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Google Pay is testing a new fashion and lifestyle shopping feature in India, bringing personalised recommendations, virtual try-on and visual search to its payments app. The pilot lets users browse clothing and fashion trends, see how select items look on them and find similar products using images. Google is currently testing the experience with a limited number of users and has not announced when it will reach a wider audience.
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OpenAI announced in September that it planned to reduce the usage limits for select ChatGPT Pro subscriptions without reducing the price tag. The company now seems to be reversing its decision. In a statement, an OpenAI official has revealed that it will double usage limits for several ChatGPT Pro subscriptions. The revised limits are also said to be applicable to Business Premium. OpenAI will also continue to offer extended usage limits for its personal AI agents.
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Google launched its latest frontier AI model late last month, named Gemini 4 Carbon, with agentic and enhanced cybersecurity capabilities. The AI model was released just two weeks after the Mountain View-based tech giant released Gemini 4 Argon, another AI model with agentic capabilities. During the announcement, Google published multiple benchmark results to showcase the capabilities of the Gemini 4 Carbon. Now, SemiAnalysis has published results from its new GDPx benchmark that tests the agentic capabilities and cost efficiency of AI models, comparing results of multiple models. On the benchmarking platform, Google's frontier AI model came in second position, while being the most cost-efficient among the top five.
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Samsung's first AI-powered smart glasses, reportedly called Intelligent Eyewear, are expected to go on sale in South Korea and the US in mid-December. According to a report, the initial launch will exclude markets such as Japan and Europe, with the company planning to expand availability depending on early demand. The smart glasses are built on Google's Android XR platform and integrate Gemini AI. They are expected to offer features such as real-time translation, voice navigation and automatic note-taking.
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Over Halloween weekend, some Grok Bot users awoke to something especially spooky: Their weekly usage limits were unexpectedly maxed out, even if they hadn’t been using SpaceX’s agentic AI overnight. Some users reported on X that theirusagejumped from as low as50% to 100%. SpaceX CEO Elon Musk confirmed in an X post on Sunday that this was not a glitch. “The cost of serving Grok Bot was unsustainably high relative to revenue,” hewrotein a reply to a question from a user. He has not addressed the issue on his main feed but has continued boasting that Grok Bot can help youbuy giftsfor your family. The cost of serving Grok Bot was unsustainably high relative to revenue Grok Bot has been positioned as a success story for the newly public SpaceX, which said in afilinglast month that AI now makes up 52% of company income. Its renamed “Super Intelligence” division actually turned a profit of $431 million, which is a rarity in the AI industry. Its profit would’ve beenhigherif not for $1.2 billion of one-time costs related to its acquisition ofCursor. Musk recently referred to Grok Bot as having “thefastest growthof a product that I’ve ever seen in my life.” Musk has not yet shared how subscribers’ usage limits will be impacted moving forward.
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Here’s a question straight out of sci-fi: What if AI could predict what you were going to do before you decided to, even in a completely new situation? Israeli startup42AIsays it is developing a “behavioral world model” that can do just that, and it has just come out of stealth with $62.5 million in seed funding. The company is initially running its predictive system at a handful of financial brokerages, where it says the model can help predict how individual traders will respond to volatile conditions. “Behavior is not deterministic; it’s constantly probabilistic,” 42AI’s CEO and co-founder Amit Gal-Or told TechCrunch when I asked him to justify his company’s tall claim. He argues that people have underlying psychological tendencies that influence their decisions even when their circumstances change, and that’s what the startup is trying to measure and convert into data that the model can understand. “A week from now, you might be a different person, but your tendencies will always play out in certain areas to a probability or distribution,” his co-founder and CTO Ehud Ezra added. And that, the company argues, is what the model does: show theprobabilityof a person making a certain decision. 