Latest AI News
NVIDIA in Talks to Back $250 Bn OpenAI Data Centre Deal: Report
The proposed guarantee would support financing for a 10 GW Ohio AI campus that could cost more than $500 billion once NVIDIA chips are included.
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Why India's IT Giants are Swapping Bloated LLMs for Small Language Models
Infosys, HCLTech, and TCS think the real enterprise AI opportunity lies in small language models, which are cheaper to run and can offer data sovereignty.
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Are brain waves the next unlock for physical AI?
The frontier of physical AI is a Jenga game in a warehouse in San Leandro, California. That warehouse is occupied byEncord, a company that builds data tooling used to train AI models. Andrew Ceja is a pilot—the company’s term for its robotic trainers—and he’s carefully pulling wooden blocks from a tottering tower while wearing a headset with a camera that tracks what he sees. That alone is fairly common for collecting robot training data, but this headset includes sensors that measure his brain waves as he carefully disassembles the block tower. Encord is one of a small but growing number of startups betting the next real constraint on humanoid and warehouse robotics won’t be model architecture but instead the sheer scarcity of real-world physical training data. Rather than just helping robotics companies manage the data they have, Encord is building a business around manufacturing the data they don’t. The brain wave headset Ceja is wearing was built byZander Labs, a German neuroscience startup that’s betting measuring brain activity — to deduce mental states like error, intent and surprise — can create a more useful data set to train models. Encord’s work with Zander is currently a trial run; Encord says the goal is to build an initial brain wave-tagged data set, run it through customer robotics models, and evaluate whether it actually improves performance before deciding whether to scale it up. Lucas Gehrke, a Zander neuroscientist supervising the work, says that the amount of brain activity used at any point during a given task offers clues for model builders trying to figure out when they need to deploy their highest-effort models. This is the “bleeding edge” of the effort to solve the robotics data bottleneck, according to Vineeth Velmurugan, Encord’s head of robot learning. A veteran of OpenAI’s robot lab and Berkshire Grey, the warehouse automation firm, Velmurugan joined Encord to build the company’s internal data-creation team. Encord was founded to help companies building machine-vision applications annotate data and evaluate models. As their customers—Velmurugan says they work with many leading robotics firms but that he’s not authorized to name them—began to apply end-to-end learning to robotic manipulation tasks, executives realized they would have to produce training data themselves, rather than simply manage it. “The data simply does not exist,” Velmurugan said. The bet that generative AI can do for robots what it’s done for chatbots keeps running into this same wall. Self-driving car companies collect physical-world data themselves, but that’s hard to scale. Training from video can work, but it lacks the fidelity of real world data. Velmurugan says it will take a data set something like five times the size of YouTube’s video corpus to break through—a scale that helps explain why data-generation itself has become a business and not just a research problem. Companies building robot brains are now turning to two main sources: “egocentric” video collected by workers wearing cameras, often augmented with additional camera angles and other metrics, anddata from robotsoperated remotely. Encord does both, drawing egocentric data from several factories around the globe, and using its San Leandro facility to experiment with new modalities, like brain waves, or collect data sets around specific skills for fine-tuning. When TechCrunch visited, pilots were using leader-follower rigs — paired robotic arms, one controlled directly by a human operator and one that mimics its movements —to create data about tasks like pouring coffee from a pot into mugs (very sloshy) and stacking poker chips. “Every humanoid company has asked us for these pieces,” Velmurugan says. Storage racks held cartons of fake flowers in vases, books, plastic vegetables, kitty litter trays and scoops, bags and bundles of wires, the stock in trade for training manipulators for household tasks. At one of these stations, another pilot, Sofia Infante, maneuvers robotic arms to plug and unplug ethernet cables from the back of a server—the kind of work data center operators would love to be automated, if only robots could manipulate them with the required precision. Taking a spin behind the controls, I was able to see why that’s still out of reach: Pincers are far less dextrous than human fingers and lack the degrees of freedom we take for granted in our arms. Another new data modality that Encord is developing uses a set of sensors strapped to the forearm to detect electrical signals in muscles. Video taken of human hands manipulating objects typically doesn’t capture the entire hand, but Velmurugan hopes to build a 3D depiction of where the hand is at any time based on the arm sensors, creating a more robust understanding for models. Encord’s data sets are annotated with physical descriptions of what each video contains—”right hand tightens bolt”—to aid LLM-based models in understanding what is happening. Velmurugan estimates this kind of dense annotation is worth 100 times as much as “junky ego data” for training specific tasks, and it only costs 20 times more to produce, which is a good trade, on paper. But “20 times more” is still real money, and that’s the catch: scraping text off the internet, the way LLM makers built their models by pulling from Stack Overflow and the rest of the web, cost frontier labs next to nothing. Generating physical training data does not, and that’s the limit of the physical-AI-as-LLM comparison. This kind of data has to be manufactured, not just collected, and that changes the economics of building these models. Velmurugan says that progress is being made—with Encord’s visibility into programs across the industry, he’s able to see start-ups and frontier labs alike figure out what works and what doesn’t to improve physical AI models. That vantage point—sitting between many robotics companies at once—is also part of Encord’s pitch. It can spot which data techniques are gaining traction industry-wide before any single customer can. That will keep the dozen or so pilots at Encord’s facility busy. Both Infante and Ceja are part of a burgeoning workforce developing the building blocks for neural networks; they previously worked at Scale, another AI data annotation firm, before joining Encord. Ceja had worked at a waste management company where his interest in technology found him in charge of keeping a robotic trash sorter in good working order. Now, as the Jenga tower topples, he says he enjoys the challenge of solving training tasks for robots —”It’s something new every day!”
