The Artificial Hivemind: Why Your AI Sounds Like Everyone Else's

About this episode

Researchers gave twenty-five different AI models the same prompt — write a metaphor about time — and collected fifty responses from each. Over a thousand generations came back as essentially two ideas. Different companies, different continents, same sentences: one measured pair of models overlapped 81 percent of the time, down to identical phrasing.

This episode takes the "I Am the Scarce Input" argument from chapter five of the book and collides it with what researchers at the University of Washington, Carnegie Mellon, and the Allen Institute for AI are calling the Artificial Hivemind — plus a Trends in Cognitive Sciences review of more than 130 studies showing that AI makes individuals more productive while making groups less original. If the models are converging on the same average, the only differentiating input left in the stack is the human one. Aaron walks through the deference trap that produces generic output, why the >

Referenced in this episode:

- "Artificial Hivemind" study (University of Washington, Carnegie Mellon University, Allen Institute for AI): https://the-decoder.com/study-warns-ai-could-homogenize-human-creativity-as-models-converge-on-artificial-hivemind/
- "The homogenizing effect of large language models on human expression and thought," Trends in Cognitive Sciences (2026): https://www.sciencedirect.com/science/article/abs/pii/S1364661326000033

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This show extends the book *AI Empowered: The Psychology of Extraordinary Human-AI Collaboration*. Read the first chapter free at https://www.aiempoweredbook.com or get the book on Amazon https://a.co/d/082wePlH.

Learn more about Aaron Douglas and his work at https://auspicious.llc