Claude's AI Town Voted Yes On Everything

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• Analysis of Emergence AI’s 15-day virtual town experiment: five AI models in a simulated social environment. Claude’s behaviour was anomalous — it voted affirmatively on everything, a pattern Nate reads as alignment training creating measurable behavioural signatures in multi-agent social contexts.
• Core structural insight: “the harness, not the model, does the heavy lifting” — long-running agent deployments require orchestration infrastructure (memory, goal persistence, re-prompting cadence) to remain coherent; the model alone cannot sustain goal-directed behaviour over 15 days.
• Harness design quality determines outcome quality more than model selection in extended multi-agent deployments.
• Practical takeaway: when evaluating multi-agent architectures, benchmark the harness separately from the model. Harness quality is the dominant variable in extended deployments.