42AI says the model tries to figure out people’s internal motivations instead of extrapolating patterns based on their previous actions. To do that, 42AI examines how people react to seemingly unrelated events. For an investor or trader, for example, such events might include completing KYC processes, onboarding, support calls, money transfers, or trading stocks. “When the person is talking to support or sales in the initial process […] we’re looking at the sentiment, we’re looking at the voice, we’re looking at a lot of interactions that they’re doing, how many times do they call, and so forth,” added Ezra. “And all of those data points are going into the model. Later on, you have the interactions: how you buy and sell, and when, and we’re looking at the market at that stage.” “So we’re trying to look at how they’re looking at the world. In a world model, every individual is the world,” he added. The company then converts those observations into mathematical representations of traits, which are then fed into a transformer-based world model that lays out probabilities for possible decisions under different circumstances. (The company uses a separate language model to generate explanations for its predictions.) Ezra said the model is trained to preserve a person’s underlying tendencies during simulations. So if someone is usually risk-averse, but the model suddenly treats them as a risk-taker, “the target is off,” he said, explaining how the model’s training rewards are structured. 42AI spent three years developing this approach, combining psychological research with neuroscience and genetics, as well as advice from experts in psychology and PTSD, and Dan Shechtman, who was awarded the 2011 Nobel Prize in Chemistry. Gal-Or said the team first studied thousands of people via psych assessments and behavioral scenarios, before adding government and biobank datasets covering more than 2 million people. Its customer datasets have since expanded to cover almost 30 million people, he said. In live deployments, the model analyzes customers’ existing records, including transactions and support calls. Gal-Or pointed out the system doesn’t need personally identifiable information, arguing that “the model never knows who the person is.” However, it seems the company still creates individual behavioral profiles from linked customer activity. In predicting market trades, 42AI claims its model achieves 98% accuracy, but Gal-Or clarified that this is more a measure of whether trading flows fall within a predicted range, not if each trade was predicted correctly. He also said the model consistently achieves an F1 score of 0.8 to 0.85 for predicting trading direction and volume. (F1 score is a metric in machine learning that measures how well a classification model can balance correct predictions against false alarms and missed events.) And it’s not like the model can work everywhere, given it’s still a machine in a box. “There’s a natural ceiling because you really don’t have access to everything that a person experiences,” Gal-Or said. Ezra explained that the system’s accuracy also suffers when it misses important situational context, like if someone chooses to change their trading choices based on advice from a friend or a colleague. Gal-Or said 42AI is working with 10 large brokerages, who are using it to manage liquidity, margin requirements, and hedging exposure. The company is onboarding more customers, he said, and is generating more than “seven figures” in revenue, though he wouldn’t specify a number. Customers pay the startup a flat fee, or a percentage of the value the platform helps them gain. He claimed one brokerage generated approximately $30 million in additional value through dynamic hedging. The $62.5 million seed funding came from the founders and Infinity Group over three years, and wasn’t entirely raised from external investors, Gal-Or said. Infinity was founded by his father, Amir Gal-Or, who is also 42AI’s chairman and a co-founder. 42AI now plans to raise more money. Beyond finance, 42AI is now exploring applications for its model in gaming, fraud prevention, and cybersecurity, and one day wants to use it to help robots and AI agents understand people. But that may be a ways off yet: Ezra acknowledged that the model needs to be trained on rules and context for each new domain. And like with all AI tools, there’s a potentially troubling side to 42AI’s technology: If a brokerage can anticipate what its customers may do, what’s to stop someone from using that knowledge to their own benefit? Ezra admitted that this was “a valid concern” when I raised it, and the founders pointed to regulations, monitoring, and an internal ethics committee, though they didn’t explain what, if anything, would stop their customers from using the technology to gain an advantage over the very people it’s profiling. Amit Gal-Or, however, was confident that the technology could help address such risks. “We are very proud that actually we are part of the solution; we are the solution to this problem,” he said.
The New York Times sued Brave, the 11-year-old, privacy-focused browser company, on Monday, alleging it scraped the outlet’s copyrighted content and unlawfully distributed it to customers the suit names as Anthropic, Perplexity, Mistral, xAI, and Cohere. Separately on Monday, the Daily News, the Chicago Tribune, and the Orlando Sentinel filed a similar lawsuit. Both were brought in federal court in Manhattan. The New York Times, which has also sued OpenAI and Perplexity over similar claims, likens Brave to a “supplier, dealer, and distributor of stolen copyrighted content” for AI companies. “In this way, Brave acts both like a burglar that misappropriates the content of publishers, and an AI ‘fence’ that sells stolen content to third parties,” the suit reads. It says Brave built an index of more than 30 billion web pages using an unidentified crawler, leaving publishers unable to block it. The suit also targets a Brave browser feature that bypasses paywalls and the company’s practice of blocking publishers’ ads while selling its own. Founded in 2015, Brave has raised roughly$250 millionto date, according to private-market trackers. The Times seeks an injunction and damages. Brave did not immediately respond to a request for comment.
