Latest AI News
Google turns Chrome into an AI co-worker for the workplace
As part of its slate ofGoogle Cloud Nextannouncements on Wednesday, the company shared plans to bring “auto browse” agentic capabilities to Chrome users in the enterprise, along with enhanced security measures. With auto browse, Chrome users can take advantage of Gemini to understand the live context in their open browser tabs, and then use the AI to handle various tasks like booking travel, inputting data, scheduling meetings, and others related to web-based work. Google suggests the tool could be used for things like inputting information in the company’s preferred CRM system based on content in a Google Doc, comparing vendor pricing across tabs, summarizing a candidate’s portfolio before an interview, pulling key data from a competitor’s product page, and more. The company notes that its workflows will still require a “human in the loop,” meaning that the user will have to manually review and confirm the AI’s input before any final action takes place. However, the idea is to help speed up these types of more tedious tasks to free up people to focus on what Google refers to as more “strategic work.” This is the larger promise from AI advocates: that you’ll get your time back by using this new technology. But in practice, studies have shown thatAI isn’t reducing work — it’s intensifying it.It remains to be seen how this will play out at the enterprise level as AI becomes a standard part of the workflow. Presumably, that could mean managers will expect that people can get more tasks done in less time. Google says the new feature will initially be available to Workspace users in the U.S., as a part of Google’s push to infuse its AI into one of its most-used apps in the workplace, the web browsernearly everyoneuses. It can be enabled via apolicy, and Google states that an organization’s prompts won’t be used to train its AI models. (A disclosure that is increasingly necessary these days, given thatMeta is even using its own employees’ keystrokesto train its AI.) Like theconsumer-facing version of the feature,Workspace users will be able to save their most common workflows for later use. These “Skills,” as they’re called, can be pulled up by either typing a forward slash (” / “) or by clicking the plus sign to access the needed Skill. In addition to the infusion of AI into Chrome, Google is touting its ability to detect unsanctioned AI tools in the workplace viaChrome Enterprise Premium. Now, it’s expanding those capabilities to help IT teams look for compromised browser extensions or other AI services — specifically “anomalous agent activity.” Google is correct to position this as a security feature, but it has another advantage, too. The tech giant is essentially leveraging corporate IT to shut down any other AI agents that could be taking root in the enterprise world organically. Years ago, this was how many web services established themselves in the workplace, amid an employee-driven “Enterprise 2.0” rush to adopt new technology like cloud storage, collaborative docs, or file sharing. This new feature, which Google somewhat ominously dubs “Shadow IT risk detection,” will give IT teams visibility into the usage of both sanctioned and unsanctioned GenAI and SaaS sites across their organization. IT teams will also receive a “Gemini Summary” of the Chrome Enterprise release notes and other AI-powered suggestions. This will surface critical changes, new policies, and upcoming deprecations, along with recommendations about things like configuring new settings or reviewing managed browsers. The company also announced an expanded partnership with Okta to secure the agentic workplace with added features to reduce session hijacking and other protections. It’s also upgrading its security controls for extensions and introducing Microsoft Information Protection (MIP) integration to help organizations enforce consistent security policies.
View
Google Cloud launches two new AI chips to compete with Nvidia
Google Cloud on Wednesdayannouncedthat its eighth generation of custom-built AI chips, or tensor processing units (TPUs), will be split in two. One chip, named the TPU 8t, will be geared for model training and another, the TPU 8i, is aimed at inference. Inference is the ongoing usage of models, aka what happens after users submit prompts. As you might expect, thecompany toutssome impressive performance specs for these new TPUs compared to the previous generations: up to 3x faster AI model training, 80% better performance per dollar, and the ability to get 1 million+ TPUs to work together in a single cluster. The upshot should be a lot more compute for a lot less energy — and cost to customers — than previous versions. It calls these chips TPUs, not GPUs, because its custom low-power chips were originally named Tensor. But Google’s chips are not a full frontal assault on Nvidia’s future, at least not yet. Like the other giant cloud providers, including Microsoftand Amazon, Google is using these chips to supplement the Nvidia-based systems it offers in its infrastructure. It is not flat-out replacing Nvidia. In fact, Google promises its cloud will have Nvidia’s latest chip, Vera Rubin, available later this year. One day the hyperscalers building their own AI chips (which includes Amazon, Microsoft, and Google) may grow to need Nvidia less, as enterprises move their AI needs to their clouds and port their apps to these chips. Still, as things stand today, it’s not profitable to bet against Nvidia. As notable chip market analystPatrick Moorhead jokingly posted on X, he had predicted that Google’s TPU could be bad news for Nvidia (and Intel) back in 2016 when the search giant launched its first one. Nvidia is now a nearly $5 trillion market cap company, meaning that prediction didn’t exactly hold up to the test of time. If all goes according to Nvidia’s plan, Google’s growth as an AI cloud provider would result in more business for the chip maker not less, even if many a workload runs on Google’s chips. In fact, Google also says it has agreed to work with Nvidia to engineer computer networking that allows Nvidia-based systems to perform even more efficiently in its cloud. In particular, the two tech giants are working to beef up the software-based networking tech called Falcon,which Google created and open sourced in 2023under the godfather of all open source data center hardware organizations, theOpen Compute Project.
