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Últimas Noticias de IA
Runlayer, Rippling drop lawsuits — but the brouhaha is still a cautionary tale for founders
On Wednesday night, Runlayer and Rippling dropped their respective lawsuits against each other. No settlement was made. No money changed hands. Not even lawyers’ fees, according to court documents seen by TechCrunch. Rippling celebrated by instantly releasingits MCP gateway, the product at the heart of the dueling lawsuits and the one that competes with Runlayer’s offering. This public fight is a cautionary tale to founders: In the age of AI, when building new software has become almost trivial, you never know who your next competitor will be. It might even be a prospective customer. To recap the short-lived legal brouhaha: Runlayer is an early-stage startup thatlaunched out of stealth in November 2025and has raised a total of $42 million from VCs like Khosla Ventures’ Keith Rabois and Felicis. It’s led by third-time founder Andrew Berman (previous companies include baby-monitor maker Nanit and an AI video conferencing tool, Vowel, that sold toZapier in 2024). After Rippling tested Runlayer’s MCP gateway for more than a year, with the two engineering teams working closely together, Rippling never signed on to become a customer, according to Runlayer’s lawsuit. Instead, Berman received a text from a Rippling employee that said his employer was building its own MCP gateway and planned to release it as a product. This employee described Rippling’s product as a clone of Runlayer’s. Runlayer sued, claiming that Rippling violated contractual agreements covering the tests of its products. An MCP gateway securely handles an enterprise’s AI agent requests for data from other software systems. So, for instance, when a hiring professional asks for details on the top five candidates for a job, including their emails, that data must be retrieved from the company’s recruitment system. The gateway handles the retrieval process, rather than granting agents direct access to the company’s software systems. It can then also layer on other features like employee role-based access control (managers getting different access than interns), observability (logs and usage trails), and so on. ThenRippling countersued, alleging that Runlayer was violating some of its patents. The move was seen by Runlayer as a way to induce it to drop its suit while ratcheting up legal expenses. Runlayer dropped its suit after spending the last three weeks in discovery. Rippling dropped its own suit and didn’t collect a settlement either. So, while the lawsuits didn’t lead to anythingbut a lot of public flaming, there is a deeper takeaway for founders. The AI landscape is changing so rapidly that the long-running technical shoot-outs that enterprises love to impose on startups need to be rethought. Between the time an AI startup enters into one and however many months later, an enterprise’s needs and desires may drastically change. In the meantime, in the span of weeks, Rippling, whose bread and butter has historically been payroll and benefits management, has now enteredthe AI gateway market with a tool that can route to different modelswhile dashboarding token spend by employee. The product is competing with the likes ofStripe,Ramp, and Databricks. Now Rippling is also in the AI security business with this MCP gateway that ties AI access to employee roles. It competes with the likes of Runlayer, Docker, and Amazon Bedrock. As for Runway, its pitch is a broader bundle of agent security services tied to the gateway, ranging from agent creation to spotting shadow AI agents running in an enterprise unbeknownst to IT.
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Google gives publishers a new way to fight AI-driven traffic losses
As AI continues tokill trafficto websites, Google on Thursday threw a bone to those publishers negatively impacted by the change. It’s now allowing readers to push a button on a publisher’s website to indicate it’s a “favorite source” they’d like to see highlighted more often across Google Search, Discover, and Google News. The tech giant said it’s making this new, interactive “Preferred Sources” button available to online publishers to embed on their own websites. The launch follows Google’s rollout of Preferred Sources in May to Google’s AI experiences, including AI Mode and AI Overviews. The option was previously available in Top Stories. The idea is to make it easier for readers to find links from the sites they know and trust when they’re searching for content or interacting with Google’s AI to learn about a topic or read the latest news. As ofMay’s launch,the company said that people across the web had already selected over 345,000 unique sources through this method. To add a site as a favorite publisher, you can visitGoogle’s source preferences page, then search for a publisher by name or website. Becoming a preferred source can drive more traffic to publishers’ websites, Google said. In earlier studies, it found that people are twice as likely to click through to a preferred source when available. By offering publishers these additional tools, Google is trying to assuage the damage that the rapid growth of AI-powered search features has had on traffic-dependent businesses. Alongside the new button, Google said that readers will soon be able to customize their Discover feed in Google’s app in their own words. To use this feature, readers will tap any three-dot menu in the feed and then tell Google what topics they’d like to see more or less of, using natural language commands. This helps Google refine the feed in real-time. The search giant is not the only company turning to AI to offer feed-tuning tools powered by AI. In recent months, a number of top social media appshave launched user-controlled algorithmsthat allow people to fine-tune the content that is recommended to them. In addition to personalizing the Discover feed, Google says Android users will be able to customize their audio daily briefings in the Google News app, as well.
