🚀 Zaprep: Your Socials on Steroids. Start free — automate 1,000 DMs/month & turn engagement into leads. with 1,000 automated DMs/month.
Submit your Tool
PoweredByAI.app is an AI Tools Directory helping individuals, businesses, and creators discover the best AI tools for writing, coding, design, productivity, and more.
© 2026 , Product of011BQ. All rights reserved.
Googleannouncedon Tuesday that it plans to disable AI tools for K-12 students next year unless school administrators explicitly turn them on. The tech giant says the change follows consultations with schools, teachers, leading researchers, and policymakers. As a result, Google is revising its approach across Gemini, NotebookLM, Gemini in Classroom, and Google Search. The decision means that schools choosing to restrict AI altogether will no longer be able to access Google Search, as AI features are now integrated into the search engine. Chris Phillips, vice president for education at Google, toldThe New York Timesthat “Search and AI are deeply integrated,” which means that if a school chooses to restrict AI altogether, students “will not have access to search, and they’ll see a message from their school that indicates it’s not available.” The change raises an issue for schools. Although teachers may not want students to use AI Overviews in Google Search to conduct research or complete assignments, they likely still want them to be able to have access to the search engine to find sources and other learning materials. However, Google Search and its AI features are now inseparable. Given the dominance of Google’s suite of apps in classrooms and the widespread use of Chromebooks, the decision underscores the company’s influence over the education sector. Schools that want to continue to have access to Google Search will have to accept its AI features. “We’ll share more specifics with schools in the coming months ahead of the changes rolling out so they have time to prepare and enable a smooth transition,” Phillips wrote in ablog post. “Our goal is simple: provide school leaders the control and confidence to use AI products in classrooms as they see fit.” Google announced the upcoming change alongside the launch ofGuided Learningin schools. The tool, which was unveiled earlier this year, sort of functions like an AI tutor, as it’s designed to help users build a deep understanding instead of just getting answers. The launchcomes amid concernsthat AI chatbotsundermine the learning processbecause they spit out direct answers.
View
There’s a lot of mystery around the finances of major AI labs, and as Anthropic inches towards it IPO, we’re starting to get some answers. In particular, the company is finally disclosing more about the economics of compute, and how much of Anthropic’s skyrocketing revenue is being lost to compute costs. Ina new filing on Monday, Anthropic released data that showed those margins on compute actually getting worse. The filing was an update tothe S-1 released last week— but where that filing’s financial disclosures only covered through June 30 of this year, the new data extended to disclose results through an extra quarter, to September 30. Broadly, Anthropic had a good quarter: revenue reached $17 billion, up from $11.5 billion in the previous quarter, suggesting durable growth. But the operating loss grew at the same time, thanks in large part to growing compute costs. Looking at the compute margins makes the picture even worse. In the new quarter, compute took up 58% of the company’s revenue, up from 48% the quarter before. At a moment when the company could win over IPO investors by showing margins improving, they’re getting worse. A chart in the filing shows this even more clearly, tracking the company’s revenue for every dollar spent on compute. Since the start of 2025, it’s been steadily growing, peaking at just over 2x this past quarter, then falling back down to 1.72x. Many indications imply that this shift in compute margins is more of a blip than a long-term problem, but it still tells us important things about the business of running a major lab. Trying to keep up with demand, Anthropic’s compute bill shot up to $10 billion for the quarter, including, as the filing notes, “$5.0 billion of compute under short-term commitments, which currently costs significantly more than comparable compute procured under our multi-year commitments.” Basically, demand was growing so fast that Anthropic had to scramble for compute, which meant paying higher rates. Some of those deals have already played out in public. Anthropichas been paying SpaceX$1.25 billion a month for compute, alongside multibillion-dollar deals withNscale,Akamai, andVolta. In the parlance of the industry, these are neoclouds rather than hyperscalers — smaller outfits that offer more flexible compute deals at higher rates. Anthropic’s margins got worse in Q3 because of deals like these, and there’s no sign they’ll be able to do without them anytime soon. As the new filing puts it, “We expect compute and infrastructure expenses to increase in absolute dollars and remain our largest operating expense for the foreseeable future as additional committed compute becomes available to us under multi-year arrangements with CSPs, and as we continue to invest in additional compute.” Anthropic is still trying to lower its compute costs. In particular, the company talks quite a bit about its efforts to build its own compute base under a project it calls “Anthropic Direct.” But with demand growing so fast, those efforts aren’t going to be able to keep pace, and the neoclouds will continue to be the best option they have. In a sense, these are champagne problems. As long as revenue is growing at nearly 50% a quarter, few investors will worry about high compute costs — and the moment demand slows down, investors may be comforted by a compute picture that has gotten a lot sunnier. But it’s a reminder of how much AI labs still depend on the trillion-dollar infrastructure buildout — and how vulnerable they are to hiccups in that process. It’s easy to imagine the data center buildout slowing down over the next year if bond markets get skittish or local opposition gets more intense. There’s not much Anthropic can do to address either of those risks, and either one would make a messy compute picture a whole lot messier. In the meantime, the future of Anthropic and other labs will be at the mercy of compute prices.