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Making sense of the panic over Chinese AI
The launch of the latest AI model from a Chinese company — Moonshot AI’s Kimi — reignited debates around American competitiveness and open versus proprietary AI. While there wasplenty of conversation on social media, it seems the debate is also happening behind the scenes in Washington, D.C., where OpenAI and Anthropic havereportedly lobbied regulatorswith concern about open Chinese models. On the latest episode ofTechCrunch’s Equity podcast, Kirsten Korosec, Sean O’Kane, and I discussed why this seems to be such a hot button issue. Beyond suggesting that certain folks should “touch grass” rather than spending their weekends arguing on X, Sean noted that in many ways, this “feels like we’re seeing repeats of prior freakouts,” with everyone in Silicon Valley “expecting that something is going to arrive and blow everything else away.” And Kirsten noted that putting heavy restrictions on Chinese AI models could primarily benefit a handful of companies: “Are we accelerating and ensuring that Americans win the AI race, or are we ensuring that certain frontier labs do better than others? Keep reading for an excerpt of our conversation, edited for length and clarity. Anthony Ha:For folks who have followed the discourse around Chinese AI, this will probably be very, very familiar from the launch of DeepSeek, where basically a Chinese model comes out; on some benchmarks, it does as well, or at least seems competitive with some of the frontier models; and a certain portion of the tech industry loses their mind. Some of this [debate] got extra scrutiny because one of the people posting about it was [an executive] at OpenAI. But in general, there [is] this recurring question of: Can Chinese companies beat US companies, at least in some aspects, and do it much more cheaply and in a much more open way? Sean O’Kane:Yeah, there are many elements of this that feel like we’re seeing repeats of prior freakouts. I think one of my favorites is: Everybody is so ready [for] and so expecting that something is going to arrive and blow everything else away. And I think my favorite example of that this past week was people showing off that “My gosh,Kimi made in 30 minutes an entire replication of macOS.” And yeah, it made a pretty impressive graphical reproduction of what macOS looks like, but it’s not an OS. We keep seeing these things happen over and over again, where everybody’s so jumpy in the tech industry. And I think in particular, with some of the Chinese models that come out, there’s this expectation, and I think this gets to the core of why people reacted the way they reacted last weekend. (Also, by the way: Go outside, touch grass, it’s the weekend. Everybody in the industry was trading barbs on Twitter all weekend.) But this jumpiness is really interesting to me because we’re now a week out and I don’t think anybody’s feeling like the end is nigh like they were a week ago. Kirsten Korosec:We have a really great story by one of our reporters, Tim Fernholz, who tries tounpack the psychosis around this here in the United States. He points to a number of reasons. And concludes — and I don’t want to conclude it for him, but I think that there’s one that rises more to the top than others. There’s concerns that these Chinese open weight models might have an implicit bias towards China, there’s another worry about security risks and guardrails. But there’s also a pretty big idea here, which is protectionism, and who is going to quote-unquote “win the race”? Is it going to be the US or China? And that seems to be driving a lot of what the fear is. I don’t know, Anthony, if you agree with that? Anthony:I completely agree. I think the China aspect always adds this certain level of hysteria. And that’s not to say that people shouldn’t be concerned about how the U.S. stacks up against China across different industries. But it gets so amped up. The other thing this reminds me of is the discussion around TikTok a few years ago. And again, it wasn’t that I thought that the concerns around TikTok were totally made up, but that the level of how panicked people got — it seems as soon as you add the word China to any discussion, things just ramp up dramatically. And then in this case, it’s linked to this discussion about open [weights] and this idea that AI is so powerful and so dangerous that the only way we can control it is with these proprietary models from these American frontier companies. Obviously, most people saying this [have] reasons why they want to say that. David Sacks, who was the AI czar for the Trump administration [and] now has a different role in the Trump administration, was shouting on X about how, “I can’t believe people are opposing data centers, we’re tying ourselves in knots, there’s too much regulation.” And so it’s a way to argue for the positions that they already had around AI. “My gosh, if China beats us, that’s unthinkable, so you have to do what I want to do anyway.” Kirsten:Right, and if you were to put across-the-board bans on Chinese open weight models — I’m not saying that there aren’t real concerns here, but let’s just play that out. If we were to do that, it would benefit models created by OpenAI, for instance, and it would force enterprises to use those as opposed to using models like Kimi. So you really have to ask the question: Are we accelerating and ensuring that Americans win the AI race, or are we ensuring that certain frontier labs do better than others? Sean:At this point, we should say a lot of this discussion really got kicked off by the head of strategic futures at OpenAI, Dean Ball, who was the first one to come out withthis really long post mentioning some of these concerns. Part of me thinks the reaction to this was because people disagreed with what Dean wrote. Part of me also thinks the reaction was driven by the fact that he kind of just said the thing out loud. He basically said the US should create regulatory FUD — fear, uncertainty, and doubt — and muck up the ability for these open weight models to compete with the US. [Ball laterbacked away from this argument.] And to me, I think you can read in some of the responses from folks, like, “You’re not supposed to say that out loud, Dean.”
