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A three-month-old AI startup wants to make repetitive office work something humans no longer have to do and investors could value the bet at $1 billion. Prentis, founded by serial entrepreneur Ritankar Das alongside LinkedIn co-founder Reid Hoffman and Zynga founder Mark Pincus, is in talks to raise $100 million at a $1 billion valuation, according to TechCrunch. Launched in April, Prentis is building “computer-use” AI models. Instead of simply answering questions like a chatbot, these models are designed to understand what is happening on a computer screen and operate software directly. The goal is ambitious: teach AI how people actually work, then let agents perform those workflows themselves.<h2>Prentis wants AI to learn your office work</h2>Many workplace processes are never properly documented. Employees simply learn which file to open, which button to click and what to do when something goes wrong. Prentis wants its models to learn these patterns by observing and interacting with real workflows. That could eventually allow customised AI agents to handle tasks such as insurance claims or customs duty refund exceptions without employees manually searching through documents and different systems. The startup has reportedly already signed contracts worth up to $50 million with customers across healthcare and manufacturing.<h2>Prentis claims its smaller AI can beat bigger models&nbsp;</h2> At the centre of the company’s pitch is Hive-32B, its computer-use model. Prentis claims Hive-32B outperforms OpenAI’s GPT-5.4 and Anthropic’s Claude Opus 4.6 on two computer-use benchmarks, WindowsAgentArena and ScreenSpot-v2. More importantly for businesses, Prentis reportedly claims its model can perform tasks at roughly one-tenth the cost of frontier AI APIs. However, these performance claims have not been independently verified.<h2>Why office work could become AI's next battleground</h2>Prentis is betting that computer automation could eventually become a bigger AI use case than coding.But it is entering an increasingly competitive market.OpenAI and Anthropic are also developing AI systems capable of interacting with computers. The attraction is clear: an AI agent that can actually operate workplace software could do far more than simply draft an email or summarise a document. Prentis argues that its advantage comes from training AI on something frontier models may lack exposure to the undocumented steps, exceptions and hand-offs involved in real work. Its small team already includes researchers with experience at OpenAI, Google DeepMind, Meta, Tencent and Alibaba. For Prentis, the bigger bet is that the next stage of the AI race will not simply be about building smarter chatbots. It will be about building AI that can sit at a computer and actually get the work done.

Prentis, founded by Ritankar Das, Reid Hoffman and Mark Pincus, is reportedly seeking $100 million at a $1 billion valuation. Its big bet: AI agents that learn how employees use computers and automate everyday office work.


A three-month-old AI startup wants to make repetitive office work something humans no longer have to do and investors could value the bet at $1 billion. Prentis, founded by serial entrepreneur Ritankar Das alongside LinkedIn co-founder Reid Hoffman and Zynga founder Mark Pincus, is in talks to raise $100 million at a $1 billion valuation, according to TechCrunch. Launched in April, Prentis is building “computer-use” AI models. Instead of simply answering questions like a chatbot, these models are designed to understand what is happening on a computer screen and operate software directly. The goal is ambitious: teach AI how people actually work, then let agents perform those workflows themselves. Prentis wants AI to learn your office work Many workplace processes are never properly documented. Employees simply learn which file to open, which button to click and what to do when something goes wrong. Prentis wants its models to learn these patterns by observing and interacting with real workflows. That could eventually allow customised AI agents to handle tasks such as insurance claims or customs duty refund exceptions without employees manually searching through documents and different systems. The startup has reportedly already signed contracts worth up to $50 million with customers across healthcare and manufacturing. Prentis claims its smaller AI can beat bigger models At the centre of the company’s pitch is Hive-32B, its computer-use model. Prentis claims Hive-32B outperforms OpenAI’s GPT-5.4 and Anthropic’s Claude Opus 4.6 on two computer-use benchmarks, WindowsAgentArena and ScreenSpot-v2. More importantly for businesses, Prentis reportedly claims its model can perform tasks at roughly one-tenth the cost of frontier AI APIs. However, these performance claims have not been independently verified. Why office work could become AI's next battleground Prentis is betting that computer automation could eventually become a bigger AI use case than coding. But it is entering an increasingly competitive market. OpenAI and Anthropic are also developing AI systems capable of interacting with computers. The attraction is clear: an AI agent that can actually operate workplace software could do far more than simply draft an email or summarise a document. Prentis argues that its advantage comes from training AI on something frontier models may lack exposure to the undocumented steps, exceptions and hand-offs involved in real work. Its small team already includes researchers with experience at OpenAI, Google DeepMind, Meta, Tencent and Alibaba. For Prentis, the bigger bet is that the next stage of the AI race will not simply be about building smarter chatbots. It will be about building AI that can sit at a computer and actually get the work done.

