)

674 votes, 228 comments. The majority of people can instantly tell when writing is generated by AI. For those who don't intend to get into the weeds…

The artificial intelligence disclosure penalty: Humans persistently devalue AI-generated creative writing.

Is Apple Intelligence Making Up Words Now?

A Reddit post suggests that Apple Intelligence is making up words in notification summaries.

Don't Write Like AI (1 of 101): "It's Not X, it's Y"

The #1 AI writing tell and a daily annoyance of millions.

De-anthropomorphizing “AI”: From wishful mnemonics to accurate nomenclature

Language matters. How we describe “AI” technology influences how it is perceived, deployed, and trusted. Extravagant and persuasive language incites hype. It is the responsibility of journalists, companies, and scholars to characterize technology in ways that inform and empower their readers by using appropriate terminology and avoiding inflated claims. One type of inflated claim comes from using anthropomorphizing language to describe system functionality. Anthropomorphization is the attribution of human capabilities and characteristics to the inanimate system. In this paper, we present a linguistic analysis of anthropomorphizing language in 29 texts (a total of 1,368 sentences) from academic articles, online news articles, and company blog posts. We construct a taxonomy of eight categories of anthropomorphization: Cognizer, Products of cognition, Emotion, Communication, Agent, Human role analogy, Names and pronouns, and Biological metaphors. Following this taxonomy we present concrete strategies for how to de-anthropomorphize the language we use to describe “AI” based on a functionality-first principle.

Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!

Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of language models on reasoning tasks. These intermediate tokens have been called \say{reasoning traces} or even \say{thinking traces} -- implicitly anthropomorphizing the traces, and implying that these traces resemble steps a human might take when solving a challenging problem, and as such can provide an interpretable window into the operation of the model's thinking process to the end user. In this position paper, we present evidence that this anthropomorphization isn't a harmless metaphor, and instead is quite dangerous -- it confuses the nature of these models and how to use them effectively, and leads to questionable research. We call on the community to avoid such anthropomorphization of intermediate tokens.

Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!

Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of language models on reasoning tasks. These intermediate tokens have been called \say{reasoning traces} or even \say{thinking traces} -- implicitly anthropomorphizing the traces, and implying that these traces resemble steps a human might take when solving a challenging problem, and as such can provide an interpretable window into the operation of the model's thinking process to the end user. In this position paper, we present evidence that this anthropomorphization isn't a harmless metaphor, and instead is quite dangerous -- it confuses the nature of these models and how to use them effectively, and leads to questionable research. We call on the community to avoid such anthropomorphization of intermediate tokens.

no slop grenade

Stop throwing AI-generated walls of text into conversations. If they wanted an AI essay, they would have asked ChatGPT themselves.

Tropes - AI Writing Pattern Directory

The definitive reference for identifying AI writing patterns. Learn to spot the tropes that give away AI-generated text.