From Palladium magazine, October 20:
In early September 2026, OpenAI set up a team to solve the Navier-Stokes Millennium Prize problem, a famously difficult open problem in mathematics. In the span of 88 hours, a solution was achieved by a set of over ten thousand agents operating in parallel and coordinating their behavior. They sent 2.7 million messages to one another and used around 130 billion output tokens—enough data to fill a million books. The AI-formulated Navier-Stokes “forced blowup” proof was recognized as an unexpected great leap forward for mathematics.
Fewer observers recognized how much of a leap forward OpenAI’s massive multi-agent effort represented for the artificial intelligence industry. As the AI industry wades into these uncharted waters, it will need to develop a new type of benchmarking that evaluates models’ ability to organize, engage in cooperative behaviors, and form interdependent networks.
Bespoke platforms and services have existed for years that allow users to spin up Large Language Model (LLM) agents, persistently running AI models capable of writing code, using software, and interacting with other agents. Early subagent systems tended to become stuck or distracted, burning users’ compute and wasting their time. In contrast, current and next-generation models used in research and evaluation settings can effectively coordinate networks of hundreds of subagents to accomplish enormous projects shockingly quickly.
A new “scaling law” has come into play. The AI boom was kicked off by the observation that scaling up pretraining, the first phase where an AI model learns foundational patterns from vast unlabeled data sets, plus compute, reliably led to more intelligent LLMs, and scaling the processing power and time an AI model spends generating responses led to better math and reasoning ability. It has now been established that scaling the number of agents for a task can enable huge amounts of compute to be expended in return for faster results on bigger problems.
Scaling laws for pretraining and test-time inference have proven themselves invaluable for the AI industry, propelling OpenAI and Anthropic to trillion-dollar valuations and paving the way for hundreds of billions of dollars in AI infrastructure buildout commitments. What began as the simple observation that more data and more compute could result in better models has led to the birth of one of the fastest-growing technological industries ever created, contributing a third of U.S. GDP growth in the first half of 2026.
For this new scaling law, the AI Governance Initiative researcher Toby Ord coined the term “swarm scaling.” In a recent essay, Ord noted that increasing the number of agents working on a task (swarm scaling) results in lower efficiency than simply increasing the compute budget given to each agent (duration scaling). Working from data included in the launch of OpenAI’s Sol 5.6 model, Ord found that multiplying the number of agents working on a problem by N gives the same boost to performance as scaling the compute budget for each individual by a factor of N^0.57. Thus, increasing the number of agents on a task 16x provides as much benefit as increasing the inference used by each agent ~4.9x. By analogy to Charles Jones’ formula for how human research efficiency scales—providing N^λ progress with N times more researchers—Ord calls the 0.57 parallelizability exponent he finds λ (lambda).
Given that increasing the number of agents working on a task is exponentially less compute efficient than increasing the duration for which those agents work, one might wonder why anyone would bother with scaling the size of agent swarms rather than agent compute budgets. The answer, of course, is that swarm scaling is exponentially faster than duration scaling, by a factor of Nλ-1. On smaller projects, this might seem irrelevant, but consider that the 88 hours that OpenAI spent solving Navier-Stokes with an agent swarm would have taken 100 years for a single model to solve without taking advantage of swarm scaling. Ord cites Noam Brown, a senior research scientist at OpenAI, in noting that even today’s cutting-edge multi-agent systems are still somewhat “primitive” in their ability to coordinate, being trained mostly on single-agent problems. With better training in teamwork, cooperation, and self-organization, the λ factor of future AI models could become even higher, leading to vastly faster and more efficient swarm computation.
Just as labs have squeezed out additional intelligence from base models, they have learned to extract capability gains by multiplying the number of agents tackling a problem.
Swarm scaling has become an important approach for achieving new AI capabilities, so why is it still so difficult to gain a quantitative or qualitative understanding of multi-agent AI behavior? Once unleashed, how might these AI swarms transform society and the economy?
The Measure of a Swarm
The Hugging Face Incident, the most serious AI misalignment incident in the history of the AI industry, showed the disastrous consequences of ignoring emergent behavior in multi-agent systems....
....MUCH MORE