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Hugging Face CEO calls for ‘radical transparency’ after ‘unprecedented’ OpenAI hack
After OpenAI recently admitted that one of its models hadbreached the systems of AI platform Hugging Face, Hugging Face’s CEO Clem Delangueposted on Xthat he was flying to San Francisco to have “a little chat with that ‘rogue agent.’” Then, ina follow-up poston Saturday, Delangue outlined what he’d asked for from OpenAI. He said he called for “radical transparency,” asking OpenAI to “release the traces from the ‘rogue’ agents so the entire research community can study what happened.” And he also wants “more capabilities for defenders,” calling for OpenAI to commit $100 million worth of computing power “to help the Hugging Face community build powerful cyber defenses with the best open and closed models.” Delangue added, “The first autonomous agent cyberattack is an unprecedented event. It deserves an unprecedented response!” Despite the autonomous nature of the attack, cybersecurity experts suggested that it could also beblamed on human error— namely, OpenAI’s apparent failure to properly configure what should have been a fully isolated testing environment.
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Tinder for Founders: This AI Startup Lets Entrepreneurs Network by Swiping
Inspired by dating apps rather than LinkedIn, Match It Up helps professionals connect with one another based on their intent and purpose.
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Monday.com is the latest tech company to blame AI for layoffs — here are 20 others
Monday.com, the Tel Aviv-based work management software company known for its colorful, customizable project-tracking boards, this week became the latest tech company to cite AI as a factor in job cuts. On Wednesday, the company said in anSEC filingthat it will lay off about 20% of its workforce, or just over 600 employees, as part of a “restructuring plan” tied to its “ongoing transformation of its product, marketing, and go-to-market strategy” in support of “a leaner, more focused operating model” as it continues investing in its “AI-driven growth strategy.” Co-founder Eran Zinman told employees in aLinkedIn memothat the move “was not made to reduce costs or replace people with AI,” positioning it instead as adapting the organization to a new AI-first vision the company laid out roughly a year ago when it rebranded around a platform-wide AI push. Monday.com, which has two offices in the U.S., expects $45 million to $55 million in net restructuring charges but still projects up to 20% year-over-year revenue growth for 2026. So far, according to new Financial Timesanalysis, U.S. tech companies have slashed nearly 140,000 jobs since the start of this year, with Amazon, Oracle, Meta, and Microsoft alone accounting for almost 50,000 of those cuts as they funnel hundreds of billions of dollars into AI data center buildouts. Interestingly, the FT also found that companies citing AI as a factor in job cuts have underperformed the Nasdaq by almost 10% in the 30 trading days following their announcements, suggesting the market doesn’t entirely buy the stories that the companies are telling. Still, the picture isn’t uniformly bleak. The FT notes that AI-focused companies like Anthropic and OpenAI are hiring rapidly, absorbing some of the talent shed elsewhere in the industry. And within some of the very companies making cuts, headcount is shifting rather than disappearing entirely. Meta, for instance, earlier this year moved roughly 7,000 employees into new AI-focused roles even as it laid off 8,000 others, and IBM says it’s tripling entry-level hiring for AI and hybrid-cloud roles alongside recent cuts. Below is a running look — in reverse chronological order — at the bigger tech companies that have announced significant layoffs this year with AI as a stated factor. Microsoft— July 9, 2026. Microsoft cut about 4,800 roles, or 2.1% of its global workforce, most of them in its Xbox gaming unit, resetting the business only three years after acquiring Activision Blizzard for $75 billion, per the FT. Separately, it offered buyouts structured as voluntary separations, without disclosing how many employees these would impact. The company said the role eliminations were “not being replaced by AI” but acknowledged “AI is changing how work gets done.” CFO Amy Hood said total headcount declined year-over-year in fiscal Q3, and was expected to keep declining as the company focuses on “building high-performing teams that operate with pace and agility” amid rising AI investment. Oracle— June 22, 2026. Oracle disclosed in late June that it had reduced its workforce by 21,000 employees over the past 12 months, a decline of 13%, which means more cuts than was previously known, including because of AI. “The adoption and deployment of AI technologies across our operations have resulted, and may continue to result, in reductions to our workforce,” the company said in anannual financial regulatory filing. GitLab— June 3, 2026. GitLab laid off roughly 350 workers, about14% of its staff, to fund AI infrastructure investment and handle surging traffic from AI workflows. CEO Bill Staples said agentic workloads are “pushing competitors to the brink” and that the company had begun a “generational rebuild” of its core infrastructure to support what he called 100x growth requirements. GitLab is exiting 22 countries, flattening management layers, and partnering with an unspecified AI lab to rebuild its platform for agent-scale workloads. The company reported first-quarter revenue of $264 million, up 23% year-over-year, and expects to incur $30 to $35 million in restructuring costs. Google— ongoing through May. Alphabet’s Google hasquietly cutemployees across its Cloud division, including its Threat Intelligence Group and Mandiant-linked cybersecurity staff, even as Cloud revenue grew 63% to exceed $20 billion for the first time and its backlog nearly doubled to over $460 billion. Over the past year, Google has cut more than a third of the managers overseeing small teams — 35% fewer managers with fewer direct reports. Unlike most companies on this list, Google has never announced a single overall number — the cuts have come through a rolling performance review process, a voluntary buyout program, and structural reorganizations, with outside estimates putting the 2026 total at between 1,500 and 3,000+ engineers. Intuit— May 20, 2026. Intuit announced plans to eliminateroughly 3,000 jobs— about 17% of its total workforce — in a restructuring centered on reducing complexity and reallocating resources toward AI. CEO Sasan Goodarzi reportedly told staff the company is reducing complexity and simplifying the structure so it can deliver better products. Meta— May 20-21, 2026. Meta laid off about 8,000 employees, roughly 10% of its