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Squarespace, the web company that helps customers build and host their own websites, is laying off about one-fifth of its workforce, in a move that will affect hundreds of jobs. Jeff Seedman, a public relations specialist representing Squarespace, confirmed the layoffs to TechCrunch. The Vergeinitially reportedthe layoffs. “Squarespace is restructuring its organization and reducing its global workforce by approximately 19%, representing around 300 employees,” said Seedman. “The changes are intended to simplify how we operate, sharpen our priorities and enable the company to move faster as we position Squarespace for its next phase of growth.” Website building has changed immensely over the last few years as a result of the generative AI boom. “Vibe-coding” website builders, which only require simple prompts from users to spin up functional front-end designs, have largely upended the industry, making it possible for users to create sites with significantly less effort. These kinds of sites have become an increasingly central part of the website-building business. Last year, web host rival Wix boughtBase44 for $80 million. Similar offerings, like Lovable, have generated significant investor interest; Lovableraised a $400 million Series Cround in August that valued the company at $13.3 billion. Google also recentlylaunched Stitch, a feature that allows users to swiftly generate user-interface designs. Squarespace has also begun a big push into AI. In 2024, the company launched an AI tool called Blueprint. The company also recently announced Forma, an upcoming AI native platform, whosewaitlist users can join. Squarespace was founded in 2004 and went public in 2021 at a $10 billion valuation. However, the companystruggledin the ensuing years to generate enthusiastic public market interest. Squarespace ultimately decided togo privateagain by way of a $7 billion private equity deal in 2024. An earlier version of this report incorrectly stated that Squarespace had not yet launched AI tools; the story has been updated.
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While much attention has focused on the updated financials Anthropic revealedin its revised S-1/Aon Monday (revenue is soaring, but so are expenses), the AI lab dropped another little bombshell in the fine print. The filing seems to warn that the company is on the cusp of creating a model capable of recursive self-improvement (RSI). RSI is the point at which AI grows so good at training itself that it spawns a dramatic acceleration of AI capabilities. To the AI doomers, RSI represents the cataclysm from which there is no return, in which AI builds itself into something powerful enough that it slips beyond human control and may be indifferent to the damage it causes while achieving its goals. To AI enthusiasts, RSI, or some version of AI reliably using itself to improve, represents the technology’s promise, where it becomes a tool of unimaginable productivity, scientific discovery, and more. Anthropic seems to believe that RSI is both a promise and a peril. It says in the S-1 that it uses AI in its R&D, but it cautions: “Using AI to help build more capable AI could also pose a considerable safety risk, particularly if it sharply accelerates the pace of progress. This is one of the areas covered by our Responsible Scaling Policy, which sets out our plans to monitor and mitigate the risks as we approach what has been termed ‘recursive self-improvement.’” When the termRSI became popular earlier this year, many believed it was a buzzword or, at best, a goal too hard to achieve, define, or prove. But on Monday, Anthropic added new statements to its Risk Factors section, saying 1) it has already used AI to develop new models whose leaps in capability it didn’t recognize at the time, and 2) it may soon meet its own definition of RSI (bold emphasis ours, below): In addition, our increasing use of AI systems to accelerate our own research and development may cause the capabilities of our models to advance more rapidly than our ability to develop, validate, and implement corresponding safety, alignment, and security measures. As a result,we have in the past not identified, and may in the future not identify, a significant acceleration in our rate of development until after it has occurred. For example, our internal estimates and our expectations regarding future models, rather than our current models,indicate that we may imminently meet our Responsible Scaling Policy’s definition of “dramatically accelerated progress attributable to AI.”We have not designated any of our current models as exceeding this threshold, and we continue to assess the underlying data. However, Anthropic also said in the filing that none of its models have crossed this RSI threshold yet and that it is promising to keep humans in the R&D process for the foreseeable future. “We do not anticipate being able to safely use Claude to fully autonomously design and build successive generations of frontier models without meaningful human supervision or understanding, if and when that becomes technically possible. Accordingly, we are considering updates to our Responsible Scaling Policy to keep humans in control of frontier model development at Anthropic.” Skeptics mightpoint outthat an S-1 functions as both disclosure and marketing, and that claims of being close to a model so powerful that it can build itself could fall into the latter category. Nevertheless, there’s no question that Claude’s capabilities, especially in terms of coding, math, and cybersecurity, have grown leaps and bounds this year. If Anthropic thinks the possibility of RSI is close, maybe we should believe it and act — perhaps govern — accordingly.
