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AI NewsFormer Infosys chief has a new startup that wants to challenge the IT services world

Former Infosys chief has a new startup that wants to challenge the IT services world

8:10 AM IST · June 25, 2026

Former Infosys chief has a new startup that wants to challenge the IT services world

For decades, IT services firms made billions of dollars by allowing companies to outsource tech tasks like customizing, integrating, and maintaining enterprise software. Vishal Sikka, former CEO of Infosys, one of the largest such firms in India, is now betting that AI can do much of that work instead. His new startup,Hang Ten Systems, has raised a $32 million seed round led by Mayfield,it said Wednesday, with a strategic investment from Aramco Ventures and participation from angel investors. The startup, whose board includes Yahoo co-founder Jerry Yang, said it helps enterprises continuously build, modify, and operate software using AI-driven development and automation. Hang Ten enters a market where IT services firms, including Infosys, are racing to adapt to AI through partnerships with companies likeAnthropicandOpenAI. The startup’s launch comes amid a growing debate over whether AI will expand the industry’s addressable market or fundamentally alter how enterprise software is built, maintained, and delivered. Clearly, some enterprises are eager to try the AI-services idea, especially from someone as experienced as Sikka, who spent 12 years building enterprise software at SAP, and later as a board member for Oracle. Mayfield Managing Partner Navin Chaddha told TechCrunch that the company “just got started a month back” and already has customers. The startup said it is working with customers including Siemens Gamesa Renewable Energy and Fresenius on AI-native project delivery. In a separateblog postannouncing the venture, Sikka, 59, said Hang Ten was already helping large enterprises “hang ten on the biggest wave of our lifetimes.” Headquartered in the Bay Area, Hang Ten told TechCrunch that it is hiring across delivery, engineering, sales, and leadership and plans to expand across multiple locations globally to meet enterprise demand. The early crew at the startup includes executives who have worked with Sikka for years across SAP, Infosys and his previous enterprise AI startup,VianAI, according to their LinkedIn profiles. Among them are co-founders Navin Budhiraja, the startup’s CTO, Sanjay Rajagopalan, its chief design officer, and Tao Liu, its senior vice president of forward deployed engineering. After stepping down as Infosys’ chief executive in 2017, Sikka founded VianAI, which emerged from stealth in 2019 with$50 million in seed fundingandlater raised $140 millionin a 2021 round led by SoftBank Vision Fund 2. Chaddha told TechCrunch Hang Ten is distinct from VianAI, describing Sikka’s earlier venture as focused on a different market. VianAI focused on enterprise AI applications and analytics tools designed to help businesses use artificial intelligence in decision-making. Hang Ten, by contrast, describes itself as an enterprise AI services company built around agentic code generation, reusable AI skills, and domain expertise. Mayfield backed Hang Ten because of Sikka’s career experience, as well as its belief that the startup’s AI-native model can scale differently from traditional services firms. “Traditional services scale linearly with headcount,” Mayfield said. “Hang Ten is built so its leverage grows with every project.” Hang Ten emerges as investors debate how AI will affect the economics of the IT services industry. Analysts at Jefferiesarguedearlier this year that IT services may be among the first sectors to face meaningful AI disruption. Infosys chairman Nandan Nilekani, however, this weeksaidAI could expand the industry’s addressable market. Infosys itself has sought to position AI as an opportunity rather than a threat, telling investors this month that “AI-first services” couldrepresent a $300 billion-$400 billion marketby 2030. The debate comes as investors reassess the outlook for traditional IT services firms, with Infosys shares down over 35% this year.

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The White House is asking OpenAI to slow roll the release of its new model over safety concerns

The White House is asking OpenAI to slow roll the release of its new model over safety concerns