Netflixannouncedon Tuesday that it’s rolling out a new “AI Discover” tab where users will be able to experiment with new ways to find their next watch. The first experimental feature launching under the tab will allow users to get recommendations based on what they’re currently in the mood for. Users can choose from curated themes featuring clips from shows and movies, based on their mood, viewing preferences, how much time they have, or what’s new and trending. Themes include “something light,” “scare me,” “family movie night,” “quick watches,” and more. The addition of the new tab suggests that Netflix wants to make its growing library easier to navigate while shifting discovery away from an experience where users are endlessly scrolling through titles toward a more personalized and AI-powered experience. Netflix says AI Discover is powered by a new intelligence layer it’s been building that responds to what users tell the platform in real time and keeps learning. Once users pick a theme, they’ll see matching recommendations. They also have the option to select more specific themes so they can keep searching until they find something they’re interested in, or choose the “something different” option to start over. Each title features a “Tune Your Recommendations” button beside “Play” and “My List” to allow users to save recommendations they like or exclude titles they don’t want suggested again. “There’s more to enjoy and discover on Netflix than ever, but what sounds right for a quiet night in may look different for family movie night or a few spare minutes,” Netflix wrote in ablog post. “We’ve spent years refining how you discover, with recommendations tailored to your tastes, from what you Thumbs Up to what you add to ‘My List.’ Now, AI Discover joins the mix as a new way to explore.” Netflix describes AI Discover as its “test kitchen,” where it will “try out new recipes for discovery.” The company says users’ feedback will help it decide which features to keep or improve before bringing them over to the main Netflix experience. AI Discover is starting to roll out in the U.S. to users who use Netflix in English on supported TVs. The company plans to bring the new tab to additional users in the coming weeks. If users have access to the new feature, they’ll see a “Discover” button with a red gem icon and a “New” label in the top navigation bar on their TV.
Fal, a San Francisco-based startup that hosts and runs AI video, image, and audio models, has raised $1.3 billion in its latest funding round led by Bessemer Venture Partners, with participation from Nvidia, Sound Ventures, and T. Rowe Price. The deal values Fal at $16 billion, double its $8 billion valuation in April,Bloomberg reported. The startupannouncedits Series E and F rounds at the same time, noting that a combined $1.65 billion was raised across both. (Sequoia led the earlier round.) The latest round (Series F) comes amid soaring demand for inference services, the process of running an AI model that’s already been trained to generate outputs, particularly from customers relying on open source models. Fal surpassed $800 million in annualized revenue earlier this year, Bloomberg’s report noted. Companies providing inference services, including Baseten, Fireworks AI, and Modal, have also been growing rapidly and raising large funding rounds athigher valuations. But their gross margins remain thin because of the high cost of buying or leasing compute, according to several people familiar with inference companies’ financials. Fal was co-founded in 2021 by Burkay Gur, a former Coinbase machine learning leader and Oracle engineer, and Gorkem Yurtseven, who was previously a developer at Amazon.
