You may have heard claims that OpenAI's Astra has opened the AGI era, but OpenAI's internal model is already achieving mathematical breakthroughs.
On Tuesday, OpenAI announced that it used an internal model "significantly more capable than GPT-6 Astra" to solve the Navier-Stokes equations, a set of problems for fluid motion and one of the Millennium Prize Problems.
To put it simply, while the Navier-Stokes equations generally work well in determining the movement of fluids, under certain circumstances, OpenAI claims that its model proved that under certain circumstances, fluid can develop a "singularity" in finite time, meaning that a fluid's speed can grow infinitely without bound. This major implications for potential scientific breakthroughs.
In a press briefing, OpenAI said it used roughly 10,000 agents to determine that this equation breaks down, and that viscosity doesn't always keep fluid motion well behaved. While this sounds complicated, these equations explain some of the most complex phenomena in our world, including things like aerodynamics and weather forecasting, Ven Chandrasekaran, a mathematician at OpenAI, said in the press briefing. "Given that kind of enormity, understanding their fundamental nature carries very deep significance."
However, OpenAI didn't just decide to solve this problem randomly. Sebastien Bubeck, a member of the technical staff at OpenAI, said that the company saw rumors that rival Anthropic had solved two Millennium Prize Problems, including Navier-Stokes, in which Anthropic had made more progress. Because of this, Bubeck said, the company decided to "pull together all of our compute and to put it all on that question," a decision that cost the company millions of dollars.
Mark Chen, chief research officer at OpenAI, refuted allegations made by mathematician Tristan Buckmaster, who was working with Anthropic employee Levent Alpöge on solving the equation over the past month and claims that OpenAI took their work and finished solving it with a single prompt. "That would be a huge breach of user trust," said Chen.
While solving this problem is a major step forward for mathematics, it has far greater implications for AI's utility in scientific and mathematical research, said Bubeck. "What happens when we are able to spend that amount of compute on problems that really matter? Developing new materials, finding cures to diseases — all of those things that we have been talking about for a long time — now they seem to be at our fingertips." Additionally, solving this problem may have been a stress test for OpenAI's longstanding push towards AGI, serving as an evaluation of a system that the company has been training for "general purpose intelligence," Jakub Pachocki, chief scientist at OpenAI, said in the briefing.
"This pace of progress is something to be taken very seriously," said Pachocki. "Even a week ago, we definitely were not expecting we'd be talking about a solution to Navier-Stokes today. It's a serious moment. We're developing these systems, and their capabilities are advancing faster than our understanding of them in some sense." In an interview with The Deep View last week, Pachocki suggested a may be necessary to better coordinate between labs and nations.
Our Deeper View #
Finding a solution to some of the most complex and foundational problems in mathematics is probably one of the most valuable use cases for powerful frontier AI. It fits directly into the utopian vision that these companies paint for an AI-powered future: systems that are finding solutions to humanity's hardest problems, such as new medicines or cures for diseases. It's also interesting to see how these labs are pushing one another by their competitive nature alone, with Anthropic being the main driver for OpenAI to want to solve this problem. While that competition could result in advances to science and medicine that benefit everyone, it's important to remember the sheer power that these companies hold in their hands, with Pachocki himself admitting that these models are advancing beyond our ability to understand them. As a society and an industry, we also have to be very careful about letting competition fuel breakneck technological advancement when we're dealing with such a potent, dangerous, and unpredictable force.