Últimas Noticias de IA
GitHub Adds 3 Million CPU Cores Post Outage, Monthly Commits Double Since April
Monthly commits have more than doubled from 1.4 billion in April to 2.9 billion in August, while GitHub has accelerated its migration to Microsoft Azure.
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AI data startup Micro1 reaches $500M gross run rate amid AI training boom
The near-bottomless demand for unique AI training data from top labs and corporations is driving a massive boom for a cohort of data-labeling startups. One of these fast-growing businesses is Micro1, a four-year-old startup that expanded its gross annual run rate from $100 million to $500 million over the past eight months, according to a person familiar with the company. Like its peers that hire domain experts such as doctors, lawyers, and scientists on a contract basis, Micro1 retains roughly 60% to 70% of that figure, putting its net annual run rate between $150 million and $200 million. While Micro1 still lags competitors like Mercor (which hit$2 billionin gross annualized revenue this summer) and Handshake (which reached$1 billionearlier this year), the startup’s revenue growth shows that there is more than enough demand to support multiple players supplying AI training data. The rapid growth is bound to continue, with some researchershypothesizingthat future AI spending on data could rival spending on compute. That outlook bodes well for Micro1, which is seeing its contract sizes grow at an accelerated pace and expects its margins to expand over time. The startup is increasingly generating synthetic data without human involvement, such as by creating automated descriptions of video content. Additionally, some of the data it generates can be sold to multiple customers, driving gross margins for this “off-the-shelf” data as high as 80% to 90%, a person familiar with the startup’s finances told TechCrunch. Selling the same datasets to multiple clients has sparked recent controversy, withcritics arguingthat distributing off-the-shelf data to Chinese AI developers helps make their models as powerful as top U.S. models. Micro1’s founder, Ali Ansari,said last month on Xthat unlike some of its competitors, the startup doesn’t sell its data to Chinese model makers. “Some human data companies work with foreign adversaries. [A]nd the results show today in Kimi K3. We believe it’s shameful to claim American AI dominance desires while selling millions worth of data to countries that we are in adversarial competition with.” Like Mercor, Micro1 began as an AI recruiting startup. But after noticing that data-labeling clients were using his AI platform to vet and recruit engineers for annotation, Ansari decided to pivot and enter the data-labeling business, too. Ansaripreviouslytold TechCrunch that in addition to having its experts evaluate model outputs — a concept known as reinforcement learning gyms — the company is building a robotics pre-training dataset by having hundreds of generalists record everyday object interactions in their homes. Micro1 raised its Series A at a$500 millionvaluation last September, and TechCrunch understands that the startup may have recently raised another round at a significantly higher valuation. Micro1 didn’t respond to a request for comment.
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OK, can we actually cool data centers with our pee?
In acheeky marketing campaign, Liquid Death teamed up with former Philadelphia Eagles star Jason Kelce to share a solution to mitigate theenvironmental impactof AI data centers, which require massive quantities of water to prevent servers from overheating. “AI data centers waste millions of gallons of water,” Kelce quips in the video campaign. “That’s why Liquid Death and Garage Beer have teamed up. We want your pee to cool these data centers.” Then, as a crowd of people walk through a field sipping their branded beverages, they sing in unison: “Let’s pee on computers together to save humanity!” It’s a funny commercial. What’s even funnier is that Kelce has unwittingly stumbled upon a real tactic for cooling down data centers. “The Liquid Death commercial is funny and tongue-in-cheek,” Michael Obradovitch, vice president of Data Center Global Accounts at Ecolab, told TechCrunch. “But in reality, there is a fair amount of alternative water sources already being used to a similar extent to cool these data centers.” These alternative water sources, when used in data centers, at least partially offset the demand for potable drinking water. One such alternative water source is recycled water, which is made by treating wastewater and sewage water so that they’re safe to use again. Wastewater and sewage water contain many things, including — you guessed it! — human urine. “You wouldn’t just use pee, but you can clean it and make it into useful water, and that’s what we advocate,” Bruno Pigott, executive director of the WateReuse Association and former acting assistant administrator in water for the U.S. Environmental Protection