View
The data analysis company Palantir delivered such a huge third-quarter earnings report on Monday that its press release even bragged that the numberswere “crushing consensus expectations.” Yet retail investors were lukewarm on the company on Tuesday, with the stock trading slightly down. That’s because Palantir’s enterprise sales, its big growth area, showed signs of slowing. First, the good news for the company’s investors. Revenue grew 96% from a year earlier, and 20% from the previous quarter, to $2.31 billion, beating analysts’ expectations of $2.18 billion. It was the first time Palantir has topped $2 billion in a quarter. Palantir also reported adjusted earnings per share (profit per share, excluding certain one-time items) of 44 cents, compared with analysts’ expectations of 41 cents. The company even raised its revenue guidance, or forecast, for the full year. Normally, that alone would send a stock soaring. The clincher was that Palantir’s U.S. commercial revenue surpassed U.S. government revenue for the first time ever. The company reported $984 million in commercial contracts (up 148% from the year-ago quarter), versus $953 million in U.S. government revenue. The reason for all this love from enterprises? They are signing up for the company’s AI software that can run frontier models and custom-train open-weight models securely, without sharing any of their data and business processes with outside AI labs. Plus, Palantir has long offered what is today known as “forward-deployed” software engineering, in which its own engineers work directly with customers to customize the software. However, Palantir also revealed a less-than-rosy signal: Even as enterprises are moving faster than ever, they are often signing short-term trials. “What Ryan and I are seeing on the front line and commercial is this much quicker interaction, much quicker to deployment, shorter duration of the contract but then an ability to expand that contract dramatically,” CEO Alex Karp (pictured above) told Wall Street analysts on the quarterly earnings call. Palantir is experiencing one of theunique phenomena of AI in the enterprise right now.Corporations are willing to quickly try AI software, skipping the usual bureaucracy, but they are also more willing to cancel contracts. Karp used one off-color metaphor after another to describe how unconcerned he was about holding onto his enterprise business. “We’ve gone from being the most attractive person in the physics department to being the captain of the football team, kind of thing,” he said, describing the influx of prospective corporate customers flowing in. “Conversely, you know, once you go Palantir, you don’t go back, and that’s just how it is, and you see it in our numbers.” The numbers don’t yet confirm Palantir’s long-term success. While the company closed a hefty $1.46 billion of total contract value from U.S. enterprises in the quarter, that’s actually a decline from the record $2.13 billion in the previous quarter. That suggests slowing sales, at least in the most recent quarter. The next earnings will show whether Palantir has solidified its role as a rising U.S. enterprise AI provider. Historically, many enterprises sign their biggest tech purchases in the final quarter of the year. Karp and his team certainly sound confident. They increased guidance for U.S. commercial revenue to be “in excess of $3.559 billion, representing a growth rate of at least 143%.”
Ballistic Ventures, the cybersecurity-focused venture capital firm co-founded by former Kleiner Perkins partner Ted Schlein (pictured above, second from the right), is seeking to raise $490 million across two new funds, TechCrunch has learned through regulatory filings. The San Francisco-based VC firm filed with the U.S. Securities and Exchange Commission this week toraise $360 millionfor its third flagship fund,matching the size of its previous fundraised in 2024, andanother $130 millionfor a separate vehicle called Ballistic Ventures Fund B. Neither fund had recorded any sales at the time of filing. The fundraising plans come as the growing use of artificial intelligence has raised concerns about more sophisticated cyberattacks. The rise of AI agents has also created new opportunities for cybersecurity startups and the venture capital firms backing them. Founded in late 2021, Ballistic Ventures’ founding team includes Schlein, former AT&T Cybersecurity president Barmak Meftah, and Roger Thornton, who co-founded security companies Fortify Software and AlienVault. The firm has backed companies, including Talon Cyber Security,acquired by Palo Alto Networks, and Pangea,bought by CrowdStrike, alongside startups such asArmadin,GetReal, Nudge Security, and Oligo Security. Ballistic’s ties to storied venture capital firm Kleiner Perkins, where Schlein spent more than two decades, help set it apart from rivals, a cybersecurity investor at another VC firm told TechCrunch. Its investment strategy, however, is similar to those of other cybersecurity-focused investors, including Forgepoint Capital and Ten Eleven Ventures, the person said. In 2022, Ballisticannounced its $300 million debut fundand raised another $360 million for its second flagship fund two years later. It also raised about $130 million for a separate vehicle called Ballistic Ventures Fund A, per PitchBook data. The new Fund B is targeting a similar amount. U.S. cybersecurity startups raised $11.5 billion in 2025, the highest since 2022, a recentreportby JPMorgan Chase said, citing PitchBook data. AI-enabled companies accounted for about 72% of U.S. cybersecurity venture deals through May this year, compared with 36% in 2019, the report said. Separately, Microsoft, in its annual Digital Defense Report released last month,warnedthat hackers are increasingly using AI for activities ranging from phishing and social engineering to malware development. The technology is allowing attackers to operate faster and at greater scale, even as many of their underlying techniques remain unchanged. Ballistic Ventures declined to comment on its fundraising plans.
