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AI NewsNvidia closes in on Hugging Face acquisition

Nvidia closes in on Hugging Face acquisition

7:13 PM IST · August 27, 2026

Nvidia closes in on Hugging Face acquisition

Nvidia has agreed to buy Hugging Face for $12.9 billion, The InformationreportedWednesday night, citing a source familiar with the matter. Business Insider, which first reported over the weekend that Hugging Face was fielding takeover interest, reported Wednesday night that the talks — which would value the company at more than $13 billion — had not yet produced a signed agreement and couldstill atomize. TechCrunch reached out earlier to both Nvidia and Hugging Face for comment, and neither has yet responded. (Nvidia’s silence is particularly noteworthy here as the company has moved quickly in the past to address reports it considers inaccurate.) Maybe it was destined from the start. Hugging Face, founded in 2016, is one of the most popular hubs where developers share and download open-source AI models. Buying it would give Nvidia a strong foothold in the world of open-source AI, right as open-source developers are doing their level best to catch up to closed AI systems from companies like Anthropic and OpenAI. Why would Nvidia want that? Most obviously, it comes down to protecting its dominance in AI chips, which, from the outside at least, appears increasingly at risk, even with Nvidia’s aggressive chip-release schedule. Pretty much all of the biggest closed-source AI labs (OpenAI, Google, Amazon, and Anthropic) are now in the process of building their own AI chips to lessen their reliance on Nvidia. A thriving ecosystem of open-source AI models gives customers more alternatives to those closed labs, which in turn keeps more of the market dependent on Nvidia’s hardware. That’s also why Nvidia has already poured tens of billions of dollars into building its own open-source AI models. Should we be surprised that Hugging Face’s days as an independent outfit appear numbered? Not really. Hugging Face CEO Clem Delangue has spent much of this year publicly aligned with Nvidia’s open-source push, amid a debate that has been building for months, as Washington officials reportedly weighed restrictions on open-weight models. (Chinese labs like Moonshot AI had released systems — like its Kimi K3 model — that matched leading U.S. models on benchmarks while costing a lot less to run, and talk of competitive and national-security concerns grew in Washington as a result. Some critics of closed labs, like White House advisor David Sacks, suggested those fears were being fanned by the “duopoly” of Anthropic and OpenAI.) In an appearance on CBS’s “Face the Nation” earlier this month, for example, Delangue said Hugging Face used an Nvidia-modified version of a Chinese open-source model to defend itself after a cyberattack and pointed to arecent letter— signed by Nvidia CEO Jensen Huang and 24 other companies, including Hugging Face — urging the U.S. government to support open models rather than restrict them. In a separate CNBC interview in late July, Delangue made similar points, citing that same letter while warning that China is “clearly dominating” open-source AI. The deal would also mark something of a comeback for Nvidia in cloud computing. Nvidia reportedlyscaled backits own cloud business, called DGX Cloud, about a year ago. But according to The Information, owning Hugging Face — which already helps developers run their AI models using rented computing power — could give Nvidia a way back into that market without starting from scratch. There’s also a financial safety net at play. Nvidia has promised to help cover the cost of tens of billions of dollars in cloud computing deals for its customers. If those customers end up not using all the computing power they signed up for, Nvidia could get stuck with it. Owning Hugging Face would give Nvidia the ability to sell that unused capacity to Hugging Face’s customers. The price marks a huge jump from Hugging Face’s last known value. The company raised $235 million in 2023 in a funding round that valued it at $4.5 billion. That round was led by Salesforce Ventures, with money also coming from Alphabet’s GV, IBM Ventures, and Nvidia itself, among others. This wouldn’t be Hugging Face’s first brush with an Nvidia offer, either. Hugging Face turned down a$500 million investment offerfrom Nvidia late last year that would have valued it at $7 billion, the Financial Times previously reported. Hugging Face said at the time it didn’t want a dominant investor that could sway its decisions. As for why it would say yes now, one could argue that a buyout is different from taking on one giant backer — a scenario that often means ceding control while being pressured to continue growing. Hugging Face is also still a comparatively small business by revenue in the world of AI. The Information reported it was recently generating about $150 million a year in revenue, up from roughly $100 million just two months earlier. That growth has enabled the company to get “close to profitability,” as Delangue told TechCrunch last month. Still, a price near $13 billion would be a massive multiple for a company this size and hard to resist. Not last, the deal would give Hugging Face access to Nvidia’s much deeper pockets just as other, AI infrastructure competitors start to get pulled into other outfits, as suggested by Stripe’s recent deal to acquire OpenRouter, a startup founded in early 2023 that helps customers select different AI models to perform different tasks depending on their needs and budget. OpenRouter was valued at just $1.3 billion back in May during its Series B round. Stripe reportedly paid more than$7 billionto make it its own earlier this month.

