Latest AI News
Nvidia doesn’t mess around: A week after open AI industry group formed, it’s already showing progress
The week-old Open Secure AI Alliance (OSAA), an industry group spearheaded by Nvidia that has already grown to over 120 companies, has developed a cutely named working group, the Shared AI Findings Exchange, or SAFE. The group is alreadypresenting proposalsfor open comment, and The Linux Foundation, a member of the group, is managing the proposals. The group developed them while members gathered at the nexus of the cybersecurity world, the Black Hat conference, taking place this week in Las Vegas. The guidelines are nothing terribly earth shattering for now. The proposals cover areas like how to confidentially report AI cybersecurity incidents, alert those affected, and then do blame-free analysis so all can learn from them. At the same time, members of the OSAA arealso contributing and cataloging bits and pieces of their open source technologythat might be useful. This may, as these types of industry organizations go, eventually coalesce into an open source means for an enterprise to secure their AI agents, or defend against rogue AI attackers, such asthe OpenAI model that infiltrated Hugging Face. (Hugging Face is also a member of this group.) For instance, Nvidia has noted that it offers an entire family of open models, as well as an open source LLM vulnerability scanner calledGarak; Okta is working on agent identity tech; Red Hat is working on agent governance; Amazon has contributed both an open agent building tool,Strands Agents, and an authorization languageCedar. And there are many more examples. The group now includes a host of big names including Adobe, BlackRock, Cisco, Intel, Microsoft, and Visa, but there are some notable absences, like Anthropic, OpenAI and Google. Interestingly, both OpenAI and Google signed the original open letter that spawned this group. Theletter, published last week, urged the White House to support open source AI efforts, not squash them. It was championed by Nvidia and signed by over 200 tech companies. WhileAnthropic’s cold shoulder to the letter and the industry group to date is not a surprise, both OpenAI and Google have released open weight models of their own. Google is generally known as a big supporter of open source, too. We’ll see if they join as this group builds momentum. Meanwhile, the group is operating at AI speeds. It’s only been a couple of weeks since news broke that the Trump Administrationwas considering banning Chinese open weight models, causing the industry consternationthat resulted in the open letter. Whatever ultimately happens with Chinese open weight models in the U.S., the fast-action by this heavyweight group appears to be a good thing for the U.S. open AI ecosystem Some in the ecosystem, like the co-founder and chief technology officer of U.S. open weight AI lab Arcee, say that’s ultimately the way tobest any threat — real or imagined — that Chinese AI labs pose to the United States. “Openness may be one of the most important paths to AI safety and security,” this industry group wrote in their letter. Looks like they are ready to immediately put their effort — and their tech — where their mouths are.
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EON wants to move the data superhighway from ocean fiber to space lasers
As hyperscalers build out data centers around the world, they need to move the bits back and forth, and that often relies on a somewhat brittle network of undersea fiberoptic cables crisscrossing the oceans. Those cables aretricky to access and repair, much less install. But alternatives aren’t easy to find: Radio transmissions don’t have the bandwidth, which rules out most wireless approaches on the ground or in orbit. But now, maybe lasers could do the job. Endeavor Optical Networks, a start-up founded in May and emerging from stealth today with $10.75 million in seed funding from General Catalyst and Andreessen Horowitz, is betting on that plan. The co-founders, CEO Charlie Horowitz and CTO Tyler Pressler, aim to launch a network of laser-equipped spacecraft to link data centers from orbit. Most satellite communications networks, even those that provide broadband internet service, aren’t robust enough to carry data at 200 terabits a second or more, the speed of undersea fiber. However, more powerful satellites and advances in optical technology are making space-to-ground communications with lasers more feasible. NASAused laser commsto beam