Latest AI News
Google closes in on another billion- user product with Gemini
Google is about to add another name to its long list of products with more than a billion users, a list that already includes Search, Gmail, Drive, Android, YouTube, and Chrome. The company said during its Q2 2026 call that the AI assistant Gemini now has over 950 million monthly users. The company noted that Gemini users have tripled from last year. Earlier in February, it said that the Gemini app crossed750 million monthly active users. With this growth, Google’s assistant is in line to compete more closely with OpenAI’s ChatGPT, which hit 1 billion monthly active usersin June. “Users love new agentic features like Daily Brief and our personalized agent, Gemini Spark, which is now available in the U.S. and internationally. We’ve been shipping helpful new features like this at an incredible pace,” Alphabet CEO Sundar Pichai said during the call. Apart from users on Android, the Gemini app has found astrong user base on iOS with launches like the Nano Banana image generation model. According to Appfigures, the app has been downloaded over 137 million times on iOS in the last 12 months. Inits latest “State of AI” report, the analytics firm Sensor Tower noted that ChatGPT’s market share among AI assistants fell below 50% for the first time. The report, which looked at H1 2026, also noted that Gemini’s share rose to 27.7%. Many people already use AI assistant apps as a substitute for search. However, Google reported that its search vertical is going strong, partially thanks tothe AI-centric overhaul. During this quarter, its Q&A-style AI mode crossed 1 billion users. The company said that it is driving “an incremental increase” in search queries. Google also noted that through hardware engineering, it has reduced the cost of AI mode for the company, despite introducing new models and features.
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Nvidia is sending GPUs to the Moon
Nvidia’s effort to deploy its GPUs far and wide is aiming at a new target: the Moon. Lunar Outpost, a startup building robotics for space infrastructure, announced on Thursday that its next Moon rover will use Jetson chips to control its LiDAR system. When it does, it is likely to be the first GPU on the lunar surface. “We’re taking the NVIDIA Jetson and comparing it to our flight compute platform that has a little bit more spaceflight heritage,” Lunar Outpost CEO Justin Cyrus said. “[We’re] seeing what the pros are, seeing what the cons are. And we’re really pushing towards trying to adopt these more capable GPU-powered systems in these extreme environments.” NASA has launched a campaign to pay private companies to explore the lunar surface ahead of plans to return human astronauts, perhaps as soon as 2028. The space agency’s program, modeled on its work with SpaceX, asks tech companies to develop vehicles that carry scientific sensors to the Moon to understand its terrain and hunt for substances like water that could prove useful or even lucrative to exploit. Nvidia also recentlyannounceda partnership with Firefly Aerospace, the first private company to safely land a robot on the Moon, that will see the Jetson platform operate on a satellite orbiting the Moon to process imagery. That satellite aims to collect data for scientists trying to map the Moon, but also to track the growing number of robots on the surface. Lunar Outpost’s next rover is packaged onboard a lander built by Intuitive Machines, another Moon-focused tech company. The rover is designed to carry a package of sensors into craters and other places that are difficult to explore from orbit. The following mission will be exploring a place on the lunar surface called Reiner Gamma, where a magnetic anomaly has puzzled scientists. Each mission is expected to take flight on a Falcon 9 rocket before the end of the year. Nvidia’s Jetson platform isn’t as well known as AI workhorses like Blackwell and Vera Rubin, but it’s a vital part of many physical AI systems. Designed to be compact and power-efficient, Jetson lets robotic systems process sensor inputs locally, allowing them to understand and react to the world around them more quickly. “Our autonomy stack was a bit more deterministic five years ago, and now it’s a combination of deterministic and physical AI, which is pretty fun,” Cyrus said. “We still run both in parallel, and then it’s our job to figure out where’s physical AI can actually plug into our stack and help us do things that no one’s done before.” Using GPUs in space is challenging because of the extreme environment. Most space-faring GPUs are in orbit close to the Earth, where they are relatively protected from radiation and dramatic temperature changes. The Moon is more directly exposed to cosmic radiation and swings in temperature as goes through its phases. “Your system has to survive lunar night and has to do so on very low power,” Cyrus said. If they can operate the chips in that environment, Lunar Outpost’s engineers hope that their vehicles will be more capable—able to more quickly process their environment and make decisions. NASA has extensive ambitions for a sustained human presence on the Moon, but that will likely require autonomous systems to pave the way and do the heavy lifting. “What we at Lunar Outpost are currently working on is how do we go from exploration to permanence, how do we actually build that outpost on the moon?” Cyrus said. “We do think combining our deterministic models plus the physical AI layer—that’s what allows us to make a human presence in space sustainable, actually having that robotic workforce.” In the next few years, Lunar Outpost has several smaller autonomous rovers scheduled for launch to the Moon, and one larger one, Pegasus, that is intended to carry astronauts. Pegasus is waiting on a rocket built by Jeff Bezos’ space company, Blue Origin, to carry it to the Moon, but that launch vehicle suffered an anomaly this summer and it’s not clear when it will return to flight. “It does seem like they’re making pretty darn good progress on the pad,” Cyrus said. “I can’t really answer Blue Origin’s timelines or NASA’s timelines on the pad reconstruction, but everything we’ve been told, we’re on the same timeline [for 2028].” That leaves futuristic visions ofspace data centersand fleets of robots at work on the Moon both waiting on the same thing: A bigger rocket.
