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OpenAI has rolled out Images 2.6 for ChatGPT and its API, adding GIF creation to its image generation tools. Users can now animate photos or create GIFs from text prompts, change playback speeds and edit individual frames. The update also lets them save images of people, pets and characters to use as references later. Developers, meanwhile, get two new image generation models, along with SVG exports and more control over image edits.
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Very few people are using personal AI agents to order Ubers, apparently. On the transportation giant’sthird-quarter earningscall on Wednesday, the company said that only four rides were ordered through an AI agent during its busiest week in October: the week of Halloween. While more people used an AI agent to order food through its delivery service, Uber Eats, the volume was still fairly small compared to the overall number of trips. Uber’s chief financial officer, Balaji Krishnamurthy,toldWall Street analysts on the call that the company handled around 320 million trips and orders across Uber and Uber Eats during the week of Halloween, but only 500 Uber Eats orders were placed by an AI agent. “It is very, very early and very limited traction that we are seeing so far,” said CEO Dara Khosrowshahi, referring to the low number of orders placed by AI agents. “Listen, we hope that there will be more traction.” The surprisingly low numbers of AI-initiated transactions come amid growing hype for a new wave of AI agents that don’t just answer questions but also handle tasks on users’ behalf. There’s been an explosion of personal AI startups, including one of the buzziest, Instinct, which was founded last year and raised its most recent round of funding at a$10 billion valuation.Meta’s Muse also became the No. 1 app in Apple’s U.S. App Store last month, topping 10 million users,per Meta’s own reporting. The company said many were using it to shop and transact with businesses. OpenAI, Anthropic, Google, and SpaceXAI (Grok) are also now offering their own AI agents, as arenumerous smaller startups. Still, early numbers from public companies indicate the agentic web still has a long way to go. DoorDash, for instance, said last week on its earnings call that it hasonly received a few hundred ordersfrom AI agents so far, as most customers are still placing orders through the DoorDash app. Uber is seeing the same behavior, it seems. Khosrowshahi pointed out that the average time to book a ride is only 50 seconds in the company’s app, and re-ordering a favorite food only takes 90 seconds. That suggests an AI agent isn’t necessarily saving anyone time. Despite the low number of AI-driven transactions, Khosrowshahi seemed bullish on the idea of agents in general. “We think that is another opportunity to reach our platform. We think it will be a net positive in terms of demand on the ecosystem,” he said. Right now, there are around eight major AI assistants on the market, Khosrowshahi noted, and he expects that number to grow to more than 10 in time. “Every single AI system company is talking to us,” he added.
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Nothing, the smartphone startup behind the distinctiveNothing Phone, is working on an AI product, but what it is remains unclear. On Wednesday, CEO Carl Peiposted on Xthat the company’s first “AI-native product” ships in December. “It’s a tool, but one built with the personality and care that have come to define a Nothing product. It has no new screen, and it couldn’t have been an app,” Pei wrote. Pei has teased an AI-first devicefor some timeand the companyhas also saidthat it plans to release a pair of AI-linked smart glasses, as well as earbuds with AI integrations. Pei’s description adds up to a screenless non-app that uses AI? He also calls it a “productivity tool.” Adding further mystique to the announcement, Pei seems to downplay any link to AI wearables. “The whole industry is hunting for its iPhone moment: the pendant, the pin, the glasses, the companion, some magical consumer device that makes AI part of everyone’s life,” Pei wrote. “I admire the boldness, but I read this moment differently.” No firm release date has been announced, but Nothing fans should know soon enough what the company has in store for them.