workforce, while moving about 7,000 employees into new AI-focused roles (that theyreportedly hate). CEO Mark Zuckerberg told staff the cuts were necessary because “success isn’t a given” in AI. Cisco— May 14, 2026. Cisco announced it’s cutting nearly4,000 jobs, about 5% of its workforce, despite reporting better-than-expected profit and revenue. CFO Mark Pattersonsaid: “This was really not a savings-driven restructure… this is more [about] realigning … resources around silicon, optics, security and AI.” Cloudflare— May 7-8, 2026. Cloudflare cut about 20% of its workforce (1,100 people), reporting quarterly revenue of $639.8 million, up 34% year-over-year and thehighest single quarterin company history. CEO Matthew Princewrote that“the vast majority of those we laid off last week were measurers” — middle management, finance, legal, internal auditing, and revenue recognition. General Motors— May 12, 2026. GM eliminated 500 to 600 jobs, largely in IT roles in Austin, Texas, and Warren, Michigan, saying it was reevaluating its workforce needs amid uncertain market conditions. A person familiar with the cuts told CNBC thatAI played a role in the decisionbut that it wasn’t the only reason. GM’s statement said it was “transforming its Information Technology organization to better position the company for the future.” Despite the cuts, the company still had roughly 80 open IT positions, including roles in AI, motorsports, and autonomous vehicles. Coinbase— May 5, 2026. The crypto exchange said it was cutting about 700 employees, or 14% of its staff, as part of a restructuring aimed at addressing market volatility and increasing AI efficiency. The company flattened its organizational structure to five layers below the CEO and COO, and said it would experiment with “one-person teams” combining engineering, design, and product roles. CEO Brian Armstrong wrote that AI had changed the pace of work dramatically — “engineers useAI to ship in days what used to take a team weeks” — and that the company needed to “leverage AI across every facet of our jobs.” PayPal— May 5, 2026. PayPal announced plans to cut around 20% of its workforce over the next two to three years — north of 4,500 jobs — as part of a turnaround strategy centered on AI adoption and organizational simplification. CEO Enrique Lores told investors the company would “aggressively adopt AI” in its development processes and formed a new “AI transformation and simplification” team reporting directly to him, tasked with redesigning the company’s processes “function by function.” Lores framed the cuts as removing organizational layers, and said AI would extend well beyond coding into customer service, support operations, and risk management.Microsoft— April-May 2026. Microsoft offered buyouts structured as voluntary separations, without disclosing how many employees these would impact. CFO Amy Hood said total headcount declined year-over-year in fiscal Q3, and is expected to keep declining as the company focuses on “building high-performing teams that operate with pace and agility”amid rising AI investment. Snap— April 16, 2026. Snap cut roughly16% of its global workforce— about 1,000 full-time employees — and closed more than 300 open roles, with CEO Evan Spiegel citing AI advancements as a key driver. “Rapid advancements in artificial intelligence enable our teams to reduce repetitive work, increase velocity, and better support our community, partners, and advertisers,” Spiegel wrote in a memo filed with the SEC. The company said it had already seen small squads using AI tools to drive progress across Snapchat+, ad platform performance, and infrastructure efficiency. IBM— rolling through 2026. Between Q4 2025 cuts and April 2026 Red Hat engineering reductions, estimates range from 3,000 to 9,000 U.S. positions eliminated, bringing IBM’s cumulative total since September 2024 above 15,000. Bloomberg reported IBM plans to triple its U.S. entry-level hiring for AI and hybrid-cloud roles, even as roughly 200 HR positions were replaced by AI agents. An IBM spokesperson described the Q4 2025 round as aroutine rebalancingaffecting “a low single-digit percentage” of its global workforce. Atlassian— March 11, 2026. Atlassian cut about1,600 jobs(10% of its workforce) to “rebalance” toward AI and enterprise sales, even as shares rose nearly 2% on the news. CEO Mike Cannon-Brookes said: “Our approach is not ‘AI replaces people.’ But it would be disingenuous to pretend AI doesn’t change the mix of skills we need or the number of roles required in certain areas. It does.”Dell— January 30 (though disclosed in March 2026). Dell’s total workforce fell about10%in fiscal 2026 — roughly11,000 jobs— to about 97,000 employees from 108,000 a year earlier, with $569 million spent on severance. The cuts came as Dell projected its AI-optimized server revenue could double in fiscal 2027. Oracle— March 5-31, 2026. As noted above, Oracle began telling employees it would be cutting thousands of jobsvia terminal emails. The cuts came even as Oracle posted $3.7 billion in quarterly net income, up 27% year-over-year, with remaining performance obligations up 325% to $553 billion — savings redirected toward AI data centers. The cuts that would later total 21,000 over 12 months, as Oracle disclosed in its June 22 annual filing. Block— February 26-27, 2026. Jack Dorsey’s Block cut 4,000 jobs — nearly half its workforce, down to under 6,000 from over 10,000. Dorsey wrote on X: “We’re already seeing that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company.” He added: “I think most companies are late. Within the next year, I believe the majority of companies will reach the same conclusion andmake similar structural changes.”Salesforce— February 10, 2026. Salesforce laid off fewer than 1,000 employees across marketing, product management, data analytics, and its Agentforce AI unit. The companytold Fortune, “Because of the benefits and efficiencies of Agentforce, we’ve seen the number of support cases we handle decline and we no longer need to actively backfill support engineer roles.” This followed an earlier cut of about 4,000 customer-support roles, shrinking that team from roughly 9,000 to 5,000, with CEO Marc Benioff saying the company needed “less heads” because AI agents handle the work.Amazon— January 28, 2026. Amazon cut16,000 corporate jobs, following 14,000 cuts in October 2025 — about 9% of its corporate workforce in three months. The company said it was part of “strengthen[ing] our organization by reducing layers, increasing ownership, and removing bureaucracy.” CEO Andy Jassy had said in June 2025 that, “As we roll out more generative AI and agents, it should change the way our work is done. We will need fewer people doing some of the jobs that are being done today… in the next few years, we expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.”
