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When large language models (LLMs) are allowed to interact without any preset goals, scientists found distinct personalities emerged by themselves.
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Our personalities as humans are shaped through interaction, reflected through basic survival and reproductive instincts, without any pre-assigned roles or desired computational outcomes. Now, researchers at Japan's University of Electro-Communications have discovered that artificial intelligence (AI) chatbots can do something similar.
The scientists outlined their findings in a study first published Dec. 13, 2024, in the journal Entropy, which was then publicized last month. In the paper, they describe how different topics of conversation prompted AI chatbots to generate responses based on distinct social tendencies and opinion integration processes, for instance, where identical agents diverge in behavior by continuously incorporating social exchanges into their internal memory and responses.
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Graduate student Masatoshi Fujiyama, the project lead, said the results suggest that programming AI with needs-driven decision-making rather than pre-programmed roles encourages human-like behaviors and personalities.
How such a phenomenon emerges is the cornerstone of the way large language models (LLMs) mimic human personality and communication, said Chetan Jaiswal, professor of computer science at Quinnipiac University in Connecticut.
"It's not really a personality like humans have," he told Live Science when interviewed about the finding. "It's a patterned profile created using training data. Exposure to certain stylistic and social tendencies, tuning fallacies like reward for certain behavior and skewed prompt engineering can readily induce 'personality', and it's easily modifiable and trainable."
Author and computer scientist Peter Norvig, considered one of the preeminent scholars in the field of AI, thinks the training based on Maslow's hierarchy of needs makes sense because of where AI's “knowledge” comes from.
Sign up for the Live Science daily newsletter nowContact me with news and offers from other Future brandsReceive email from us on behalf of our trusted partners or sponsorsBy submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over."There's a match to the extent the AI is trained on stories about human interaction, so the ideas of needs are well-expressed in the AI's training data," he responded when asked about the research study.
The future of AI personality
The scientists behind the study suggest the finding has several potential applications, including "modeling social phenomena, training simulations, or even adaptive game characters."
Jaiswal said it could provide a shift away from AI with rigid roles, and towards agents that are more adaptive, motivation-based and realistic. "Any system that works on the principle of adaptability, conversational, cognitive and emotional support, and social or behavioral patterns could benefit. A good example is ElliQ, which provides a companion AI agent robot for the elderly."
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But is there a downside to AI generating a personality unprompted? In their recent book "If Everybody Builds It Everybody Dies," (Bodley Head, 2025) Eliezer Yudkowsky and Nate Soares, past and present directors of the Machine Intelligence Research Institute, paint a bleak picture of what would befall us if agentic AI develops a murderous or genocidal personality.
Jaiswal acknowledges this risk. "There is absolutely nothing we can do if such a situation ever happens," he said. "Once a superintelligent AI with misaligned goals is deployed, containment fails and reversal becomes impossible. This scenario does not require consciousness, hatred, or emotion. A genocidal AI would act that way because humans are obstacles to its objective, or resources to be removed, or sources of shutdown risk."
So far, AIs like ChatGPT or Microsoft CoPilot only generate or summarize text and pictures — they don't control air traffic, military weapons or electricity grids. In a world where personality can emerge spontaneously in AI, are those the systems we should be keeping an eye on?
"Development is continuing in autonomous agentic AI where each agent does a small, trivial task autonomously like finding empty seats in a flight," Jaiswal said. "If many such agents are connected and trained on data based on intelligence, deception or human manipulation, it's not hard to fathom that such a network could provide a very dangerous automated tool in the wrong hands."
Even then, Norvig reminds us that an AI with villainous intent need not even control high-impact systems directly. "A chatbot could convince a person to do a bad thing, particularly someone in a fragile emotional state," he said.
Putting up defences
If AI is going to develop personalities unaided and unprompted, how will we ensure the benefits are benign and prevent misuse? Norvig thinks we need to approach the possibility no differently than we do other AI development.
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"Regardless of this specific finding, we need to clearly define safety objectives, do internal and red team testing, annotate or recognize harmful content, assure privacy, security, provenance and good governance of data and models, continuously monitor and have a fast feedback loop to fix problems," he said.
Even then, as AI gets better at speaking to us the way we speak to each other — ie, with distinct personalities — it might present its own issues. People are already rejecting human relationships (including romantic love) in favour of AI, and if our chatbots evolve to become even more human-like, it may prompt users to be more accepting of what they say and less critical of hallucinations and errors — a phenomenon that's already been reported.
For now, the scientists will look further into how shared topics of conversation emerge and how population-level personalities evolve over time — insights they believe could deepen our understanding of human social behavior and improve AI agents overall.
Article SourcesTakata, R., Masumori, A., & Ikegami, T. (2024). Spontaneous Emergence of Agent Individuality Through Social Interactions in Large Language Model-Based Communities. Entropy, 26(12), 1092. https://doi.org/10.3390/e26121092
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Drew TurneyDrew is a freelance science and technology journalist with 20 years of experience. After growing up knowing he wanted to change the world, he realized it was easier to write about other people changing it instead. As an expert in science and technology for decades, he’s written everything from reviews of the latest smartphones to deep dives into data centers, cloud computing, security, AI, mixed reality and everything in between.
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