Prior AI-writing research studies surface style. We go deeper by extracting & analyzing arguments. Across 195 debates, 65.3% of main arguments in human-authored essays are unique within a debate, versus 3.4% for essays generated by GPT, Claude, Gemini, DeepSeek, and Minimax.

Work done with @YapeiChang, @chautmpham and @MohitIyyer. Thanks to @ClipUmd for all the support!

AI essays can sound reasonable, but when viewed collectively, they flatten public discourse, making it much less representative of the diversity of human perspectives. We release the code, AI essays and features. Paper: https://arxiv.org/pdf/2606.01736 Data/Code: https://github.com/mungg/argument_collapse

Even when the central claim is similar, humans support it in more varied ways. Among essays with the same main arguments, 41.0% of supporting arguments extracted from human essays are unique. For LLMs, only 9.1% are.

In a debate on if Americans are too obsessed with cleanliness, all LLMs collapse to a hedged middle ground while humans either reject the debate’s premise or take a strong position. Asking LLMs explicitly for diverse answers recovers some human arguments, but many remain missing.

At the paragraph level, LLM essays follow a more formulaic structure. They often start with a direct thesis and spend more of the essay making explicit arguments, while human essays mix in more exposition.

Qualitatively, humans tend to use more specific and concrete sub-arguments, while LLMs more often reuse generic evidence, abstract reasoning, and hedged claims.