back data from its most recent Moon mission, while a handful of private space companies, including York, Kepler, and Cailabs, have demonstrated links between Earth orbit and the ground. Those connections, however, aimed for a throughput of 2.5 Gbps, and EON has a bigger starting goal, Horowitz says: Throughput of 2.4 terabits a second. That will require some secret sauce to deal with one of the biggest problems with laser comms—how the signal is distorted by the atmosphere as it passes through it, particularly when clouds are blocking the way. EON intends to build a network of about 20 satellites, each able to provide a dedicated link between two continents, with the initial fleet providing 24 hour coverage for early customers. The company will carefully choose ground stations in different regions to serve local data centers and CDNs, using redundant sites and leveraging weather data to ensure a reliable link. The startup is talking to hyperscalers and AI labs as customers, since they move more data than anyone else, with the focus on underserved or expensive routes: Lengthy ones, like France to Australia, or those without extensive existing infrastructure, like crossing between Africa and South America. They plan to sell dedicated capacity to entice customers interested in full control of their data transit. First, though, EON will use its seed round to build out an optics lab, hire more engineers, and perform ground tests ahead of a demo satellite they hope to launch around the end of 2027. Horowitz expects that spacecraft to offer the highest optical downlink throughput yet seen—at least 800 gigs and perhaps a terabit. Doing that will require careful engineering. EON will focus on producing the optical communications terminal, carefully allocating spending to the components that must be exquisite, like the gimbals that will point the laser. The company plans to buy powerful off-the-shelf satellite busses, like those made by Apex Space, Horowitz’s previous employer. Horowitz served as Apex CEO Ian Cinnamon’s chief of staff and then as the company’s director of special projects. “Charlie is a force of nature—he can move seamlessly from strategy to the details required to make something real,” Cinnamon told TechCrunch. “Charlie is the ideal founder, and I invested personally because I believe deeply in Charlie and what he’s building at EON with Tyler.” In addition to Pressler, a PhD astronautical engineer who has planned frontier missions for NASA, the company’s technical bench includes Michael David Francois, a long-time Google executive focused on global network infrastructure, and Wesley Baxter, an optics engineer who most recently worked on Amazon’s LEO satellite network. Jeannette zu Fürstenburg, General Catalyst’s president and managing director, who led the investment, said she sees it uniting the fund’s two key themes—AI and resilience. “I don’t worry about demand,” she told TechCrunch. “I think all of that will solve for itself. It’s really all about can you actually get this thing into space in the time that we discussed? We really think about founder-product fit, [Horowitz] is just the right caliber of guy to go after a problem like this.” They aren’t the only one chasing this problem—Blue Origin, Jeff Bezos’ space company, has announced plans forTeraWave, a 5,048 satellite network that aims to provide speeds of up to 6 Tbps to large-scale users. Blue’s plan is more ambitious, but will also require more time to launch and deploy. EON’s smaller fleet of satellites should be easier to get into space quickly, but they will need to solve many of the same technical challenges. “Data centers have high standards for quality and redundancy,” points out Caleb Henry, the director of research at Quilty Space. “Satellite internet is just now progressing from a technology of last resort to dependable, high-bandwidth infrastructure. That’s not to say it will be impossible to make satellites optimized for data center connectivity, just that it will be harder and take longer than most entrepreneurs suggest.”Still, a project like may be more practical compared to the popular idea of building the data centers themselves in space. “We have one rule at the company: no physics problems,” Horowitz says. “There’s a market that exists today that we can go serve. Down the road, we’ll go and take on more as it comes, but we know that this is a problem that exists today, that’s only getting worse. That’s our bet—more data is moving terrestrially than ever.”