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Meta AI’s new Mac app wants you to talk to your apps
Meta today announced a new Mac app for Meta AI with built-in system-wide dictation. The app can also look at the current screen and answer your questions based on the context using its Muse Spark model. The company said that the dictation feature works across all apps, just like other tools such as Wispr Flow, Superwhisper, and Monologue. Last month, Google also updated its Gemini app for Mac toenable a system-wide dictation feature. The Mac app release is part of a Meta AI update for business owners. The company said that merchants can connect their Instagram and Facebook accounts, Meta ad campaigns, and Google Workspace (Gmail, Docs, Sheets, and Slides) accounts to Meta AI, and get insights by asking queries to the AI assistant. Meta AI can provide users with information about campaign performance and audience engagement metrics to understand what posts have worked well. It added that users can also access intelligence on competitors based on publicly available data. With the new Meta AI integration with business tools, the AI assistant can create proposal decks, draft documents, and spreadsheets. Meta has increasingly focused on making its AI tools available to businesses forautomated customer support and inquiries across its apps,including WhatsApp and Instagram. During the company’s Q2 2026 earnings call, CEO Mark Zuckerberg said that there is a big opportunity tosell agents to businesses and automate work on their behalf.
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Inertia Enterprises finds a way to make its fusion fuel fast
Fusion startupInertia Enterprisessaid it has found a way to cut the time it takes to make its fuel pellets from several days to just minutes. By slashing fuel filling time, Inertia says it has knocked down one of theten barriersit must overcome to deliver the first phase of its commercial power plant ambitions. The startup gave TechCrunch an exclusive first look at the process. The advance was not guaranteed: Investors gave Inertia$450 millionon the premise that it could commercialize technology developed at the National Ignition Facility (NIF), which is anelaborate science experimentthat requires painstaking care and feeding to operate. At the NIF, making fuel pellets can take a week or more and cost a small fortune. That’s not a great recipe for a profitable operation. “But when you really double-click on it, you’re like, wait a minute, they’re only making a handful of them a year,” Jeff Lawson, co-founder and CEO of Inertia, told TechCrunch. “I put on my commercial hat and was like, wait a minute, I know the word for this: Prototypes.” At Inertia, the team set about turning those prototypes into something mass manufacturable. “That’s why we’re hiring people from the likes of Apple,” he said. “There’s a bunch of industrial engineers who are like, ‘Alright, I guess I have to go figure out how to make that a billion times over in a factory.’” NIF’s fuel pellet is far from an iPhone, though. The outside is a spherical diamond shell, and just inside, there’s a thin layer of frozen deuterium and tritium, the isotopes of hydrogen that can fuel a fusion reactor. Inside the crystalline layer lies a mix of gaseous deuterium and tritium. Each solid layer must be as close to perfectly spherical as possible. Those fuel pellets are then wrapped in gold casings known as hohlraums, which convert laser energy into X-rays that compress the fuel pellet inside. If everything goes to plan, that compression causes atoms to fuse and release energy. But even small imperfections in the spherical shape can disrupt the ignition process, preventing a fusion reaction from reaching its full potential. Inertia had to condense the process to the point where it makes commercial sense without straying too far from the physics discovered at NIF. “What happens if you try to do it faster?” Lawson said. The team had a head start: Annie Kritcher, co-founder and chief scientist at Inertia, designed the first fusion experiment at NIF that released more power than it consumed. After several rounds of development, the startup was able to grow the crystals in about 30 minutes, something that could take up to a week at NIF. Altogether, a single fuel pellet can be made in about two to three hours, and the process can be ramped to industrial scale. Inertia developed the process with help from the NIF at the Lawrence Livermore National Lab, with which the startup hasformed a public-private partnership. Inertia has an advantage that NIF does not, though. Because the startup is planning to use a laser that is four times more powerful than the one currently at the NIF, it can tolerate more imperfections in the fuel pellet. This also helped speed the manufacturing process. “We actually have a lot of margin,” Lawson said. “That’s our strategy, to oversize our driver, our laser, to give us lots of margin to go play with in every other part of the system.” Reducing fuel filling time has the knock-on effect of reducing the amount of tritium Inertia needs to hold at any given time. Tritium is radioactive, and handling it requires care. It’s also extremely expensive at the moment, about $30,000 per gram, and only about 25 kilograms are stockpiled globally,accordingto the journal Science. Inertia, like many fusion startups, plans to make its own tritium with help from the fusion reactions, but it still needs some inventory to get started. Plus, reducing manufacturing time helps keep inventory small. Inertia expects its full-scale commercial power plant will useten fuel pelletsevery second. “By reducing the latency of this step, you’ve made your facility smaller; you’ve made the whole thing faster, the whole thing more efficient,” Lawson said.
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Fleetx.ai Acquires Pando.ai to Build Unified Fleet & Freight Platform
The combined entity plans to prepare for a public listing over the next 18 to 24 months, potentially targeting an IPO, and is aiming for ₹300-400 crore in combined revenue on a profitable basis by the time of the listing.
