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It\u2019s a familiar feeling: You start a text message, and your phone\u2019s auto-complete function suggests several choices for the next word, ranging from banal to hilarious. \u201cI love\u2026\u201d you, or coffee? Or you\u2019re finishing an email, and merely typing the word \u201cLet\u201d prompts your app to suggest \u201cLet me know if you have any questions\u201d in light gray text.<\/p>\n
Predictive language technologies have\u00a0become so routine<\/a>\u00a0\u2013 baked into smartphones, email services and chatbots \u2013 that we barely notice them anymore. But they raise a difficult question: What happens to a writer\u2019s unique voice when AI routinely completes their thoughts \u2013 or generates them altogether from scratch?<\/p>\n As the chair of a large English department \u2013 and as a scholar\u00a0who researches the effects of predictive writing<\/a>\u00a0\u2013 I\u2019ve witnessed firsthand the challenges that generative AI systems such as ChatGPT, Gemini and Claude pose for individual expression.<\/p>\n This technology has been\u00a0incorporated into the writing process<\/a>\u00a0so fully that it\u2019s almost impossible to imagine encountering a scene from the not-so-distant past: a writer, alone, with a pen and a piece of paper, wrestling with how to best translate their ideas, arguments and stories into something legible and interesting.<\/p>\n As many scholars have noted, though, this vision of writing was never fully accurate.<\/p>\n Essays have always incorporated guidance from teachers, professors or writing tutors. A friend might give feedback, or your favorite novelist\u2019s turn of phrase might offer inspiration. The language we use\u00a0is never fully<\/a>\u00a0\u201cours,\u201d but draws on millions of sources absorbed over the course of our lives.<\/p>\n Just as it\u2019s a myth to imagine that writers compose in a vacuum, there has never been a clear line between genuine human expression versus machine-generated text.\u00a0As scholars have pointed out<\/a>, we have been using machines to communicate for a long time. Every technological development \u2013 from the quill pen and the typewriter to the word processor \u2013 has brought with it changes in how humans express themselves.<\/p>\n However, the ubiquity of predictive language technologies directly threatens human creativity \u2013 or, as one study put it, \u201cPredictive Text Encourages Predictive Writing<\/a>.\u201d<\/p>\n Because generative AI composes and suggests text in highly standardized, predictable patterns, its outputs can read as if they\u2019re dressed-up versions of what linguists call \u201cphatic expression<\/a>.\u201d These are the overly common phrases that function as social glue more than as conveyors of sentiment: \u201cHow are you?\u201d, \u201cHave a good day\u201d or \u201cSee you soon.\u201d<\/p>\n But this glue can lose its hold if the technology is used in the wrong situations. Using artificial intelligence\u00a0to compose a social media post<\/a>\u00a0in the wake of a tragedy, or using it\u00a0to write a fan letter to an Olympic athlete<\/a>,\u00a0comes off as insincere<\/a>.<\/p>\n People are starting to catch on to\u00a0generative AI\u2019s prose<\/a>, not because it\u2019s clunky or poorly written, but because it all sounds the same. That\u2019s because\u00a0large language models are trained<\/a>\u00a0on gigantic masses of examples of human writing, and they predict text based on probabilities and commonalities. I wonder if anyone's written about LLMspeak from a linguistic perspective. This doesn't have a single em dash and nary a "it's not X, it's Y," but you can tell *immediately* that it's an LLM. In the abstract I would not have assumed machine-produced prose would be so obvious https:\/\/t.co\/3CoLUKCEBZ<\/a><\/p>\n — LBark (@franzsherbert) February 27, 2026<\/a><\/p><\/blockquote>\n Predictive text leads to predictive writing<\/strong><\/h4>\n
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