David Turturean, a recent MIT graduate and a member of the technical staff at the startup lab Physical Superintelligence (PSI), used early access to Anthropic’s Fable 5.2 model to solve an 18-year-old problem about quantum computing.
“I’m totally accepting the idea that this might be the last paper I ever put out,” Turturean, who deferred plans to pursue a master’s at MIT’s prestigious computer science and AI lab to join PSI, told TechCrunch. “In a few months, like, why would you even put out a paper?”
The problem in question, the Aaronson-Ambainis conjecture, concerns the ability of quantum computers to solve problems faster than classical ones. Scott Aaronson, one of the two researchers who posed the problem, writes that he doesn’t quite understand the proof but that he believes it’s correct because it was autoformalized in Lean, a coding language for math, and explained by a team of top researchers with whom PSI shared the solution.
The solution appears to close the door on one avenue quantum computers could follow to solve certain difficult problems at high speed, but not all of them. “It’s a negative result that allows people to know, okay, we’re no longer looking there,” Turturean said.
More broadly, it reflects the upheaval in the scientific world caused by AI problem-solving.
“A proof of full [Aaronson-Ambainis conjecture] was one of the last remaining open problems in quantum query complexity that everyone seemed to agree was centrally important,” Aaronson, who described himself feeling satisfied, awestruck, disappointed, and fatigued by the news, wrote. “I was surprised, honestly, to see it missing from OpenAI’s Mathocalypse dump a few weeks ago. Surely they tried it, so was it really so much harder than all the problems that were solved?”
That’s a point in favor of PSI’s model, which is to solve scientific problems with specialized model harnesses and scaffolding.
“An effective kind of research system should have lots of things related to harnesses, lots related to tools, memory systems, simulation infrastructure,” PSI CEO Matt Pines told TechCrunch. “You want to be able to really go after a problem at breadth and depth, and be able to consider a wide range of potential alternatives.”
For now, the company’s revenue comes from using its tools to optimize data centers, simulating the variables of heat, power, water, sound — “where the performance of that site as well as its impact on the community are a direct function of its physics.” The long play, however, is converting theoretical physics breakthroughs into actual technologies.
“Six to nine months ago, when I was talking to investors, like I had to sort of stretch to validate that conditional, right? Like, ‘no, no, no, trust me, pretty soon, theoretical physics is going to be mostly the domain of machines,’” Pines said. “And it’s not there yet, but it’s on its way.”
Still, while AI has claimed important solutions in fields like mathematics and computer science where it’s possible to express them in validated code, doing so in less abstracted disciplines like the life sciences or physics has proven more difficult. Last week, the startup Discovered Materials released a $1 million grand challenge for researchers using AI to understand problems like superconductivity and heat dissipation.
“At the end of the day, physics is different than math because it’s the real world … nature gets a vote in your models of reality,” Pines said. He said the startup is focused on understanding how to take computational results and validate them with experiments in the most efficient way possible.
“If it’s a true fact of nature, that’s a trick that you can exploit,” Pines said. “You could build a device out of it, right? The AI suggests an idea; it allows you to do something in the world you weren’t able to do before … and so each of those are kind of loops that PSI is building.”
As “neolabs for science” become a pathway for potentially groundbreaking science, the participants are still figuring out what the standards are for this new world. Because PSI had to hold its result until Anthropic’s model was nearly ready for release, they were able to share it with relevant researchers to better understand it.
“As an academic, we wanted to play our role in doing things the right way,” Jim Halverson, a professor at Northeastern University who advises PSI, said. He’ll be attending a summit at MIT next week where leading physicists will try to hash out a response to the new pace of science. “Best practices are still emerging … We figured that transparency is a good way to go, and contacting experts is a good way to go.”
“We had some time to sit on it … and it was the first time that I actually took one of my AI-elicited solutions on print, and I read through it, and I was like, okay, how can I make this … be presented more naturally for a human?” Turturean, who said the academic community will expect him to be able to explain the result, told TechCrunch. “And I think it also made me realize, oh my God, the weight of what AI can do.”
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