Agency (EPA), told TechCrunch. To be clear: You should not actually contribute gallons of your pee to help cool data centers, as Kelce facetiously suggests. But just for the sake of the thought experiment: What would happen if youdidtry to cool a data center with a steady stream of pee? “Pee contains all sorts of stuff. It contains salts, it contains urea, bacteria, organic matter of all sorts that can leave mineral deposits. If you just put that into a cooling tower or something else, it would require constant cleaning,” said Pigott. “One of the methods of cooling is called evaporative cooling, where hot air is passed through water to remove heat through evaporation. Can you imagine if you just poured urine through hot air?” We do have the technology to turn our urine into potable drinking water — that’swhat astronauts do in space, since they can only bring so much water with them on their spacecraft. But that isn’t efficient at a large scale, and even if it were, it’s not like scientists can just access millions of gallons of pee at will (well, not unless Kelce really commits to the bit). Instead, our toilet water ends up in wastewater and sewage. That’s where water treatment facilities come in, providing recycled water to spare us from the smell of evaporated urine. These facilities use membrane bioreactors, reverse osmosis, ultraviolet light, and other processes to treat water until it’s clean enough for industrial use. In some cases, this water can even be treated to the point that it’s drinkable. “We use recycled water for cooling for all kinds of industries, and we have for decades,” Dr. Greta Zornes, practice leader for water reuse at the engineering firm CDM Smith, told TechCrunch. “So this is only one application, but definitely, there’s been a boom in recycled water for data center cooling.” Though Zornes has worked on water reuse technology for more than two decades, her day-to-day work has shifted with the rising demand for data centers. “Every day right now, I’m working on recycled water for data centers,” she said. When data centers use more recycled water, they don’t pose as much of a burden to the local potable water supply. But industries can only pivot to recycled water use when there is proper infrastructure in place to treat millions of gallons of water every day. “You have to be somewhat near a waste water treatment facility that’s sizable enough that you have enough water to use,” Zornes said. “So when data centers go out into rural areas, a lot of times the wastewater treatment plants just aren’t big enough — they’re not treating enough water for them to be able to take it and treat it and use it.” Loudoun County, Virginia, located outside of Washington, D.C., is home to more than 250 data centers, withplansto construct at least another two dozen. As of 2025, Loudoun data centers collectively used about200 million gallonsof recycled water each day, but the water footprint of these data centers is so extreme that this only accounts for 43% of daily data center water usage in the area. The other 260 million gallons, or 57% of daily data center water usage, come from potable water supplies, according toLoudoun Water. “There’s a lot of infrastructure that has to be built out and usually isn’t existing today, and that takes time,” Zornes said. “That’s one of the problems — it’s just the time that it takes to get that done.” Obradovitch thinks that the AI industry could even drive resources toward building out this kind of infrastructure to scale water treatment. Meta, for example, will investat least $270 millionin wastewater infrastructure projects near its data centers (the company also losesabout $4 billioneach quarter on its Reality Labs division). “That’s where data centers can actually come in and be anchors of water infrastructure,” Obradovitch said. “There’s a number of cases and examples where data centers, as part of their engagement with communities, have committed funding and capital to some of these municipalities to help in addressing some of those exact challenges.” On the policy side, Pigott is advocating forlegislationthat would provide a 30% tax credit to help industries scale their recycled water infrastructure. “We think it would greatly accelerate the pace with which data centers and other industries entered into this area,” he said. While there’s some unintentional science behind Liquid Death’s joke, the commercial and its virality serve as a reminder to the tech industry that the environmental demands of data centers have become a mainstream concern. According to arecent Gallup poll, about seven out of 10 Americans oppose data centers in their communities, and AI products continue toface backlashfrom consumers who feel as though the technology is being forced into their lives. “I’m glad that people are concerned about water, and anything that raises awareness of water, however crude it may be, could actually be beneficial,” Pigott said. “It gives us a chance to educate the public about what we’re doing today.”
