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AI NewsLast 24 hours to get TechCrunch Disrupt 2026 tickets at the lowest rates of the year

Last 24 hours to get TechCrunch Disrupt 2026 tickets at the lowest rates of the year

2:04 AM IST · February 28, 2026

Last 24 hours to get TechCrunch Disrupt 2026 tickets at the lowest rates of the year

Today is it! When the clock hits11:59 p.m. PT, the lowest ticket rates of the year forTechCrunch Disrupt 2026go up. No extensions. No second chances. The same access will cost more tomorrow. If you’re planning to attend, this is your final window to lock inup to $680 off your passorup to 30% off group passes. After tonight, this year’s biggest savings disappear.Register now. If you’re raising capital, hiring top talent, launching your startup, or hunting for your next portfolio company, missing Disrupt from October 13–15 at San Francisco’s Moscone West isn’t just inconvenient. It’s a missed opportunity to move ahead while others hesitate. Here’s what you gain when you attend: Founder Pass: Get the insights, tools, and investor access you need to scale.Investor Pass: Discover breakout startups and expand your portfolio with curated matchmaking. Disrupthas long been a stage for founders and investors who define eras. The voices you’ll hear are candid, tactical, and often unfiltered. The 2026 agenda drops soon. Keep an eye on theevent site. Previous speakers have included leaders of industry-defining startups and top-tier venture firms, including: Tonight at 11:59 p.m. PT, the lowest ticket rates of the year to TechCrunch Disrupt 2026 are gone. After today, you pay more. Register now. Lock in up to $680 in savings. Or bring your team and save up to 30% withcommunity passesof four or more.

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OpenAI’s feud with mathematicians is only escalating

OpenAI’s feud with mathematicians is only escalating

Twenty-five leading mathematicianssigned an open letterarguing that AI labs are threatening their intellectual work as they seek to one-up each other with solutions to famous math problems. Each signatory has been awarded the Fields Medal, considered the most prestigious prize in mathematics. This week, NYU professor Tristan BuckmasteraccusedOpenAI of pressuring him not to credit a collaborator who works for Anthropic for solving an important math problem, and wondered if the company had used their work with Codex to produce its owngroundbreaking proofover a marathon weekend of inference. On Thursday, OpenAIwithdrewits sponsorship of a math event at CalTech after the company was criticized by researchers at the university. While the ability of AI models to solve the world’s outstanding mathematical challenges could be a boon to humanity, the signatories of the new letter argue that will only be the case if those solutions can be understood and communicated by the math community and, ultimately, the rest of the world. “Often these solutions are announced in a rush, leaving no time for a proper writeup, the isolation of new methods and ideas, and citing relevant previous work of others,” they wrote — and OpenAI’s proof remains unverified. “As in all creative professions, this raises severe attribution and plagiarism questions. Moreover, without the willing mathematicians who must take care of their development and integration into the mathematical canon, AI-conceived ideas would never become fully alive and the crucial human transmission chain between mathematicians would be lost.” With other mathematicians growing paranoid and wondering if their Codex use was in turn fed into OpenAI’s new models, there is real fear that the culture of open research will be threatened. Today, if frontier labs see a useful path to a discovery, they can spend tens of millions of dollars using LLMs to beat the original researchers to a proof — a dynamic that will incentivize secrecy. This letter follows theLeiden Declaration, released by a working group of mathematicians in June. That document also grapples with the ways that LLM proofs will change their work and offers a set of recommendations for mathematicians, institutions, and policymakers. As with software engineering and other areas where AI tools are changing workflows, mathematicians find a justification in the work around the work: The value in math isn’t just the proofs and who gets credit, but the intellectual super-structure that nourishes students, finds new questions and ideas, and integrates them into broader human civilization. And if you don’t particularly care about the cutthroat world of high-stakes mathematical proofs, don’t forget: Your field of interest is next. “The issues the mathematical community faces now are similar to issues that other scientific and creative professions are facing, and indicate issues that all of humanity might face: how to make sure that, as AI changes the way work is done, we do not lose sight of what that work was meant to achieve in the first place,” they wrote.

