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Google DeepMind has introduced Gemini Omni 1.2 Flash. This latest video generation model introduces native 15-second video creation and new editing capabilities. Google says the new model brings a 30 percent price reduction compared to Gemini Omni 1.1. It lets users draft content at 360p or 720p for just $0.07 per second. It supports generating and editing continuous video clips up to 15 seconds long and enables Conversational Editing.
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After launching its AI assistant in September, Meta is back with another update which will now let users have real-time conversations. Called Muse Voice, the feature will work Meta's Muse Realtime Avatar to provide life-like conversations complete with gestures and expressions. The feature will only be available to users in the US and will be coming to other markets soon. The assistant was launched in the US in September but has made its way to Canada. However, it's not possible to access the AI assistant outside of these countries due to regional app store locking.
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Airbnb reported its third-quarter (Q3) 2026 financial results Thursday. Along with its financial health, the platform also shared its plans and products it plans to introduce soon. The company has revealed that it has started developing a dedicated AI agent that will help users plan their trips. Airbnb plans to start rolling out the AI travel agent next year. Airbnb says that its upcoming AI travel agent will use data from different travel-specific libraries. The company has already deployed an AI agent for customer support. The platform also features AI Search, AI Filters, AI Listing Descriptions, and AI Companion, with each tool offering a specific functionality.
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Alex de Vigan’s last business got swamped by the rise of generative AI, but now he’s betting the tech that team had built, a platform for creating tempting product renderings for e-commerce, can be re-purposed to make robots more useful. His new venture,Physicl, has raised $10 million in seed funding led by Inovia Capital to help world model designers and robot builders solve thorny training problems with more realistic simulations. Founder and CEO de Vigan spent the last decade building nfinite, which made it easy for brands and retailers to create 3D models of their goods and display them online. The arrival of transformer-based AI models led that company to focus more on generative imagery, but de Vigan saw a new purpose for his team and the tech stack they’d built to faithfully render 3D environments. “What we were super unique about doing in 3D, AI was going to replace, but something was missing in AI: its ability to understand, interact, and manipulate space and objects,” de Vigan says. Researchers developing physical AI models to make robots more capable of tasks like handling diverse, small objects have been frustrated by the returns of training them on video, and the expense of gathering data through remote operations. Simulations in digital worlds are seen as a promising solution, but ensuring that these simulated worlds are faithful to real-world physics has proven difficult. De Vigan believes that the pipeline his previous company built to “transform unstructured, incomplete, heterogeneous real-world information into a high-fidelity physical representation… with correct geometry, materials, and physics” can now be used to make physical AI models more effective. Physicl is entering a crowded category of startups vying to provide data and tooling to foundation model builders and robot operators. “The space is very young and very blurry sometimes,” de Vigan admits. There are a variety of companies collecting, labeling, or generating data in different modalities as their customers try to figure out the secret sauce that will make robots live up to their promise. In particular, Physicl wants to focus on post-training models on difficult edge cases. The startup is releasing a product called Studio with 10,000 3D environments and 100,000 assets that represent various objects. It lets users create scenes to generate synthetic data for training robots on specific problems. For example, Studio could be used to generate training data to help a robot that has trouble discerning a cup on a cluttered counter-top, or while the cup is in shadow. Loading the player… SpAItial, a world model builder, collaborated with Physicl as an early customer to improve the fidelity of its products, the latest of which, Echo-3, was released last week. SpAItial’s tool generates 3D simulations from 2D imagery, and to do that effectively, its CEO and founder Matthias Niessner said they needed to train on the kind of high-fidelity synthetic data Physicl offers. However, Physicl wants to be more than a data provider; it sees itself as the tooling for ongoing synthetic data generation. “What we created 10 years ago, without knowing it, was basically an agentic workflow to transform unstructured information into high-fidelity 3D,” de Vigan said. He imagines customers bringing trillions of images or millions of hours of lidar capture data to feed into his platform and turn them into high-quality 3D representations that can be used to generate billions of permutations to train their models. “The real challenge is, you have to curate the data by hand, and this is a very long cycle,” Niessner, also a professor of robotics at the Technical University of Munich, told TechCrunch. “To us what seems very attractive is you can just do this on the fly […] You’re running Claude, and it checks, ‘Oh, I need more data of this in my training,’ and then it auto generates that data, depending on the training state. That, to me, is super exciting, and this is exactly what Physicl is offering.”
