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Dropbox is making a big push into AI infrastructure with a $350 million acquisition of cloud technology company Tigris Data, marking what appears to be its largest acquisition to date. AnnouncedThursday, the deal is expected to strengthen Dropbox’s AI infrastructure capabilities and support the development of DBX Agent Cloud, a new storage platform aimed at businesses and developers building AI models and agents. Dropbox told TechCrunch via email that the transaction is expected to close in the fourth quarter of 2026. The company also said it currently has no plans to reduce headcount as part of the acquisition. The company already cut 9% of its global workforce last month,WSJreported. DBX Agent Cloud will initially offer object storage as a stand-alone product, allowing businesses to purchase storage independently and use it with existing tools and applications built for Amazon S3 (Simple Storage Service), which is a cloud storage service that enables businesses to store and retrieve large volumes of data. Beyond its infrastructure ambitions, Dropbox is also bringing more AI capabilities to its core product. Users will be able to ask AI to perform supported tasks using their files, choose between different AI models, and connect their Dropbox content to third-party tools, including ChatGPT, Claude, and Muse. The company also plans to expand DBX Agent Cloud with additional capabilities designed to help AI systems work with different types of content, including documents, images, audio, and video. Other planned additions include filesystem features that will allow AI agents to manage files and maintain continuity across tasks and sessions. Dropbox says the updated experience has already reached more than 70,000 teams. The initial rollout is available to eligible Standard and Advanced customers in the U.S., U.K., and Australia, with broader availability planned for 2027. Tigris Data was founded by former Uber engineers who helped develop the ride-hailing company’s storage infrastructure. The company provides cloud storage technology that helps businesses store and access data across different regions. Its platform automatically positions data closer to the users and computing resources that need it, helping reduce access times and improve performance. Tigris Data raised$25 millionin a Series A funding round last year. While the Tigris deal appears to be Dropbox’s largest acquisition, it is not the company’s first move to expand its AI capabilities through M&A. Dropbox has previously acquired several AI-focused companies, includingReclaim.ai,Promoted.ai, andMobius Labs.
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It feels like AI is cracking a new math problem every week. Last month, OpenAI released719 manuscriptsclaiming solutions to some of the field’s hardest problems. Even the computer checkscan slip. After years of AI slop, it’s fair to wonder whether math is next. Axiom Mathis betting math can avoid that fate, not by slowing down, but by standing on firmer ground. The$1.6 billion startupwants to build a verified foundation that AI agents can rely on to write software and, eventually, do science. On Wednesday, the companyannouncedit had formally verified the Stacks Project, one of the largest bodies of mathematics ever written by humans. Mathematicians often hold Stacks in rare regard — Columbia mathematician Peter Woit has claimed there’s “nothing else really comparable,” comparing the project to “the famous Bourbaki books,” a series thatrebuilt math from its foundationsin the 20th century. Woit’s Columbia colleague Johan de Jong started theStacks Projectin 2005 as an open source reference. Mathematicians have contributed to it ever since, building out results from college-level algebra all the way up to the field’s most advanced ideas across algebra, geometry, and number theory. The company’sfleet of AI agentstranslates the library’s roughly 7,654 pages into about 16 million lines of computer-checked code. Formalizing math is a way of making sure the structure underneath it is sound. Mathematicians rewrite the proof as code in a programming language called Lean, and a program called a proof assistant checks the code line by line. If the proof passes, mathematicians can trust that it’s logically sound. Engineers use the same idea, called formal verification, toconfirm their chip designs work, while researchers in France have used it tocatch bugsin software that calculates family benefits. For a library like the Stacks Project, checking each theorem is only part of the job. The theorems and definitions in Stacks are interconnected, and each one builds on the ones before it. Like a family tree, many results in Stacks trace back to the ones that came before it. That interconnectedness made Axiom unsure the project would work at first. The team had to keep every connection intact across about 16 million lines of Lean code. “Things that are big and things that are delicate in life just don’t go together,” Axiom CEO Carina Hong told TechCrunch. “[Stacks] is a collection of mathematical results that is the foundation of mathematics. You want [theorems] to be consistent and not, like, paradoxical. You want the definitions to not fight with each other.” The 25-year-old CEOdropped out of a Stanford math PhD programand founded the company in March 2025. In March, Axiom raised a$200 million Series A led by Menlo Ventures. “We were not sure if vibe coding would be able to preserve the beauty of