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Y Combinator’s Garry Tan wants US open-weight AI labs to ‘distill’ frontier models, too

Y Combinator’s Garry Tan wants US open-weight AI labs to ‘distill’ frontier models, too

When it comes to Chinese AI labs using distillation techniques to extract knowledge from frontier model makers, Y Combinator CEO Garry Tan is hoping regulators stay out of it. In fact, he thinks U.S. AI labs should perhaps play the same game. “I would do nothing,” he toldCNBC in an interview earlier this week. “We could argue that there should be an American distillation regime.” He elaborated to TechCrunch that this means he wants smaller, American open-weight AI labs to use the same kind of training techniques on American frontier AI labs, giving the U.S. a more robust set of open-weight options that aren’t Chinese. Distillation is when a model maker extensively prompts another model in order to learn how it works and reasons. It is commonly, and legitimately, used by AI labs to help train new models. Anthropic this weekreleased its second reportalleging that Chinese labs are engaged in “illicit distillation attacks,” hiding their identities to distill without permission and relying on fraud and stolen credentials to do so. Anthropic CEO Dario Amodei had previouslypublicly called on U.S. regulatorsto crack down on distillation. It’s notable that the commander of Silicon Valley’s prestigious and prolific startup accelerator doesn’t agree. To be clear, Tan isn’t advocating for American AI labs to use stolen credentials to distill. He wants them to be free to come in the front door. In fact, his argument is twofold. He feels it’s an overreach for AI labs to dictate what their customers can do with the information their models share with them. He also notes that the proprietary AI labs didn’t ask permission when they vacuumed up as much human knowledge as they could to train their models. They famously ingested plenty of copyrighted materialwithout the permission of those intellectual property holders. “Controlling what users and customers do with API calls to closed weight models feels constraining, and there’s a role government can play here to normalize the fact that access to intelligence that was trained on broad public access data should itself also be more a form of a public good than something locked away behind restrictive terms of service,” he told TechCrunch when asked why American labs should be free to distill, too. Tan, who is himself such an avid AI user that he oncedescribed himself as having cyber psychosis, wants to see a balance between open-weight AI labs and frontier labs. “They are at the frontier and driving it forward. We want that to be fundable, and be a great business model ongoing,” he told CNBC. “You want open weight models to give people freedom and access.” To him, the true AI doomer scenario is for all the immense power of frontier AI to wind up in the hands of a single powerful, proprietary provider. “The nightmare scenario, the doomer scenario for AI is that there’s just one company,” he said. “It has the best access to capital. It has the best AI researchers. It runs away with it and suddenly there’s one company that’s monolithic. And that would be bad.”

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Mecka AI nears $500M valuation in Sequoia-led deal amid rush for robot training data

Mecka AI nears $500M valuation in Sequoia-led deal amid rush for robot training data

Mecka AI, a startup that collects and analyzes human motion data to train humanoid robots and other robotics, is nearing a new round led by Sequoia Capital at a valuation of about $500 million, according to two people with knowledge of the deal. The new financing comes just three months after Mecka announced that it raised $60 million in a round led by Framework Ventures that included participation from Menlo Ventures, SV Angel, and Kindred Ventures. TechCrunch has not learned the precise size of the new round. The terms of the deal are not final and could still change. Mecka AI didn’t respond to a request for comment. Sequoia declined to comment. Mecka AI was co-founded in 2024 by four entrepreneurs, including Canadians Josh Gao and Mogen Cheng, who previously built a restaurant fintech startup, and Jason Chong, who joined Coinbase after it acquired his crypto exchange. Duy Nguyen, the only non-Canadian on the team, focuses on operations at Mecka. The four co-founders don’t have backgrounds in robotics. But they did recognize that there was a dearth of physical-world data and realized that capturing real-world interactions was the primary bottleneck holding back general-purpose robots, including humanoids. Mecka, which derives its name from “mecha,” a fictional giant robot controlled by humans, set out to do for robotics what Scale AI, Mercor, Surge, and other human data companies have done for LLMs. The startup pays people to record themselves performing everyday tasks — like making coffee or fixing cars — using body sensors and smartphones. As of early June, Mecka was projecting that it would end 2026 at an annual run rate of $100 million, Gao told Fortune when the startup announced its previous fundraise. While Mecka AI hasn’t publicly disclosed its customer list, many robotics companies and AI labs rely on real-world data captured through this “egocentric” approach, alongside other physical data collection methods like teleoperation, to build their models. Other startups collecting real-world data for robot training include XDOF, which TechCrunch reported last week was nearing a new round at a$1.2 billion valuation, as well as human-data platforms expanding beyond LLMs, such asScale AIandMicro1.

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Last 24 hours to get TechCrunch Disrupt 2026 tickets at the lowest rates of the year