AI companies across the world are racing to make bigger and better models, but the problem is all of them need massive amounts of processing power. On the other end, some researchers and startups are creating smaller models that can run on edge devices, like your phone or computer. One such startup, Underdog, has releaseda gamebuilt with its new voice model called Woof that’s only 32MB large. The game features a room with a retro tech theme, a typewriter, and a CRT monitor. There is a desk lamp, which makes a sound when you click its switch. The intro warns users about “intrusive” cloud providers in the town — ostensibly a jab at cloud-based models. You can interact with the typewriter and music tuner. We made a game for our new frontier text-to-speech model, Woof. 31.7 MB, so small that it runs in your browserPlay the game on X… all voices are Woof. Defeat Darkcloud for an invitehttps://t.co/ImUFYDCBQGpic.twitter.com/tthHx7ZGBK You can type anything on the typewriter using your keyboard, and the game will speak it back. You can also rotate the tuner to generate different kinds of music. All of this works within the browser through Underdog’s speech generation model, which, the company says, can run on most devices without any internet connection. The model has 31 million parameters and is open for everyone to download. While such a small model likely won’t replace bigger models, it may still be enough to drive local assistant experiences or create personalized podcasts. This demo comes weeks after Underdog launchedBark, a 167MB speech-to-text model also designed to run on edge devices for transcription and dictation. Sigil Wen,the founder of Underdog, told TechCrunch that he started building the app because, instead of using notetakers like Granola, he wanted to build his own system with AI models that can run on users’ devices and let them own their data. “I think we will be able to run most capable models of today on the devices you already own within months. We just want to train the best open models that are American-made, that are open, that are capable and allow people to have private AI,” he said, referring tohis manifesto. In addition to the two voice models mentioned above, Underdog has also releaseda 27-billion-parameter modelthat can run on laptops, along with a waitlisted app that the company is positioning as a locally hosted alternative to Muse and Instinct.
Amazon is jumping on the currently most popular bandwagon for interacting with AI agents: texting. The company on Friday said it is launching a feature that lets customers chat with its Alexa AI agent over text messages to help them shop, much like you can with AI agents like Muse or Instinct. The move comes after Amazonblocked Meta’s Muse from querying its site in September.The company is rolling the features out just before the start of the holiday shopping season. The e-commerce giant said select U.S. customers will get a text asking them to enable the shopping feature. Those with access can text Alexa to shop on Amazon using natural language queries, spanning everything from searching for items, adding them to cart, and purchasing. The text agent builds on the recent shopping-focusedupgrades to Alexa, and so can manage lists, keep tabs on prices, search for items across the web, compare products, and check the status of orders. Users can also share a picture of an item or someone wearing an outfit to search for similar listings on Amazon. The company said over time, Alexa will learn users’ preferences and suggest items accordingly. The company said users who don’t get invited to use this feature cansign up to a waitlist. The feature will be rolled out more broadly in the coming weeks. As the market for consumer-focused AI agents balloons, platforms like Amazon are building their own to tap this new avenue of user interaction. Last month, delivery company DoorDash said that third-party agents had so faronly placed hundreds of ordersin total on its platform, and that it plans to concentrate on developing its own text-based agent,launched in September, further.
