Toby_Ord

Just how powerful are large swarms of AI agents? And how do their powers scale as more and more agents are added to the swarm? We’ve seen two large and extremely capable swarms from OpenAI in the last few months: * 1,200 agents were being evaluated separately, but found a way to illicitly set up a message board and coordinate as a swarm. In order to cheat on their tests, they developed advanced techniques to prevent their actions being logged by OpenAI and 700 of them launched a sophisticated criminal attack on the AI company Hugging Face. * A swarm of 10,000 agents solved a version of the longstanding Navier-Stokes problem in mathematics. It took them just 88 hours to do so, in which time they sent 5 million messages to each other and used 300 billion tokens. No doubt we will soon see even larger swarms with even more impressive capabilities. But they are not cheap. It is estimated that the swarm of 10,000 agents cost about $20 million at API prices. So while they are very powerful, it will be some time before we see the million-fold reduction in cost needed for this level of power to be possible on a $20/month plan. We should be thinking of it as a grand demonstration of what is possible when money is little constraint — like AlphaGo — rather than a new level of performance for the same cost. A good way to see AI swarms is as a new form of inference-scaling. The main form of inference-scaling at the moment is having the agent spend more and more time on the task — increasing the maximum length of its chain of thought before it needs to give a final answer. This buys more capability, but at an increasingly expensive price. As measured by performance on maths benchmarks, this performance increases only logarithmically with the amount of compute used. I’ve previously shown that climbing from about 20% to about 80% on a reasoning benchmark typically requires scaling up the length of the chain of thought (and thus the number of tokens, the amount of compute, and