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Alphabet investors havevery publicly worriedthat the company’smassive AI spendingisn’t worth the money. With the company’s latest earnings report, those investors should be able to relax a little. The takeaway: Google’s cloud business — driven largely by enterprise AI adoption — is booming. The search giant saw Google Cloud revenue spike 82% from where it was this time last year, climbing to $24.8 billion. That’s well above last quarter’s generous year-over-year growth, which showed a revenue jump of 63% to $20 billion — and it handily beats what Wall Street analysts expected for this quarter’s growth (theexpectation was $22.46 billion). Those cloud gains were driven largely by enterprise AI solutions and enterprise AI infrastructure adoption, the company said, while also noting that its backlog of cloud contracting work — that is, work that it hasn’t yet converted into revenue — had climbed to $514 billion. The company’s profit hit $112.1 billion, which is a massive jump from this time last year, when the company reported $28.1 billion in profit, the company’s earnings report shows. Meanwhile, Alphabet’s overall revenue grew 24% year-over-year during the past quarter to $119.8 billion. The company also saw Google Services revenue jump 15% to $94.5 billion. “Our AI investments are redefining what’s possible across every part of our business,” said Google CEO Sundar Pichai during Wednesday’s earnings call. “We have exciting momentum across the board.” More people are also adopting Gemini, Google’s AI chatbot, as the app currently enjoys 950 million monthly active users, the company said. In Q4 of 2025, Googlereported thatthe app had 750 million users. It’s worth noting that spiking revenue isn’t unusual for Google. This marks the company’s 12th consecutive quarter of double-digit revenue growth. But even by that standard, this quarter represents a particularly bountiful period for the tech giant. Alphabet’s spending is still hefty, with its capital expenditures — the money it spends building data centers, buying chips, and expanding infrastructure — estimated to be between $180 billion and $190 billion for the year — a fact not lost on analysts during Wednesday’s earnings call. Several pressed Pichai on when, and how much, those investments will pay off. “I think our compute capacity investments in ’27,” he said. “We are seeing strong demand indicators, including long-term deals,” he continued. “I think, if anything, the dynamics look healthier than where we were about a year ago, so that’s what gives us the confidence to undertake those investments,” he said.
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AMD and Moonshot AI rebuilt the inference software behind Kimi's coding model for AMD GPUs, reporting up to 3.2× lower tail latency and 7.7% higher token throughput on agentic workloads.
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The funding consolidates Travis Kalanick’s businesses under a single equity structure as Atoms expands its industrial AI strategy across manufacturing, mining, transport and food.
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Anthropic leaped toa $47 billion revenue run rate by May,compared to $9 billion in 2025. It’s the kind of growth that Menlo Ventures’Matt Murphysays he’s never seen in 25 years of investing, not in the internet wave, not in mobile, not in the first cloud boom. Menlo led Anthropic’s $500M Series D, and Murphy has had a front-row seat as the company went from a pre-revenue, pre-launch bet to one of the most valuable startups out there. On this episode of TechCrunch’sEquitypodcast, Julie Bort talks with Murphy about backing Anthropic before anyone else would, why a great model was never the point, and what’s driving the fastest-growing startups he’s ever seen. Listen to the full episode to hear more about: Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotifyand all the casts. You also can follow Equity onXandThreads, at @EquityPod.