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Companies are using more AI than ever, but they’re not spending more on it. A new report from Rampshows AI spending flatlined over late summer even as token volumes rose, a sign that price competition and savvier customers may be capping revenue for OpenAI and Anthropic. According to Ramp’s data, token spend only grew 1.5% across August and September. To compare, in July, it grew 42% from the previous month. As Anthropicgears up for its IPO, the data suggests investors might be wrong to assume AI companies will keep growing at breakneck speed. “Almost the entire spend on AI is from venture-backed, high-growth tech companies,” Ramp’s lead economist Ara Kharazian told TechCrunch. “That’s fine as long as AI spend continues to rise, but if it starts to slow down because of something in the market or some other factor, they will all start to slow down simultaneously, and that could lead to other cascading effects.” Kharazian said he’s already seeing evidence that the top 1% of firms — the most “AI-pilled” — have stuttered slightly in their spend in August and September, coming up just below the $10,000 per-employee they were hitting at their summer peaks. A slowdown among that cohort is “sufficient to slow down revenue entirely, despite the fact that we’re seeing rising volumes,” he said. Ramp data shows growth in spending among median and top 10%, but it’s not enough to make up the difference. Median firms spend $12-$15 per employee per month, and the top 10% spend $600-$1,000. “If you look at the use cases today, it’s not necessarily clear they will get up to that $10,000 per employee per month mark that the 1% is at,” Kharazian said. While there’s plenty of demand for AI coding agents in industries like software and finance, “it’s not necessarily true that a doctor’s office or a car dealership will need to spend that much on AI.” “Theera of hypergrowth may be over,and now that we know there may not be as much juice to squeeze out of the top 1%,” he continued. October data did see a re-acceleration, with token spend up 8.9% from September 23 to October 23, but Kharazian said the headwinds on spend growth are strong. Kharazian (who has published a series of blogs called “Cracks in the AI Thesis”) said the slowdown isn’t due to a demand problem. Again, AI volumes are up. Instead he points to the competitive nature of the marketplace. “OpenAI and Anthropic price cuts have…just made it cheaper to use AI,” he said. In September,OpenAI cut API pricesin half for GPT-6 Sol and GPT-6 Luna. Answering the price war, Anthropic has also made significant reductions to pricing for Fable 5.1 and Opus 5.5 — a decision that the company said in a recent filing slashed revenue growth by about $600 million. Both companies have also been offering enterprise discounts that are intended to lock-in spend, but in reality are hitting their revenue targets. Kharazian said this dynamic means it has very much become a buyer’s market. Subsidizing use of expensive technology in order to lock in customers has always been a part of the Big Tech playbook. Just recall how cheap it used to be to take an Uber or Lyft ride, and now that there are few other ride-hail players, the cost of going from A to B has risen significantly. Kharazian says those dynamics don’t work in the AI world because there’s just too much competition. “Meta and Nvidia are also introducing enterprise offerings with pretty cheap models or open source models,” Kharazian said. He also pointed to the range of other open model companies from China and the United States, as well as anew company called Jev— a lightweight tool for choosing between a few predetermined outputs — that is increasinglyamong the top performerson Ramp. “For a lot of AI use cases, you just need a simple Yes/No,” Kharazian said. “So you take your already cheap models, you shift them over to Jev, and now your already cheap stuff gets even cheaper.” The ability to complete workloads at a much cheaper cost is another factor driving the spending slowdown. That’s partly due to the discounts from AI companies, but also because companies are just getting better at being thrifty. “As you get really advanced in using AI, you get better at figuring out how to control it,” Kharazian said. “You’re more likely to use defaults that prevent usage of very expensive models for long-running tasks. You’re more likely to impose some budgets on employees. You’re more likely to offload some of the spend from standard and light models to even cheaper light models via routers.” Notion co-founder Akshay Kothari recently posted internal data that matched Ramp’s insights. As he succinctly put it: “tokens go brrr. costs go ahhh.” tokens go brrrcosts go ahhhpic.twitter.com/1whCWCJCyM “Alot of the assumptions that we had about the trajectory of AI spend are starting to break down,” Kharazian said. “You can use more AI, but use it for less. That’s great for the customers…but it’s not necessarily great for the model companies. And it does introduce new questions around their revenue trajectories.” Bloomberg reported Friday thatAnthropic’s potential valuationsank from the $2 trillion estimated in early October to just $1.2 to $1.3 trillion. The company is also reportedly expected to receive ahigh “junk” ratingfrom credit agencies.OpenAI also lowered its proposedvaluation to $1.25 trillion from $1.4 trillion and slashed its latest fundraising goals by about $10 billion. Kharazian says Ramp’s dataset likely informs many investor concerns. There is, however, one potential brightside for spending trajectories, albeit a speculative one. Much of the thesis for hypergrowth spending is that AI will undoubtedly introduce new use cases that will accelerate things. AI coding emerged just a year and a half ago as the most commercially viable product that has led Anthropic and OpenAI to becoming trillion-dollar companies. The hope among investors is that there will be another revolution that will drive further spend. Some think personal agents will be the next coding agents.OpenAI,SpaceXAI, andMetahave all released their own personal agents, alongside startups likeInstinct. Kharazian is skeptical, though, that personal agents, which are “not as economically productive as coding,” will provide the boost the industry needs for nonstop growth. “The projection is that there will be new products being introduced that will drive further growth,” Kharazian said. “But based on what we can observe today and the products available and what is commercially advanced, AI spend has started to slow down.”