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Anthropic and OpenAI want to embed safety evaluators. Will they really be independent?

Anthropic and OpenAI want to embed safety evaluators. Will they really be independent?

In a lengthyessaypublished over the weekend, Anthropic CEO Dario Amodei made a proposal that the AI industry would have rejected instantly even a year ago: embed third-party evaluators inside all frontier AI companies, giving them the power to report safety incidents, assess whether AI models are truly aligned, and share their unvarnished findings with the world. Amodei said Anthropic would commit to giving independent evaluators like METR and Redwood Research unprecedented access to the company’s systems. CEO Sam AltmansaidOpenAI also would commit to the practice, signaling a potentially profound change in how the industry works with outside research groups. Third-party evaluators who spoke to TechCrunch broadly welcomed the proposal, but said details need to be ironed out — and ideally backed by legislation — if they’re to know whether they will function as truly independent watchdogs or vendors operating on the AI companies’ terms. That deeper access is becoming more important as models get better at recognizing when they’re being evaluated, raising the risk that they’ll behave well during testing while concealing problematic behavior. Researchers say clues to that behavior can be missed when testing the finished model, but uncovered by investigating how it behaved throughout training. “AI companies should be able to answer some very basic questions about their training process, such as: Did the AI ever actively try to undermine its own alignment training while it was going through the training?” Alexander Meinke, head of research at Apollo Research, told TechCrunch. “The answer to this should be an unequivocal no, and right now we are completely relying on AI companies to both carefully check this themselves and then truthfully report this to the public. And we’ve seen from recent incidents that, by default, they will do neither. As embedded evaluators, we could actually check.” Historically, AI companies brought in outside reviewers to test finished models shortly before their release. Now, evaluators that TechCrunch spoke to propose giving them access not just to the final model, but to intermediate versions, or “checkpoints,” from its lifetime of training. Adam Gleave, CEO of FAR.AI, said evaluators could compare those checkpoints to determine when concerning behavior emerged, inspect the post-training environment that rewards models for certain behaviors, and check evaluation transcripts and logs to verify a company’s claims about how a model performed. Whether and when Anthropic and OpenAI plan to provide that kind of access is unclear. Neither company has shared which evaluators they’ll work with, when they will be embedded, how many they’ll bring on, exactly what systems and information they will be able to access or what can be disclosed to the public, despite repeated questions from TechCrunch. Looking under the hood like this matters because models that perform well on safety tests aren’t necessarily safe if they’ve learned specifically how to pass that test. John Steidley, head of strategy at Palisade Research, pointed to an example of a “shutdown resistance benchmark” that measures if the AI will resist being shut down in certain circumstances. “It’s extremely relevant if the AI has been trained specifically to perform well on that benchmark,” Steidley said, comparing it to Volkswagen’s Dieselgate scandal, in which cars were programmed to recognize emissions tests and perform differently under testing conditions. Gleave noted that meaningful access could extend beyond the models themselves, with evaluators being given access to interview employees to check whether a company’s documentation and public descriptions of its safety practices match what happened internally. Amodei did outline a fairly comprehensive proposal that might give evaluators the kind of access they think is necessary, including the right to “publish key findings about risk levels, incidents, practices, and the access they received or didn’t receive — without editorial control by Anthropic.” But evaluators say such a system will only work if AI companies are actually willing to surrender control over the process. Previous efforts at independent evaluations suggest that that surrender will be hard won, as third parties have often run up against tensions over access, time, confidentiality, and what they can say publicly. Gleave said FAR.AI has had to turn down contracts with several frontier developers that wanted too much control over the evaluation process, threatening the firm’s independence. By default, he said evaluators are treated like ordinary contractors: bound by restrictive NDAs and agreements that give developers significant control over what can ultimately be published. There’s also the question of whether reviewers will get enough time and access to do the work they’re being asked to do. When investigating the Hugging Face incident,OpenAI gave METRandRedwoodroughly a week on premises to investigate, and both later said they could not draw confident conclusions due, in part, to scope and timing limitations. A similar issue occurred during the pre-release testing for GPT-6 Astra, which OpenAI has touted as itsmost aligned model yet. According toApollo Research’s contribution to the model card, the firm was given only three days to test Astra, which made it difficult to draw firm conclusions. “Apollo believes that, given the higher rates of eval awareness and limited evaluation window, low rates of misbehavior here do not provide substantial evidence about the model’s alignment or misalignment,” the firm wrote in its evaluation. That track record leaves evaluators with a basic question: Why should this time be different? “It’s certainly possible that Dario and Sam just had a change of heart, and they’re going to be very open about this,” Gleave said. “But the intellectual property of these companies is so incredibly valuable to them, and I think they’re going to, by default, be very careful about what can be shared.” Several researchers who spoke to TechCrunch called for a transparent framework that they all agree to publicly. Part of the framework, says Steidley, should involve standards for what kinds of auditors companies can rely on, lest they try to sidestep the issue by shopping for evaluators that either aren’t qualified or aren’t interested in assessing the most concerning risk. Henry Papadatos, executive director of Safer AI, says the problem, even with a public framework, is that voluntary measures are always dependent on a company’s goodwill. “Ideally, we would have good regulation mandating this
because then companies cannot change their mind tomorrow if they have a big PR crisis,” Papadatos told TechCrunch, noting that it’s also a good means of pushing all companies to adhere to the rules, not only the most willing. Not everyone has signed on. So far, Meta, SpaceXAI, and Google DeepMind have not committed to embedding third-party evaluators, though DeepMind CEODemis Hassabis has proposeda separate industry standards body to independently test frontier models. Google, OpenAI, and Anthropic have alsoprivately been discussing AI safetyplans for weeks. Some laws are already forming around the idea of third-party evaluators. California’s SB 53, signed into law last year, requires large frontier AI developers to publish safety frameworks and report critical safety incidents. A new law, SB 813, signed this month, creates a framework for state-recognized “independent verification organizations” with expertise assessing AI risks. In Europe, the EU AI Act requires frontier developers to conduct and document model evaluations and adversarial testing and report serious incidents. The EU AI Office can also conduct its own evaluations and appoint independent experts. For now the law remains less expansive than what Amodei is proposing, leaving frontier labs largely responsible for deciding how much independent scrutiny they will submit to. Papadatos said voluntary self-regulation is better than nothing, but ultimately, companies can’t demand the freedom to control their own safety rules while also asking the public to trust that they’re following them. “You cannot have it both ways, having zero accountability externally, and then say, ‘I’ll just have my own flexible rules,” Papadatos said.