Prentis, founded by Ritankar Das, Reid Hoffman and Mark Pincus, is reportedly seeking $100 million at a $1 billion valuation. Its big bet: AI agents that learn how employees use computers and automate everyday office work.

Three-month-old AI startup Prentis, backed by Reid Hoffman, eyes $1 billion valuation with a bet on automating office work

Elon Musk has rejected allegations that he is racist, pointing to his half-Indian partner Shivon Zilis, their four children and the diversity of senior executives working across his companies.During an interview with The Economist, the Tesla and SpaceX chief was confronted directly with the criticism: “People say that you’re a racist.” Musk responded by bringing up his family. “My partner is half-Indian. I have four children with her; one of them was named after a famous Indian physicist,” he said. “So I would say I’m not racist,” Musk added.

<blockquote class="twitter-tweet" data-media-max-width="560">Q: People say that you&#39;re a racist. Elon Musk: My partner is half Indian, and I have four children with her. One of them was named after a famous Indian physicist. So I would say I&#39;m not racist. And also, if you look at the people that are employed at my companies, we have… <a href="https://t.co/w8sfy3F3R7">pic.twitter.com/w8sfy3F3R7</a>&mdash; Clash Report (@clashreport) <a href="https://x.com/clashreport/status/2080339432305996222?ref_src=twsrc%5Etfw">July 23, 2026</a></blockquote> <script async src="https://platform.x.com/widgets.js" charset="utf-8"></script>

<h2>Who is Elon Musk’s half-Indian partner?</h2>Musk was referring to Shivon Zilis, a Canadian-born technology executive with Indian heritage.Zilis joined Musk’s brain-computer interface company Neuralink in 2017 and has served in a senior role at the company. The couple have four children together. One of their sons carries the middle name Sekhar, in honour of Indian-American astrophysicist Subrahmanyan Chandrasekhar. Chandrasekhar won the 1983 Nobel Prize in Physics for his groundbreaking work on the structure and evolution of stars.<h2>Musk points to diversity at his companies</h2>Musk also cited the workforce across his businesses while rejecting the racism allegations. “If you look at people that are employed at my companies, we have senior executives of all races,” Musk said. “I don’t think there is any racism there.” The billionaire was also questioned about whether he was anti-Muslim. Musk rejected that characterisation, saying his objection was to people entering a country with views or behaviour that conflict with its laws and accepted norms.<h2>Musk says he ‘got carried away’ with DOGE</h2>The wide-ranging interview also turned to Musk’s involvement with the Trump administration and the Department of Government Efficiency, or DOGE. Musk admitted he would approach that period differently if given another chance. “I got carried away, frankly,” he said, adding: “I think instead of doing DOGE, I would have basically worked on my companies.” His latest comments come as Musk continues to face scrutiny over his political interventions and statements on race, immigration and culture. The billionaire has also recently drawn controversy over Christopher Nolan’s The Odyssey, criticising the casting of Oscar-winning actress Lupita Nyong’o as Helen of Troy. But when confronted directly over allegations of racism, Musk’s response was clear: he rejected the label and pointed to both his multicultural family and the leadership of his companies.&nbsp;

Elon Musk has rejected racism allegations, citing his half-Indian partner Shivon Zilis, their four children and his son’s middle name honouring Nobel-winning Indian-American astrophysicist Subrahmanyan Chandrasekhar.


Elon Musk has rejected allegations that he is racist, pointing to his half-Indian partner Shivon Zilis, their four children and the diversity of senior executives working across his companies. During an interview with The Economist, the Tesla and SpaceX chief was confronted directly with the criticism: “People say that you’re a racist.” Musk responded by bringing up his family. “My partner is half-Indian. I have four children with her; one of them was named after a famous Indian physicist,” he said. “So I would say I’m not racist,” Musk added.

Elon Musk has rejected racism allegations, citing his half-Indian partner Shivon Zilis, their four children and his son’s middle name honouring Nobel-winning Indian-American astrophysicist Subrahmanyan Chandrasekhar.

‘I have four children with her’: Elon Musk cites half-Indian partner while denying racism claims

Anthropic has launched Claude Opus 5, promising users near-frontier AI intelligence without paying the company's highest price. Released on July 24, Opus 5 is designed for everyday professional work, including software development, research and complex tasks. Anthropic says the model comes close to the intelligence of its more powerful Claude Fable 5 at half the price.The launch also shows where the AI race is heading. Companies are no longer competing only to build the smartest model. They increasingly need models that can deliver strong performance without making every task prohibitively expensive.