Whether developed by OpenAI or Anthropic or built on open source alternatives, current AI models share a critical limitation: They don’t learn from their mistakes without further training. Businesses adapting these models for specialized fields, such as law and finance, typically get around this by fine-tuning general models. However, that often requires hiring internal research teams or relying on expensive consultants, commonly known as forward-deployed engineers (FDEs). Trajectory, a startup founded about a year ago by three Stanford classmates with experience at Google DeepMind and Apple, wants to automate that process, allowing AI models to learn from feedback and continuously improve at specialized tasks. The 18-person company just announced that it raised $45 million in a Series A round at a $300 million valuation, led by Sequoia, with participation from Nvidia, Bessemer Venture Partners, and Bain Capital Ventures. Trajectory co-founder Ronak Malde compared foundational models to a very smart PhD student who’s always starting a new job. “But what the economy values is someone who worked on the job for 30 years,” he told TechCrunch. The startup aims to solve this problem through an approach known as continual learning. Businesses using Trajectory can start with an open source AI model and connect the platform to their applications, allowing it to collect feedback on how the model performs. Trajectory then uses that information to retrain the model, helping it improve at specialized tasks over time. With Trajectory, lawyers, doctors, and other specialists can improve the models near automatically themselves, Malde said. That capability could make deploying specialized AI faster and cheaper. Companies like Harvey and Rogo are already using Trajectory’s platform to post-train open source models, helping them perform more like experienced lawyers or financial analysts by learning from previous tasks, such as reviewing a particular type of legal contract or valuing a company using a specific methodology. Trajectory’s other customers include Decagon, Mercor, and Nvidia. The startup is now making its post-training platform publicly available. The people using Trajectory decide “what good looks like for their organization and can turn that into improvements for their model,” Malde said. Trajectory’s approach to continual learning shares some similarities with recursive self-improvement (RSI), the idea that AI systems can improve their own capabilities. But co-founder Michael Elabd argues that keeping humans involved in the training process makes Trajectory’s approach safer, since autonomous self-improvement could lead to unintended or even dangerous outcomes. Trajectory isn’t the only startup trying to automate post-training. Other companies going after this market include Thinking Machines Lab, founded by former OpenAI CTO Mira Murati and the company behind the fine-tuning platform Tinker, Applied Compute, which was valued at $3.25 billion last month, andPrime Intellect, a platform that helps companies build and train their own AI agents. Malde believes the startup’s biggest advantage isits team. He was among the handful of researchers Google DeepMind acqui-hired from Windsurf. Elabd also worked at DeepMind, while Trajectory’s third co-founder, Arjun Karanam, previously worked on generative foundation models at Apple. The startup has also recruited researchers and product designers from OpenAI, Stripe, and Netflix. Why does design matter so much to a company focused on AI training? Karanam says the startup has developed an interface that makes post-training more accessible to its nontechnical customers.