View
How SpaceX preempted a $2B fundraise with a $60B buyout offer
Until a few hours before SpaceX announced its deal, giving it the option to acquire Cursor — the maker of AI-powered coding software — for$60 billion, Cursor was on track to close a $2 billion funding round later this week, according to a person familiar with the matter. The round would have valued the company at $50 billion. SpaceX said it would either buy the company at some point later this year or pay $10 billion to Cursor to collaborate on AI development. Cursor was apparently running a parallel process, negotiating a potential acquisition by SpaceX while simultaneously finalizing a private funding round with investors that include Andreessen Horowitz, Thrive, Nvidia, and Battery Ventures, details of which were firstreported by TechCrunchlast week. It is not uncommon for startups to engage in acquisition discussions while simultaneously raising new capital. While many private companies prefer to remain independent, Cursor’s $2 billion raise would have fallen short of the capital needed to reach cash-flow breakeven, likely forcing the company to raise substantial funding later, the person said. SpaceX, which recently merged with xAI, has been aiming to beef up its AI capabilities to better compete with leaders like Anthropic and OpenAI. Acquiring Cursor gives Elon Musk’s company a better chance of challenging rivals in AI coding, currently the most lucrative application of the technology. However, SpaceX is delaying the potential acquisition of Cursor until after its IPO this summer. This is largely because the company wants to avoid updating its confidential financial filings before the listing, and it will be easier to finance the $60 billion purchase using its new, publicly traded stock. The deal appears to benefit both sides for several reasons. Despite fast revenue growth, Cursor is facing fierce competition from Anthropic’s Claude Code and OpenAI’s Codex. Given that threat, the startup could face challenges in continuing to raise private capital to finance its massive computing needs. Even if SpaceX doesn’t go through with the acquisition, Cursor is receiving a $10 billion capital injection paid out over time from Elon Musk’s company. Additionally, if SpaceX goes through with the acquisition, the space giant will likely keep the entire Cursor team intact. Unlike Google’s purchase of Windsurf, which was structured as an acqui-hire of key individuals, SpaceX currently lacks a meaningful AI workforce and is widely seen as not having a significant AI business. Meanwhile, SpaceX has access to vast computing capacity at its data centers in Mississippi and Tennessee, which it can offer Cursor, potentially in lieu of part of the $10 billion “collaboration” payment promised the coding startup. The company would also like public investors to value it as more than just a space and satellite business. By promising to potentially acquire Cursor, SpaceX positions itself as an AI company, giving it a chance to garner the much higher valuation multiple that Wall Street currently assigns to AI companies.
View
AI is spitting out more potential drugs than ever. This startup wants to figure out which ones matter.
