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AI NewsGoogle Cloud races to catch up in the AI deployment wars with Accenture deal

Google Cloud races to catch up in the AI deployment wars with Accenture deal

3:39 AM IST · September 9, 2026

Google Cloud races to catch up in the AI deployment wars with Accenture deal

Google Cloud and Accenture are working together on ajoint unitdedicated to sending engineers into enterprises to help them better adopt Google’s AI tools and services. The new unit, dubbed Accenture Gemini Enterprise Business Group, is Google’s latest foray into theincreasingly competitive worldof “forward-deployed engineers,” or FDEs. Rivals in the AI race, includingOpenAI,Anthropic,MicrosoftandAmazon, have all recently launched separate business units in a bet that implementing AI models can become its own trillion-dollar business. It’s the kind of bet AI companies and hyperscalers increasingly need to make. Hyperscalers are committing hundreds of billions of dollars a year to GPUs, data centers, and power capacity even as the revenue directly attributable to AI remains a fraction of that investment. Google Cloud generated $24.8 billion in the second quarter, a big chunk of which was driven by enterprise AI. But the commitments behind that growth are enormous. Google Cloud’s parent companyAlphabet reportedly accumulated$811 billion in purchase commitments and contractual obligations as of June 30. This return on investment is not yet materializing in the way companies and investors need it to, so everything hinges on whether or not AI companies can create enough demand for their services. But that demand is not guaranteed, as enterprises themselves are struggling to see a true return on investment on their AI spending. It’s conventionally held that enterprises have simply lacked the expertise to intelligently integrate AI tools and services into their workflows in a way that not only saves them money, but helps them make more of it in the long run. That’s where the FDEs come in as a steady, guiding hand that, ideally, possesses the perfect mental cocktail of business acumen and agentic AI prowess needed to change everything. As part of its deal with Accenture, Google will train up to 1,000 of the consultancy firm’s FDEs to work with enterprises and build custom AI applications on the Gemini Enterprise platform. According toAugust data from Ramp, Google accounts for roughly 6% of enterprise AI spending among U.S. businesses, compared to Anthropic’s 43.5% and OpenAI’s 39.7%. Google’s new unit with Accenture, whichThe Wall Street Journalfirst reported, is the latest of its aggressive expansions of its FDE model this year as it attempts to resolve enterprise deployment bottlenecks and catch up to rivals. Earlier this year, Google Cloud launched a$750 million partner ecosystemcommitment that embedded Google’s own FDEs across multiple consultancies, including Capgemini, Cognizant and Deloitte. The tech giant also struck amulti-year partnership with CVCCapital Partners to deploy FDEs directly into the investment firm’s portfolio companies. Google isn’t the only giant at risk of being outpaced by newer firms. Companies that are dedicated specifically to embedding engineers into businesses to build bespoke AI workflows — like Ode with Anthropic, or OpenAI’s The Deployment Co. — threaten big consultancy firms like Accenture as well. For the professional services giant, the Google tie-up adds to its own wave of FDE programs this year, which include a similarMicrosoft FDE practice in March, anFDE initiative with ServiceNowin May, and ajoint program with SAPin June.

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Your AI agents can now control your Google Home devices

Your AI agents can now control your Google Home devices

Smart home owners will soon be able to control their devices connected to Google Home via an AI agent. On Wednesday, Google rolled out early access to its Model Context Protocol (MCP) server for its Google Home ecosystem, which will allow any AI agent that supports MCP — like Claude, Hermes, OpenClaw, ChatGPT, and Google Antigravity — to securely work with their smart home devices and access their event history. This update will allow people to use natural language instructions to do things like review their camera summaries, monitor smart home activity, control their connected devices, and build their own custom smart home dashboards. To get the connection set up, users will need to create a Google Cloud project and configure it to use Home MCP. They will then provide the MCP configuration details to their agent of choice and ask it to set it up. The agent will then ask them to sign in and grant permissions. (A setup guide will also be offered in the Google Home Developer Center.) The system will support any device in the Google Home ecosystem, including Google Nest doorbells and thermostats and “Works with Google Home” (or Matter) devices, like light bulbs. Google already supports MCP in other areas of its business, including in itsGoogle Cloudanddata platforms, developer tools, andGoogle Workspace, for instance. Support for Google Home is targeted more directly at consumers who are experimenting with AI agents designed to handle everyday tasks. The company said access to the MCP will roll out starting today and continuing over the coming weeks to subscribers who pay forGoogle Home Premium Advancedin the U.S. This is the more expensive, $20-per-month subscription tier that provides features like longer event-based video history, descriptive notifications and detailed alerts, tools to search video history, daily summaries, and more. Google wouldn’t comment on if or when the MCP would roll out more broadly, such as to other subscription tiers or markets. Google is also soliciting feedback from early adopters during this period by way of itsSmart Home for Developers Community, it says. An earlier version of this post misstated the rollout’s start. It is today, Wednesday, not Tuesday.