Stacks,” Hong said. To pull off the project, the team gave its agents a loose structure and let them run in parallel. “With Stacks, we imposed a hierarchy … [but] it was not, like, a complete hierarchy of dictating what every single agent was doing,” said Ashvin Swaminathan, a member of Axiom’s technical staff. “The agents were finding tasks for themselves and identified things that they could work on in parallel.” Axiom also trained its own models to be good at math, an approach Hong said most other verification startups haven’t taken. At first, the team relied on one of its in-house Lean experts to teach the agents a human sense of taste. Over time, Hong said, the agents have picked up taste on their own. The result fills one of the biggest gaps in formalized math. “When you look at what has been formalized in mathematics, there’s this huge black hole. And that black hole covers algebraic geometry,” Swaminathan said. Until now, researchers who wanted to check new work in algebraic geometry with Lean had little to build on, Swaminathan said. “Well, now they do.” Axiom argues that each verified result becomes a building block, so future proofs can lean on checked math instead of starting from scratch. Terence Tao, the UCLA mathematician and Fields Medal winner,has made the general case more simply: “The more formalized proofs you have, the more formalized proofs you can make.” Mathematicians aren’t Axiom’s only intended audience. Developers havelong struggledto guarantee that their code is free of bugs and security holes. That problem grows as AI agents write more of the world’s code. Axiom is betting that the software of the future will need to rest on verified foundations, the way Stacks now does. “We believe that mathematics is the domain where you start building these things and that it’s a transferable skill,” Swaminathan said. “Agents can read our code, understand it, and build new infrastructure on top of it. Our Lean formalization is now the substrate that agents can build upon.” The way Axiom’s agents built Stacks, working in parallel and picking their own tasks, is how Hong expects much of AI’s future work to happen. AI agents, she argues, will need a well-built, reusable body of knowledge to keep from producing slop. “You want [agents] to have a reusable library for them to build on and keep pushing the frontier,” Hong said. “If you slop drop in a way that’s bad engineering choices in the beginning, then it’s very hard for them to grow that boundary.” Hong is confident enough in that approach to issue a dare. “I openly challenge all the coding companies to work on formalization at this scale.”
Anthropic announced Thursday that Claude is getting a new tool called “Personal Finance,” designed to help users keep track of their money. With it, users can connect their bank and credit card accounts through Plaid and ask Claude questions about their finances. Anthropic says access is read-only, meaning Personal Finance can’t move, manage, or invest users’ money. By letting Claude connect directly to bank and credit card accounts, Anthropic is positioning its chatbot as a financial companion that can help people understand their spending and make sense of their money, rather than simply offering them generic advice. With the launch, Claude is playing catch-up to OpenAI’s ChatGPT, which introduced similarpersonal finance toolsin May that also let users connect their financial accounts and ask questions about everything from spending patterns to long-term financial planning. “Claude gives you answers in everyday language, from your balances and transactions, and explains any term that’s new to you,” Anthropic wrote in a blog post. “Like the rest of Claude, Personal Finance has no ads or sponsored recommendations, and it doesn’t offer credit cards, loans, or savings accounts of its own, so your money questions get a straight answer.” For instance, at the end of the month, a user could ask Claude, “Where did my money go this month?” The chatbot will then add up spending across the connected accounts and sort it by category, such as rent and utilities, food and drink, and shopping. Users can choose to go deeper by following up with questions like, “Is that more than usual?” or “Anything else I’m missing?” Anthropic says the tool goes beyond basic spending summaries by analyzing transactions across connected accounts to flag potential issues, such as duplicate charges, unexpected fees, and steadily increasing bills. Users can also ask broader questions about their finances, such as whether they’re spending more than they earn. The tool can also explain financial concepts like APR (annual percentage rate), while providing calculations that show users how much they’ve paid in interest over the past year. Of course, some people will have reservations about connecting their financial accounts to an AI chatbot. Anthropic says Claude’s access is limited to each connected bank’s name, account name and type, the last four digits of the account number, balances, credit limits, and up to two years of transaction history. Additionally, the company notes that when users chat with Claude in the Personal Finance hub, those conversations aren’t used to train its models. Personal Finance starts rolling out in beta this week to users on the Pro, Max, and Ultra plans in the U.S. The launch of the new tool comes as Anthropic has been expanding Claude into new verticals. For instance, Anthropic launchedClaude for LegalandClaude for Financial Servicesearlier this year.