Stuck in years-long lines for grid connections and new turbines, some AI data centers are making their own electricity with converted jet engines,the New York Times reportedTuesday. The engines, pulled from retired aircraft and rebuilt to burn natural gas, aren’t anyone’s idea of a permanent power plant. But they’re another sign of how far power supply has fallen behind the AI buildout. For companies stuck in those lines, the main draw of converted jet engines is how quickly the engines can be up and running. “It’s all about getting power as fast as possible,” Rystad Energy analyst Audun Martinsen told the Times. With jet-powered units, “they can have a data center running in 24 months,” he said. Hooking up 100 megawatts to the grid in Northern Virginia, by comparison, takes aboutseven years, according to the International Energy Agency. Most power capacity is expected to come on by 2031, an executive at Mitsubishi Power, one of the world’s top makers of power generation equipment,told Bloomberg last week. In some cases, these delays are already costing some data center operators business. Last month, Microsoftpulled outas the anchor customer for Nscale’s data center in Loughton, England, after the local utility said it couldn’t deliver enough power until the early to mid-2030s. Elon Musk’s xAI is among the companies already using the engines, having installed eight converted jet-engine turbines in Mississippi to help power its Colossus 2 data center in Memphis, the Times reported, citing state permits and satellite imagery. The units come from ProEnergy, which rebuilds a GE engine commonly found on Boeing jets. In 2025, OpenAIfollowed Musk’s leadwith gas turbines at its first Stargate site in Abilene, Texas. Crusoe, a Denver-based startup that builds AI data centers, is constructing that Abilene campus andordered 13of ProEnergy’s converted units in April. In September, Crusoewalked awayfrom a $1.25 billion deal to buy 29 natural gas turbines from Boom Supersonic, a startup developing a supersonic passenger jet that has also begun selling a version of that jet’s engine as a power plant for data centers. A Crusoe spokesperson told TechCrunch at the time that the company still plans to use turbines. Currently the Trump administrationis adding to the pressureby encouraging data centers to generate their own electricity. Converted jet engines come with trade-offs, though: They’re less efficient and pollute more than turbines designed for conventional gas plants, and it takes more of them to make the same amount of power, according to the Times. That may ultimately make converted jet engines a short-term play with some experts saying that efficiency and environmental concerns may grow in importance in a year or two.
Anthropic is working on an open source system to verify which AI model produced a given output, according to a blog post published Tuesday. The system,called FlashProve, is built to provide what researchers call “provable inference” — a cryptographic proof, essentially a mathematical guarantee, that a given output came from a specific model. It’s meant to let users confirm that the answer they received really came from the model they think they’re using. Similar tocryptographic email signaturesthat let recipients confirm a message really came from its claimed sender, FlashProve would be robust against impostors attempting to disguise one model as another, even when the impostor is the lab that built and runs the model. In particular, the researchers note that the system could be used to prevent an AI company from secretly swapping in a cheaper model to cut costs, or a more expensive model to inflate its performance results. FlashProve could also be used to verify claims by third parties that a specific task was done by an advanced model. “A developer might claim that their software library was scanned for vulnerabilities by a frontier cybersecurity model, or a company might claim that its business plan was given the go-ahead by an appropriate model,” the researchers write. “How can a user verify these claims?” To work, the system requires AI labs to publish a “fingerprint” of each model they release that can later be matched to specific outputs and verified cryptographically. Crucially, the fingerprints don’t reveal a model’s weights, the internal numbers that define how it behaves and which closed-model makers like Anthropic and OpenAI keep private. That means labs could publish the fingerprints without giving up their most valuable asset, enabling the system to work with commercial models, too. The system is also designed to be lightweight, so verification could be performed on a local device. As part of the announcement, researchers releaseda detailed technical specificationand working code for various elements of the system, which is publicly availableunder an open source license on GitHub. Research in the system is ongoing, and there are no plans to deploy it at Anthropic or any other lab. “This research post is meant to share intermediate progress, and not a completed research agenda,” researchers explain in the announcement post. “We are sharing this explicitly with the goal that others look at what we have done, take it, and improve it.”
The offering combines specialised agents and conversational assistants for system administration, observability and optimisation, with human oversight.
View
Coforge's 18-year relationship with Pega underpins the launch, adding another offering to its customer experience portfolio.
View
The company will use the capital to expand its AI team and develop its Large Logistics Spatio-Temporal Model, which it uses to improve delivery allocation and reduce idle time for riders.
View
The divide is visible in the difference between the resources available to urban and rural students.
View
View
View
View
View
View
View