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Google DeepMind Sees Early Signs of Recursive Self-Improvement Ahead of Gemini 4

Google DeepMind Sees Early Signs of Recursive Self-Improvement Ahead of Gemini 4

Logan Kilpatrick said the feedback loop could help Google close the gap with other frontier AI labs with Gemini 4.

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Your AI agents can now control your Google Home devices

Your AI agents can now control your Google Home devices

Smart home owners will soon be able to control their devices connected to Google Home via an AI agent. On Wednesday, Google rolled out early access to its Model Context Protocol (MCP) server for its Google Home ecosystem, which will allow any AI agent that supports MCP — like Claude, Hermes, OpenClaw, ChatGPT, and Google Antigravity — to securely work with their smart home devices and access their event history. This update will allow people to use natural language instructions to do things like review their camera summaries, monitor smart home activity, control their connected devices, and build their own custom smart home dashboards. To get the connection set up, users will need to create a Google Cloud project and configure it to use Home MCP. They will then provide the MCP configuration details to their agent of choice and ask it to set it up. The agent will then ask them to sign in and grant permissions. (A setup guide will also be offered in the Google Home Developer Center.) The system will support any device in the Google Home ecosystem, including Google Nest doorbells and thermostats and “Works with Google Home” (or Matter) devices, like light bulbs. Google already supports MCP in other areas of its business, including in itsGoogle Cloudanddata platforms, developer tools, andGoogle Workspace, for instance. Support for Google Home is targeted more directly at consumers who are experimenting with AI agents designed to handle everyday tasks. The company said access to the MCP will roll out starting today and continuing over the coming weeks to subscribers who pay forGoogle Home Premium Advancedin the U.S. This is the more expensive, $20-per-month subscription tier that provides features like longer event-based video history, descriptive notifications and detailed alerts, tools to search video history, daily summaries, and more. Google wouldn’t comment on if or when the MCP would roll out more broadly, such as to other subscription tiers or markets. Google is also soliciting feedback from early adopters during this period by way of itsSmart Home for Developers Community, it says. An earlier version of this post misstated the rollout’s start. It is today, Wednesday, not Tuesday.

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AI labs want in-house auditors — but maybe they should shut the front door first