<blockquote class="twitter-tweet">Introducing Claude Opus 5. It&#39;s a thoughtful and proactive model that comes close to the frontier intelligence of Fable 5 at half the price. <a href="https://t.co/GQWhcq2CQL">pic.twitter.com/GQWhcq2CQL</a>&mdash; Claude (@claudeai) <a href="https://x.com/claudeai/status/2080699495453528290?ref_src=twsrc%5Etfw">July 24, 2026</a></blockquote> <script async src="https://platform.x.com/widgets.js" charset="utf-8"></script>

<h2>Claude Opus 5: What has changed?</h2>Opus 5 costs the same as its predecessor, Opus 4.8, but Anthropic says it delivers significantly better performance. On coding and knowledge-work evaluations including Frontier-Bench and GDPval-AA, Anthropic says Opus 5 has reached state-of-the-art performance. It is also designed to use computing resources more efficiently, reducing the cost of completing many tasks. The model can adjust how much effort it spends on a problem. Users can therefore favour deeper reasoning for difficult work or use fewer tokens when speed and cost matter more. Opus 5 also has a Fast mode, which Anthropic says operates around 2.5 times faster than its standard mode.<h2>Opus 5 or Fable 5: Which should you use?</h2>Anthropic is not positioning Opus 5 as a replacement for Fable 5. Dianne Penn, an Anthropic product leader, told Reuters that users should choose Opus 5 for value, while Fable 5 remains better suited to highly autonomous projects that could continue for days.That distinction could matter to businesses. Everyday coding and office tasks may not need the company's most expensive frontier model. Anthropic says Opus 5 is now the default model for Claude Max users and the strongest model available to Claude Pro subscribers. It is also available through the Claude API.<h2>Anthropic says Opus 5 is safer, too</h2>There is another notable difference. Opus 5 remains behind Anthropic's Mythos 5 on cybersecurity tasks. Reuters reports that it was less capable of exploiting cyber vulnerabilities than Fable 5, allowing Anthropic to deploy it with less restrictive cyber safeguards. Anthropic also says its automated behavioural testing found Opus 5 to be its most aligned model so far, with lower rates of problematic behaviour. The bigger story, however, is economics. If AI models approaching frontier-level performance can become significantly cheaper, businesses may not always need the most powerful model available. For Anthropic, Opus 5 is its bet that the next stage of the AI race will be fought not just over intelligence, but over how much useful intelligence customers can get for their money.

Anthropic has launched Claude Opus 5, an AI model aimed at coding and everyday professional work. The company says it approaches Fable 5 intelligence at half the price, while offering stronger efficiency and major gains over Opus 4.8.

Anthropic has launched Claude Opus 5, promising users near-frontier AI intelligence without paying the company's highest price. Released on July 24, Opus 5 is designed for everyday professional work, including software development, research and complex tasks. Anthropic says the model comes close to the intelligence of its more powerful Claude Fable 5 at half the price. The launch also shows where the AI race is heading. Companies are no longer competing only to build the smartest model. They increasingly need models that can deliver strong performance without making every task prohibitively expensive.

Anthropic has launched Claude Opus 5, an AI model aimed at coding and everyday professional work. The company says it approaches Fable 5 intelligence at half the price, while offering stronger efficiency and major gains over Opus 4.8.

Anthropic launches Claude Opus 5 with near-Fable 5 intelligence at half the price

Google has discontinued Google Play services for devices running Android 6.0 Marshmallow, bumping the minimum required version up to Android 7.0 Nougat. Devices stuck on the older OS won't stop working immediately. Still, they may gradually lose the ability to download new apps or get Play Store updates through as developers phase out support for older Android versions. The change impacts phones that cannot be updated past Android 6.0, an operating system which came in 2015. Google has updated the minimum Play Services requirement now set at Android 7.0 (API level 24), effectively closing the book on Marshmallow support after years of backing it. Functioning as a background service, Google Play Services underpins a range of core Android features, from signing into Google accounts and receiving push notifications to location tracking, Maps functionality, security verification, and data syncing. It also gives developers the APIs needed to integrate Google-powered capabilities into their apps. Meanwhile, Google Play Services is different from the Google Play Store. Several Android apps depend on it to install, update and function properly. After the end of Play Services support, developers are likely to slowly drop compatibility with Android 6.0 devices, resulting in users eventually discovering certain apps that can no longer be downloaded from the Play Store; however, others might stop receiving updates or fail to work correctly.&nbsp;Also read: <a href="https://www.wionews.com/technology/nvidia-is-digging-its-own-grave-deepseek-ceo-backs-china-s-ai-chip-push-1784913193475">'Nvidia is digging its own grave': DeepSeek CEO backs China's AI chip push</a><h3>Will your phone stop working?</h3>Not entirely. Devices running Android 6.0 Marshmallow will still be able to handle basic functions like calling, texting, viewing photos, and running apps already installed on them. That said, the overall experience will likely worsen with time. Since newer app versions demand updated Play Services and more recent Android builds, users can expect growing compatibility problems. Core functions like signing into Google accounts, using location-based features, and receiving push notifications could also start acting unreliably. Additionally, without further Play Services updates, these phones won't get future security patches, leaving them increasingly exposed to newly emerging threats.