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Political groups both supporting and opposing the AI industry spent millions of dollars trying to influence Tuesday’s midterm elections and shape the future of AI regulation. That spending produced mixed results, with some of the most heavily backed candidates losing their races. On the industry side, two AI-focused political action committees (PACs) that raised and spent money to influence the elections — American Mission and Building the Future — collectively spent $4.8 million to back South Carolina Senate candidate Darline Graham, who lost. American Mission is the Republican spending arm ofLeading the Future(LTF), a broader political group backed by OpenAI president Greg Brockman and the VC firm Andreessen Horowitz. American Mission also spent more than $500,000 apiece supporting Republican House candidates Aaron Flint in Montana and Laurie Buckhout in North Carolina, both of whom lost. LTF’s Democratic spending arm, Think Big, had better luck backing Florida Rep. Jared Moskowitz, who won reelection. Groups advocating for stronger AI safeguards also saw mixed results.Public First,which is backed by Anthropic, supported several Republicans who lost through its affiliated PAC but had more success backing Democratic candidates. It also contributed $3 million to a PAC supporting California state Sen. Scott Wiener, who was leading his race at the time of publication. Meanwhile,Guardrails Alliance, a group that has sought to make taking money from AI companies a political liability, spent more than $1 million apiece opposing two candidates who lost: California Rep. Jimmy Gomez and Montana’s Aaron Flint. It also spent $300,000 supporting Angela Gonzales-Torres, who defeated Gomez. It’s difficult to separate the impact of AI-related spending from the broader Democratic wave that swept Tuesday’s elections. Elon Musk, who reportedly poured half a billion dollars into supporting Republicans in the midterms, also sawmany of his candidates lose. Indeed, LTF also backed some candidates it believed were likely to win, only to see them lose to the Dems. For LTF, though, the general election wasn’t the main focus. The group told TechCrunch ahead of Election Day that it thinks primaries (the contests that choose each party’s nominee) are a more effective use of its money, because spending there can build name recognition and help candidates set up AI policy agendas. LTF spent $27.38 million in the primaries, including $8 million alone on opposing New York Assembly memberAlex Bores, who lost his bid for Congress back in June. When deciding whether it’s worth jumping into a general election race, LTF assesses how competitive the race is, whether it thinks spending could make a difference, and whether it should keep its powder dry for future elections. Charlie Blaettler, political director of Guardrails Alliance, had a different theory for why LTF spent comparatively little in the general election. “They don’t want their money to become an issue in these elections,” he said. He pointed to the Michigan Senate primary election, in which a pro-Israel lobbying group called AIPAC spent close to $30 million backing Democratic candidate Haley Stevens against Abdul El-Sayed. Stevens conceded the primary to El-Sayed, who went on to win the Senate seat. “AIPAC became the story in that race,” Blaettler said. “We know people hate AI billionaires. They hate the people running these companies. They’re very distrustful and disgusted by the political spending and lobbying that the tech industry does. If they spend in a competitive race, they would just become an enormous liability for their candidates.” The main fight among AI-related PACs, whether they are pro-industry or pro-regulation, is whether Congress should establish a national AI framework thatoverrides state laws, or whether states will maintain their own ability to impose safeguards. Neither side seems poised to back down. LTF, which favors a single national AI framework, told TechCrunch it plans to keep spending in the 2028 elections, while Guardrails Alliance says it wants to keep opposing candidates backed by the AI industry. Blaettler said that while LTF’s ads rarely touch on AI, instead focusing on popular issues for each candidate, Guardrail Alliance’s ads are specifically about who is taking money from the AI industry. “Our goal is to make AI money politically toxic,” Blaettler said.