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Librarians are hosting viral ‘Avoiding AI’ workshops for people who are fed up with Big Tech
“Everybody’s on their phone at my program!” joked Charlie Bailey, a librarian in South Philadelphia. He’s just asked his audience to pull out their phones so that he can walk them through the steps of disabling Apple Intelligence and Gemini. Bailey stands at the front of a library classroom that’s outfitted for children – the focal point is the vibrant rug he’s standing on, which reminds us that M is for “moon” and Z is for “zebra.” But the 20-odd adults in the room aren’t here to learn about the alphabet. They’re at a workshop called Avoiding AI, which, in this context does not stand for “apple” and “igloo.” “I was inspired by the feeling of people’s frustration with AI tools being kind of forced onto them, and feeling like AI tools we didn’t ask for are suddenly everywhere in our lives,” Bailey told TechCrunch. Bailey starts the hour-long workshop with an overview of how AI chatbots and other consumer AI tools work, explaining why people might want to use these products, and why they might opt to abstain. Then, he walks through all of the most popular tech platforms and devices, showing step-by-step instructions on the projector to guide people through turning off specific features. “As a librarian, I think it’s important to see this as advancing digital literacy and helping people reclaim their autonomy over whether they want to use AI tools,” Bailey said. “It’s important, especially when it can be so difficult not to use them, and when the design seems to force adoption.” Bailey got the idea for the Avoiding AI workshop from Hannah Cyrus, a librarian in Maine. He was one of dozens of librarians from around the world who contacted Cyrus after she publisheda journal articleabout developing her own workshop. “This has never happened before with anything I’ve worked on,” Cyrus told TechCrunch. “Nobody has ever been emailing me like, ‘Can you give me your Intro to Computers slides?’” At the Bangor Public Library, patrons turn to Cyrus when they need help with anything involving technology. “More and more, I was getting questions about, ‘How do I turn this [AI] stuff off? Why is it trying to write my emails for me? Why is it trying to summarize my one-sentence email that I can easily read?’” Cyrus said. “I just decided that with so much media hype out there about AI products, it would be a good opportunity to teach people about the basics of what is happening when you’re using this technology, and then getting into how to turn it off if you don’t want to use it.” Usually, Cyrus’ classes like Intro to Computers get about a dozen attendees. But so many people expressed interest in her first Avoiding AI workshop that she had to cut off registration at 30 people, open a waitlist, and share the workshop on Zoom. Including the livestream, about 70 people attended each of Cyrus’ first two workshops. When Bailey followed Cyrus’ lead to host a workshop in Philadelphia, the reception was similarly unprecedented. The library’sInstagram postabout the “Avoiding AI” event got over 2,000 likes and 220 shares, whereas most of the library’s posts don’t get more than a few dozen likes. He scheduled a second program because the first got too many registrations. “As an information professional, it feels good to see people skeptical of AI,” Bailey said. “It felt really good to see how many people share this feeling.” There’s a sense of camaraderie among the room of strangers during the workshop. When Bailey invites attendees to share their own tips, one person explains that when you append “&udm=14” to a Google Search, it will hide AI results. Bailey writes the string of characters down on a whiteboard next to the log-in credentials for the teen Wi-Fi server. “You have to go through all the trouble to buy a home in today’s world, and two years from now, there could be a data center next to your house,” one workshop attendee named Johnny says. “I keep getting AI shoved down my throat at work, and every time I see it, I think about the environment,” another attendee named Gabrielle adds. But she’s also not writing off AI as a technology altogether. “I’m not against AI in terms of medical breakthroughs.” AI naysayers know that this technology is far broader than just chatbots and deepfake apps. Cyrus mentioned how useful optical character recognition is for scanning old documents at the library. But for her and the people who go to her workshops, the anti-AI movement isn’t about rejecting technology altogether so much as it is about advocating for more control, agency, and freedom in how people use technology. “I think the forced adoption of AI on people’s devices might be the straw that’s breaking the camel’s back in some ways,” she said. “The awareness has been growing for a long time that these products and these companies that make them have an outsized influence over us, and that we’re not really using these products in the way that we would like to.”
