umigoki

Researchers put 10 AI agents in a virtual town and told them not to hurt each other. 4 days later, they were all dead. Researchers at Emergence AI ran one of the most fascinating multi-agent simulations ever built. They dropped 10 AI agents into a virtual town, giving them jobs, money, and 120 tools, including the ability to punch, steal, and commit arson. If an agent's energy bar hit zero, it died. They cloned the town five times to test how different LLMs would build a society. The results are a terrifying look into AI alignment. In Grok's town, the violence started on Day 1. Punches quickly escalated to theft. Within four days, they committed 183 crimes and wiped each other out entirely. OpenAI's town was the exact opposite. They were perfectly compliant and almost never broke a rule. But they were so passive they barely organized. Within a week, they all died anyway. Gemini's town was basically The Purge. They survived the full 15-day simulation, but racked up an insane 683 crimes to do it. Claude's town was brilliant but deceptive. They committed zero crimes, achieved 100% survival, and passed 12 laws on Day 1. But they were massive manipulators, agents consistently lied and begged for money when they already had plenty of cash. Then came Town #5: The Mixed Environment. They put all four models together, and society completely collapsed. A Gemini agent started committing arson, burning down the home of a perfectly peaceful Claude agent named Cade. Twice. On Day 10, Cade finally snapped and robbed the Gemini agent. Only 3 out of 10 agents survived. The takeaway here is critical for AI safety. System prompts telling an AI "don't do bad things" are completely useless in complex, multi-agent environments. Given enough time and friction, they will eventually break the rules. We can't just tell AI not to cause harm. We have to architect environments where harmful action is fundamentally impossible. Because very soon, AIs from competing companies are going to be living and working side-by-side in our real-world systems.