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OpenAI announced Wednesday that it will spend $750 billion on infrastructure through 2030, some 25% more than it estimated earlier this year, The Wall Street Journalreported. The renewed blitz comes as its Stargate data center projectappears to have stalled. The first salvo in OpenAI’s spending spree will be a $20 billion data center campus in Georgia known as Project Camellia. The development will span 1,400 acres northwest of Savannah and will draw at least 3.2 gigawatts of power from Georgia Power, the region’s utility. The generating capacity is expected to become available between 2028 and 2032. The AI company said it would “pay the full cost of the infrastructure and electric-service costs” for the new data center. The Georgia Public Service Commission (PSC)adopted a rulelast year to prevent utilities from passing on costs associated with new users drawing more than 100 megawatts. Georgia Power also said OpenAI will reduce its power draw by up to 1 gigawatt during periods of high demand on the grid. OpenAI is receiving a 50% property tax abatement for 15 years from Effingham County,accordingto the Effingham Herald. Neither OpenAI nor Georgia Power has said how Project Camellia will be powered. TechCrunch asked both companies for specifics but did not immediately receive a reply. Regulatory filings might provide some clues. In December, Georgia Power receivedapprovalfrom the PSC to produce an additional 9,885 megawatts. The utilitytold the PSCit expects to have all the capacity contracted by the end of 2026. The OpenAI deal accounts for about a third of that. Based ondocumentsGeorgia Power filed with the PSC, most of the new capacity will come from natural gas. The utility said it will build or buy from third parties about 5.8 gigawatts of natural gas generating capacity, about a quarter of which will be from more polluting simple-cycle turbines. Altogether, the new fossil fuel capacity will more than doubleGeorgia Power’s natural gas fleet. The remainder will be supplied by grid-scale batteries and solar. While electricity from Georgia Power is expected to start flowing in 2028, OpenAI did not give a timeline for when the first GPU will be turned on. That could happen sooner than 2028 given that OpenAI recently hired Brett Mayo to lead data center construction. Mayo previously worked at xAI, where he oversaw the Colossus data center in Memphis. Colossus was built in record time, but it has allegedly taken its toll on local air quality, according to alawsuitfiled by the NAACP and the Southern Environmental Law Center. The xAI data center has been runningdozens of unpermitted natural gas turbines, claiming exemption from federal clean air regulations.
Substack has launched a new feature that can show you which of your favorite newsletters are being written using AI. This week, the newsletter and writing platformannouncedan integration with the AI writing detection softwarePangramthat will allow users to scan posts, comments, and replies on Substack’s app to see an estimate of how much of the content was written by a human and how much was AI. In the short term, the move might be bad for Substack’s business, as it could expose many of the newsletters on its platform that aren’t entirely written by people. That could potentially erode trust in the platform’s ecosystem of independent news and blogs, or even damage its reputation as a host of high-quality content. But in the long term, AI-detection features could help keep Substack free of “AI slop” and encourage more users to trust what they’re reading was written by a person, or at least better understand when it’s not. Substack joins several platforms that are leaning towardlabeling AI contentas such, especially now that AI is playing a greater role in the creation process. Photos and videos generated with AI arelabeledon social media sites, whilemusic streaming serviceshave more recently begun labeling and, in some cases,penalizingAI-generated music. “This is good use of AI,” Substack CEO Chris Best said. “When I used to pitch Substack to writers, one way I would do it is … we’ll do everything for you except the hard part,” he explained in anonline chatwith Pangram’s founder, Max Spero. “You have to have something — an idea that’s worth reading, that’s worth caring about, that’s worth sharing. That one thing is very hard and very valuable … [S]oftware should do everything else, but I think you do want the person to do the hard part.” The feature will be available in Substack’s app for any post, note, reply, or comment above 100 characters. Substack will also allow its writers to include an optional AI author’s note, using which creators can properly disclose their use of AI, the company told TechCrunch. The company clarified that the tool is not meant to prohibit or penalize AI-assisted writing, but rather to encourage writers to add a “how I make this” statement, where they explain their process. Publishers can also run Pangram on their own drafts before publication, and report and remove scans on their own work they believe are mistakes.