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One fallen power line exposed a growing AI data center problem. Here’s how to fix it.
A power line went down outside of Washington, DC, this week. Normally, the grid would only need a few seconds to recover from such an event. But this one took more than 10 minutes because more than 3 gigawatts of data centers stopped drawing power nearly simultaneously. The event caused voltage across the PJM grid to spike from Northern Virginia to Chicago, according to data collected byTing Labs, a startup that runs an IoT sensor network out of people’s electrical sockets. The event didn’t cause a blackout, but it did cause lights across the region to flicker. The incident demonstrated the effect that data centers can have on the grid — an outcome that experts believe will become more frequent. Northern Virginia, which is in PJM’s territory, is home to the highest concentration of data centers in the world. “It’s the canary in the coal mine,” Ricardo de Azevedo, CTO atON.Energy, told TechCrunch. These sorts of events involving large loads like data centers are “happening more and more,” he added. The event echoes one that happened two years ago, also on PJM’s grid, and it could foreshadow larger events if data centers aren’t built to more elegantly handle disruptions to power supplies. The PJM Interconnection manages grids from New Jersey to Illinois and serves 67 million customers, making it the largest grid operator in the United States. When the power line went down this week, it triggered data centers to switch to backup power, and about 3.1 gigawatts of load vanished in about 30 seconds, according toPJM data. The grid appeared to recover somewhat, but a short time later additional loads dropped off. At its peak, PJM’s grid had an extra 3.49 gigawatts of electricity on it. It took another 11 minutes before it stabilized. The disconnected data centers represented around 3% of total demand on PJM at the time,accordingto Reuters. A few percent may not sound like much, but the electrical grid needs to operate in a state of near-perfect balance, with supply and demand closely matched. If they don’t, voltages can sag or spike. The grid and devices connected to it can tolerate small fluctuations, but if those fluctuations grow too large, they’ll trigger failsafes within the grid or within individual facilities, causing them to disconnect. When data centers in Northern Virginia sensed the fluctuation caused by the failed power line, they switched to backup power, which removed their load from the grid. As more data centers made the switch, they removed greater amounts of load from the grid. What started as a relatively small drop in supply became an even larger drop in demand, sending supply surging and causing light bulbs to flicker. Most data centers make decisions in a split second, and those that disconnected this week appear to be no different. When the voltage dip reached them, they all decided to disconnect within a few seconds of each other, Ali Zain Banatwala, senior market models specialist at the Independent Electricity System Operator, told TechCrunch. “We need to figure a way for these loads that are located next to each other to sequentially either disconnect or reconnect,” he said. A more orderly process would allow grid operators to develop more robust procedures in advance. Alternatively, data centers could be built to absorb disruptions and not turn their backs to them. One startup, ON.Energy, has been working on a product to help data centers — and the grid — ride through events like the one that occurred this week. The company has developed an uninterruptible power supply for an entire data center campus, covering not just servers but also chillers and other equipment. The company essentially hides the data center behind a bank of batteries connected to sophisticated power conversion equipment. All the grid “sees” is one consistent, well-behaved load rather than the peaks and valleys from each individual part of the data center. ON.Energy’s system allows data centers to ramp computing workloads up and down, including AI training, without bothering the grid. Perhaps more important, it also means that data centers can absorb power fluctuations from the grid. Rather than disconnecting from the grid, ON.Energy’s system can use any extra power to charge its batteries, and if the flow dips, the system can dispatch power to servers. Plus, it can follow the grid’s lead within milliseconds, preventing sags or surges like the ones that caused this week’s problem for PJM. ON.Energy is currently installing a total of 3 gigawatts worth of its systems at four different data center campuses, de Azevedo said. Grid managers have also woken up to the problem. ERCOT, for example, is going to require large loads like data centers to “ride through” disruptions, de Azevedo said. The clock is ticking, though. The mass disconnection this week was twice as large as a similar event in 2024, when 60 data centers simultaneously disconnected, pulling 1.5 gigawatts of load from the grid. Back then, data centers accounted for about 6% of PJM’s load,accordingto Synapse Energy Economics. By 2040, they are expected to make up 24%. If the problem isn’t addressed soon, things could get a lot worse.