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ChatGPT can now send texts for you with new Apple Messages plug-in
If you’ve ever wanted to share all of your digital conversations with OpenAI, we have good news for you: The AI lab has just launched an Apple Messages plug-in for ChatGPT, allowing interested users to connect their Messages inbox with the chatbot. The benefits of doing this, OpenAI argues, are numerous. Users can use the plug-in to sort, analyze, or edit their messages directly from ChatGPT. The plug-in also works with Codex and ChatGPT Work, so users can use it professionally, not just personally. Abrief commercialadvertising the new plug-in shows a user asking the chatbot to suggest follow-up messages to their contacts based on the messages received the previous day. You can also ask ChatGPT to delete messages for you, draft and send messages on your behalf, or search for information buried deep in your message history. As with most things related to AI, this new feature raises some privacy questions. OpenAItold Bloombergthat the plug-in runs locally on a user’s machine and that it “doesn’t create an index of all someone’s messages.” Still, the specifics of what that means aren’t immediately clear. TechCrunch has reached out to OpenAI for more information. When it comes to message sending, OpenAI encourages users to keep an eye on what ChatGPT is doing and discourages turning on persistent approval, warning that doing so “removes your final chance to review a message before ChatGPT sends it as you,” the companywrites.
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OpenAI is gaining on Anthropic with business users, new data indicates
Until both OpenAI and Anthropic get close enough to their planned IPOs to release their financials, we have to look to other sources for signs of how well their businesses are doing. One of those sources, Ramp, the corporate credit card and expense management company, has just released some surprising new data: OpenAI has started gaining on Anthropic with U.S. businesses. OpenAI, which was once the runaway leader with both businesses and consumers, lost the lead among Ramp’s paying business users back in May. That’s when Anthropic hit 41% market share to OpenAI’s 39%. The ChatGPT maker has never regained that lead. As of July, Anthropic has nearly 44% to OpenAI’s nearly 40%. The data covers more than 70,000 American businesses that spend billions via Ramp’s bill pay and corporate card products. Ramp’s customers are spread across industries but, as a popular Silicon Valley corporate credit card, they do skew toward the tech industry. A closer look at the most recent data, according to Ramp economist Ara Kharazian, shows that OpenAI is currently growing faster among this segment in Q3 to date than Anthropic. Mind you, there’s still a month left in the quarter and that’s like 30 AI years, so the trend could easily shift again before it’s over. Ramp also declined to provide actual dollars spent, sharing only percentages. To borrow ChatGPT’s own hedging style for a moment: This isn’t a measure of the total market. It excludes large enterprises that use spend-management tools from providers like American Express, rather than Ramp. But it’s enough data to show market indications. And what it shows is that Anthropic hasn’t won permanently. Businesses are willing to flop back and forth as each lab releases new models, volatility that should give both companies’ investors pause about how “sticky” enterprise AI spending really is. “GPT-5.6 Sol is really good, increasingly the choice for developers,” Kharazianposted on Xabout OpenAI’s new growth. “Fable 5, meanwhile, disappointed both in adoption and real-world application given price + data retention requirements imposed by regulators,” he continued. That may be an over simplification. Fable — Anthropic’s higher-end model tier — is expensive but it’s also built for a more targeted set of use cases than a general chatbot. Still, Anthropic did cause some outrage when it warned Fable users that it must retain their datafor 30 days. Ramp’s data also suggests that both companies should be growing business revenue, even as they duke it out for market share, because the market overall is expanding. The percentage of companies that pay for AI among these Ramp customers has been steadily climbing. It topped 50% in March. It reached nearly 56% by July.
