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Airbnb hasteasedits plans to build its own AI travel agent before, but on Thursday the company announced that the new AI feature will launch in the first half of 2027. In the company’sQ3 shareholder letter on Thursday, Airbnb also provided more detail about how the agent will work, what it will actually do, and how it’s different from a typical AI chatbot. In prior interviews, CEO Brian Chesky sketched out the travel company’s approach to AI,describing a plan to build an AI agentthat has a richer interface than a conventional chatbot. He also expressed his opinions about how chatbots aren’t the right interface for travel, noting that people take pleasure in the planning process and in anticipating a trip. Any agent that it built, then, shouldn’t take away from the inspiration and the browsing,he told TechCrunchin an interview last month. Chesky today reiterated that belief, telling analysts on the earnings call that people won’t use one AI agent for everything. “Today’s AI agents like Muse and Instinct are good at simple routine tasks like making a restaurant reservation or scheduling an appointment. But planning a trip is different,” he said. “Travel is aspirational. Discovering where to go and where to stay is part of the experience. In fact, 80% of travelers say they enjoy spending time researching trips. People want to look at photos. They want to compare homes. They want to make decisions with people they’re traveling with,” he continued. “And I don’t think the general-purpose agents are the best to do that. So that is why we are building our own AI agent,” Chesky said. Airbnb described its agent as having a personalized interface that’s designed specifically for travel, where it will “build an interface” around a user’s question instead of simply responding as a chatbot would. “What we are doing is developing a custom-designed AI-native experience that responds to what guests are looking for,” Chesky said. “So instead of relying on traditional chat alone, which is very text-heavy — which, again, is definitely not the way you should plan travel, our agent draws from a library of hundreds of travel-specific interface components — maps, photos, comparisons, calendars, itineraries — and composes them in real time to match what they’re looking for.” The exec said the agent will feel “a little bit like generative UI” as it’s designed to offer up “the right interface at the right time” for someone’s specific travel needs. In its shareholder letter, the company touted that its nearly 20 years of experience in bringing hosts and guests together is informing how the agent is built, as it incorporates insights about how people both book and host on its platform. The agent will also be able to help travelers beyond the booking itself. “Our agent will be able to help guests throughout their trip — from changing a reservation or resolving a payment issue to getting help with refunds, rebooking, or 24-hour safety support. These are things an outside agent can’t easily do on its own,” the company wrote. Over time, Airbnb expects the agent will be able to help travelers with other aspects of their trip, including not only homes and hotels, but also services and experiences. Services and experiences are two areas that have become a bigger focus for the company followingits redesign last year. While the company previously said to expect its AI agent sometime in the next year, it’s now more specifically committing to roll out the new technology to consumers in the first half of 2027. The news of the agent’s release was delivered alongside strong third-quarter earnings, with revenue growing 19% to $4.87 billion, topping Wall Street estimates. It also raised its 2026 revenue growth forecast, which it now predicts will be in the high teens, instead of the mid-teens. “Airbnb is back in growth mode,” Chesky told analysts on the Q3 earnings call on Thursday. “It’s rare for a marketplace of our size to reaccelerate growth, but that’s exactly what we’re doing. Q3 was our fastest revenue growth in more than three years,” he added.
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Palo Alto Networks is in talks to acquireClutch Security, a startup that secures AI agents’ access to company systems, for $500 million in cash plus additional milestone-based payouts, according to a person familiar with the discussions. Three-year-old Clutch was last valued at $85 million when it raised a $20 million Series A round in January 2025, led by Lightspeed and SignalFire, according to our source. The startup is currently approaching $10 million in annual recurring revenue (ARR). That means the acquisition, if completed, would value Clutch at about 50 times its ARR, a standard way of pricing software companies. Palo Alto Networks and Clutch Security did not respond to a request for comment. The Information was the first to report the acquisition discussions. The high valuation is not an anomaly in a market increasingly focused on securing software against bad actors using AI to breach systems more easily. Upwind, a startup using AI to secure cloud infrastructure, recently turned down an acquisition offer from DataDog that valued it at roughly 40 times ARR, TechCrunchreportedlast week. If the deal happens, it would be Palo Alto Networks’ seventh acquisition this year, according to PitchBook. The cybersecurity giant has a history of buying innovation rather than building it in-house, and it has ramped up dealmaking this year in response to rising AI-driven cyberattacks. In September, Palo Alto Networks acquired Console, a two-year-old startup that uses AI agents to automate routine IT help desk tasks. The transaction valued Console at $500 million in stock and cash, TechCrunch reported. Other VC-backed companies that Palo Alto Networks has scooped up this year include NEA-backed Embrace, for$325 million; Chronosphere, an observability platform backed by Greylock and Lux Capital, for$2.95 billion; and Koi, a cyber startup backed by Battery and Team8, for$231 million, according to regulatory filings. Several of Clutch’s competitors have also been acquired. Cyera, a cybersecurity startup valued at $12 billion earlier this year, acquired Oasis Security, which, like Clutch, focuses on managing AI agents’ access to systems, for$1 billion. Meanwhile, Cisco bought Menlo Ventures-backed Astrix for roughly$400 million, Calcalist reported.
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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 167 MB 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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Their departures come as Claude Code expands its role in Anthropic’s AI coding business.
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Altman’s recent comment on RAM shortage has reignited the debate over memory allocation between AI data centres and consumer devices.
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The new capabilities give AI agents business context, enterprise controls and the ability to execute recurring tasks autonomously.
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Security, governance and model dependence are emerging as major concerns for businesses, even as executives report productivity gains from AI investments.
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FDE salaries in India range from Rs 32 lakh to Rs 1.55 crore annually, with compensation rising alongside experience
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Chatbots such as ChatGPT, Anthropic’s Claude, and Meta’s new Muse assistant are becoming alternatives to traditional search.
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GPT-6 Astra, used through Codex, helps Mazda engineers explore different vehicle nose shapes, simulate airflow and identify designs that could reduce aerodynamic drag.
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