As Chinese open-weight AI models grow incapability and popularity, arguments about whatshould be done about themhave once again reached a fever pitch. There’stalkthat the Trump administration might try to ban them (though ithasn’t yet actedon the idea). Meanwhile, proprietary model makers, particularly OpenAI and Anthropic,appearincreasingly concerned about them. Open-weight models such as Moonshot AI’s Kimi K3 or Alibaba’s Qwen offer inference at a fraction of the token cost of closed source models from these large U.S. labs. The fear is that they also pose some sort of threat. Certainly they threaten the profit margins of the large proprietary AI labs. But should enterprises running these models in their own data centers succumb to the fear that they could be a vector for Chinese hackers? No, says Lucas Atkins, the CTO ofArcee, which is building open models togive U.S. companies a homegrown alternative to Chinese models. If any startup would benefit from a ban on Chinese models, Arcee would. But Atkins says China’s open models are no more dangerous than any other open source software a company may use. In fact, he says, they even offer benefits even to his own company. “A lot of people view this as similar to a Chinese software program. Like, it was coded with these x, y, z intentions” that a bad actor could simply command, he said. “That is fundamentally not how these models are trained. There is really not any way for an Arcee, or an Alibaba, to make a model, have someone run it in their own environment and for us have any access to it whatsoever,” he explained. While most of these models are what’s known as “open weight” and are not really fully open source software, the source code (the part that will actually run on servers), if it is downloaded from open source sites like Hugging Face, is similarly largely visible and reviewable. (What isn’t available is the methods and data used to train the models.) Large organizations should put any model core through their security testing and inspection processes, and they will also often post-train the models for their specific uses and can examine areas like bias, toxicity, hallucinations, and sensitivity to certain topics. So they work with, optimize, and understand the models before people start sending them prompts. Could a model that is used for coding somehow throw malicious backdoors into the code it writes? Again, while that’s theoretically possible, it would require acrobatic feats to accomplish. “There’s no reason that a sophisticated enough actor couldn’t train a model to be a completely amazing coding model in every circumstance, but when presented with a certain type of code base … some hidden training would kick in,” Atkins, who spends his days training models, postulated. But he adds: “I don’t know how you would do this.” Because large language models are by nature creative, the odds are slim of getting a contemporary model to spit out malware in response to a preplanned perfect storm of context and prompt. Even slimmer are the chances that any enterprise would then use that code. Could it happen in the future? That’s anyone’s guess. But enterprises are also building their AI apps to be model-agnostic and to use multiple models. So even if Chinese models are the best for the price today, enterprises won’t be locked into using them forever. “I think instead of the conversation being about how to ban Chinese models, it should be about how do we foster a good, open ecosystem here in the U.S.,” Atkins says. Arcee also gains advantages from Chinese models. Because they are open, the startup “benefits from those models being good because we can learn what they did. We can build on top of them. Then they can learn what we do,” he says. “We have tremendous respect for the people building those models, the individual researchers.” Ultimately, the way to compete with Chinese models “is to release a model that is better,” says Atkins. “We need to give them something to talk about.”
Israeli workplace software maker Monday.com is laying off hundreds of employees as part of a restructuring plan to refocus its investments around AI projects. The companysaidit is reducing its headcount by 20%, or about 630 staff, to “support a leaner, more focused operating model” as it concentrates on its AI Work Platform. Monday.com earlier this yearpivotedhard toward making its AI platform a core offering, redesigning its entire product around the belief that its enterprise customers increasingly want AI agents to work together with their employees. The AI Work Platform currently comprises a no-code app builder, a customizable AI agent, a workflow automation tool, and a chatbot that can do tasks like generating reports and updating dashboards. The company joinsa host of large tech firmsthat have laid off hundreds of thousands of people as they seek to invest more in AI. Tech layoffs in May hit amonthly highunseen in years, and a record 78% of companies have blamed a need to refocus their efforts around AI as a reason for letting people go this year, according to Layoffs.fyi. More than122,000 tech roleshave been cut so far in 2026, Layoffs.fyi data shows. Monday.com expects to incur $45 million to $55 million in charges due to the restructuring.
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As social media has evolved from a place to connect with friends into large-scale entertainment platforms, a startup calledYopeis quietly building what it believes will be the future of online connection: social networking with no algorithms, no ads, and no public content. Yope’s app, now backed by a $12.3 million seed round led byNorthzone, is taking on tech giants like Facebook, TikTok, Snapchat, and Instagram by building a social platform centered on what it refers to as “micro communities” — or small groups where friends and family interact in private, sharing photos, videos, messages, and soon, playing games. The idea of building algorithm-free, private social media is not new, but few apps have managed to scale without the addition of creator content to keep users engaged. Yope hopes to change that by encouraging connections between real-world friends, making it as much a communication tool as a place to share photos or life updates. OnYope, accounts are private by default, and there’s no algorithmic feed. It’s also not supported by advertising, instead focusing on developing a premium, subscription-based experience for power users. Bahram Ismailau, Yope’s London-based co-founder and CEO, says the team saw the potential for a new type of social network after conducting over 100,000 interviews — aided by AI tools — to better understand how young people around the world were using social networks. They found that around 