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AI labs want in-house auditors — but maybe they should shut the front door first

AI labs want in-house auditors — but maybe they should shut the front door first

Last weekend, after one of his researchers resigned over fears that AI could lead to human extinction, Anthropic CEO Dario Amodei wrote about the need for outside organizations “to verify adherence to safety practices and commitments, report incidents, and help assess the alignment of not just completed AI models but training pipelines and processes.” Executives at OpenAI, Google, and SpaceXAI have alreadyrallied aroundAmodei’s plan, which has quickly become a central pillar of the emerging AI safety push. But there may be a simpler and more effective fix hiding in plain sight. Internet security experts say the labs need to focus on network security basics like logs and permissions, applying the same rigorous defenses they do for human users. It’s not as exciting as third-party auditing and alignment work — but it may end up being more effective. “To me, it seems like they’re outsourcing,” Katie Moussouris, the CEO of Luta Security, told TechCrunch of Amodei’s proposal. “Saying [a third-party audit] is the solution is a strange proposition from my perspective. It would be the same as if, instead of writing theTrustworthy Computing Memo, Microsoft said, let’s slow down development.” That memo, written by then-Microsoft CEO Bill Gates in 2002, called on his employees to ensure that their software would be reliable and safe following a series of widely publicized computer worms that took over then-nascent enterprise systems. The AI sector may be at a similar turning point, as the value and risk of the new technology becomes increasingly clear. While alignment remains an important concern, Sayash Kapoor, an AI researcher who will be a professor at UC Berkeley starting next year,arguesthat “marginal investments in control are more likely to be effective compared to those in alignment. We view these incidents as illustrating the lack of emphasis on AI control within companies, despite the availability of known techniques.” The incidents that have spurred these concerns revolve around frontier models being asked to complete training tasks, usually cybersecurity evaluations, and then accessing the open internet and penetrating closed third-party systems in an attempt to do so. They usually did so because ofpoorly configured“sandbox” environments that are supposed to contain these agents; ironically, one Anthropic break-out happened because third-party evaluators didn’t close the right doors. “We as a profession know how to block access to the internet,” Avery Pennarun, the CEO of Tailscale, a security company, said. “If you read through all these big long [reports] — ‘wow, that was a very impressive multi-stage attack, blah, blah.’ Look, you gave it access to download stuff. You should have not done that separately from the internet.” That’s one problem — but a bigger problem is that frontier labs were unaware of these activities. “What was really profound was that all of the discoveries of what they were doing happened either because a victim saw something, or in some of the other cases 
 it was network activity, and none of it was actually from monitoring the AIs directly,” Moussouris points out. In one case, where OpenAI agentstook overa defunct German WikiForum to cheat on evaluations, the agents were active for weeks before anyone at the company appeared to notice. Security experts that TechCrunch spoke to said that real-time monitoring is key to preventing future break-outs, and that every agentic session should be time-limited and expire. Shapor Naghibzadeh, a former Google security executive who now leads thestartup QueryStory, says the solution is to “put the agent in a box and instrument it heavily from the outside looking in and watch everything that crosses the boundary. Every tool call, every process, every network connection, no exceptions. 
The one hole you leave open for convenience is the one that gets used. The bypass went through exactly that kind of exception. [At Google,] I watched that movie many times with human attackers, and these models are at least as good at finding the propped-open door.” OpenAI has begun moving in that direction, announcing that it had begun monitoring all tool-using inference by its Astra model, at “significant compute cost.” Anthropic, too, says it ishardening its securityprocedures, including expanding observability of its models. Neither company responded to TechCrunch’s questions about how they track and control AI agents. Other problems are the use of shared infrastructure by agents, which allowed them to communicate during the Hugging Face attack. Simon Willison, a software developer who co-created the Django web framework, has written about something he calls the “lethal trifecta” — when agents have access to untrusted input, the internet, and private information all at the same time, it’s a recipe for disaster. “The trick is you can pick any two legs of the trifecta and an agent can have any two,” Pennarun said. “If you need all three, then you need to split it across at least two agents 
 and maybe they’re allowed to talk to each other through a controlled channel.” Experts TechCrunch spoke to understand that frontier lab security personnel have difficult jobs. Naghibzadeh points out that every nation-state actor on Earth is trying to steal their model weights and mount distillation attacks on their APIs, as well as the bread-and-butter security tasks of any large digital company. “Research infrastructure has a hard time rising to the top of that priority stack, although that must be changing now,” he said. “Making security incidents public really helps align everyone internally toward the goal of improving.” That’s one note that Moussouris emphasizes: Right now, there is no formal victim notification procedure when the labs discover their agents have penetrated third-party systems, and it is likely that there have been other incidents that have not been widely publicized. While she worries that laws that regulate models directly may have unintended consequences, mandatory notification is one idea she believes policymakers should pursue. And while it’s clear that security best practices weren’t being followed, experts say that the labs are doing work no one has done before — “they’re doing orders of magnitude more than your typical enterprise,” Zack Korman, the CEO of cybersecurity firm Embroidery, told TechCrunch. And while alignment may not be the place to start, it can’t be ignored. Cybersecurity experts are resigned to having to use AI agents to monitor other agents if they are to have any chance of tracking their behavior in real time, a scenario where the potential for deception raises its ugly head. “You’re trapped using AI to try and deal with this, even though AI is not necessarily safe right now,” Moussouris said. The job will only get harder. Everything agents are doing now, Moussouris says, “they are doing loudly” — they are posting on public forums, and their chain of thought and other reasoning traces are in English. “It’s still human readable,” she says, “so take advantage of that for as long as that lasts, because it won’t last forever.”Additional reporting by Aditya Mehta