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AI chip startup Etched defies skeptics, hits $10.3B valuation from big-name investors
Etched,the AI chip startup founded by three Harvard dropouts in 2022, has closed a $300 million Series C funding round at a $10.3 billion valuation, co-founder and COO Robert Wachen tells TechCrunch. The round was led by Sequoia, with Andreessen Horowitz, SK Hynix, Jane Street, and Diffusion Capital also participating, along with other, earlier investors. Other backers of the company include names like Peter Thiel, Andrej Karpathy, Dylan Field, Amjad Masad, and more. Etched was previously valued at $5 billion in December when it raised a $500 million round, meaning it has doubled its valuation in about seven months. The company says this is the highest valuation ever for a Sequoia-led Series C. Last month, Etched announced that it hadsuccessfully manufactured its homegrown chips, that its first full systems were being tested by clients, and it had already booked $1 billion worth of orders. Etched launched at a time when the idea ofbuilding a chip specifically for AI modelsbased on transformer technology (the architecture behind most modern AI systems, including ChatGPT and Claude) wasconsideredwild if not wacky. The company is still battling the perception that its products — which are sold as full systems, not just chips — involve chips designed to run only specific LLMs. That’s not the case, Wachen explains. The systems can run any AI model, including Mixture of Experts models like DeepSeek and Qwen — an architecture that splits tasks across specialized sub-models rather than relying on one large model — as well as non-transformer designs like Mamba, which is built on a differentunderlying architectureknown as a state-space model. (Interestingly, the idea of etching parts of a specific AI model directly into silicon to boost performance isn’t considered far-fetched anymore. Google is reportedly pursuing the same concept with itsFrozen v2 chipfor Gemini.) Still, Etched’s claim to fame today is that it designed two new components from scratch to speed up inference — the computing process that happens after a user submits a prompt. “Inference is built in two stages,” Wachen says, “prefill and decode.” The “prefill phase” involves understanding the prompt, including context. It’s mathematically and compute-intensive. The “decode” phase generates the output tokens (the actual answer the user sees). It requires less computation but needs massive amounts of memory. Etched created a prefill chip that operates “dramatically” faster, he promises, “by running at a much lower voltage than any other AI chip. We call this low-voltage inference.” Lower voltage generates less heat, which allows the chip to pack in more transistors. For the decode process, Etched created a new type of memory and “interconnect technology that we call cluster scale memory. It allows many chips to connect together and use a shared memory pool at a very, very fast, low latency,” he says. The result, Etched promises, is high speeds but lower costs. Because the startup was launched before most of the tech world (besides Nvidia) understood AI’s specialized compute needs, the founders, CEO Gavin Uberti, Wachen and CTO Chris Zhu, havefaced plenty of skepticswho kept doubting even after announcing the company announced that its first batch of silicon had been successfully manufactured by TSMC. Much of that comes from how few people have had access to the systems. So far, access has been limited to investors and early customers. In fact, that’s how Etched landed its list of famous investors in the first place — by showing them private demos in its office. “Andrej Karpathy from Anthropic, Noam Brown from OpenAI, Geoffrey Hinton, as well as all the investors in the funding round — these are all people who actually tried the hardware and are very excited about it,” Wachen says. Still, it’s been a long, difficult road with more to go until the rack systems are mass produced and delivered. The trio famouslydropped out of Harvardto launch Etched, not knowing then how to raise cash (much less the loads of it they would need) or how to hire. “We had no idea how hard it was going to be,” he said. “I think we still have to be humbled by what it will take to actually get to scale.” Wachen remembers landing in the Bay Area after telling his parents he was leaving school to do a startup, with no office or apartment arranged. He slept on the floor of a friend’s unfurnished house. “I remember staying in my friend’s house that they were about to sell, using a towel as a blanket,” he laughs. The founders eventually set up the servers they needed to run the chip-design tools in the garage of an early employee and “every time it needed to be rebooted, he would call his wife, and she would go and hit the reboot button.” Today, there are 400 people bustling in an office, and Etched operates a 2 megawatt data center. “We’re running tokens in our in our lab today, working with some of the largest AI companies in the world,” he says. Wachen also has a blanket now, and a mattress “and a pillow even. Multiple pillows,” he jokes. More importantly, he and his co-founders never let the doubters stop them. “It’s come a long way. It’s a very, very different world. But I think, when you really think something’s possible, and you just work at it for a long time, you can do it.”