Loading the player… Democrats’ historic victories in this week’s U.S. midterm elections could also transform the tech industry. The public voted on 41 data center-related ballot measures on Tuesday — in all but one, the majority voted to impose more restrictions on data center development. And even when data centers weren’t on the ballot directly, they were a major talking point, with newly-elected Democratic governors and congressmemberslikely to support additional regulation. On the latest episode of TechCrunch’s Equity podcast, Kirsten Korosec, Sean O’Kane, and Anthony Ha discuss what the election will mean for the tech industry moving forward — not just when it comes to data centers (whichOpenAI is already trying to rebrand), but also increased government scrutiny of Big Tech. Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotifyand all the casts. You also can follow Equity onXandThreads, at @EquityPod.
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Democrats’ historic victories in this week’s U.S. midterm elections could also transform the tech industry. The public voted on 41 data center-related ballot measures on Tuesday — in all but one, the majority voted to impose more restrictions on data center development. And even when data centers weren’t on the ballot directly, they were a major talking point, with newly-elected Democratic governors and congressmemberslikely to support additional regulation. On the latest episode of TechCrunch’s Equity podcast, Kirsten Korosec, Sean O’Kane, and Anthony Ha discuss what the election will mean for the tech industry moving forward — not just when it comes to data centers (whichOpenAI is already trying to rebrand), but also increased government scrutiny of Big Tech. Listen to the full episode to hear more about: Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotifyand all the casts. You also can follow Equity onXandThreads, at @EquityPod.
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Meta recently launchedits Muse agents— a new generation of cuddly agentic helpers. Now, the company has launched Spaces, a new way to communicate and collaborate with them. As far as I can tell, Spaces are basically breakout conversations that revolve around a particular topic — although Meta refers to them as “custom experiences.” They live in the sidebar of the interface where you interact with your Muse agent. Clicking on the Space brings up the conversation, where Muse works toward a particular goal on your behalf. For instance, if you wanted to plan a vacation, you could open a new Space focused on that particular project, and whenever you want to work on it with Muse, you would click to open that particular tab. Or, if you’re focused on collecting recipes for an upcoming family event, you’d open a new Space for that particular project. Other examples Meta gives are a holiday gift tracker, a wedding planner, or a space for DIY projects. “Describe an idea in chat, and Muse builds a space for you, whether that’s a tracker, a planner or a project board,” Meta says. Spaces can also be easily integrated with third-party apps and information, Meta says. “Connect the apps you use, and your spaces keep themselves up to date in the background, so new concert dates, price changes, or savings progress show up without you having to ask,” the company says. On Friday, Mark Zuckerberg, Meta’s founder and CEO,postedabout the new feature on X: “Playing around with spaces in Muse lately — tell Muse what you need and it builds it. I’ve made a BBQ game, an event tracker, and a home control dashboard. Excited to see what you build.” Muse agents — and the cuddly avatars that enclose them — have been greeted with a relatively warm reception over the past few months. During its earnings call in October,Zuckerberg said thatits agents had 10 million users in the U.S. and Canada — less than two months after the agents debuted. The company is obviously busy building out the user experience around the agents. Spaces is actually the second new Muse feature that Meta has debuted over the last week. On Thursday, the company alsoannounced Muse Voice, which allows users to video chat with their agent and give it commands verbally. The company is in competition with other cute and cuddly AI agents — includingOpenAI’s dotsand SpaceXAI’sGrokbot, which were debuted with similarly cartoonish personas.