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After accusations of selling ‘perv glasses,’ Meta prepares to sell a pair without a camera

After accusations of selling ‘perv glasses,’ Meta prepares to sell a pair without a camera

Meta’s camera-equipped smart glasses haveproven more successfulthan other entrants into the market, but they have also deeply disturbed certain consumers who see them as invasive emblems of a dystopian surveillance society run amok. Now, as the company weathers complaints that it’s selling “pervert glasses,” it has reportedly decided to sell a pair that doesn’t come with integrated spy equipment. The Informationreports thatMeta is developing a new smart glasses model, dubbed Luna, that, sans cameras, comes equipped with a convenient system to communicate with the company’s AI chatbot and Muse, its AI consumer agent. The glasses include six built-in microphones so users can communicate with the chatbot, as well as a button on the side of the glasses that, when pressed, activates the AI system. Luna may be unveiled as soon as Meta Connect, Meta’s annual hardware/developer event happening in Menlo Park next week, the outlet notes. TechCrunch reached out to Meta for more information. The smart glasses industryhas grownover the past few years, with Metapositionednear the front of the pack of providers. However, concerns over usability, cost, and privacy have dogged the business, with many consumersstill unsureof how, why, or if they should use the devices. Meta’s Reality Labs, which is responsible for developing its smart glasses line,is still losing a gargantuan amountof money, as its earnings report from April revealed.