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Is the future of data centers portable? Runware builds a pod to find out
On Tuesday, AI infrastructure company Runware announced the launch of its own modular data center called Sonic Inference Pod. Designed as a single transportable unit, the Pod represents a more flexible kind of compute that can sit alongside hyperscalers’ massive data center projects. Runware says the Pod can offer inference at a higher quality but lower cost than other serverless inference platforms and GPU clouds. The modular design means it’s easy add capacity quickly by creating new pods rather than having to expand a fixed data center. In some ways, this is the future, Flaviu Radulescu, co-founder and CEO of Runware, told TechCrunch. “We believe distributed compute, positioned closer to end users for faster inference, is what will win in the long term,” he said, noting his company as an example. Aside from a lower price, Radulescu noted that the runware system can scale and add capacity fast, deploy anywhere there is power, and adapt quickly to new hardware releases. The Runware pods also do not use water, but rather a closed-loop cooling system that can be built in days, compared to the months or even years it takes to build traditional data centers. “Demand for inference is growing faster than facilities can be built,” Radulescu said. “What we want is to power the world’s intelligence, to be the backbone every AI model runs on with capacity that keeps up with demand instead of throttling it.” Runware currently has 10 pods in deployment across the U.S., Europe, and Asia-Pacific, Radulescu said. The company already provides inference to a few companies, including Higgsfield AI and Wix, and has 160 sites available to power its pods right now. Runwareannounced a $50 million Series Ain December to provide the infrastructure needed for companies to generate images. They see the expansion into pods as part of the company’s core mission: providing inference to companies, rather than a single product. AI labs like OpenAI and SpaceX are still racing to build data centers throughout the U.S. OpenAI, for example, is close to striking a $500 billion deal that would see it build a data center in Ohio,according to reports. But Radulescu doesn’t see those projects as a threat to the Sonic Inference Pods, describing the flexibility of the pods as a key differentiator. “Every pod runs as part of a single network, so requests go wherever there’s capacity, closer to the users, and if one pod goes offline, traffic moves to another,” he said, adding that a system failure means one pod is down rather than a whole fixed facility. “Customers who want dedicated hardware get whole pods to themselves.” He’s also not too worried about other companies building this for themselves, saying simply that hardware is slow and finding the talent pool to build and fix this technology is small. “A mistake in a circuit board design costs months between redesign, simulation, fabrication, testing and delivery,” he said. “Every one of those calls needs someone who understands exactly what each component does and what breaks if it’s gone.” Building AI data centers is a controversial topic, however, especiallybecause of how many resources it uses.Already, communities where data centers are located have reported seeing a rise in utility costs. One day, Runware sees a world where it can run on renewable power and doesn’t draw on the resources communities need, but that day is not necessarily today. Radulescu said that AI power use is going to increase regardless, “driven by demand for inference, not by who supplies it.” What Runware is focused on right now is how that demand gets met, he said. “No transmission losses, no water in cooling, and we’re using power that already exists instead of asking for new grid capacity to be built. More inference built this way means less new grid, less water, for the same amount of compute.”
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Apple says more ex-employees may have taken confidential data to OpenAI
Apple is now seeking a preliminary injunction in itstrade secrets case against OpenAI,which aims to stop the AI model maker from moving forward with developing an AI device or other products based on Apple’s technology. The iPhone maker also claims that more of its former employees may be involved with the trade secrets theft. In a newfiling, Apple is requesting expedited discovery from the accused OpenAI employees, senior systems engineer Chang Liu and Chief Hardware Officer Tang Yew Tan; OpenAI, and its foundation; and io, the device startup co-founded by Apple’s former lead designer Jony Ive. Apple also notes that its continued investigation has so far revealed 11 other former Apple employees beyond Liu and Tan may have been witnesses or otherwise involved in the case, and others who were previously named in the original complaint, like OpenAI employee Yu-Ting Peng. The filing marks an escalation in Apple’s legal battle with OpenAI, as it suggests Apple has uncovered new evidence that the misconduct goes beyond the former employees named in the original complaint. “For example, another former Apple employee seems to have met with Mr. Liu and Ms. Peng in advance of Ms. Peng’s interview at OpenAI and discussed with them during that meeting Apple proprietary information relating to unannounced products,” the filing states. “Yet another former Apple employee took screenshots of confidential Apple documents relating to an unannounced Apple product before an interview at OpenAI.” “And, after Apple filed its complaint, multiple former Apple employees now working at OpenAI reached out to discuss returning Apple-issued work devices they kept when they left Apple,” Apple claims, suggesting there were more who were possibly involved with the scheme. Apple is pushing the court to allow for expedited discovery because it believes it has good cause to suspect that there are others involved in the theft of its intellectual property. The company noted that its motion for a preliminary injunction is also pending. OpenAI responded publicly to Apple’s latest,saying in a blog postthat Apple’s request for a preliminary injunction is “both based on false information and completely unnecessary because we do not have, nor want, any of their trade secrets.” “We’re much more interested in building innovative products and technologies that push the frontier,” OpenAI’s statement reads. The AI model maker also pointed to earlier mistakes Apple made,which had been reported,including that Apple emailed the wrong person when it made contact with OpenAI after confusing two similar surnames. OpenAI also alleges that Apple lied about discussing matters with its general counsel. And, the company said that Apple didn’t admit to the claim that the “residual access” allowing former employees to access Apple’s system was the result of poor security procedures on Apple’s part.