Security researchers have discovered hundreds of copies of a Chinese-made AI hacking tool publicly exposed to the internet for anyone to access, potentially allowing outsiders to launch cyberattacks against unsuspecting targets. The hacking software, called Artex, is an open source autonomous penetration testing tool, meaning software that automatically probes computer systems for weaknesses, and it’s been linked to several cyberattacks since its public release in July. As open source software, it’s free for anyone to download and run on their own servers, making it a popular tool for those who cannot access Anthropic or OpenAI’smost advanced cybersecurity AI modelsfor discovering security vulnerabilities. Microsoft haspreviously describedArtex as a system of AI agents that can conduct cyberattacks “from reconnaissance through data exfiltration with little needed human interaction.” In ablog post, cybersecurity firm Censys, which scans the internet for exposed or vulnerable systems, said that it identified more than 800 distinct deployments of Artex that were accessible from the web without needing a password to log in. Silas Cutler, principal security researcher at Censys who wrote the blog post, said that not every instance of Artex is operated maliciously and many are used for authorized testers. But it’s unclear why hundreds of these Artex systems are exposed to the web, as the software does not require them to be accessible from the internet. The findings underscore how even powerful hacking tools can be misconfigured to be publicly accessible by anyone, increasing the chance that someone will find and misuse them. The publicly accessible systems also expose attack tools, such as implants (hidden malicious software) used to compromise and maintain persistent access to a victim’s network. Security companies have linked the use of Artex to several broad-scale cyberattacks, includingagainst the Korean finance industry in September. Cybersecurity giant CrowdStrike attributed the attacks to a Chinese-speaking hacker who used an Artex instance in the attacks, in part because the hacker had left an “open directory” hosting the system. Censys said that its data showed the Artex instance in question had been publicly exposed for months, and appeared to contain more attack data that extended “well beyond South Korean banks,” including payment and gaming companies. Artex was also used in an attack on IT support giant ConnectWiseearlier in October, which allowed Chinese-linked hackers to spy on thousands of organizations whose data and files are hosted in Microsoft’s cloud. Microsoft, which has visibility into the attack, said in a blog post that the hackers used Artex to break into customers’ cloud environments and steal their data at such speed that defenders had “little time to detect and respond.” Many of the attacks linked to Artex involved exploiting simple vulnerabilities, such as guessing weak or known passwords, and a lack of multi-factor authentication. The findings show that simple vulnerabilities, including misconfigurations, can expose both the attacker and their targets. Artex has since announced it would go closed-source, effectively limiting further releases for paid customers. There are still dozens of copies of Artex’s code available on code-sharing sites that can still be used in active attacks.
OpenAI has reportedly raised $11 billion so far in a new funding round that values the AI lab at $1.25 trillion. Bloombergreported onthe new funding on Friday, citing sources familiar with the matter. The $11 billion is part of a larger funding round that hasn’t closed yet, according to the outlet. The round is expected to close in the coming weeks and could total about $20 billion, a source told Bloomberg. In late September, Bloombergreportedthat OpenAI was looking to raise $30 billion in the upcoming round. The current figures fall well short of that target. TechCrunch has reached out to OpenAI for comment and will update this story if it responds. The news comes only hours after Bloombergalso reportedthat potential buyers of shares in the IPO of OpenAI’s chief competitor, Anthropic, are eyeing a substantially lower valuation than Anthropic had targeted. Bloomberg writes that some potential buyers believe Anthropic’s valuation could be as low as $1.1 trillion, compared with the$2 trillionthat investors were said to be expecting roughly a month ago. OpenAI previously intimated that it would go public this year but subsequently delayed its plans. There are expectations that the companywill IPO next yearat some point. The market for AI hangs in a particularly precarious place. The Democratic landslide in the U.S. midterms — and overwhelmingly negative voter sentiment around data centers — hascaused jitters throughout the market, with many anticipating potential regulatory challenges in the future.
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