Researchers find six patterns that make AI text so exhausting to read

A study by researchers at NYU and UT Austin had 14 language models produce text in casual conversation, coding sessions, and argumentative essays, then compared it against human-written text, identifying six constructions AI overuses, such as unusual word combinations, distant placement of related sentence parts, dropped connectors, stacked nouns, nouns turned into verbs, and animate verbs applied to inanimate subjects. Smaller models like Llama and Olmo base come closest to human text, while within the Olmo family scores rise from base through instruct to the reasoning model Olmo Think, pointing to post-training as the likely cause. The researchers argue that training on math, code, and reasoning tasks rewards correctness over clarity, and that the shift from RLHF to RLVR removes humans from the feedback loop.