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Bengaluru Bets on AI to Predict Water Shortages Before They Happen
The Bengaluru Water Supply and Sewerage Board will inaugurate its ₹91.12-crore JICA-backed Integrated Intelligent Water and Sewerage Management Centre under the Cauvery Stage V Project.
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Infosys AI Revenue Share Climbs to 8.2% in Q1 Even as it Lowers Guidance on Uncertain Demand
IT major Infosys has named insider Ashiss Kumar Dash as CEO Designate, who will succeed Salil Parekh from April 2027.
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With 650 Startups & 5 GCCs in Sight, Mysuru Thinks Bigger at Big Tech Show 2026
Exclusive remarks from Karnataka’s leadership reveal how the state plans to accelerate Mysuru’s technology ambitions.
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Experts say exploiting Anthropic’s Fable isn’t how Kimi K3 got so good
White House science advisor Michael Kratsios said that Moonshot, the Chinese company behind the Kimi K3, the largest available open-weight LLM, built its model by copying Anthropic’s Fable LLM while using chips that aren’t cleared for export to China. “Large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable,” Kratsioswrote, amid reported discussions aboutbanning Chinese open-weight modelsthat have roiled the AI sector. Moonshot did not respond to questions about its training process, and Kratsios did not share more details about the sources of his allegations. Kratsios’ tweet echoed comments from Treasury Secretary Scott Bessent that “we are finding watermarks of our U.S. large language models on many of the Chinese models, and that that’s unacceptable.” It’s not clear what those watermarks consist of, and the Treasury Department did not respond to a query. However, experts are skeptical that distillation—the process of querying an LLM to determine its inner workings and copy its capabilities—is responsible for the advanced capabilities that Kimi K3 displays. “I don’t think you get a model this strong and this quickly on the heels of Fable doing strictly distillation,” Braden Hancock, a researcher at the Laude Institute and co-founder of Snorkel AI, told TechCrunch. “There’s just not even frankly time, right? Fable’s only been publicly available since July 1st. You can’t distill that much data, train a model, and release it in two weeks.” “I’ve been of the opinion that distillation has becoming less and less impactful over time as the Chinese models get closer to the frontier and the training regime shifts to [reinforcement learning],” Nathan Lambert, an AI researcher at the Allen Institute for AI, said in apodcastreleased yesterday. “[I]f it were the case, everyone would be easily able to catch up to a GLM or to a K3 by using its data for distillation. But we have not, or we won’t see this, from supervised fine-tuning alone.” Performing distillation requires a lab to systematically query its target model in order to generate data that can be used for post-training. Sometimes this explicitly involves asking the model to articulate its chain-of-thought to understand how it solves problems. Other times, the prompts and responses from a model are used to train a new model in a process called supervised fine-tuning, or SFT. It’s this fine-tuning process that can result in a model ostensibly created by a third party claiming that it is Claude. Fine tuning is where, in Lambert’s view, the “model picks up its manners.” But Lambert says that the benefits of SFT are becoming less important as models become more complex. To distill Fable-like capabilities would likely require reinforcement learning techniques. In many cases, that means having an agent of the larger model grade the smaller model’s responses, and adjusting based on the grade. The more advanced techniques also require more significant infrastructure. Large reinforcement learning runs can require tens of millions of agents. Using a frontier lab’s API to do that “would be insanely expensive and potentially it would probably be a time bottleneck because these models are pretty slow and to be frank might not even give you a performance uplift.” It seems likely that previous frontier models might have contributed to Kimi; Anthropicpublicly accusedMoonshot, DeepSeek and MiniMax of systematically distilling its models earlier this year. Anthropic said it discovered millions of exchanges between its models and users it identified at those companies through IP addresses and other meta data. Those queries were “distinct from normal usage patterns, reflecting deliberate capability extraction rather than legitimate use.” Anthropic didn’t respond to TechCrunch’s queries about Fable distillation. However, distillation is seen as common among AI companies, not just in China. Elon Musktestifiedearlier this year that his company SpaceXAI distilled OpenAI models to develop Grok, and that the practice was common in the industry. The line between distillation and developing synthetic data sets, for example, can be fairly blurry. “[I]n general, Americans are understating the technical expertise of these Chinese teams,” Hancock said. “One of the founders of Moonshot was a CMU PhD student. These are legitimate researchers and engineers doing solid work. …if American models ground to a halt, I think China’s progress would slow, but would still continue. They’re not just riding coattails here.” It’s also hard to disentangle distillation from the second part of Kratsios’ comment — that Moonshot had obtained advanced Nvidia Chips, Grace Blackwell 300s, and also accessed GB300 equipped-servers in Thailand. Those chips are banned from export to China, but a black market exists, according to Sam Bresnick, a research fellow at Georgetown’s Center for Security and Emerging Technology. In May, the founder of Supermicro, a US server builder, was indicted for smuggling advanced chips into China. “I am a proponent of know your customer laws for data centers across the world,” Bresnick said. “If you are letting a company conduct huge training runs on your state-of-the-art hardware, there needs to be a reporting mechanism for who that company is and what they’re doing.” President Joe Biden’s Department of Commerceproposedfederal know-your-customer rules for data centers in 2024, but no further progress appears to have been made under Donald Trump. Exporters shipping advanced chips abroad, however, aresupposed to ensurethey are only used for approved purposes.