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Former Infosys chief’s AI startup nabs another $53M

Former Infosys chief’s AI startup nabs another $53M

Hang Ten Systems, an AI startup founded by former Infosys CEO Vishal Sikka just four months ago, has expanded its seed funding by another $53 million. This new investment round was closed just five weeks after theinitial $32 million seed round was closed, the company tells TechCrunch. The fresh investment, led by Temasek’s early-stage investment platform Xora, takes Hang Ten’s total funding to $85 million. Mayfield, which led the startup’s earlier investment, also participated. Other investors include Aramco Ventures, Intel CEO Lip-Bu Tan, Micron CEO Sanjay Mehrotra, and Yahoo co-founder Jerry Yang, who is also on its board. Founded in May, Hang Ten is betting that AI can fundamentally reshape how large companies build and maintain software. The startup advises companies — typically with over $10 billion in annual revenue — on AI strategy and also helps build and modernize software. “The build part itself has basically become close to zero marginal cost, close to zero time,” Sikka (pictured above) told TechCrunch. He argued that AI is shifting the focus of software development from writing code to defining requirements and validating whether systems perform as intended. The thesis appears to be resonating with customers. Within 25 days of the first meeting with one customer, Hang Ten signed a multimillion-dollar contract to build a mission-critical software system, Sikka said, adding that he had never seen enterprise deals move that quickly. Hang Ten is now working with a mix of existing customers and late-stage prospects spanning 21 major enterprises, Sikka said, with some prospects in the proposal or contract-negotiation stage. The startup has secured multiple seven-figure contracts and is pursuing eight-figure deals. Moreover, its customers span the U.S., Europe, the Middle East, and Asia, and include Fresenius Kabi, Saudi Aramco, and Siemens Energy. The early customer wins prompted Xora to approach Hang Ten about investing, Sikka said, adding that Xora saw an opportunity to introduce the startup to other companies in Temasek’s portfolio. Sikka declined to disclose Hang Ten’s valuation but said the second round of seed funding was a bump up. With the fresh capital, Hang Ten plans to expand its engineering, consulting, and sales teams. The Palo Alto-headquartered startup currently has about 20 to 25 employees across the U.S., the Middle East, and Australia, and expects to hire in Europe and India, Sikka said. Hang Ten enters a crowded market in which AI model developers such as OpenAI and Anthropic are expanding their enterprise offerings, while traditional consulting firms are racing to embed generative AI into their own services. “Enterprises still need trusted partners who are independent of the underlying platform and who work with and solely in the interest of the enterprise,” Sikka said. “There will always be room for companies like us.” Hang Ten says its edge lies not in building another AI model, but in adapting AI to the complex software development processes of large enterprises. Co-founder and chief design officer Sanjay Rajagopalan told TechCrunch the startup’s engineers rely on an in-house framework, Hobie, that packages reusable AI “skills” for regulated industries and complex enterprise software projects. “We are actually delivering production software,” Rajagopalan said. “It’s not like we are doing some kind of PoC.” Hang Ten’s approach is already putting it in competition with traditional systems integrators. Many of the startup’s engagements, Sikka said, are replacing incumbent providers, though more than half of its current opportunities involve new projects that companies had previously put off, rather than work taken from existing vendors. Rajagopalan noted that Hang Ten can use teams of about two to four people for some projects that might previously have required about 30, although customers or independent third parties still handle final quality checks and certification. The startup promises customers a 10-fold improvement in cost, speed, or a combination of the two, he said. Even in four months of its debut, Hang Ten has received serious acquisition offers from “very big companies,” Sikka told TechCrunch. He, however, declined to identify the prospective buyers. The startup, he said, has turned them down for now as it focuses on expanding the business. Before founding Hang Ten, Sikka co-founded enterprise AI startupVianAIin 2019 after leaving Infosys in 2017. It raised$50 million in seed fundingand laterraised $140 millionin a 2021 round led by SoftBank Vision Fund 2. Sikka departed that startup in April, according to his LinkedIn profile. He told TechCrunch that VianAI is “going through a transaction,” declining to provide further details. As for how startups like Hang Ten could disrupt services providers like Infosys, Sikka noted, “We are fortunate that we don’t have the burden of legacy that we have to transform.” The startup takes its name from “hang ten,” the surfing maneuver in which a surfer rides with all 10 toes over the front of the board. For Sikka, it reflects the startup’s ambition to help enterprises ride the ongoing AI wave, what he called “probably the biggest wave of our lives.”

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