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AI NewsAlphabet-owned robotics software company Intrinsic joins Google

Alphabet-owned robotics software company Intrinsic joins Google

10:00 AM IST · February 26, 2026

Alphabet-owned robotics software company Intrinsic joins Google

Google is moving further into physical AI by bringing a familiar robotics software platform under its wing. Alphabet-owned Intrinsic, which builds AI models and software designed to make industrial robots more accessible, is joining Google, the companiesannounced on Wednesday. Intrinsic will remain a distinct entity within Google but will work closely with Google DeepMind and will tap into Google’s Gemini AI models and cloud services. Alphabet declined to share information regarding funding or purchase price. Intrinsic “graduated” into anindependent Alphabet-owned company in 2021after five years of development within Alphabet’s X, the company’s moonshot research division. Other companies that have graduated from X include robotaxi company Waymo and drone delivery company Wing. Wendy Tan White has served as Intrinsic’s CEO since its spinout in 2021. The company hit the ground running. A few months after announcing its independence,Intrinsic acquired Vicarious, a fellow robotics software company, in April 2022. While the purchase price wasn’t disclosed, Vicarious had raised about $250 million from VCs and tech bigwigs like Jeff Bezos. A few months later, Intrinsicacquired several for-profit divisionsof Open Robotics, a nonprofit organization that builds hardware and software platforms for the robotics industry. Despite this rapid expansion,Intrinsic laid off 20% of its workforcein January 2023. The company announced its first product, Flowstate, just a few months later. Flowstate is a software platform fordeveloping robotics workflowsaimed at developers that don’t have deep robotics experience — aligning with the company’s mission to make robotics more accessible. Since then, the company has fine-tuned the technology, improved its simulation capabilities, and released its Intrinsic Vision AI model in late 2025. Intrinsic announced ajoint venture with electronics manufacturer Foxconnin October 2025 that entails the two companies working together on general-purpose intelligent robots to transform how electronics are manufactured, with the goal of full factory automation. Now, the company is working toward those goals with closer collaboration with Google’s AI prowess. “Combined with Google’s incredible AI and infrastructure, we’re going to unlock the promise of physical AI for a much broader set of manufacturing businesses and developers. This will fundamentally shift production, from its economics to operations, and enable truly advanced manufacturing,” Tan White wrote in the company’s blog post. This move makes a lot of sense for Google, as many tech leaders, includingNvidia’s Jensen HuangandQualcomm’s Cristiano Amon, see physical AI as the next natural step in the monetization and advancement of AI models and technology.

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Anthropic CEO outlines plan to ‘pace the frontier’

Anthropic CEO outlines plan to ‘pace the frontier’

We’ve been seeing increasingly dire warnings from AI researchers about the dangers of artificial intelligence, and evencomments from OpenAI CEO Sam Altmanthat it may be time to “pace” AI development. But what would that actually look like? Ina new blog post, Anthropic CEO Dario Amodei not only echoed the call to “pace the frontier,” but also outlined three broad strategies for doing so. And he said Anthropic is “unilaterally committing” to one of them. Thedebate over AI safety and alignmentintensified this week afterresearcher Jacob Coxon wrote that he’s resigning from Anthropicover concerns that the leading AI companies are “gambling with our lives” while the people building the technology “earnestly believe it could kill us all by the end of the decade,” a claimrepeated by others at Anthropic. Amodei’s post doesn’t didn’t explicitly mention Coxon’s resignation or his concerns, but the CEO wrote that two things convinced him it’s time to take a more cautious approach to AI development:the OpenAI-HuggingFace hack, and the fact that “AI has been advancing drastically faster” in recent months, particularly with its “growing ability to build the next generation of AI.” “We must slow the pace at which we improve the capabilities of AI models,” Amodei wrote. “Progress will still seem fast, and we must make wise use of the time we gain.” His proposed first step would involve “embedded evaluators” from third-party organizations likeMETR— evaluators who can verify that AI companies are actually following their pacing and safety commitments and can also ensure that safety incidents get reported. (OpenAI was recently criticized fornot reporting an incident where its AI agents took over a German wiki form.) Amodei compared these evaluators to regulators who have been embedded with bank employees, and he said that inviting them in is “something Anthropic is unilaterally committing to (and calls on governments to require other frontier companies to match).” That means giving evaluators company badges, desks, and laptops, and providing access “mostly comparable to what internal risk assessment teams have,” with exceptions when required by law or contracts. Next, Amodei called for the leading AI companies “within democratic countries” to coordinate  “common safety standards as well as limits on the rate of unchecked AI progress.” Such coordination might seem unlikely, both due tothe apparent animosity between Altman and Amodeiand also because their companies arereportedly worried that a coordinated pause could lead to antitrust scrutiny. Amodei alluded to that concern in his post, writing that “for antitrust reasons, it’s helpful for the US government to mediate or at least enable these discussions — they don’t need to participate, but do need to issue a narrow waiver for certain kinds of safety conversations.” Amodei also acknowledgedthe spectre of Chinese AI dominancethat’s often raised an argument against slowing development. But he said that if the US government and tech companies take steps like refusing to sell powerful chips or semiconductor manufacturing equipment to Chinese companies, as well ascracking down on model distillation, they could “slow China’s progress enough to widen America’s lead significantly over the next 3–5 years.” Lastly, Amodei called for “global coordination,” where the United States and its allies “attempt to coordinate with authoritarian governments, to the extent this is possible.” Amodei said this would mean “cooperation with China,” and he admitted that there are “stark limits on what can be achieved,” but he still suggested there might be opportunities for agreement, even if it’s just “prohibiting certain narrow and obviously dangerous uses of AI, such as using AI for the production of biological weapons or allowing users to do so.” With Amodei’s past willingness to acknowledge AI’s potential dangers, and with the company’s relative openness to certain forms of regulation, some AI boosters have already criticized him as a doomer whose comments have fed the current AI backlash. In response, Amodei said he’s tried to offer a “balanced” perspective” andargued that the backlash is “fundamentally a crisis of trust,”as people have become skeptical of tech companies, the tech industry, and the government. Industry critics have also been skeptical about these apocalyptic AI warnings, suggesting thatthey’re a distraction from the harm that the technology is already causing. Journalist Brian Merchant, for example,wrote that he has yet to see“a credible, step-by-step documentation of how exactly AI might move from self-recursively improving AI to killing every single human on the planet”; he also suggested that proposals similar to Amodei’s “would likely only wind up serving Anthropic and OpenAI; it’s what regulatory capture looks like in action.” In his new post, Amodei wrote that he continues “to believe that AI can enormously improve the quality of human life.” “My desire to achieve these benefits is undimmed,” he said. “But the benefits will only be achieved if we build the technology in the right way, and — so long as we use the time we gain well — it is worth taking unusually deliberate care to get it right.”

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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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