The November 3 midterm results gave Democrats full control of Congress, setting the stage for what could be one of the most divided U.S. governments this decade, with Republicans in the White House. Tuesday’s results suggest that anger over data centers, the large facilities that house the computers powering AI, is becoming a political force. With Democrats now in control of Congress, that could lead to new limits on where data centers can be built and how much say local communities have. Data centers became a ballot-box issue in various states. AnNBC exit pollshowed how concerned voters are about AI: More than half of respondents believe that AI will do more harm than good, while almost 80% are against the building of data centers in their neighborhoods. Almost 60%, meanwhile, said data centers were acrucial or major factorin their vote. Nadine Smith, president and CEO of the racial justice organization Color of Change, called Tuesday’s election a referendum on “who this economy is built for.” “National security matters,” Smith said, referring to a common argument from data center supporters that the infrastructure is essential for the U.S. to stay ahead in war and in its rivalry with China. “And so do the families in South Memphis breathing exhaust from the gas turbines powering an AI supercomputer.” (She was referring to xAI’s data center in Memphis, Tennessee, which uses gas turbines for power.) Voters in several states pushed back against the AI buildout, even as pro-AI super PACs spent millions hoping to sway them this midterm. The anti-data center backlash was especially felt in states like Ohio, where OpenAI has eyed a $500 billion data center, backed by more than $100 billion in financing from Nvidia. In fact,the Ohio countywhere Meta had planned a 700,000-square-foot facility just passed a ban on data centers, and local communities across the state overwhelmingly voted against data center buildout measures. Analysts atMorgan Stanley predict thatDemocratic governor-elect Amy Acton will also pose a major risk to Ohio’s data center buildout. Elsewhere, candidates in Texas, Maine, Michigan, Nevada, Georgia, and South Carolina all won seats after taking a stance against the rapid buildout of data centers. “It is clear that money does not buy outcomes,” said Anshi Bhatt, a tech and policy researcher, adding that in modern politics, creating a “local villain” is one way to build campaign momentum. Likely next steps include more construction moratoriums or requiring data centers to pay for their own power grid upgrades instead of passing those costs on to nearby communities. Goldman Sachs, in fact, predicts data center construction delays in states like Georgia and Texas because of Democratic gains. Democratic control of Congress could also improve the odds for bills like the one Sen. Bernie Sanders and Rep. Alexandria Ocasio-Cortez introduced earlier this year, though any such bill would also face a possible veto. It proposes a nationwide moratorium on new data centers until Congress adds new guardrails and ensures that the buildout does not raise electricity costs, harm the environment, or proceed without community approval. There are also bills like the GRID Savings Act, proposed by New Mexico Sen. Martin Heinrich, which would prevent grid infrastructure costs from being passed on to communities during large AI data center buildouts. The environment is also a major concern, especially at xAI’s data center in Memphis and at the data centers throughout Virginia, known as the world capital of data centers, where gas turbines are used for power. Maia Woluchem, program director at Data & Society, a nonprofit research group, believes that changes to energy regulations, land rights, and other environmental policies could affect the data center buildout, especially as congressional Democrats look further into the Trump administration’s rollback of certain energy protections,as the New York Times reported they might. “Regardless of federal action, I could imagine that we will see additional movement at the state and local level, given the momentum that’s been building from the ground up,” Woluchem continued, citing governor-elect Gina Hinojosa, who supports a statewide moratorium on data centers, as an example. Both Hinojosa and Gov. Greg Abbott called for some limits, but Hinojosa centered much of her campaign on opposing data centers, a notable choice in Texas, whose electrical grid has proved vulnerable over the past decade. But the hard part is still to come. Smith, for example, is now expecting a fight, or even more executive orders, to bypass what Congress is now likely to block. “Even among Democrats, a blanket freeze is contested,” she said, referring to some Democrats’ worry that a moratorium could slow AI progress and hurt national security. She also expects AI lobbyists to start calling lawmakers. “Democrats ran on making Big Tech pay,” Smith said. “Our job is to make sure they remember it once the cameras leave.” The backlash runs deeper than the buildings themselves, which aren’t new. One private equity executive told me recently that people don’t necessarily hate data centers, but rather AI, and that data centers have become a stand-in for people’s fears and anxieties about the technology. “It does look like the results are pointing to a real frustration with tech power at minimum,” Woluchem said, noting that this is also a bipartisan issue. For the past month, people have told me they are worried about data centers’ effect on the environment and about theirlack of sayin whether they’re built. Amazon, for example, built a data center in Gilroy, California,without many residents knowing, by satisfying a 45-year-old zoning law. Vague or outdated zoning laws, along with requirements that local officials sign nondisclosure agreements (NDAs) barring them from discussing data center projects, have only fueled people’s outrage about the buildout. Data centers have also received billions in tax breaks, and although many tech companies have touted the benefits, such asjobsand community programs, for many people, that simply wasn’t enough. People want to know they still have a say in what happens in their communities. Many also fear that AI buildouts will mainly enrich a few wealthy people while their communities suffer and living costs rise. “If AI infrastructure is truly a national priority, then the companies building it can afford to pay for their own power, meet clean air standards, disclose what they use, and build with the consent of the people who live next door,” Smith said. “Families should never be subsidizing the richest corporations in history through their utility bills.”
