Toby Ord infers the parallelizability of agent-cluster scaling from OpenAI cluster data

Toby Ord estimates from OpenAI's public charts that a 10x increase in the number of agents is roughly equivalent to a 3x to 5x increase in inference tokens for a single agent. The collaboration scaling parameter across three benchmarks is about 0.48 to 0.68, close to estimates for some human teams and recursive self-improvement models. The author believes clusters are currently mainly trading higher cost for speed, and discusses the parameter's implications for intelligence-explosion models. The extrapolation covers only three benchmarks and at most 16 agents, so it cannot be directly extended to large clusters; a September 27 revision removed a speculation about another scaling chart.