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ServiceNow AI Crosses $1 Bn ACV as Enterprise Demand Lifts Q2 Revenue
The AI annual contract value has topped $1 billion as agentic AI deployments surged ninefold in nine months. The company raised full-year subscription revenue guidance after beating Q2 estimates.
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JAN AI & VTU Bring Human-Centred AI Training to Women Engineering Students
The company is training women engineers to use AI to address local challenges instead of focusing solely on automation.
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[Exclusive] Ex-GitHub CEO Targets Indian Developers for AI-Native Venture
Former GitHub CEO Thomas Dohmke's startup Entire has launched an India region for its Distributed Git Network to meet data residency requirements.
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ServiceNow bets $40 million on Indian banking software specialist to expand its financial services push
ServiceNow, the U.S. enterprise software company known for automating workflows like IT service management and HR operations, is betting on an Indian banking software specialist to deepen its push into global financial services. The company has invested $40 million inBusinessNext, valuing the 24-year-old Indian firm at $700 million and taking a roughly 5% stake. The deal gives BusinessNext access to ServiceNow’s global sales network as the companies expand their partnership in AI for financial services. ServiceNow’s investment reflects BusinessNext’s growing profile beyond India. The profitable, Noida-based company, which generated about $32 million in revenue in its latest financial year, serves more than 70 banks across India, Southeast Asia, the Middle East, and the U.S. Its customers include the Reserve Bank of India, the country’s central bank, and State Bank of India and HDFC Bank, which are India’s largest public- and private-sector lenders, respectively. About half of BusinessNext’s revenue comes from outside India, with overseas markets expected to drive much of its future growth, founder and CEO Nishant Singh said in an interview. The company chose ServiceNow over potential financial investors to accelerate its expansion by tapping the U.S. software group’s global reach. Singh told TechCrunch that the partnership would help BusinessNext “borrow” its go-to-marker “machinery” — referring to ServiceNow’s sales infrastructure — in markets where it has a limited presence. “Think of it as a strategic partnership, which is cemented with funding,” he said. BusinessNext’s software, Singh said, manages customer-facing banking workflows, while ServiceNow is stronger in workflow automation and back-office systems, a combination the two companies plan to sell jointly to financial institutions. “India’s financial services sector is at an inflection point — institutions are moving from digital experimentation to full-scale AI-led operations,” Kulmeet Bawa, ServiceNow’s group vice president and managing director for India and SAARC, said. He added that the partnership combines ServiceNow’s enterprise workflow platform with BusinessNext’s banking expertise. Founded in 2002, BusinessNext — known as CRMNext until 2022 — has spent several years building what Singh calls an “autonomous banking” platform, using AI agents to automate banking workflows while keeping sensitive customer data on private AI infrastructure to meet regulatory and privacy requirements. Singh told TechCrunch that AI was built into the company’s platform from the outset rather than added later. “We actually renamed our company and we kind of rewrote our stack to put that fundamentally at the core,” he said. BusinessNext employs more than 1,300 people across its operations and waslast valued at $181 millionin 2021, per private market intelligence platform Tracxn. It has raised more than $60 million in external funding and counts Avataar Ventures, Norwest Venture Partners, and Ascent Capital among its existing investors. The deal comes as established enterprise software vendors face pressure from customers who are questioning whether traditional SaaS tools are worth paying for when AI-native alternatives are emerging. For ServiceNow, the deal builds out its position in banking by partnering with a company focused on AI-driven banking software, as it expands its enterprise software portfolio through acquisitions, investments, and partnerships.
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AI is Becoming the Recruiter’s Lie Detector
As candidates lean on generative AI to polish resumes and rehearse answers, recruitment platforms are deploying their own AI to tell the authentic from the artificial, and to stop human bias creeping back into the process.
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