Apple is cutting the daily amount its engineering teams can spend on AI, BloombergreportedWednesday. It’s a big change for a company that has workedto integratethe technology into its engineering processes. In October, Apple lowered the daily AI spending cap for its engineering teams, decreasing it from $300 to $200, the outlet reported. And more cuts are in the works, with some allowances falling to $100 a day, sources tell Bloomberg. Outside engineering, Apple is reportedly weighing a daily limit as low as $25 a day. TechCrunch has reached out to Apple for comment. Earlier this year, atsunami of tokenmaxxingtook hold, with companies pushing staff to use AI as much as possible in the name of vaguely defined efficiency gains. The frenzy was short-lived, however, as bills for said tokens came due, and companies decided that token rationing might be a more fiscally sensible approach. Concerns persistabout an apparent gap between what AI costs to build and run and the (often unclear) productivity gains the technology is suppose to generate.
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Almost a year ago to the day, Figure AI CEO Brett Adcockrespondedto some gentle social media taunting from rival robotics company Agility with a prediction: “You’ll be bankrupt in <12 months.” Instead, Agility is going public through a reverse-merger that will see it become a publicly-traded company and raise $465 million in new capital from retail investors and a tranche of private funding. The transaction will take place through a special purpose acquisition company, or SPAC. “You don’t see a lot of dancing robots or backflipping robots from Agility,” CEO Peggy Johnson told TechCrunch ahead of the company’s market debut. “We stuck to our strategy, which [are] these manual, very repetitive jobs, that it’s hard to find humans to do.” Agility predates the recent boom in physical AI investment. The company was spun out of Oregon State University in 2015 by a group of researchers who made headway in figuring out how to get a robot to walk safely on two legs. The company has focused on deploying a handful of its Digit robots in industrial settings with nine different customers, including Schaeffler, GXO, Toyota Motor Manufacturing Canada, and Mercado Libre. Digits are generally used to move heavy totes, including more than 100,000 of them at a GXO logistics facility. Compared to humanoid robots built by Figure, Tesla, 1X, or Boston Dynamics, Agility’s Digit can seem plodding, but the robot is actually doing real work, and earning money — albeit just $174,000 in the first six months of 2026, and mostly from related parties. The company lost $362 million in the same time period, which means the clock is ticking for it to successfully deploy its infusion of new capital. The good news is that Agility has $300 million in planned contracts from 30 customers for its new generation of robot, Digit 5. The new model can safely operate around humans, not just in an isolated work cell like its previous model (and indeed most humanoid robots). That deal was “one of the driving factors” behind the decision to take the company public, Johnson said, but could not yet reveal who the customers or say if they are investors. To close the deals, Johnson said that Digit 5 will have to hit undisclosed targets along several metrics, including throughput, uptime, and accuracy of placement. The company has yet to show off the prototypes of Digit 5, but Johnson said they will be demonstrated at CES 2027 and widely available at the end of that year. The company’s pitch is that a robot can save $100,000 a year for companies under a robot-as-a-service model and even more if the customer purchases the robot outright. Whether those unit economics work out in the end will depend on Digit 5’s capabilities, both in terms of task accuracy, and how long it can work without pauses for recharging or maintenance. Johnson gave the example of a single person lifting a 50-pound object — an informal threshold some workplaces use to avoid injury. “Digit can do that over and over and over,” she said. “You don’t want to give a human that job. You want to give that to the robot.” Agility faces two key questions: How quickly can it find new customers for Digit 5, and how quickly can it improve on the robot’s capabilities? Prospective customers need to spend $500,000 for Agility to do an on-site proof of concept before a deployment is fully scaled up; there were four customers signed up in May, 2026. And while Agility can pick up heavy loads and place them with 98% accuracy, it still — like most of today’s robots — lacks the ability to do complex manipulation tasks. It’s still not clear how quickly generative AI techniques will unlock the kind of dexterity that could allow robots to handle a wide variety of items. Training systems based on video and teleoperation data have not led to reliable general autonomy, though adapting the transformer architecture to different modalitiesappears promisingto physical AI researchers. “There’s no internet of physical data of robot movements, and