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Meta brings Pocket, an app that lets you vibe-code and share games, to US users
Meta’s experimental vibe-coding gaming app,Pocket, is now rolling out to everyone in the U.S. The app, whicharrived quietly last monthin the test market of Brazil, allows people to generate small, interactive games using AI prompts, which are published to a scrollable feed. Based on Meta’sacqui-hireof the team at thevibe-coded gaming platform Gizmoearlier this year, these interactive experiences and games — or “gizmos” as Meta calls them — respond to touch and the tilt of your phone, play sound effects, and can include clips of your favorite songs. They can also use photos from your camera roll or access your camera. The resulting games can then be shared on your profile, where others can save them, remix them into other creations, or simply repost them. The app is the latest example of Meta’s push to make AI-creation tools mainstream, following its earlier efforts that included making AI-generated images withits Meta AI appand creating AI videos withan experimental app called Vibes. Pocket now joins these and several other stand-alone mobile applications from Meta in recent months. CEO Mark Zuckerberg has credited the increased output to AI-enabled software development, which makes it faster for the company to test and ship new ideas. “Earlier this year, we shipped Instagram Instants. We also just launched Forum, a stand-alone Groups app, and Seller, a stand-alone Marketplace app. I expect it to become a lot easier to ship new apps,” he told analysts on July’s earnings call. “So we are planning to build out more ideas and use our recommendation systems to scale them.” In addition toInstants,Forum, and the Seller app, Meta has shipped an experiment involving AI bedtime stories, and this week, aMeta AI app for Mac. With the launch of Pocket, Meta isshutting downthe original app acquired from Atma Sciences. It's bittersweet to shut down Gizmo, a labor of love by so many people for the last couple years, but I'm excited for Pocket and what's next.Starting a company is something I wanted to do for a long time, and it was way more fun than I imagined. I owe everything to my…https://t.co/5KwjsgyAtb
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Ramp launches its own AI model router, called Router
Corporate expense management platform Ramp is hot on the heels ofStripe in setting up toll houses for AI inference. Ramp on Wednesday evening launched its own AI model routing service, dubbedRouter, that lets users and companies use and switch between various large language models through an API. The company says it has been using the router it built for its own AI usage needs over the past three years. The service is only available in the United States for now. It’s free to use for the remainder of 2026 (users will still have to pay for AI model inference costs), and it comes with a $26 credit launch offer. The company did not say how much the service will cost next year. Router functions similarly to OpenRouter, though the latter offers many more AI model options than Ramp’s current offerings. Router offers access to models from OpenAI, Anthropic, DeepSeek, Moonshot, Minimax, Nvidia, xAI, and Z.ai. It also provides several “strategies” to help its customers route AI requests to models based on their preferences. For example, one lets users set a preference for model providers’ flex usage tiers, while another lets Router choose which model to route queries to based on up to three user-specified benchmarks. Users can also choose to route only difficult problems to expensive models or test models easily without having to switch. Users get a dashboard, too, that lets them see token spend, cost, latency, fallback attempts, and other details. Notably, Router has an opt-out data retention policy: It will record model inputs, outputs, and tool calls for one year by default, though the company says it will remove “personally identifiable information before using that content to improve the product.” For Ramp, entering the model routing business offers a two-pronged opportunity: It gets to tap the booming AI inference marketandoffer its existing clients a model routing service that fits in neatly with its existing products, which includes AI token usage monitoring and token spend management. And, if Router proves as attractive of a model testing arena as OpenRouter has, Ramp may also be able to build long-standing relationships with AI labs and inference providers worldwide. That could help Ramp, whichraised$750 million at a $44 billion valuation in June, gain new customers and a new point of entry for selling its expense management products.
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A third of web pages published since ChatGPT’s launch show signs of AI authorship, study finds
Over one-third of web pages published after the release of ChatGPT show signs of being written by AI, according toa new study from Pew Researchreleased on Thursday. The report corroboratesother studiesthat detail how much of the web’s newer web pages are now either written by or “substantially edited” by AI, the firm says. It also arrives shortly after internet infrastructure provider Cloudflarereportedthat bot web traffic had overtaken human web traffic — a milestone that was reached sooner than the company had estimated. Pew’s data, however, is focused not on who or what is browsing the web, but on what is being browsed. And apparently, much of it is bots reading web pages written by other bots. To compile the report, Pew said it used theCommon Crawlweb archive to collect nearly half a million English-language web pages from the past five or so years, starting a couple of years before ChatGPT’s November 2022 release. Pew then usedOpen Pangram’stechnology to detect how many were likely written or heavily edited by AI. In a random sample of 10,000 web pages collected in July 2026, around 10% showed “significant signs of AI authorship,” Pew said. However, Pew pointed out that a random sample like this would inevitably include older web pages — ones published before AI writing tools even existed, and therefore couldn’t have been AI written. To get a better sense of how much of the web is now being written by AI, Pew filtered out the older web pages and focused only on those published after the release of ChatGPT. In this same snapshot with the older pages removed, signs of AI authorship were found in over one-third (35%) of the pages. Drilling down into domains themselves, Pew found that URLs with a .com domain showed signs of AI authorship at around 10x the rate of a .edu or .gov domain (both of which were around 1% AI authored). In addition, .org domains had only a 4.6% rate of AI authorship. Of course, this analysis is not perfect. Pangram, like other AI-detection tools, can misclassify pages as AI written when they weren’t. But at scale, the data is likely at least directionally correct. In addition, Pew found that other supposed tells of AI authorship had also increased over the years, like the use of em dashes, Oxford commas, and phrasing like “it’s not X, it’s Y,” among other things.