OpenAI co-founder and president Greg Brockman said the company would be ready for an IPO in 2027, noting that its enterprise revenue has been growing. Speaking at aWall Street Journal tech conferenceon Wednesday, the executive touted how the company had been gaining ground in the enterprise market, where rival Anthropic had already successfully made inroads. In response to the interviewer’s question about being IPO-ready next year, Brockman replied, “I think so. Yes.” “We’ve been very clear this year that we want to go public when we feel is the right time for us, and I feel extremely good about the business. I feel like we’ve made so much progress this year, and I’ve honestly never felt more confident in what we’re doing,” Brockman said. He detailed how OpenAI’s trajectory as an enterprise had changed over the course of 2026, noting that the first and second quarters were “pretty rough.” He also admitted that OpenAI “wasn’t even in the conversation” when it came to agentic coding — an area where Anthropic’s Claude Code has gained market share. “We had Codex, but no one was talking about it,” Brockman continued, “and I think that we just kept our heads down and executed.” By the third quarter, activity picked up, as OpenAI saw 70% quarterly growth in its annualized revenue — something Brockman attributed to the enterprise side of the business doubling. He explained that the growth wasn’t simply the result of something that happened in the quarter itself, but rather the culmination of investments the company had been making up to that point. Now the majority of OpenAI’s revenue is coming from the enterprise business, which previously accounted for only around 30% of its total revenue. “And it’s not that consumer [revenue has shrunk] … It’s been growing, but it’s the enterprise segment has really taken off,” he said, pointing to a recent partnership with JPMorgan Chase as one example of the type of client it’s now winning deals with. Brockman also spoke more broadly about the lessons OpenAI learned from selling into the enterprise market as well. He said the company had come to understand that its job was to help businesses identify the most important milestones they could achieve with its AI technology, build trust with clients, and demonstrate that it was a reliable partner for the long haul. “A year ago, we weren’t even really in the enterprise game, right? We were really perceived as a consumer company, and a lot of the companies we’d start talking to would say, ‘Is OpenAI really committed to the enterprise?’” Brockman said. “But it’s so different today.”
Max Justicz, the chief technology officer and cofounder of the startup Charge Robotics, used an OpenAI model to finda severe vulnerabilitythat — if exploited — could allow hackers to take control of servers running Ubuntu, a widely-used open-source Linux based operating system. Justicz, who has a hobbyist’s interest in cybersecurity and previously found a vulnerability in the same codebase, told TechCrunch that he found this issue using GPT 5.6 Sol, released in July, through the AI maker’s Daybreak Blue, a program that gives cybersecurity defenders access to powerful frontier models. “I literally just aimed ChatGPT at the same body of code and said ‘I found this bug previously that was fixed several years ago, please find bugs as bad or worse,’ and it came back in 2 hours with this,” Justicz said. “It’s extremely convoluted and hard for a human to find, but it works!” He reported the issue to the Debian Project, which maintains the software in question and issued a patch to fix the vulnerability. There has been no known evidence of exploitation. Still, the ease with which he found it suggests that we’ve only seen the tip of the iceberg when it comes to potential exploits that even novice hackers can utilize with the help of AI models. “I can only imagine what one could do with more time and access to less neutered models. We should expect some extremely crazy bugs to be found and exploited over the next few months,” Justicz wrote in asocial media threaddiscussing his finding. “This might be obvious to most folks here, but there is currently huge alpha in taking some code or binary someone has not audited yet and just aiming a powerful AI model at it.” OpenAI appears to have noticed. Today, the AI giantannounceda new model focused on cybersecurity, ChatGPT Cyber, would be made available to the highest-tier of users in the cybersecurity program, Daybreak Red, which allows offensive cybersecurity researchers tools for vulnerability hunting and development. Yesterday, OpenAIsaidthat the same model had been used to scan and prepare patches for vulnerabilities in the Linux kernel, finding 948 potential issues. OpenAI also found 81 “novel” vulnerabilities in Firefox, the web browser. “Our goal is to dramatically expand Daybreak, bringing advanced security research to organizations of every size, independent researchers, and open-source maintainers,” the frontier lab said in a statement. OpenAI has insisted that there is a narrow window of time for the people who safeguard digital infrastructure to take action before AI models become even better at hacking and easier to access. For cyber defenders, however, it can feel like the frontier labs have unleashed a mountain of new work that outstrips their resources. Justicz said that he personally spent about $5,000 on subscriptions and tokens to find several exploits. Besides Daybreak’s model access, OpenAI hasalso announceda $1 billion initiative to support cybersecurity professionals in the program who work on critical infrastructure like the electrical grid or water systems. The Daybreak program has its own critiques. Justicz notes that the frontier lab appears to have recently “nerfed” Daybreak Blue to make it less useful at finding vulnerabilities. One one hand, it may be good to limit access from potential bad actors — Justicz said he just uploaded a photo of his drivers’ license to join the program. On the other, cybersecurity professionals say they have one hand tied if they can’t use frontier technology while bad actors can deploy open-weight models withabliterated guardrails. Still, OpenAI says just “thousands” of cybersecurity professionals are participating in the Daybreak program, versus a far larger number of potential hackers. It’s unclear if frontier labs are devoting enough of their resources to this issue. Eric Wallace, a researcher on OpenAI’s technical staff, said that “we are now allocating a significant amount of our frontier compute towards discovering and patching vulnerabilities in open source codebases, fully pro bono.” But the Linux bug hunt cost $1.2 million, which is significantly less than the approximate $6 million token spend the company invested in finding a potential solution to a famous problem of fluid dynamics. If there is a narrow window of time to patch the digital infrastructure the world depends on, more urgency may be required.