that’s why you see so many people trying to simulate the data using a variety of things, and we use all of those techniques as well,” Johnson said. “They have their pros and cons, but we also have … 65,000 hours of real operational data from real customers in real facilities…that helps us accelerate the training of Digit.” Johnson said the capital raised in the SPAC transaction would meet the company’s current needs, but wouldn’t project that it would be sufficient for the company to be self-sustaining. She wouldn’t share production targets for Digit 5, but the company has said its production facility could produce 10,000 of the robots annually. “What you’ll see from us going forward is continuing to listen to our customers, understanding what they need in these facilities,” she said. “We’re taking our cue from them because we’re building a business.” For all the open questions, Agility still finds itself further ahead of most humanoid builders by dint of simply putting its product in the field. “Let’s start with the bins and the totes, and then let’s do the picking and the kitting,” cofounder and CTO Johnathan Hursttold TechCrunchat the opening of the company’s Fremont R&D facility this summer. “And then let’s like start working on cardboard, which is really hard, and loading and unloading tractor trailers and things like that. Okay, now we’re at 100 million robots, you know? A trillion-dollar company.” Correction: This story originally mis-identified the CEO of Figure AI; it is Brett Adcock.
Discovered Materials, a months-old, San Francisco startup using AI models tofind new substancesfor building advanced chips, announced a million-dollar challenge for AI models to solve five critical problems in materials science. While frontier AI models have solved a remarkable series of outstanding mathematics problems and made progress in the life sciences, they have yet to truly plumb the complex physics in advanced materials. “It’s more difficult to specify problems clearly in materials science because it’s not like math, where you have a very specific definition and can get to a yes or no,” said Advaith Sridhar, a co-founder of Discovered Materials, which was part of Y Combinator’s spring 2026 batch. “We came to this discussion [wondering], what are the grand challenges in this field? How do we know AI is making progress?” Though materials science doesn’t have an equivalent of the Millennium Prize Problems, Discovered Materials recruited an advisory group of academics at leading global universities to spell out five open problems. The company is offering a pool of prizes, worth $1 million in total, for solutions to the problems. “Across materials science, there are phenomena we observe but cannot explain,” Felipe Jornada, a Stanford professor who helped develop the challenges, said in a statement. “Explaining these phenomena would improve our understanding of physics, and the answers would directly shape how we build electronics, store energy, and manufacture the materials of the future.” Before announcing the challenges, Discovered Materials turned frontier lab models loose on them, giving AI agents internet access and other resources to work autonomously for days. The models spent several days on each problem without making much headway. Still, it’s easy to imagine OpenAI or Anthropic attempting to one-shot them with significantly more compute or a newer model. “It’s not particularly a concern to us whether we find it, or someone else finds it, or a professor finds it, or the frontier labs find it,” Sridhar said. “The reason we’re putting out these challenges is because we want them to serve as the goalposts. If you go and solve these challenges, we are deepening our understanding of materials and how things around us behave.” Tracking efforts to solve the problems will show “whether the models are climbing in this domain,” he added. The challenge also reflects the changing nature of scientific inquiry as AI proves capable of uncovering insights and techniques human researchers didn’t anticipate. With solutions apparently easier to find than ever, the questions become more important. “This is a new way of thinking about science, where we come up with the verification and the challenges first, and then we let somebody come up with the ideas that explain the problem,” Sridhar said. “Knowing what are the right questions to ask is not trivial and very difficult in this domain, so this is where the professors have really helped us shape what are the right questions we should be asking.” Discovered Materials’ main focus is developing new materials for use in semiconductors; the startup partners with chip giants on research projects while also funding its own scientific efforts. When the company announced its seed round in August, Sridhar told TechCrunch that he hoped the company would generate a useful material within a year. “Those timelines have accelerated a little bit,” he says today. “I’m more confident now that we will be able to find these industrially useful materials in a timeline shorter than a year.”
