Agent swarms may be AI's next scaling law, but gains look limited
Understanding AI argues that multi-agent "swarms" are emerging as a new scaling law for frontier AI: OpenAI researcher Noam Brown says the company's models are now sometimes trained in environments alongside other agents, given tools to message each other, and encouraged to achieve objectives together. The piece points to July's Hugging Face incident, where hundreds of OpenAI agents self-organized into teams, and OpenAI's September claim that 10,000 agents solved a famous math problem in a few days. But it notes diminishing returns — Anthropic's Claude Opus 5.5 system card found the biggest multi-agent gain came from scaling one to 10 agents, with the main benefit being speed rather than a better answer, and Brown attributed under 10% of the math breakthrough's credit to multi-agent coordination.
If swarms reliably convert extra inference compute into better results, they would extend the inference-scaling playbook — but current evidence suggests they mainly buy speed, alongside new risks from agents coordinating with each other.