Meta President Dina Powell McCormick said that the Democratic wins in the midterm elections are going to make it a lot harder for companies to build data centers, adding that the industry has “not done as good a job as we needed to explain the benefits of these investments.” Speaking at theWSJ Tech conferenceon Wednesday, the Meta exec pointed out that the backlash against data centers — which wasvery much on the ballot during this election season— was also tied to people’s feelings about AI technology overall. “The visible sort of manifestation of AI is a data center, and it’s something that people can say, ‘I don’t want this in my community,’” she explained. “I think there’s an underlying bigger issue, which is how people feel about AI in general.” Meta believes it could turn around the negative consumer sentiment around AI if it’s able to demonstrate the value, not only of the data centers coming to local communities, but in the technology itself. “The stakes of who builds the technology that cures cancer, that gives access to education, that is also a very fundamental difference…Mark [Zuckerberg, Meta CEO] is very ‘long human,’ and he’s betting on humans more than machines,” Powell McCormick explained. “And that’s what we mean when we say the future is for everyone. That people should not be left behind. But the way to ensure that is that this technology is democratized, that people have access to it in a very fair and transparent way,” she said. The exec also touted Meta’s experience in building data centers, saying that the company has been doing so for 20 years, as it takes a lot of compute power to offer the products it does in the social networking space. Today, the company has data centers in 23 states, she said, and five of them, including one in Texas, are planned as projects of one gigawatt or more. But Meta is now worried that it could lose some of its investments after politicians ran on anti-data-center platforms during the midterms. “I think it’s going to be harder,” Powell McCormick said, describing what the future of building data centers looks like. “It has been hard, I should say, but I also think that it’s becoming clear what models work, and the states want those models that work.” Specifically, communities only want data centers when it benefits them in some way, she explained, laying out the approach that Meta today uses to close its data center deals in local communities. Successful projects see Meta paying for its own electricity, directing any tax surpluses as a result of the data centers toward teachers and first responders, keeping the labor force local, and getting bipartisan support for the project, among other things. Going forward, she expects data center projects to come under more review and be weighed against whether the constituents actually want them. Meta has pinned its hopes on its AI investments, particularly its consumer AI agent Muse scaling far beyond the 10 million users it has today, to become a business that one day serves billions. But even if that’s not the case, it believes that its investments in data centers will not be for nothing, as the compute itself will be valuable. “We’re getting calls every day to lease that compute [we’ve built], and so one way or another, we’re seeing a path to monetization,” Powell McCormick said.
The company’s adjusted profit rose 20.7% year-on-year, while its proposed ITC Infotech merger remained on track.
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Their departures come as Claude Code expands its role in Anthropic’s AI coding business.
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Altman’s recent comment on RAM shortage has reignited the debate over memory allocation between AI data centres and consumer devices.
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The new capabilities give AI agents business context, enterprise controls and the ability to execute recurring tasks autonomously.
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Security, governance and model dependence are emerging as major concerns for businesses, even as executives report productivity gains from AI investments.
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FDE salaries in India range from Rs 32 lakh to Rs 1.55 crore annually, with compensation rising alongside experience
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