Y Combinator CEO Garry Tan has, in the past,jokingly admittedthat he’s suffered from “cyber psychosis” and sometimes barely sleeps because he’s so excited to be working with his AI agents. On a new episode of Walter Isaacson’s podcastThe Builders, set to publish on Tuesday, Tan put a price tag on his passion. “I spend about maybe $100[,000] to $200,000 a year on tokens,” he told Isaacson. He uses agents for countless work projects and coding tasks and has freely shared his set of 23 Claude Code skills,called gstack, on GitHub — an effort to help teach others what agents can do, he said. On the one hand, he uses his agent the way many people use AI: to help him research Isaacson’s podcast and prep for the kinds of questions he might be asked. On the other, his agent also helps him run his life. “It’s read all of my email and all of my Dropbox. It’s read, like, all of my Apple Notes, even my therapy notes going back 20 years,” he said. He describes using AI agents as getting help with “almost anything that you do in your day-to-day life, and for it to do it somewhat more autonomously. Think of it as a Jiminy Cricket on your shoulder at all times. Like, almost anything you want to do, it knows you, and it’s there to help. And it will actually sort of run ahead and say, ‘What about this?’ Or it’ll help you see around corners.” As an early OpenClaw adopter who is now a power user, Tan says one thing he’s learned is not to treat AI agents like traditional software. Some people are so worried about “repeatability and safety that they actually trade off all of the beautiful parts of working with an AI agent, which basically can be so similar to a human,” he said. For instance, Y Combinator is also using an agent that is “rebuilding the way we operate at YC,” he said. “We now have an internal AI agent, for instance, named Stray Light that kind of has a personality, and then it has a company brain. Like it actually has the total context of every founder we’ve ever backed, anyone who’s ever emailed us, like all the applications.” Tan, who is, naturally, an avid lover of technology (as anyone running Silicon Valley’s famed startup accelerator would be), believes personal AI agents will eventually become mainstream, if one major problem is solved: Costs have got to come down. “It’s too expensive right now,” he said, adding that he knows the amount of money he spends on AI agents is “a crazy privilege that not everyone can do.” But freebie agents controlled by Big Tech maybe aren’t the answer, either. When discussing the future he’d like tech to build, and how Meta as a social media company has impacted us, he said, “This is a huge company that controls the algorithm, and we all wish that they didn’t at some level,” he said. “So you could imagine a personal AI that you control, that you program.”
Apple’s services business remains at record levels, but it’s facinggrowing pressure. The central part of that business, Apple’s App Store, has faced regulatory pressure and a slump in mobile gaming, which havehurt services growth. However, on Monday’s earnings call, Apple signaled that it may look to boost services revenue through third-party apps. What does that mean, exactly? Unfortunately, it’s not entirely clear. When discussing how AI could potentially “contribute to revenue growth over time,” Kevan Parekh, Apple’s chief financial officer, said: “On the product side, you know, we feel AI offers a compelling reason for customers to upgrade and switch in order to get the latest capabilities, and we also feel there’s opportunities across third-party apps and partnerships as well as first-party opportunities.” Parekh did not elaborate on what sorts of opportunities might be available via third-party apps. However,Reuters reportedMonday that investors are wondering whether Apple could profit by distributing third-party AI agents through the App Store and taking a cut of the revenue they generate. Regulatory and legal changes have cut into App Store commission revenue in some markets, even as Apple’s services business as a whole continues to grow. Third-party estimates in August, for example, suggested the company had seenan 18% dropin U.S. commission revenue, with declines also in Brazil and Japan after those countries introduced new regulations. Apple has traditionally controlled payments within its app marketplace tightly, taking a cut of all App Store transactions. But alawsuit by Epic Gamesultimately forced the company to allow apps to redirect users to outside payment systems. Other legal challengesthroughout the worldhave further hampered the business. During the earnings call, Parekh referenced Apple’s App Store woes and returned to the idea that the company’s service business could expand into new territory. “We’ve also seen some factors that impacted the performance of the App Store, and during the September quarter, the performance on the App Store was similar to the prior year, and we had a record number of paid subscriptions with double-digit growth for third-party apps on the App Store,” Parekh said. “At the same time, we did see some headwinds in mobile gaming that we’ve talked about before, and expect to see those continue into the summer quarter.” Parekh added: “As we move ahead, we expect services growth to to be between 8 to 9% [in the December quarter]. This outlook contemplates our best estimates on sequential headwinds, primarily from third-party licensing and the additional half percentage point of foreign exchange I just mentioned.” TechCrunch has reached out to Apple for more information and will update this story if it responds.
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