AI’s biggest impact in science is Google DeepMind’s use of a deep learning modelto predictthe complex structures of proteins — the molecules that drive virtually every process in living cells. But as AI models continue to spit out more candidates for potential treatments, there’s an emerging bottleneck: actually characterizing all those candidates in practice, for testing and mass production. That’s the goal of10x Science, a startup founded in December 2025 that announced a $4.8 million seed round today, led by Initialized Capital and with backing from Y Combinator, Civilization Ventures, and Founder Factor. Its three founders are David Roberts and Andrew Reiter, experienced biochemists, and Vishnu Tejas, a serial founder with expertise in computer science and AI models. “When biopharma tries to create a drug candidate, they have all of these really nice prediction tools,” Roberts told TechCrunch. “You can add as many candidates as you want to the top of the funnel, but they all have to pass through this characterization process. Everything needs to be measured.” Understanding the structure of proteins is key for researchers developing biologic drugs, which are produced in living cells and use sophisticated design to specifically target diseases and conditions. For example, they can be designed to target specific cells, like Keytruda, a popular drug sold by Merck that helps the immune system identify and attack cancers. 10x’s three founders worked together in the Stanford lab of Nobel laureate Dr. Carolyn Bertozzi, where they studied the interactions between cancer cells and the immune system, and were frustrated by their inability to understand precisely what was happening on a molecular level. The most accurate way to assess molecules is through a complex technique called mass spectrometry, a way of determining their atomic structure by measuring them in an electric field. The relatively new technique generates complex data that requires significant expertise to interpret, and analyzing it takes up a lot of time. 10x’s platform combines deterministic algorithms rooted in chemistry and biology with AI agents that can interpret that data. The team had to do significant work to train the models on spectrometry data and make its analyses traceable, a key requirement for a tool that will be used to help companies achieve regulatory compliance. Matthew Crawford is a scientist at Rilas Technologies, a firm that runs chemical analyses for other companies — saving clients like biotech startups from having to invest several million dollars in their own spectrometry equipment and the experts to operate it. Crawford has been using the 10x Science platform for several weeks and says it is speeding up his work. Crawford said the model surprised him with its ability to explain its conclusions, find the right data for analyses on its own, and adapt to evaluating different kinds of molecules. While some AI tools he has experimented with in the past over-promised or suffered accuracy issues, he says this one makes reasonable assumptions, something he attributes to the deep domain expertise of its creators. “I ran a particular protein through it, and it just kind of figured out, from what I named the file, what the protein probably was,” Crawford said. “It then searched databases online for the sequence for that protein, so I didn’t have to program in the sequence.” 10x executives say they’re also working with multiple major pharmaceutical companies, as well as academic researchers. The plan is to use this seed funding to hire more engineers and continue to refine the model and offer it to new customers. If they are able to gain traction characterizing proteins, Roberts hopes the company will expand to offer a new kind of understanding of biology, combining protein structure with other data about cells. “The deeper thing behind what we’re building is actually a new way to define molecular intelligence,” Roberts said. For its investors, 10x offers a useful way into the biotech space that isn’t dependent on a specific drug succeeding and winning regulatory approval. If the company works out the way its founders hope, it will become an important tool for drug development, whether or not the eventual products succeed in the marketplace. “This is a SaaS platform that pharma has to pay for, every single month, to go through all of these potential candidates,” Zoe Perret, a partner at Initialized, said. She’s counting on the deep experience of the founders to protect the company from competitors; there simply aren’t that many people who understand these methods and the data they produce. What the platform could do, Crawford says, is help unlock the techniques for researchers who could benefit from these methods but lack the time or resources to deploy them. “Groups here are trying to make a new drug,” he told TechCrunch. “They just want to get a quick, simple answer out of mass spec, and then it opens up a whole can of worms. This software is going to help keep that can of worms closed and just get them the answer they actually need to then do the next thing in their research.”
View
OpenAI teams up with Infosys to bring AI tools to more businesses
OpenAI has partnered with Infosys to integrate its artificial intelligence tools, including coding assistant Codex, into the Indian IT giant’s Topaz AI platform. Infosys said the integration will be used to help its clients modernize software development, automate workflows and deploy AI systems at scale, initially focusing software engineering, legacy modernization, and DevOps. India’s IT services firms face mounting pressure from a mix of slowing client spending and rapid advances in generative AI. Shares of Infosys have fallen over 22% this year amid abroader sell-offtriggered by weak forecasts, investor concerns that AI tools could automate parts of traditional outsourcing work, and macroeconomic turmoil due to the U.S.-Iran war. The move also reflects a broader trend of AI firms teaming up with global IT services providers to scale adoption in large enterprises. OpenAI has previouslypartnered with HCLTech, and Infosys has strucka similar deal with Anthropic. OpenAI gains a distribution channel into large enterprises through Infosys’ global client base and delivery capabilities across more than 60 countries. The companies said the deal is aimed at helping enterprises move from experimentation to large-scale deployment. Infosys has been ramping up its AI business. The company said earlier this year that AI-related services generated ₹25 billion (about $267 million) in revenue in the December quarter, or roughly 5.5% of its total. The deal is part of a broader push by OpenAI to expand its enterprise footprint through initiatives such asCodex Labs, announced on Tuesday, which involves engineers working with clients to help deploy its tools. Initial partners include Accenture, Capgemini, CGI, Cognizant, Infosys, PwC and Tata Consultancy Services, as OpenAI aims to build a distribution network to scale adoption of Codex, which now hasmore than 4 million weekly active users. The companies did not disclose financial details of the deal.