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Elon Musk spends half his time talking robots and AI on Tesla earnings calls
Elon Musk wants you to believe Tesla is no longer a car company, even if it’s still shaped like one. The company shipped nearly half a million cars last quarter and made 70% of its money from car sales. Still, Musk has spent the last few years making the case that Tesla is really an AI and robotics company, even if some of the AI happens to live in cars. And whatever the company financials suggest, Musk’s attention has been moving decisively to the AI parts of the company — projects like the Optimus robot and fully autonomous robotaxis— as the everyday concerns of a carmaker get pushed to the side. To show how that shift happened, TechCrunch teamed up withHudson Labs, a New York-based financial research firm, to map what Musk and Tesla’s other executives have spent the last seven years talking about on the company’s quarterly earnings calls. The startup sourced transcripts of the calls from S&P Market Intelligence dating back to 2019 and used its Co-Analyst — an AI tool purpose-built for high-precision financial research — to determine a topic for each sentence, before counting their frequency. The data shows that Musk now speaks about artificial intelligence, along with robotaxis and Full Self-Driving software, nearly 50% of the time he opens his mouth. That’s up from prior years, like in 2022, when he typically spent 15%-20% of the time on those efforts. Over the same period, Musk was often making the case that autonomy justified the company’s soaring value. “If you value Tesla as just an auto company – fundamentally, it’s the wrong framework,” Musk said onthe Q1 call in 2024. “If somebody doesn’t believe Tesla is going to solve autonomy, I think they should not be an investor in the company.” Talk of robotics has shot up sharply in the last three years, too. Tesla revealed it was working on a humanoid robot known as Optimus in 2021. In the year that followed, Musk only spent around two percent or less of his time talking about the project. Over the past year, though, he’s spent at least 10% of his remarks talking up Optimus, with it occupying nearly a third of his focus on the third-quarter call in 2025. Musk ramped up how often he talks about these futuristic ideas at the same time that Tesla’s core automotive business stopped growing. As a result, he now spends less than a third of his time on earnings calls talking about cars and manufacturing. On that same third-quarter call last year, Musk spent less than 20% of his time talking about the automotive business. The other Tesla executives who appear on the company’s earnings calls, like chief financial officer Vaibhav Taneja and vice president of engineering Lars Moravy, have been much slower to shift their focus. Even on some of the most recent calls, they have spent around 30% of the time focusing on the automotive business, with their next-most common topics being AI, robotaxi, and Full Self-Driving. Their attention has shifted, but they are lagging behind Musk’s enthusiasm for AI and robotics, most likely because those efforts aren’t yet generating any real returns. These other Tesla executives used to spend nearly 50% of their time on these calls (or more) talking about making and selling cars. That all changed in 2024 as the car business started to suffer thanks to increased competition from legacy automakers and new Chinese entrants. But when they do join Musk in talking up the future, they match the lofty rhetoric of their boss, the world’s richest man. “The path to amazing abundance is ever challenging and requires making bold bets,” Taneja said on the Q2 call this year. “Our progress will be non-linear. The future is going to be great.”
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Sarvam Is Hell-Bent on Pre-Training a Trillion-Parameter Model
Sarvam AI really wants to put to rest the question of why it's building a model from scratch instead of post-training open-weight models.
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Activate Invests in Sarvam AI's Series B Round
The funding adds to Sarvam’s efforts to build foundation models and AI infrastructure in India.
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AI4Bharat, Josh Talks AI Design Real-World Multimodal AI Evaluation Platform
The platform will help governments and enterprises compare AI models using deployment-specific evaluations across Indian languages, accents, and dialects.
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Razorpay Hires AI Leaders from Microsoft, Salesforce, CRED and Divyam.ai
The company said the appointments will help build AI systems capable of supporting financial decision-making across payments, banking, risk and developer platforms.
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IndiaAI, Where are Your Research Papers?
Beyond GPUs, funding, and data centres, India’s next AI need is far simpler: a research paper the rest of the world cannot ignore.
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Dabur Partners with Accenture to Build AI-Powered ‘Digital Brain’ Across Enterprise
Accenture will also support Dabur in redesigning talent strategies, operating models, and governance frameworks.
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Cisco Joins IIT Delhi to Launch Technology Hub for India's AI, Cybersecurity Capabilities
The Cisco Technology Hub aims to boost AI and cybersecurity research, innovation, and talent development in the Indo-Pacific region
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