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AI NewsOpenAI claims it solved an 80-year-old math problem — for real this time

OpenAI claims it solved an 80-year-old math problem — for real this time

2:53 AM IST · May 21, 2026

OpenAI claims it solved an 80-year-old math problem — for real this time

OpenAIclaimsits new reasoning model has produced an original mathematical proof disproving a famous unsolved conjecture in geometry, which was first posed by Paul Erdős in 1946. If this sounds familiar to you, it’s because this isn’t the first time OpenAI has made such a bold claim.Seven months ago, the AI giant’sformer VP Kevin Weilposted on X: “GPT-5 found solutions to 10 (!) previously unsolved Erdős problems and made progress on 11 others.” It turns out, GPT-5 didn’t actually solve those problems; it just found solutions that already existed in the literature. Taunts from rivals like Yann LeCun and Google DeepMind CEO Demis Hassabis followed, and Weil promptly took down his premature post. Today, at least, it seems OpenAI didn’t make the same mistake twice. Alongside the announcement, the company publishedcompanion remarksin support of the disproof from mathematicians like Noga Alon, Melanie Wood, and Thomas Bloom, who maintainsthe Erdos Problems website, and previously called Weil’s post“a dramatic misrepresentation.” “For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids,”OpenAI posted on X. “An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better.” The company said this marks “the first time AI has autonomously solved a prominent open problem central to a field of mathematics.” The proof, per OpenAI, came from a new general-purpose reasoning model, not a system specifically designed to solve math problems or even this problem in particular. OpenAI says this is significant because it means AI systems are now more capable of holding together long, difficult chains of reasoning and connecting ideas across fields in ways researchers may not have previously explored. That has implications for biology, physics, engineering, and medicine. “AI is helping us to more fully explore the cathedral of mathematics we have built over the centuries,” Bloom said in a statement. “What other unseen wonders are waiting in the wings?”

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Rocket engine startup Impulse raises $500 million to hire people, not AI

Rocket engine startup Impulse raises $500 million to hire people, not AI

Impulse Space, a startup founded by SpaceX engine guru Tom Mueller to build highly-maneuverable spacecraft, announced a $500 million Series D this week that it will use to hire as many as 200 new employees. The round, led by 137 Ventures and BANNER VC, with participation from Founders Fund, Lux Capital, and Linse Capital, reflects investor interest in space and defense tech as the U.S. government hurls cash at national security problems and SpaceX gears up for its IPO. Impulse is focused on in-space mobility. The company has developed a highly maneuverable platform called Mira that is targeted at U.S. Space Force buyers. It’s also building Helios, a vehicle designed to carry satellites rapidly to high orbits after they are dropped off in space closer to Earth. President and COO Eric Romo told TechCrunch that the new capital will help the company build and test more space vehicles and emphasized the company’s hiring plans at a time when aerospace talent is in high demand. While the company’s software teams are adopting AI coding tools, Romo said that when it comes to solving engineering problems in the real world, deep learning models aren’t quite ready for prime time. As the 13th employee at SpaceX back in 2003, Romo’s job was creating computer simulations of the company’s engine design to assess its performance. “I considered it success if I got within 20% of the right answer, because the simulations were just not that good,” Romo said. “They’ve improved, but they’ve not improved that much, and so there’s not really any substitute for designing the thing, analyzing the thing, building it, and then getting it on the test stand.” Romo suspects AI tools for hardware design may be slower to arrive because the right training data is hard to find, compared to the amount of text and code available on the internet to train LLMs. “If you want to go, say, find the best designs for a turbo pump seal package in the world, you’re not going to find those online,” he points out. Impulse started with a focus on propulsion and evolved to build spacecraft, requiring the company to add more expertise in the form of engineers who build vehicle structures and flight computers. One reason the company recently opened an office in Colorado is that aerospace talent has more options today — instead of just going to Los Angeles, engineers can find work in Seattle, Denver, or Texas. Next up for the company is another launch of its Mira spacecraft, which made its third flight late last year. That flight wasn’t without incident — a problem with its navigation system led it to expend much of its propellant early on. Romo said the company is prepping a new Mira mission that is expected to launch before the end of the year.

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ZeroDrift raises $10M to protect AI models from themselves

ZeroDrift raises $10M to protect AI models from themselves

As enterprises troubleshoot their AI systems, governance has emerged as a key challenge. Some are taking a dual approach: One model to handle incoming queries, and another to keep the first one from getting into trouble. That’s the premise ofZeroDrift, a new AI compliance service that on Tuesday said it had raised $10 million in a seed funding round that saw investments from a16z Speedrun, Reign Ventures, PitchDrive Ventures, and U&I Ventures, among others. The company deals entirely with the second part of the system, sitting between AI models and end users to flag and replace any messages that might present a compliance problem. It might seem strange to build an AI tool to correct other AI systems’ mistakes, but ZeroDrift says its system has a few architectural advantages over the models it will be correcting. The system is triggered by conventional programs that deterministically apply known compliance standards like SOC 2 or GDPR, and the LLM only comes into play once a message has been flagged, rewriting a compliant version of the same message. “We’re able to identify, deterministically, what are all the regulated areas, what’s the violation that’s being broken, and then we have LLMs that can do the rewrites,” CEO Kumesh Aroomoogan says. Critically, the company says its entire system can be run with lower latency and more reliability than a conventional LLM. This is what ZeroDrift touts as its primary advantage over big labs like OpenAI and Anthropic, which are often already present in the underlying system. The most obvious use case is for AI chatbots, which are already deployed in front of consumers where there can be serious consequences for rogue answers. But Aroomoogan sees a much larger total addressable market, potentially spanning AI-generated messages that are generated only within automated systems that humans will never see. So far, it’s a relatively small market, but it’s one that will grow as AI proliferates. If the fundraise is any indication, there’s a lot of pent-up demand for such products. “It was probably the fastest fundraising I’ve done in my life,” Aroomoogan says, crediting Andressen Horowitz for helping structure the seed round. “We closed within three weeks, and we will be oversubscribed by 3x on the amount.”

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