View
Tech Mahindra Q4 Profit Grows 16% as AI-Led Deals Lift Margins
Tech Mahindra scored deals worth $1.07 billion in Q4 FY26, up 34.5% YoY, driven by large transformation contracts.
View
Investors Sour on Indian IT as AI Demands Structural Reset, Not Just New Deals
Indian IT stocks are under pressure from investors. The Nifty IT index slid nearly 4%, triggered by HCLTech’s earnings rout.
View
Deepseek ‘In Talks’ To Raise Funds at $20 billion Valuation
Fresh capital would let DeepSeek invest more in infrastructure and pay competitive salaries to retain top engineering talent.’
View
OpenAI Unveils ChatGPT Images 2.0 With Improved Image Generation, Reasoning Capabilities
OpenAI on Tuesday launched its next-generation image generation model. Dubbed ChatGPT Images 2.0, it is claimed to deliver more precise, usable, and context-aware images, based on prompts entered by the user. The new model introduces improvements in instruction following, multilingual rendering, and composition. The San Francisco-based artificial intelligence (AI) giant says it also adds reasoning capabilities for more complex tasks. ChatGPT Images 2.0 is being rolled out across ChatGPT, Codex, and the API.
View
Exclusive: Google deepens Thinking Machines Lab ties with new multi-billion-dollar deal
Former OpenAI executive Mira Murati’s startup, Thinking Machines Lab, has signed a new multi-billion-dollar agreement to expand its use of Google Cloud’s AI infrastructure, including systems powered by Nvidia’s latest GPUs, TechCrunch has exclusively learned. The deal is valued in the single-digit billions, according to a source familiar with the matter, and includes access to Google’s latest AI systems built atop Nvidia’s new GB300 chips, alongside infrastructure services to support model training and deployment. Google has been actively striking a number of cloud deals with AI developers as it aims to wrap together its cloud offerings with other services like storage, a Kubernetes engine, and Spanner, its database product. Earlier this month,Anthropic signed an agreementwith Google and Broadcom for multiple gigawatts of tensor processing unit (TPUs) capacity (these are Google’s custom-designed AI chips for machine learning workloads). But the competition is fierce. Just this week, Anthropic also signed a new agreement with Amazon to secure up to 5 gigawatts of capacity for training and deploying Claude. Earlier this year, Thinking Machines partnered with Nvidia in a deal that included an investment from the chipmaker. But this is the first time the lab has struck a deal with a cloud services provider. The deal is not exclusive, so Thinking Machines may use multiple cloud providers over time, but it’s still a sign that Google is looking to lock in fast-growing frontier labs early. Murati left her job as OpenAI’s chief technologist and founded Thinking Machines in February 2025. The company, which soon afterwards raised a $2 billion seed round at a$12 billion valuation, has remained highly secretive, but launched its first product in October. DubbedTinker, it’s a tool that automates the creation of custom frontier AI models. Wednesday’s deal provided some insight into what Thinking Machines is developing. In a press release, Google noted that it can support the startup’s reinforcement learning workloads, which Tinker’s architecture relies on. Reinforcement learning is a training approach that has underpinned recent breakthroughs at labs, including DeepMind and OpenAI, and the scale of the Google Cloud deal reflects how computationally expensive that work can get. Thinking Machines is among the first Google Cloud customers to access its GB300-powered systems, which offer a 2X improvement in training and serving speed compared to prior-generation GPUs, per Google. “Google Cloud got us running at record speed with the reliability we demand,” Myle Ott, a founding researcher at Thinking Machines, said in a statement.