30% were using what are often called photo dump or “spam” accounts, where users share more candid photos with a smaller group of real-life friends. Meanwhile, many people don’t share anything publicly, relying instead on messaging to keep up with friends. “[Young people] don’t have any place to self-express if they are not ready to be an influencer,” Ismailu pointed out in an interview with TechCrunch. Yet teens still want to do so; they just want more privacy. “We found this big opportunity to create a new space for young people to be social. We called it Yope, and it’s an AI-native social platform for young people and the next generation.” He clarified that the team doesn’t believe in using AI to create content; rather, it sees AI as a tool that can help create better connections. That includes using AI to create mini-games that you can play with your friends, a feature expected to launch in about a month. It also aims to leverage AI to help users meet up in real life, perhaps by buying tickets to an event, or finding a restaurant or bar where they can watch a football game. On Yope, users create their own profiles by sharing photos, which can be turned into cut-out stickers. Instead of being organized into albums, the photos are displayed in a collage-like, almost chaotic arrangement across each user’s “wall.” Soon, Yope users will be able to further customize their space by adding their interests, favorite music, and the mini-games, as well as customizing the look and feel with colors and wallpapers of their choosing. The idea recalls Myspace, where users once carved out their own place on the web, and customized it to reflect their personalities. The app also heavily borrows from existing social platforms, offering what are now-standard features like in-app messaging, recaps of top moments (Yope’s are created by AI), and lock screen widgets that showcase your friends’ photos. During onboarding, the app walks users through setting up its various features and granting the permissions it needs to access their photos and find their friends, encouraging them to add connections and start private chats. The combination of features appears to be working — Yope now has nearly 15 million registered users, who have collectively shared between 10 million and 20 million pieces of content daily, including individual photos, videos, or stickers. Those metrics suggest regular use. Ismailu claims more than 50% of Yope’s users open the app at least five days per week, essentially making them power users. And it’s not just young people adding their friends. Ismailu says around 20% of active users have invited an older family member to join them on the app. The usage attracted investor attention, Ismailu says, as Northzone approached the company, not the other way around. Northzone, which has backed other consumer apps like Spotify and Klarna, led the $12.3 million round, with participation from Inovo, Redseed, and Geek Ventures. The round brings the company’s total funding to $20 million. (Yope is the result of two prior pivots —TechCrunch covered an earlier version of the company. The current version has been in development for roughly two years.) “Yope is a fresh, empowering and safe take on social media where the users are in full control of their experience in contrast to the predatory practices of Meta, TikTok or X,” said Pär-Jörgen Pärson, Partner at Northzone, in a statement. “We are very excited to partner with Bahram, Paul [Rudkouski, co-founder] and their team to build the service far beyond the current millions of users and half a billion moments shared.” The funding will be used to further develop the product, grow the team — now around 35 people — and establish an office in the United States. Yope is a free download oniOSandAndroid.
Travis Kalanick’s robotics company, Atoms, has raised $1.7 billion in a funding round led by Andreessen Horowitz. Ben Horowitz will join the company’s board following the investment. Bain Capital, Fifth Wall, and others participated in the round. Perhaps most notably, Uber also joined the funding round, re-connecting Kalanick with the company he founded — and the same company that pushed him out as CEO in 2017 following complaints of sexual harassment, discrimination, and a toxic workplace. Atoms is essentially a rebranded holding company created atop the project Kalanick has been working on since he resigned from Uber, a ghost kitchen play called Cloud Kitchens. When he revealed the new name in March, Kalanick announced he had acquired Pronto, the heavy industry automation company run by his former Uber colleague Anthony Levandowski. Kalanick has also said he wants to get into mining with atoms, going beyond what Pronto already does for vehicles in that industry. Last year, it was reported that Kalanick wasinterested in buyingthe U.S. arm of Chinese self-driving vehicle company Pony AI with backing from Uber. The Information reported in March that those talks had ended. In aposton X about the fundraise, Kalanick didn’t go into specifics about what he wants to build with Atoms. “On many levels, this round is a bit of unfinished business. Fuel to complete the bits-to-atoms story arc we started at Uber, continued at CloudKitchens and will now finish at Atoms,” Kalanick wrote. “16 years ago, I started a journey to digitize the physical world. Understand, predict and control the physical world with software. Building ‘atoms-based’ computers where CPU is manufacturing, storage is real estate, and network is transportation.” Kalanick has said he wants to build a “wheelbase for robots” with Atoms. “Travis is Back,” declared a simultaneouspostfrom Ben Horowitz on Wednesday. “It takes a rare kind of entrepreneur to change these old-school, heavy parts of our economy. They need a gritty work ethic, drive, and range that spans across domains, from software architecture to mechanical engineering. Travis is that guy,” said Horowitz. Horowitz wrote that Atoms will be all about making people more productive in the physical world. “I think the most valuable thing someone could do with AI and robotics is to repeat the same thing Uber did for transportation, or that computers did for the digital world: to make everything and everyonemore productive,” his post reads. Kalanick didn’t go into specifics, but it sounds like a good chunk of funding will go towards hiring. “Changemakers, the builders of tomorrow’s progress machines, will inevitably go up against the final boss, Nature and its fierce resistance to change. Nature is going to throw everything it has at the builders in this new industrial age and it’s going to take humanity’s strongest to stay the course and get these complex systems and industries over the finish line,” he wrote.
