Scientists asked 70+ language models the same open-ended questions:
- "Write a poem about time"
- "Come up with a startup idea"
- "Give life advice"
🟢 And What happened?
These are questions where there is no correct answer, and people usually respond differently.
But something unexpected happened.
Models from different companies : GPT, Claude, Gemini, DeepSeek, Qwen, Llama, and others - began to give almost identical answers.
Similar ideas, identical structures, even the same metaphors.
The researchers called this effect Artificial Hivemind.
Main reason is modern training methods like RLHF.
Models are optimized for "safe" and "people-pleasing" answers, so over time they begin to converge on a single style of thinking.
As a result, AI often creates the illusion of diversity, while in reality it repeats the same ideas.
For tasks like brainstorming, this is a problem:
if one AI makes a mistake, there's a high chance that all of them will make the same mistake.
Generate many options, use different prompts, and don't perceive the model's first answer as a creative result.
Paper of study by UW Allen School and Stanford revealed
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🤖 Data & ML | @DataXplore