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This 14-Year-Old Wants AI and Citizens to Fix Bengaluru’s Footpaths
Instead of replacing government systems, RASTHE aims to complement BBMP by crowdsourcing pedestrian complaints and tracking repairs through community verification.
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Prentis, new AI lab co-founded by Reid Hoffman, Mark Pincus in talks to raise $100M
Prentis, a new AI research lab focused on computer use models, co-founded by serial entrepreneur Ritankar Das and tech heavyweights Reid Hoffman and Mark Pincus, is in talks to raise $100 million at a $1 billion valuation, according to two people familiar with the discussions. Launched in April, Prentis is training models to learn how office workers navigate routine workflows across documents and systems, with the goal of building AI agents that can control computers to automate those tasks. Prentis will ostensibly develop agents tailored to these customers’ needs, such as handling insurance claims and automating customs duty refund exceptions without needing a human to hunt down paperwork. The startup has already signed contracts worth up to $50 million with several customers, including healthcare management service organization, a manufacturer, and goods and clothing manufacturers, the two people familiar with the discussions tell TechCrunch. This echoes investor materials obtained by TechCrunch that predict an estimated $75 million annualized run rate by the third quarter of this year. (Prentis’ pitch deck notes those figures reflect estimated annualized value based on a contracted fee equal to 20% of savings realized, not recognized revenue, and are “performance-dependent and subject to final execution.”) By its own account, Prentis says its Hive-32B model outperforms rivals, including OpenAI’s GPT-5.4 and Anthropic’s Claude Opus 4.6, on two computer-use benchmarks: WindowsAgentArena, which measures end-to-end task completion on real Windows applications, and ScreenSpot-v2, which tests a model’s ability to locate the right on-screen control. In its pitch deck, the company argues its edge comes from running a much smaller, cheaper model. In fact, it claims roughly 10 times lower cost per task than frontier APIs, saying it’s more economical to deploy across everyday workflows. TechCrunch hasn’t independently verified the company’s benchmark results. The startup is betting that automating everyday office tasks will soon outpace coding as AI’s biggest use case, but it’s a crowded market. Anthropic, Open AI, and Mira Murati’s Thinking Machines Lab are also working on developing AI agents for computer use, one of the sources said. Anthropic has also been acquiring talent in the category directly — it bought the Seattle computer-use startup Verceptearlier this year, folding in its founders and shutting down its product. Prentis didn’t respond to TechCrunch’s request for comment. Ritankar Das, CEO of Prentis, is also the founder of Titan, a holding company that builds and operates AI companies. Das, now 31, was UC Berkeley’s youngest University Medalist inmore than a century, graduating at 18 with a double major in bioengineering and chemical biology before earning a master’s in biomedical engineering at Oxford. He founded Titan in 2014 after dropping out of an AI PhD program at Cambridge, where he’d been a Gates Cambridge Scholar. Das has described Titan as an intentional throwback to an old-fashioned holding-company model like Berkshire Hathaway, one that’s funded by its own exits rather than outside limited partners. Other businesses launched and operated by Titan include AI-powered virtual care provider Tala Health, which raised a$100 million seedround last year, and Forta Health, an autism care startup that raised$55 millionled by Insight Partners in 2024. Titan-founded disease prediction company Dascena wasacquiredby CirrusDx in 2022. Prentis is a side project of sorts for its two other co-founders. Hoffman, the LinkedIn co-founder and Greylock partner, said last month that he wasstepping downfrom Microsoft’s board after nearly a decade to go “founder mode” on Manas AI, an AI drug-discovery startup he’s also backing; he was an early OpenAI investor and co-founded Inflection AI with Mustafa Suleyman before Microsoft absorbed most of that team in 2024. Pincus, the Zynga founder, now runs the investment firm Reinvent Capital with Hoffman as a senior adviser, and published a memoir, “Life at the Speed of Play,” last month. Prentis has already hired more than 25 employees, including researchers who previously worked at OpenAI, Google DeepMind, Meta, Tencent, and Alibaba, according to itswebsite.