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Grok keeps sending gibberish responses to users
An unusual glitch has resulted in xAI’s Grok chatbot speaking gibberish to many users. After asking the model to generate a PDF, one user received the response: “match it without and your they and two for planets can practical and often cheese…” with similar nonsense continuing for several paragraphs. Checking source links, another user found a string of links to reinforcement learning research sites. Affected users who spoke to TechCrunch said they were using Grok Lite and noticed the issues as early as Wednesday morning. TechCrunch was unable to reproduce the issue in our own testing, and it is likely affecting only a small subset of users. xAI did not respond to a request for comment. Still, the result has been a lot of confused Grok users. The chatbot’s Reddit community has beenoverwhelmedwithcomplaints. Refreshing the session often restores normal function, but some users reported that the gibberish responses continued even after multiple refreshes. The bug appears to be limited to direct queries on Grok.com. The Grok account on X.com has been unaffected. Responding to unhappy users, the Grok accountconfirmedthe issues on X: “That pure word salad is a rare temporary generation glitch. Official status athttps://status.x.aishows all Grok services fully operational with no incidents. Start a fresh chat or regenerate—it usually clears right away. Sorry about the gibberish,” the account wrote Thursday morning. xAI has seen significant staff turnover in recent months, losingmost of its founding teamand at least 50 researchers and engineers, according toa report from The Information in May. The company released its most recent foundation modelin July, describing it as “an Opus-class model, but faster, more token-efficient and lower cost.”
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Linkdaze’s smart calendar is built to run a household, not just track a schedule
With back-to-school season approaching (or already here in some places), keeping track of everyone’s schedules can get pretty chaotic. Between work, school, appointments, sports, chores, and everything else going on, a regular paper calendar just doesn’t cut it. That’s whereLinkdaze’ssmart digital calendar comes in — a touchscreen tablet built specifically to organize a household rather than a single person. One of Linkdaze’s biggest strengths is its calendar compatibility. The system can synchronize calendars from popular services, including Google, iCloud, Outlook, Yahoo, and Cozi, which is a dedicated family-organizing app. This is particularly useful for families where different members use different platforms. Instead of asking everyone to switch to a single calendar app, Linkdaze brings multiple schedules together and uses color coding to make individual family members easy to identify. Launched last December, Linkdaze is available in 15.6-inch and 10.1-inch models, giving you some flexibility depending on how much wall space you have. Beyond calendars and appointments, you can use it for chores and rewards, meal planning, shopping lists, and other family organization. It can even double as a digital photo frame for displaying family photos. The most interesting feature, however, is Linkdaze’s AI meal planner with “Snap-to-Sync.” Instead of manually entering everything into a meal-planning app, you can take a photo of a paper recipe or your kid’s school lunch menu. Linkdaze will turn that information into a digital meal plan and generate a shopping list from it. While not an entirely new idea, it’s a useful feature that helps Linkdaze stand out from a basic digital calendar. Another big plus is that Linkdaze doesn’t require a monthly subscription for its main features. It’s an interesting choice in a category where recurring revenue has become the default. Skylight, a competing smart-calendar brand, offers additional features through its $79 per month subscription. For a hardware company entering a crowded smart-display market, that decision is either going to differentiate its product or look like a lost revenue stream. Linkdaze is also less expensive up front, with the 10.1-inch model priced at $119.99 compared with Skylight’s 10-inch model at $159.99. Overall, this device could make a practical gift for busy parents who are trying to keep everyone’s schedules in one place. It could also be a great fit for college apartments, where roommates can use it to coordinate chores, study schedules, shared meals, and other household responsibilities. It’s also very helpful for those of us juggling interviews, deadlines, meetings, and story assignments.