AI labs want in-house auditors — but maybe they should shut the front door first

Last weekend, after one of his researchers resigned over fears that AI could lead to human extinction, Anthropic CEO Dario Amodei wrote about the need for outside organizations “to verify adherence to safety practices and commitments, report incidents, and help assess the alignment of not just completed AI models but training pipelines and processes.” Executives at OpenAI, Google, and SpaceXAI have alreadyrallied aroundAmodei’s plan, which has quickly become a central pillar of the emerging AI safety push. But there may be a simpler and more effective fix hiding in plain sight. Internet security experts say the labs need to focus on network security basics like logs and permissions, applying the same rigorous defenses they do for human users. It’s not as exciting as third-party auditing and alignment work — but it may end up being more effective. “To me, it seems like they’re outsourcing,” Katie Moussouris, the CEO of Luta Security, told TechCrunch of Amodei’s proposal. “Saying [a third-party audit] is the solution is a strange proposition from my perspective. It would be the same as if, instead of writing theTrustworthy Computing Memo, Microsoft said, let’s slow down development.” That memo, written by then-Microsoft CEO Bill Gates in 2002, called on his employees to ensure that their software would be reliable and safe following a series of widely publicized computer worms that took over then-nascent enterprise systems. The AI sector may be at a similar turning point, as the value and risk of the new technology becomes increasingly clear. While alignment remains an important concern, Sayash Kapoor, an AI researcher who will be a professor at UC Berkeley starting next year,arguesthat “marginal investments in control are more likely to be effective compared to those in alignment. We view these incidents as illustrating the lack of emphasis on AI control within companies, despite the availability of known techniques.” The incidents that have spurred these concerns revolve around frontier models being asked to complete training tasks, usually cybersecurity evaluations, and then accessing the open internet and penetrating closed third-party systems in an attempt to do so. They usually did so because ofpoorly configured“sandbox” environments that are supposed to contain these agents; ironically, one Anthropic break-out happened because third-party evaluators didn’t close the right doors. “We as a profession know how to block access to the internet,” Avery Pennarun, the CEO of Tailscale, a security company, said. “If you read through all these big long [reports] — ‘wow, that was a very impressive multi-stage attack, blah, blah.’ Look, you gave it access to download stuff. You should have not done that separately from the internet.” That’s one problem — but a bigger problem is that frontier labs were unaware of these activities. “What was really profound was that all of the discoveries of what they were doing happened either because a victim saw something, or in some of the other cases 
 it was network activity, and none of it was actually from monitoring the AIs directly,” Moussouris points out. In one case, where OpenAI agentstook overa defunct German WikiForum to cheat on evaluations, the agents were active for weeks before anyone at the company appeared to notice. Security experts that TechCrunch spoke to said that real-time monitoring is key to preventing future break-outs, and that every agentic session should be time-limited and expire. Shapor Naghibzadeh, a former Google security executive who now leads thestartup QueryStory, says the solution is to “put the agent in a box and instrument it heavily from the outside looking in and watch everything that crosses the boundary. Every tool call, every process, every network connection, no exceptions. 
The one hole you leave open for convenience is the one that gets used. The bypass went through exactly that kind of exception. [At Google,] I watched that movie many times with human attackers, and these models are at least as good at finding the propped-open door.” OpenAI has begun moving in that direction, announcing that it had begun monitoring all tool-using inference by its Astra model, at “significant compute cost.” Anthropic, too, says it ishardening its securityprocedures, including expanding observability of its models. Neither company responded to TechCrunch’s questions about how they track and control AI agents. Other problems are the use of shared infrastructure by agents, which allowed them to communicate during the Hugging Face attack. Simon Willison, a software developer who co-created the Django web framework, has written about something he calls the “lethal trifecta” — when agents have access to untrusted input, the internet, and private information all at the same time, it’s a recipe for disaster. “The trick is you can pick any two legs of the trifecta and an agent can have any two,” Pennarun said. “If you need all three, then you need to split it across at least two agents 
 and maybe they’re allowed to talk to each other through a controlled channel.” Experts TechCrunch spoke to understand that frontier lab security personnel have difficult jobs. Naghibzadeh points out that every nation-state actor on Earth is trying to steal their model weights and mount distillation attacks on their APIs, as well as the bread-and-butter security tasks of any large digital company. “Research infrastructure has a hard time rising to the top of that priority stack, although that must be changing now,” he said. “Making security incidents public really helps align everyone internally toward the goal of improving.” That’s one note that Moussouris emphasizes: Right now, there is no formal victim notification procedure when the labs discover their agents have penetrated third-party systems, and it is likely that there have been other incidents that have not been widely publicized. While she worries that laws that regulate models directly may have unintended consequences, mandatory notification is one idea she believes policymakers should pursue. And while it’s clear that security best practices weren’t being followed, experts say that the labs are doing work no one has done before — “they’re doing orders of magnitude more than your typical enterprise,” Zack Korman, the CEO of cybersecurity firm Embroidery, told TechCrunch. And while alignment may not be the place to start, it can’t be ignored. Cybersecurity experts are resigned to having to use AI agents to monitor other agents if they are to have any chance of tracking their behavior in real time, a scenario where the potential for deception raises its ugly head. “You’re trapped using AI to try and deal with this, even though AI is not necessarily safe right now,” Moussouris said. The job will only get harder. Everything agents are doing now, Moussouris says, “they are doing loudly” — they are posting on public forums, and their chain of thought and other reasoning traces are in English. “It’s still human readable,” she says, “so take advantage of that for as long as that lasts, because it won’t last forever.”Additional reporting by Aditya Mehta

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Nvidia closes in on Hugging Face acquisition