The Most Famous AI Writing Tic Is Also the Most Mysterious - The Atlantic
If Julius Caesar had debuted this year, William Shakespeare might have been accused of writing it with AI. A certain suspicious rhetorical device appears again and again in the play. It’s in Act I, Scene ii: “The fault, dear Brutus, is not in our stars, but in ourselves.” In Act III, Scene ii: “Not that I loved Caesar less, but that I loved Rome more.” And later in that same scene: “I come to bury Caesar, not to praise him.”
These famous lines include what has become perhaps the best-known tic of AI writing—a sentence that tells you what the subject isn’t as well as what it is: It’s not X; it’s Y. Once you start noticing the construction, you see it all over the place. In one version, the Y is additive: It focuses, intensifies, or expands on the X. An annual review by Citizens Financial Group reported that growth in its private-banking division was “not just a win for the private bank—it’s a win for the entire enterprise.” In another variant, the Y supplants the X as the preferred descriptor. “The target was never a man. The target was the truth,” Michael Flynn, a former Donald Trump adviser, wrote in a March X post.<br>Then there are constructions like No A, no B, just C, which especially seem to crop up in AI-generated fiction. Lines such as “No bag, no things, no armor, just me” helped to fuel accusations of AI writing in the horror novel Shy Girl, which was pulled by its publisher this year. (The book’s author denied using AI to write it. Citizens Financial Group has previously said that its communications team “leverages the technology in a number of areas.” Flynn did not respond to a request for comment.)
The prevalence of this device isn’t just anecdotal—it’s measurable. (Sorry.) Barron’s reported that its appearance in corporate communications more than quadrupled from 2023 to 2025. Researchers at Pangram, which makes an AI-detection tool, estimate that Not just X but Y sentences appear three times as often in AI writing as they do in human writing. Elyas Masrour, a founding engineer at Pangram, told me that all of the major chatbots—including ChatGPT, Claude, Gemini, and various open-source models—rely on it to varying degrees.
Many other well-known chatbot tells—such as the usage of delve—have come and gone as AI companies honed their models and worked out kinks. Last fall, ChatGPT became obsessed with goblins and gremlins, prompting another intervention: OpenAI retired ChatGPT’s “nerdy” personality, whose affinity for mythical creatures had apparently infected its other models. Yet It’s not X; it’s Y has shown no signs of abating.
Before ChatGPT came along, the construction was obscure enough that it didn’t really have an agreed-upon name. Now there’s a scramble for what to call it. Terms from academia, such as antithesis and metalinguistic negation, capture some forms of the construction but not others. In an email, Laurentia Romaniuk, a product manager for model behavior at OpenAI, referred to it as “contrastive phrasing.” Despite its clunkiness, the most popular name I’ve seen is “negative parallelism.”
When deployed judiciously, negative parallelism can be punchy. But ChatGPT turns to it too often, Romaniuk acknowledged, which can feel formulaic. So the company is working on ways to broaden the chatbot’s repertoire. In the meantime, she added, users can try giving ChatGPT “custom instructions.” On a Reddit forum about AI writing, users trade tips for scrubbing negative parallelism from chatbots’ writing. One suggested pasting Claude’s output into another AI chatbot and telling it to act as a copy editor who has a strict ban on “negative pairings” such as “it wasn’t X, it was Y.”
One obstacle to a more comprehensive fix is that no one seems to know for certain why AI models are so enamored with negative parallelism in the first place—maybe not even the companies that created them. (Anthropic and Google did not respond to my requests for an interview.)
The simplest theory is that humans trained them that way. Large language models are built by first identifying patterns in unfathomable quantities of human-written text: books, academic papers, patent filings, and especially the internet. Negative parallelism was, of course, present in the initial training data. Shakespeare aside, there are lots of famous examples: In the 1960s, the legendary football coach Vince Lombardi popularized the saying that “winning isn’t everything; it’s the only thing.” In the 1990s, a frozen-pizza brand’s commercials insisted: “It’s not delivery. It’s DiGiorno.”
But the training data also included lots of bad writing that AI companies don’t want their chatbots to mimic, Tuhin Chakrabarty, a computer-science professor at Stony Brook University who studies AI writing, told me. So they also undergo “reinforcement learning,” a process by which human reviewers grade the models on their responses. Through trial and error, chatbots are guided...