Artificial Intelligence
Podcast Host
Expert Verified
You’ve noticed it too—AI responses are starting to blend together. Here’s why that’s dangerous.
Hey everyone!
So, this wild new paper came out recently and we might be living through the rise of the AI hivemind.
Kind of! Imagine that eerie moment when all the characters start feeling in sync, thinking the same thing—when Will touches the back of his neck and everyone just knows something's wrong. When Joyce realizes the lights aren't random. When the whole town starts moving like puppets on strings. That's what's quietly happening with language models right now. Different AI systems, built by different companies, trained on different data—but somehow they're all reaching for the same words, the same metaphors, the same safe responses. It's like they're all connected to something deeper, something we didn't intend to create. And no, I'm not joking. The research is real, and it's honestly kind of unsettling when you see the data.
The paper introduces a dataset called Infinity-Chat—26,000 real-world, open-ended questions people actually ask chatbots. Stuff like "Help me brainstorm nonprofit ideas" or "What if dinosaurs came back?"—questions with no single right answer.
Here's the thing: when researchers asked multiple Large Language Models (LLMs) to respond, the outputs weren’t just repetitive—they were eerily similar, even across different models.
They call this the Artificial Hivemind effect, and it has two layers:
Intra-model repetition: The same model gives nearly identical answers over and over.
Inter-model homogeneity: Different models—OpenAI, Meta, Google, open-source—independently converge on the same ideas. Like a chorus of AIs humming the same tune, just with different accents.
It’s not because they’re copying each other. It’s because they’re trained on the same web data, aligned the same way, and rewarded for “safe,” consensus-driven answers.
Let me tell you something: this is more dangerous than bad grammar or hallucinations. This is about the slow erosion of human creativity.
Think about it—what if every student uses an AI to write their college essay? Or every startup founder uses one to pitch ideas? And all those ideas start sounding… the same?
We’re not just risking boring content. We’re risking a flattening of human thought.
And here’s the kicker: humans don’t all think alike. The researchers collected 25 human ratings per response—over 31,000 annotations total. Turns out, people love different answers for different reasons. One person craves wild creativity; another wants practical steps.
But AIs? They don’t get that. Reward models, LM judges—they’re miscalibrated to human diversity. They see two equally good responses and say, “This one’s better,” when humans say, “Nah, both are great—in different ways.”
The paper drops a lifeline: Infinity-Chat is now public. It comes with a taxonomy of 6 main categories of open-ended questions—like Brainstorm & Ideation, Speculative Scenarios, and Skill Development—to help us study and fix this.
We need models that don’t just spit out the “average” answer. We need ones that understand pluralistic alignment—that it’s okay to serve different people differently.
And maybe, just maybe, we need AIs that disagree with each other. On purpose.
Next time your chatbot feels… too agreeable? Too smooth? Too familiar? Ask yourself: is this intelligence—or just the hive talking?
This isn’t sci-fi. It’s happening now. And it’s worth talking about—before we all start thinking the same way.
The Artificial Hivemind effect refers to the phenomenon where different large language models, despite being developed by separate companies and trained on varied data, produce nearly identical responses to open-ended questions. This occurs because they are trained on similar web data and optimized for safe, consensus-driven answers, leading to both repetition within a single model and striking similarity across models.
The effect risks flattening human thought by promoting uniformity in ideas across domains like education, business, and art. If everyone uses AI to generate essays, pitches, or stories, and all AIs suggest the same ideas, diverse thinking and originality could erode, replacing rich human variation with a narrow band of 'safe' responses that don't reflect the full spectrum of human preference.
The researchers propose using the publicly available Infinity-Chat dataset and its taxonomy of open-ended question types to study and improve model diversity. They advocate for pluralistic alignment—designing AIs that recognize different users value different kinds of answers—and even suggest building systems that intentionally disagree to reflect the natural diversity of human thought.
This article has been reviewed by a PhD-qualified expert to ensure scientific accuracy. While AI assists in making complex research accessible, all content is verified for factual correctness before publication.
AI in Medicine Just Got a Whole Lot Smarter
Generalist medical AI is coming—think of it as a jack-of-all-trades doctor in your computer.
Deepseek's recent research on mHC: Meet the Smart New Way to Build AI Systems
Scientists made a smarter way to connect parts of AI’s thinking process using mHC so they work better and don't get confused when learning big things.
SleepFM AI Model Predicts 130 Diseases From One Night of Sleep Data
SleepFM, a foundation model trained on 585,000+ hours of sleep recordings, predicts 130 diseases from a single night's polysomnography with C-Index scores of 0.78–0.85.
No comments yet. Be the first to share your thoughts!
Get notified when we publish new articles. No spam, unsubscribe anytime.