View
Google Maps is about to get a big dose of AI
Google has unveiled new generative AI features for its mapping and geospatial apps that are designed with enterprise users in mind. The new features, announced at Cloud Next in Las Vegas this week, add generative AI capabilities to Google’s mapping platform, giving it enhanced visual and data analytics powers. One of the new features, called Maps Imagery Grounding, allows enterprise users to use generative AI to create realistic scenes in Google Street View to visualize how a particular project—be it a movie set or a planned construction site—might look. Users merely type a prompt into Gemini Enterprise Agent Platform, which then conjures the scene inside Street View, as long as the proper settings have been enabled within Google Maps Imagery. “In seconds, you can storyboard your creative vision with an accurate image—and you can even use Veo to animate the scene,” the company said in its press release. The company is also expanding the ways in which users can analyze data from satellite imagery in Google Earth. A new feature called Aerial and Satellite Insights allows users to analyze imagery that is stored in Google Cloud’s BigQuery—the company’s cloud-based data warehouse and analytics platform. The company claims that this feature shrinks “weeks of work” into just minutes of labor. Finally, the company is also launching two new Earth AI Imagery models, AI systems designed to assist with geospatial analysis. Google says that the models have been trained to identify “specific objects in imagery–like bridges, roads, and power lines.” Previously, companies had to build and train their own AI systems to do this, a process Google says could take months. The new models mean “businesses no longer need to spend months training and building AI from scratch when developing their own products.” The announcements build on Google’s broader push into enterprise geospatial AI. The company’s Earth AI platform is already being used by partners includingAirbusandBoston Children’s Hospitalfor applications ranging from environmental monitoring to disaster response. “These AI updates unlock entirely new possibilities for businesses, data analysts, and urban planners,” the company said in its release.
View
The most interesting startups showcased at Google Cloud Next 2026
Google Cloud Next is taking place this week in Las Vegas, and one clear message has emerged: Google wants AI startups on its cloud. To that end, it made several startup-related announcements. The most significant is that the tech giant has earmarked a new$750 million budgetto help its Cloud partners sell more AI agents to enterprises. This funding is available to partners ranging from startups to the big consulting firms. It can be used for costs like Gemini proof-of-concept projects, Google forward-deployed engineers, cloud credits, and deployment rebates. Google alsohighlighteda long list of startups that are using Google Cloud, either newly signed or expanding their footprint. Among them are a few standout names: Lovableis expanding its use of Google Cloud by launching a new coding agent through Google’s enterprise app marketplace. Lovable is the fast-growing vibe coding startup and was on a$400 million ARR track as of February, it said. Notion, Silicon Valley’s favorite AI-infused document productivity app, most recentlyvalued at about $11 billion, is using Gemini models to power its text and image generation features. Gamma, an AI-powered PowerPoint killerrecently valued at a $2.1 billion valuation, is using Google’s state-of-the-art image model Nano Banana 2 and other Google Cloud features. Inferact,thecommercial inference startupfrom the creators of the popular open-source project vLLM, is accessing Nvidia’s GPUs through Google Cloud, in addition to using the tech giant’s AI stack. ComfyUI, the popular open-source tool forcreating AI-generated images and multimedia, also offers access to Nano Banana 2 and is using other Cloud features. Other startups that received the Google Cloud shout-out this year include: ChorusView, which makes AI-powered smart tags that track the condition and movement of goods in real time. Emergent AI, a vibe coding platform. ExaCare AI, which makes AI software for post-acute medical care facilities. Insilica, which creates AI-generated regulatory-compliant chemical safety reports. Optii, which makes AI-enhanced hotel operations software. Parallel AI, which builds web search and research APIs built for AI agents. Proximal Health, which makes AI-powered software that automates the insurance claims adjudication process. Reducto, which does AI-powered document parsing. Stord, which handles e-commerce fulfillment and parcel operations. Stylitics, which makes AI image generation software for retailers for tasks like outfit styling and product bundles. Temporal, a developer cloud environment built to prevent failures. Vapi, which makes dev tools for building conversational voice agents. Vurvey Labs, which conducts synthetic market research via AI agents. Wand, an in-game assistant for single-player PC games. Watershed, which makes software that helps enterprises report on and manage sustainability programs. ZenBusiness, an all-in-one back-office tool for small businesses that includes an AI chat assistant.
View