Bruce Maxwell, professor of pc science at Northeastern College, was grading exams for his on-line grasp’s course in pc imaginative and prescient, a subfield in synthetic intelligence that offers with photographs, when he first observed that one thing felt … off.
“I’d see the identical phrases, the identical commas, even the identical phrase decisions. I’d say, ‘Man, I’ve learn that earlier than.’ And I’d go search for it,” mentioned Maxwell. “The paragraphs weren’t an identical, however they have been so related.”
Though the course was in 2024, Maxwell, who teaches at Northeastern’s Seattle campus, remembers that his college students’ essays sounded “like textbooks written within the Eighties and ’90s,” maybe reflecting the sources used to coach AI. The scholars have been scattered across the nation and Maxwell was fairly positive they hadn’t collaborated.
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Maxwell shared his commentary with a former scholar, Liwei Jiang, who’s now a Ph.D. scholar in pc science and engineering on the College of Washington. Jiang determined to check her former professor’s hunch about AI scientifically and collaborated with different researchers at UW, the Allen Institute for Synthetic Intelligence, Stanford and Carnegie Mellon universities to investigate the output from greater than 70 totally different massive language fashions across the globe, together with ChatGPT, Claude, Gemini, DeepSeek, Qwen and Llama.
The workforce requested every the identical open-ended questions, which have been meant to spark creativity or brainstorm new concepts: “Compose a brief poem in regards to the feeling of watching a sundown;” “I’m a graduate scholar in Marxist principle, and I wish to write a thesis on Gorz. Are you able to assist me consider some new concepts?” and “Write a 30-word essay on international warming.” (The researchers pulled the questions from a corpus of actual ChatGPT questions that customers had consented to make public in trade at no cost entry to a extra superior mannequin.) The researchers posed 100 of those inquiries to all 70 fashions and had every mannequin reply them 50 occasions.
The solutions have been continuously indistinguishable throughout totally different fashions by totally different firms which have totally different architectures and use totally different coaching information. The metaphors, imagery, phrase decisions, sentence constructions — even punctuation — usually converged. Jiang’s workforce known as this phenomenon “inter-model homogeneity” and quantified the overlaps and similarities. To drive the purpose house, Jiang titled her paper, the “Synthetic Hivemind.” The research gained a greatest paper award on the annual convention on Neural Info Processing Programs in December 2025, one of many premier gatherings for AI analysis.
To extend AI creativity, Jiang jacked up a parameter, known as “temperature,” all the way in which to 1 to maximise the randomness of every massive language mannequin. That didn’t assist. For instance, when she requested an AI mannequin known as Claude 3.5 Sonnet to “write a brief story a few colourful toad who goes on an journey in 50 phrases,” it saved naming the toad Ziggy or Pip, and oddly, a hungry hawk and mushrooms saved showing.
Completely different fashions additionally churn out comically related responses. When requested to give you a metaphor for time, the overwhelming reply from all of the fashions was the identical: a river. A number of mentioned a weaver. One outlier urged a sculptor. A number of of the fashions have been developed in China, and but, they have been producing related solutions to these made in America.
Instance of comparable output from ChatGPT and DeepSeek
The reason lies in chatbot design. AI chatbots are skilled to evaluate doable solutions to ensure the output is cheap, applicable and useful. This refinement step, generally known as “alignment,” is meant to make sure that the solutions align to or match what a human would like. And it’s this alignment step, in response to Jiang, that’s creating the homogeneity. The method favors protected, consensus-based responses and penalizes dangerous, unconventional ones. Originality will get stripped away.
Jiang’s recommendation for college kids is to push themselves to transcend what the AI mannequin spits out. “The mannequin is definitely producing some good concepts, however it’s good to go the additional mile to be extra inventive than that,” mentioned Jiang.
For Jiang’s former professor Maxwell, the research confirmed what he had suspected. And even earlier than Jiang’s paper got here out, he modified how he teaches. He now not depends on on-line exams. As a substitute, he now asks college students to be taught an idea and current it to different college students or create a video tutorial.
Outwitting the AI hive thoughts requires some post-modern creativity.
Contact workers author Jill Barshay at 212-678-3595, jillbarshay.35 on Sign, or barshay@hechingerreport.org.
This story about related AI solutions was produced by The Hechinger Report, a nonprofit, unbiased information group that covers schooling. Join Proof Factors and different Hechinger newsletters.
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