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Runlayer, Rippling drop lawsuits — but the brouhaha is still a cautionary tale for founders
On Wednesday night, Runlayer and Rippling dropped their respective lawsuits against each other. No settlement was made. No money changed hands. Not even lawyers’ fees, according to court documents seen by TechCrunch. Rippling celebrated by instantly releasingits MCP gateway, the product at the heart of the dueling lawsuits and the one that competes with Runlayer’s offering. This public fight is a cautionary tale to founders: In the age of AI, when building new software has become almost trivial, you never know who your next competitor will be. It might even be a prospective customer. To recap the short-lived legal brouhaha: Runlayer is an early-stage startup thatlaunched out of stealth in November 2025and has raised a total of $42 million from VCs like Khosla Ventures’ Keith Rabois and Felicis. It’s led by third-time founder Andrew Berman (previous companies include baby-monitor maker Nanit and an AI video conferencing tool, Vowel, that sold toZapier in 2024). After Rippling tested Runlayer’s MCP gateway for more than a year, with the two engineering teams working closely together, Rippling never signed on to become a customer, according to Runlayer’s lawsuit. Instead, Berman received a text from a Rippling employee that said his employer was building its own MCP gateway and planned to release it as a product. This employee described Rippling’s product as a clone of Runlayer’s. Runlayer sued, claiming that Rippling violated contractual agreements covering the tests of its products. An MCP gateway securely handles an enterprise’s AI agent requests for data from other software systems. So, for instance, when a hiring professional asks for details on the top five candidates for a job, including their emails, that data must be retrieved from the company’s recruitment system. The gateway handles the retrieval process, rather than granting agents direct access to the company’s software systems. It can then also layer on other features like employee role-based access control (managers getting different access than interns), observability (logs and usage trails), and so on. ThenRippling countersued, alleging that Runlayer was violating some of its patents. The move was seen by Runlayer as a way to induce it to drop its suit while ratcheting up legal expenses. Runlayer dropped its suit after spending the last three weeks in discovery. Rippling dropped its own suit and didn’t collect a settlement either. So, while the lawsuits didn’t lead to anythingbut a lot of public flaming, there is a deeper takeaway for founders. The AI landscape is changing so rapidly that the long-running technical shoot-outs that enterprises love to impose on startups need to be rethought. Between the time an AI startup enters into one and however many months later, an enterprise’s needs and desires may drastically change. In the meantime, in the span of weeks, Rippling, whose bread and butter has historically been payroll and benefits management, has now enteredthe AI gateway market with a tool that can route to different modelswhile dashboarding token spend by employee. The product is competing with the likes ofStripe,Ramp, and Databricks. Now Rippling is also in the AI security business with this MCP gateway that ties AI access to employee roles. It competes with the likes of Runlayer, Docker, and Amazon Bedrock. As for Runway, its pitch is a broader bundle of agent security services tied to the gateway, ranging from agent creation to spotting shadow AI agents running in an enterprise unbeknownst to IT.
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Google gives publishers a new way to fight AI-driven traffic losses
As AI continues tokill trafficto websites, Google on Thursday threw a bone to those publishers negatively impacted by the change. It’s now allowing readers to push a button on a publisher’s website to indicate it’s a “favorite source” they’d like to see highlighted more often across Google Search, Discover, and Google News. The tech giant said it’s making this new, interactive “Preferred Sources” button available to online publishers to embed on their own websites. The launch follows Google’s rollout of Preferred Sources in May to Google’s AI experiences, including AI Mode and AI Overviews. The option was previously available in Top Stories. The idea is to make it easier for readers to find links from the sites they know and trust when they’re searching for content or interacting with Google’s AI to learn about a topic or read the latest news. As ofMay’s launch,the company said that people across the web had already selected over 345,000 unique sources through this method. To add a site as a favorite publisher, you can visitGoogle’s source preferences page, then search for a publisher by name or website. Becoming a preferred source can drive more traffic to publishers’ websites, Google said. In earlier studies, it found that people are twice as likely to click through to a preferred source when available. By offering publishers these additional tools, Google is trying to assuage the damage that the rapid growth of AI-powered search features has had on traffic-dependent businesses. Alongside the new button, Google said that readers will soon be able to customize their Discover feed in Google’s app in their own words. To use this feature, readers will tap any three-dot menu in the feed and then tell Google what topics they’d like to see more or less of, using natural language commands. This helps Google refine the feed in real-time. The search giant is not the only company turning to AI to offer feed-tuning tools powered by AI. In recent months, a number of top social media appshave launched user-controlled algorithmsthat allow people to fine-tune the content that is recommended to them. In addition to personalizing the Discover feed, Google says Android users will be able to customize their audio daily briefings in the Google News app, as well.
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