OpenAI’s release of its newest model, GPT 5.6, reportedly won’t be like its previous releases. Instead of distributing it to the public, the company plans to share it only with a select group of close partners because the Trump administration told it to,reports The Information. At a meeting this week, CEO Sam Altman reportedly told staff that the government would be “approving access customer by customer” during a preview period. Altman reportedly added that if the limited release goes well, OpenAI hopes to follow with a general, broader release a “couple of weeks later.” In other words, the Trump administration appears to be pressuring OpenAI to do what Anthropic is already voluntarily doing: keeping its most powerful AI models under wraps. According to The Information, OpenAI’s new model is not only being reviewed by the administration, but its staffers also “worked closely” with the government on the upcoming release. The agencies that reportedly asked for a limited release were the Office of the National Cyber Director and the Office of Science and Technology Policy. The Trump administration — which originally positioned itself as taking a “hands off” approach to AI — has in recent months pushed for federal oversight of new models. Earlier this month, Trumpsigned an executive orderdirecting certain AI companies to voluntarily submit new models to the government for testing and evaluation before releasing them publicly. Earlier this year, Anthropic sparked no small amount of controversy when it announced that its new frontier cyber model, Claude Mythos, wouldonly be releasedto a small coterie of partners through a program called Project Glasswing. Anthropic argued that its model was simply too powerful and could, in the wrong hands, cause more harm than good. Observers have since debated whether Anthropic’s rhetoric is a mere marketing gimmick or a legitimate attempt to keep a powerful model from being misused. The answer may be somewhere in between. Cybercriminals have used automated tools fora very long time, but in the age of generative AI, they now have more digital ammunition than ever before. LLMs have proven adept atwriting malware, and some can evenexecute entire ransomware attacksautonomously. The specific concern with frontier cyber tools like Mythos is that they are ostensibly capable of both identifying and exploiting software vulnerabilities at speeds that no human analyst could match. Since many software systems contain hidden bugs that act as entry points into enterprise networks, this obviously poses an obvious and significant problem for any organization running complex software infrastructure. That said, since these models remain closed to the public, it’s difficult to tell just how much of a threat they really are.

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Patronus AI lands $50M to build ‘digital worlds’ that stress-test AI agents

Patronus AI lands $50M to build ‘digital worlds’ that stress-test AI agents

AI agents are becoming more sophisticated. They are evolving from answering questions to autonomously executing multi-step complex tasks. But before these agents can be trusted to book trips or conduct financial analysis on behalf of users, model providers and the startups building such agents want to ensure that they perform reliably across a vast range of scenarios. AI labs often use benchmarks to show off their model’s prowess, but a high score, even on an agent-oriented benchmark, doesn’t actually prove that an AI can accomplish various complex, real-world jobs correctly. Patronus AI, a startup founded in 2023 by former Meta AI researchers Anand Kannappan and Rebecca Qian, is helping model makers and companies fine-tune models to do just that by building simulated digital environments in which to evaluate the agents’ performance. The San Francisco-based startup must be solving an important problem. Virtually every frontier AI lab and many emerging startups are now customers, according to Glenn Solomon, a managing director at Notable Capital, who describes demand for the company’s simulated environments as nearly insatiable. Patronus’ revenue has grown 15-fold over the past year, fueling significant investor interest. On Thursday, the company announced a $50 million Series B round led by Greenfield Partners, with participation from Notable Capital, Lightspeed, Datadog, and Samsung. The round brings the company’s total funding to $70 million. Patronus uses what it calls “digital world models” to create replicas of websites and internal systems. In these environments, agents are stress-tested after training using reinforcement learning, which iteratively rewards successful task completion and penalizes errors. AI labs see great value in these digital simulations because they give agents a chance to try different, sometimes unpredictable, scenarios. The company compares its approach to how Waymo trained autonomous cars by first building synthetic worlds to test vehicles against rare hazards, such as severe weather or a child running after a ball. The difference with AI agents is that they tend to take shortcuts, which means they fail to complete the task correctly. “Patronus is really good at spotting the hacks and making sure they are holding the models accountable,” Solomon said. Patronus is currently providing its simulated digital worlds for software engineering and finance, but these are just the start, according to Kannappan. “Today we’re very focused on the problems that are verifiable, so the problems that you can immediately check and verify, but there are a ton more areas that are very non-verifiable or very hard to verify,” he said. Just because these processes are verifiable doesn’t mean they are simple. “We want to be able to actually create the environment in which you can operate an agent that can run for 10 hours or 10 days or 10 weeks,” Kannappan said. As for rivals, Patronus believes it is primarily competing against the internal teams AI labs have already built to evaluate agent behavior. While human-data firms like Mercor and Surge help model makers with reinforcement learning, Patronus operates differently by evaluating how agents behave without any human involvement.

7 hours ago

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Netris raises $15M Series A from a16z to help AI neoclouds go live faster