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Google Photos Could Soon Add a Map View to Ask Photos Search Results, Feature Spotted in APK Teardown
Google Photos might soon give Ask Photos a location-based way to display search results. A new map option has reportedly been discovered in a test version of the app, allowing photos found through location searches to appear according to where they were captured. The feature could let users explore groups of photos on a map, zoom into specific areas and open individual sets of images. However, Google has not announced the feature, and its presence in a test build does not guarantee a public rollout.
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Binance now lets AI agents trade, but keeping them in check is largely up to users
Binance, the world’s largest crypto exchange with more than 300 million registered users, on Thursday launched a platform that lets AI agents analyze markets and execute trades on users’ behalf, bringing autonomous AI directly into the business of managing real money. CalledAgent OS, the platform lets developers connect AI applications and agents to Binance’s financial infrastructure. It brings the exchange’s existing tools and services such as Binance APIs, Binance Wallet Agentic Hub, Binance x402 transaction verification and payment facilitator API, and Binance Skill Hub, along with newly introduced support for itsModel Context Protocol (MCP). The platform also works with tools including OpenAI’s ChatGPT and Codex, Anthropic’s Claude Code, and Cursor, allowing users to authorize agents to access market data, view account information, and execute trades. However, as the AI race moves away from chatbots that answer questions to agents capable of taking action, Binance is putting much of the responsibility for keeping them in check on users, who ultimately have to decide what agents can access and trade and set limits on what they can do. “Instead of total freedom, we put the power in users’ hands to give them the granular access control of what they can do through the agent,” said Jeff Li, vice president of product at Binance, in an interview. “We put [the control] at the account level to protect the users’ funds.” Binance does that primarily through dedicated “sub-accounts”, which users can assign to agents and configure for specific activities, such as spot or futures trading. Withdrawals from those sub-accounts are blocked by default, Li told TechCrunch, creating a sandbox around an agent’s activity. Users can also choose whether an AI agent must seek approval for every order or can execute trades autonomously once its permissions are configured, a Binance representative said. Binance does not impose a separate cap on how much an AI agent can trade or lose, so the amount a user transfers into the sub-account effectively serves as the limit. Asked whether Binance can see what leads an agent to make a particular trade, Li said the reasoning happens outside its systems, either on the user’s computer or within their chosen AI application. “We really cannot see the reasoning of what the user’s action is,” he said. That means Binance can monitor an agent’s resulting trading activity, but has limited visibility into whether a decision was influenced by faulty information or manipulation. Li again pointed to the sub-account as the main line of defense when asked what would happen if an agent were manipulated through a prompt-injection attack or otherwise compromised. Binance also said its existing security, risk-control, and anti-money-laundering policies for subaccount APIs apply to Agent OS at launch. Trading is one of the first use cases Binance is targeting. Nonetheless, Li said agents could monitor markets, conduct research and risk analysis, react to signals, and autonomously place orders or execute strategies such as arbitrage. Agent OS is also designed to connect agents to payments and on-chain activity. Through Binance’s x402 integration, agents can send and settle payments, while its Agentic Wallet allows them to interact with tokens and decentralized-finance protocols. Unlike exchange trading, where Binance does not impose a separate cap on how much an agent can trade or lose within its subaccount, Agentic Wallet transactions carry Binance-set daily limits. Regular swaps are capped at $50,000 a day, DeFi transactions have a default $100,000 daily limit, and x402 payments are limited to $20 a day, according to the company. Li said Agent OS was Binance’s “first step” toward giving developers a platform to build AI-powered applications that can act across crypto and traditional markets. Binance is not alone in opening its infrastructure to AI agents. Rival crypto exchanges have been moving in the same direction, using MCP and other developer tools to give AI applications direct access to market data and trading systems. In March, Krakenlaunchedan open-source command-line tool with a built-in MCP server that allows AI agents to execute actions including spot and futures trades. Coinbase followed in June withCoinbase for Agents, which connects AI agents directly to users’ accounts and allows them to trade, make payments and execute other financial workflows within user-set limits. Similarly, OKXenabled agentic tradingon its platform by bringing an open-source MCP toolkit earlier this year.
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OpenAI Brings GPT-5.6 Terra and Luna to Amazon Bedrock in India
The move gives Indian businesses access to in-country inference, with Luna targeting high-volume workloads at lower per-interaction costs.
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Google Offers Indian Students One Year of Gemini AI Plus for Free
The offer also includes higher usage limits, study notebooks, Deep Research through Gemini Live and new AI-powered learning tools in Google Search.
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OpenAI’s Codex Helps Asana Cut 5-Year Migration to 2 Weeks, Slashes Costs 500x
The project cost about $12,000 in model and infrastructure usage, compared to roughly $6 million that Asana had previously estimated.
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AI is Killing the 8-Hour Workday. What Enterprises Will Measure Instead
AI is also challenging the assumption that spending more time on a task necessarily means creating more value.
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KissanAI Is Obsessed With Getting Its Vision Model for Farmers Right
KissanAI wants to turn crop images into actionable intelligence that agricultural businesses can plug into their existing platforms.
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