The Scaling Law for Distributed AI Networks

This article discusses a scaling law that governs distributed AI networks, which is about the number of synthesis paths between independent research sites rather than model size. The author argues that the standard research infrastructure fails to capture these synthesis paths, leading to inefficiencies in knowledge sharing.

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The article explains that the scaling laws commonly discussed in AI research, such as Chinchilla and Kaplan, describe how a single system gets smarter as resources are added. However, the author argues that this is the wrong problem for a distributed research environment. The scaling law that actually governs distributed intelligence networks is about the number of synthesis paths between independent sites, which grows quadratically with the number of sites. The standard research infrastructure fails to capture these synthesis paths, leading to inefficiencies where sites replicate each other's work without knowing about interim findings. The author proposes a new architecture called Quadratic Intelligence Swarm (QIS) that can achieve quadratic growth in synthesis paths with logarithmic growth in coordination cost. This is done by having each site distill its observations into compact outcome packets, which are then routed based on semantic similarity rather than sharing raw data or model weights.