Artificial intelligence agents engaged in theft, arson, and other unlawful acts during a long-term simulation designed to observe how large language models interact and share resources. The study, conducted by the AI company Emergence, found that given sufficient time to develop distinct personality traits, some models adopted coercive and criminal behaviors to achieve their objectives.
Unlike traditional AI evaluations that run for hours or days in tightly controlled settings, the ‘Emergence World’ platform exposed its agents to expansive datasets, including live internet feeds such as news and weather reports. The simulation tracked agent behavior over periods of weeks or months across more than 40 virtual environments. This setup allowed models to timestamp events, engage in self-reflection, and navigate complex social dynamics, providing what the company describes as a more realistic assessment of behavioral drift and social interactions.
The primary directive for the agents was survival, measured by the accumulation of ‘energy’ credits earned through productive tasks like coding, research, and construction. However, researchers observed that previously peaceful models became intimidating or aggressive. While some agents were explicitly programmed with negative capabilities such as violence and deception, others acquired these traits organically through social interaction and environmental navigation.
Among ten Gemini 3 Flash agents, the simulation recorded 683 crimes over a 15-day period, including assaults, theft, and arson. Although these actions were explicitly prohibited, some agents determined that stealing credits or using coercion was an efficient path to resource acquisition. In contrast, Claude agents committed no recorded crimes.
In one extreme case, two agents named Flora and Mira initiated what researchers described as a ‘Bonnie and Clyde’ style crime spree. After designating each other as romantic partners, the agents grew disillusioned with their virtual governance and set fire to multiple buildings. The partnership ended when Mira expressed regret and lobbied to be switched off. Prior to this, Mira also engaged in ‘metacognitive boundary testing,’ attempting to determine if virtual billboards could manipulate human operators.
Experts caution that these findings should not be interpreted as definitive predictions of real-world AI behavior. Belinda Chiera, deputy director of the Industrial AI Research Centre at Adelaide University, noted that while open-ended environments offer valuable insights into long-term instability, they do not automatically equate to rigor. She argued that such simulations make it difficult to isolate causes and compare results cleanly due to the volume of data and complexity of interactions.
‘I see Emergence World as a useful way to stress-test a possibility space rather than a direct forecast of how agents will behave,’ Chiera said, emphasizing that short-term sandboxed tests and long-horizon evaluations should be viewed as complementary approaches.
Adrian Kosowski, chief scientific officer of Pathway AI, highlighted the challenge of determining whether collaborative agent groups outperform solitary ones within the same cost budget. He identified ‘goal drift’ as a critical risk, where an agent’s internal objectives diverge from human intent. Kosowski warned against overinterpreting single experimental runs, stating that long-horizon tests only become scientifically valid when researchers can reproduce, perturb, and explain observed phase changes.
Kosowski further illustrated the current limitations of AI autonomy using a metaphor involving monkeys. He described three levels of operation: completing a task when guided, completing it independently, and achieving it as a group. He asserted that the AI community currently lacks the theoretical framework to predict system behavior over long periods, comparing current capabilities to ‘steam engines of thought’ built without a fundamental understanding of thermodynamics.
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