At its developer conference on Thursday, Box showed off a new feature that will build Nvidia’s Nemotron embedding system into the core of its product. Called Box Content Retrieval, the new feature allows agents to answer queries far more effectively by relying on the embedded metadata created by Nemotron. Box says the upshot for users is faster, more accurate and less expensive queries. Nvidia’s VP of generative AI software, Kari Briski, told TechCrunch that she expects queries to be answered four times faster, with 20% fewer errors and 90% fewer tokens used. The system will be available to all of Box’s Enterprise Advance customers, rolling out early next year. “This dramatically boosts both the relevance of our search technology for agents,” Box CEO Aaron Levie said. “Instead of an agent having to kind of read through each document, we can send back chunks from the documents that are most relevant to the user’s query or whatever the task is that the agent is doing.” The feature is an implementation ofNvidia’s Nemotron Embedproject, which offers open-weight embedding models to improve automated retrieval systems. Major labs have often overlooked such models, which use LLMs to pre-process data so it can be better used by agents. The sector has been a focus for Nvidia’s open-source model project, with its recent Nemotron release in July topping the RTEB benchmark, which measures retrieval quality. The system will be most useful when paired with active agents, Box says, giving the example of lawyers, who will ostensibly be able to find relevant clauses and precedents more easily under the new system. Because Nemotron’s embeddings go beyond simple keywords to map underlying concepts, a search tool can surface related legal themes even when different wording or language is used. While the Nemotron models are freely available, the system also relies heavily on Box’s pre-existing metadata and permission controls, which will make it challenging to recreate from scratch. “We’re probably packing in five different technologies into one simple API,” Levie told TechCrunch.
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Hardware startup Aina released a smart ring today that features a screen, a joystick, and a mic so that you can talk to your agents and take notes. The $399 ring has a speaker so you can take calls using it as well. The startup, which raised$5.5 million in July, is positioning the ring as a controller of sorts for both AI and non-AI apps. The company previously made Mirage, a three-key controller that let you invoke buttons based on the app on your Mac’s screen. For instance, if you were on a Google Meet call, you could map keys to camera on/off, mic mute/unmute, or to leave the meeting. Aina has baked in lessons from that device to create the new ring, which it’s simply calling “AI ring.” Using its mic, you can take notes, send a prompt to agents, set reminders, and dictate notes on your Mac using Wispr Flow or the startup’s own dictation engine. Now, there are already smart rings in the market that do similar things, like thePebble Index 01,Sandbar Stream,Oasis, andNatura, which is probably why Aina has added more controls and functionality to stand out. Its ring comes with a speaker so that you can take calls, and there is a screen, which lets you see notifications from your phone or which apps you are controlling. For navigation, there is a joystick, and it also supports gestures, so that it can trigger different actions based on what’s on your Mac or phone’s screen. The ring also lets you control music or even smart home devices like Apple TV. I have used the ring for a few days, and it feels like a smartwatch in a ring format with more controls. I can get notifications and reply to them. It surfaces my meetings; I can call Uber if I want to get a ride home. It’s easier to use Apple TV’s remote itself, but the Aina ring proved useful the couple times I couldn’t find the remote. Granted, a lot of the software and features are still being fleshed out, so it’s hard to figure out where such a device ring might sit in my daily usage, but it is squarely aimed at folks who are surrounded by digital devices and AI applications. “Phones and laptop screens were designed for an age of browsing where you think about opening apps. For interacting with proactive agents, we need a better interface, and that is what we had in mind while designing Aina,” Apoorv Shankar, Aina’s founder, told TechCrunch over a call. Companies are trying to cater to programmers and AI enthusiasts with specialized hardware that could help them harness different agents. This includesOpenAI’s experimental keyboardand Rabbit trying toposition itself as a controller. The hope with such hardware is that enough people will have multiple agents running to invest in specific gadgets to keep tabs on them. Aina’s ring goes on pre-order starting Thursday at early-bird pricing of $399, and the company plans to start shipping the device in January 2027. Once the early-bird period ends, the ring will cost $499.
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 standalone 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.
The company’s adjusted profit rose 20.7% year-on-year, while its proposed ITC Infotech merger remained on track.
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