AI writing is hard to spot, and it is changing human writing too
AI-written text is much harder to identify than many people assume. In linguist Claire Hardaker’s “Bot or Not” test, people correctly pick AI-written reviews only about 60% of the time on average. Common clues such as cliches, long dashes, and neat groups of three are weak evidence because human writers use them too. This uncertainty has led to public suspicion around writers and publishers, including the Jamir Nazir short-story dispute, the withdrawal of the novel Shy Girl after denied AI-use rumors, and Steven Rosenbaum’s book The Future of Truth containing that he later acknowledged.
tools are also imperfect: Pangram claims a very low and has tested well independently, but style changes can still fool detectors, and naturally “AI-like” human writing can be misread. output has visible habits at scale, including heavy use of words such as “delve,” “showcase,” “underscore,” and “intricate.” Some researchers think with human feedback may push models toward words that human raters treat as signs of quality. Studies now suggest some AI-favored words rose in real conversations after ChatGPT became common, while public attention to those words can also make writers avoid them. Models have their own speech habits: Gemini often uses certain explanatory openings, while DeepSeek has its own stock responses.
AI editing can also flatten different forms of English toward an Anglo-American norm, a pattern researchers call cultural ghosting. Writers and linguists argue that AI is strong at familiar, functional prose but weaker at deep story shape, lived feeling, and true artistic novelty. Jennifer Egan avoids AI for creative writing because she worries about style contamination and training-data theft, while Jeannette Winterson sees AI as a usable tool but not as a replacement for human inner life.