Netris raises $15M Series A from a16z to help AI neoclouds go live faster

The AI boom has encouragedeveryone and their uncleto launch a data center business. But spinning up a data center isn’t easy. Even if you solve theproblem of securingthe GPUs, network switches, and storage, you still have to get everything configured, running, and be able to cater to customers’ various needs. Getting a data center ready to provide cloud-computing services specifically for AI inference and training services can take months of work. And the longer you take to get to market, the higher the cost of having all those precious GPUs sitting idle. Network automation startupNetrisclaims it can make that problem disappear for neoclouds. The company provides software that runs on network switches, and it also offers a platform that connects to switches to help neocloud operators reduce the time it takes to go live by automating setup, configuration, and operations. The platform also provides network abstraction, so hardware configurations can be changed as required, and it isolates servers and resources at the hardware layer so neoclouds can serve multiple customers (multi-tenancy). If that sounds like a solution to an obvious problem, you’re not wrong. Until recently, data centers were largely the domain of large infrastructure operators like Equinix, NTT, Digital Realty, Oracle, Microsoft, AWS, or Google. Those companies pretty much solved network setup, configuration, and multi-tenancy for themselves by hiring ranks of engineers or building the automation themselves. Small neocloud businesses rarely have such resources at their disposal. “As a GPU cluster operator, you need to make configuration changes to every link, every day. At traditional data centers, they were using something called SDN [software-defined networking] to do this, but SDN is falling short, because it’s a software technology,” Netris’ CEO Alex Saroyan told TechCrunch. “For AI, software is not okay, because the amount of traffic is so high, everything must be hardware accelerated. So you need something like SDN, but completely hardware accelerated. This is what we do, and this is what we’ve been doing for eight years.” Saroyan said Netris’ platform is vendor-agnostic, compatible with networking equipment and standards used at data centers, both for Nvidia and AMD’s servers. The startup’s promise has found many believers, one of which is Nvidia. Two years ago, the chipmaking giant was so impressed by a demo of Netris’ technology that it recommended the company to several customers. Today, Netris is live at more than 35 GPU clusters around the world (about a million GPUs total), operated by the likes of Lightning AI, Foxconn, Visionbay, Hewlett Packard Enterprise, TensorWave, Telus, and others. To build on that momentum, Netris has now raised $15 million in a Series A round from Andreessen Horowitz, TechCrunch has exclusively learned. Notably, there’s no AI at work here. Saroyan said the company only uses algorithms it had developed previously for running and configuring automation and operations. “We started way before AI. We understood the challenge early on, and we started developing this algorithm early on. AI is not deterministic, right? Sometimes it likes to do things on its own. It’s good for creative work, but for changing many thousands of switch configurations, you don’t need to be creative. You need to be very persistent and repeatable.” a16z partner Guido Appenzeller is joining the company’s board. Looking forward, Netris aims to use the funding to hire more engineers and sales staff, add support for more hardware vendors, and implement more functionality in its algorithm.

11 hours ago

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Databricks’ former AI chief thinks he can cut AI’s power bill by 1,000x

Databricks’ former AI chief thinks he can cut AI’s power bill by 1,000x

The drive to discover the next big thing in AI has funded some pretty ambitious projects — but one company is taking it as a chance to rebuild computing architecture from the ground up. Led by Naveen Rao, formerly the head of AI at Databricks,Unconventional AIpromises to make inference processing vastly more power efficient. The secret weapon: a new kind of oscillator-based computer architecture. On Thursday, the company released its first model AI — called Un-0 — an image-generation system tool that shows for the first time how the company’s technology can replicate conventional AI systems. In an accompanying new paper, the company’s research team details how they built a fully functional image-generation model using a software simulation of the new architecture — one that performs just as well as state-of-the-art diffusion models. “This is the ‘hello world’ of a new kind of computer,” Rao told TechCrunch. “Over the next year, you’re going to start seeing some pretty interesting news around this.” The output from the new Un-0 model is similar to that of image-generation models like Stable Diffusion or OpenAI’s GPT Image 1. The impressive part is how it arrives at that performance. The model is built on an oscillator-based architecture that is completely different from the chips that power conventional computing and traditional LLMs. The advantages of the oscillator-based computing are complex, but Rao believes it will ultimately reduce power use by as much as 1,000 times. Much of the infrastructure to get there is still being built. The current version of Un-0 runs on a software simulation of Unconventional’s oscillator chips, but the company plans to release schematics for an actual chip soon. From there, the plan is to build an entire inference stack from the ground up, with Unconventional AI eventually supplying compute capacity just like any other provider. “We will build a new kind of system composed of our chips,” says Rao. “We will run AI models there, and we will have a network cable where prompts come in and inferences go out, but it’ll be done at 1/1000 of power.” It’s a stunningly ambitious goal, particularly for a company that still counts less than 50 employees. But given the scale of the AI buildout and the anticipated cost of meeting the growing demand for inference, it may be one of the few efforts to meet the scale of the problem. As Rao sees it, the available supply of power will be one of the hard limits for AI in the years to come — and Unconventional is one of the few projects able to address it. “AI scaling is hard because of energy. It’s going to be the fundamental limit in the next few years. You just can’t go past it. It’s going to be an energy-limited problem, at the end of the day,” he says.

11 hours ago

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