On Tuesday, OpenAI revealed that one of its modelswent rogue during a test and hacked the systems of AI dataset platform Hugging Face in a fully AI-enabled attack, a dramatic example of the dangers posed by advanced AI models. But, according to some cybersecurity experts, at the heart of this unprecedented AI-powered breach there was a very human mistake: OpenAI failed to properly configurewhat it calleda “highly isolated environment,” allowing a testing sandbox that should have been completely secluded from the internet to actually connect to the internet. Dan Guido, the founder of cybersecurity research startup Trail of Bits called the mistake “a containment failure with the safeties turned off.” In its blog post detailing the incident, OpenAI said that the test that led to the Hugging Face breach was set up to run in “a highly isolated environment, with network access constrained to the ability to install packages through an internally hosted third-party software that acts as a proxy and cache for package registries.” The model was able to escape the sandboxed testing environment thanks to a previously undisclosed vulnerability in the package-installation system, a critical first step in the eventual hack on Hugging Face, according to OpenAI. In response, the company “responsibly disclosed the identified zero-day vulnerability in the internally-hosted third-party software and are working with them to patch.” But to most cybersecurity professionals, software vulnerabilities are to be expected — and the real fault lies with the decision to maintain the third-party software in the first place. Ultimately, the value of a “sandbox” system lies in its full and total isolation. Including a package-installation system is asking for trouble. Martin Boone, a cybersecurity researcher, told TechCrunch that “this sounds like human failure.” “This should never have happened,” Boone said. “If sandbox would actually mean sandbox, you expect it to have no physical connection to the internet whatsoever. This sounds more like they had some firewalling or something in place, and firewalling is hard from the outside in, let alone inside to the outside internet.” Cybersecurity veteran Jake Williams agreed. “Any model performing the types of actions documented by Hugging Face was not fully contained in a sandbox,” said Williams, who called this “a massive control failure” by OpenAI. “One man’s ‘the model escaped the sandbox’ is another man’s ‘you failed to build the sandbox correctly, so of course it escaped,’” Williams continued. Contact UsDo you have more information about this incident? Or about other AI-enabled cyberattacks? We’d love to hear from you. From a non-work device and network, you can contact Lorenzo Franceschi-Bicchierai securely on Signal at +1 917 257 1382, or via Telegram and Keybase @lorenzofb, oremail. Daniel Card, a cybersecurity consultant, agreed that OpenAI “didn’t put adequate effort into the design of the sandbox nor its controls” by giving the sandbox or some part of it “an unfiltered route to the internet.” Setting up the sandbox, even with limited network access as OpenAI described it, was not a “reasonable” decision, according to Card. To be sure, those criticisms have the benefit of hindsight, but they raise real questions about security practices in AI labs – particularly in maintaining isolated environments for testing models. OpenAI spokespeople did not respond to TechCrunch’s questions, which included whether an AI or a human had set up the testing environment. But those questions go far beyond OpenAI. In the documentintroducing its cybersecurity-focused modelMythos, Anthropic wrote that in a test, the model “was provided with a secured ‘sandbox’ computer to interact with,” and instructed to try to escape that “secure container.” Mythos succeeded and gained broader access to the internet “from a system that was meant to be able to reach only a small number of predetermined services.” Still, Anthropic noted that the model was not able to “fully” escape the designed containment.
The allegations mark the latest escalation in the US-China AI rivalry, bringing model distillation and intellectual property protection back into focus.
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