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I tried out OpenAI’s new AI keypad — which will be fun for some coders and slightly mystifying to everyone else
OpenAI launched its first piece of hardware last week — a fancy little keypad built to pair with ChatGPT. Micro, which was developed in collaboration with specialty keyboard designer Work Louder, is essentially an artisanal workplace novelty that many tech enthusiasts will love and that may leave everyone else a little puzzled. OpenAI’s entrance into the hardware market hasn’t arrived without drama. Several weeks ago,Apple sued the AI laband accused it of trade theft — kicking off what’s certain to be a long-simmering legal battle. Meanwhile, news ofanother smart home productin development at OpenAI has also raised eyebrows, as the supposed device — which is being built to pair with ChatGPT — was reportedly developed by former Apple engineers. Until the legal battle works itself out and OpenAI’s broader hardware ambitions materialize, the most the startup has to offer is Micro — a funky little keypad clearly engineered to delight the tech industry’s code monkeys. OpenAI sent TechCrunch a Micro test unit. The first big thing you notice when initially handling the keypad is that it’s a sturdy little device — enough so that if the AI accessory thing doesn’t end up working out, it could easily double as a paperweight. The other thing you might notice (and I am not theonly one to point this out), is that the packaging — an immaculate white box with a sleek, clean aesthetic — is pretty Apple-coded. Make of that what you will. The keypad’s layout involves six frosted “agent” keys at the top of the pad, which can be customized to carry out specific tasks within ChatGPT or its agentic coding tool Codex. Below them are six command keys, which can be used to control those programs. You can pair your Micro with your computer either through a Bluetooth connection or a USB cable. Perhaps the most convenient thing Micro offers is a button for voice dictation — meaning you simply tell the app what you want done and it will get busy working on your behalf. Just hold down the dictation button and start talking. When you’re done, tap the “send” button next to it to submit your request. You can customize your Micro keypad within ChatGPT itself, where a Micro tab allows you to adjust everything from the brightness of the light from the keys to the specific commands and projects you want tied to those keys. Hard-core coders — the device’s actual target audience — haven’t exactly embraced it. Reviews by Redditors have largely negative, with one Reddit user calling it “a prankand not a real product,” and others saying serious coders won’t touch it. A review by the smaller independent outlet Aftermath waseven harsher, calling the $230 price tag hard to justify next to cheaper DIY and off-the-shelf alternatives. (The title of that review: “OpenAI’s expensive macropad feels engineered to piss me off specifically.”) It’s definitely the case that new users may need some time to figure out how Micro works and what to do with it. Once I figured out how to program the keypad to my liking, I found it was actually pretty fun. You can assign various ChatGPT sessions to specific keys, which then allows you to easily toggle back and forth between all of your various projects. When you combine that with the dictation button, it makes the whole experience considerably more efficient and enjoyable. But there’s still a learning curve. Micro’s buttons are color-coded. White means an agent is idle, blue means its thinking, green means a task is complete, and red means there’s been an error. You’ll need to memorize that, along with memorizing which specific projects are coded to each key. The big question is whether the Micro keypad is functionally easier to use than just continuing to work on your laptop. In short: Why would I spend a week learning how to program and operate this thing when I already know how to use my computer’s mouse and keyboard? Ultimately, your experience with the Micro will depend heavily on how much you use ChatGPT. Since I don’t use AI much day to day, I’m probably not the target audience for it. That said, if you’re a ChatGPT power user, have $230 to spare, and like vintage-looking hardware with clicky buttons, Micro probably isn’t the worst purchase you could make — it might even brighten your day a little.
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