Functioning as a background service, Google Play Services underpins a range of core Android features, from signing into Google accounts and receiving push notifications to location tracking, Maps functionality, security verification, and data syncing. 

Google has discontinued Google Play services for devices running Android 6.0 Marshmallow, bumping the minimum required version up to Android 7.0 Nougat. Devices stuck on the older OS won't stop working immediately. Still, they may gradually lose the ability to download new apps or get Play Store updates through as developers phase out support for older Android versions. The change impacts phones that cannot be updated past Android 6.0, an operating system which came in 2015. Google has updated the minimum Play Services requirement now set at Android 7.0 (API level 24), effectively closing the book on Marshmallow support after years of backing it. Functioning as a background service, Google Play Services underpins a range of core Android features, from signing into Google accounts and receiving push notifications to location tracking, Maps functionality, security verification, and data syncing. It also gives developers the APIs needed to integrate Google-powered capabilities into their apps. Meanwhile, Google Play Services is different from the Google Play Store. Several Android apps depend on it to install, update and function properly. After the end of Play Services support, developers are likely to slowly drop compatibility with Android 6.0 devices, resulting in users eventually discovering certain apps that can no longer be downloaded from the Play Store; however, others might stop receiving updates or fail to work correctly. Also read: 'Nvidia is digging its own grave': DeepSeek CEO backs China's AI chip push Will your phone stop working? Not entirely. Devices running Android 6.0 Marshmallow will still be able to handle basic functions like calling, texting, viewing photos, and running apps already installed on them. That said, the overall experience will likely worsen with time. Since newer app versions demand updated Play Services and more recent Android builds, users can expect growing compatibility problems. Core functions like signing into Google accounts, using location-based features, and receiving push notifications could also start acting unreliably. Additionally, without further Play Services updates, these phones won't get future security patches, leaving them increasingly exposed to newly emerging threats.

Functioning as a background service, Google Play Services underpins a range of core Android features, from signing into Google accounts and receiving push notifications to location tracking, Maps functionality, security verification, and data syncing.&nbsp;

Google ends Play Services Support, likely to stop these Android phones from downloading apps & games

DeepSeek founder Liang Wenfeng has laid out an unusually ambitious vision for the Chinese AI company: focus on artificial general intelligence (AGI), keep commercialisation secondary and help build an AI ecosystem less dependent on Nvidia. His remarks emerged from a lengthy discussion with investors and offer a rare look at how the normally low-profile entrepreneur thinks about the global AI race. While companies across the industry are spending heavily to turn AI into profitable products, Liang said DeepSeek's main objective remains AGI broadly referring to AI capable of performing intellectual tasks across many areas at or beyond human level.<h2>DeepSeek wants the 'watermelon', not the 'sesame seeds'</h2>Liang compared near-term revenue opportunities with “sesame seeds”, suggesting DeepSeek will pick some up without allowing them to distract the company from a much bigger prize. “Our goal should be AGI,” he said. The philosophy does not mean DeepSeek will ignore commercial opportunities. Rather, Liang argues that building more advanced intelligence should take priority over maximising revenue from today's products. He described AI development as a staircase. Language models provide the foundation for reasoning, reasoning enables more capable agents, and future systems could eventually learn and improve continuously. If an AI system can help develop its own successor, Liang believes progress could accelerate dramatically.<h2>Why Liang thinks Nvidia is 'digging its own grave'</h2>Perhaps his most provocative remarks concerned Nvidia. US restrictions have limited Chinese companies' access to some of Nvidia's most advanced AI hardware. But Liang argues that those restrictions are creating a powerful incentive for China to develop alternatives. “If Nvidia cards can't be bought, everyone is forced into it everyone has to do domestic chips,” he said. Liang went further, saying he believes “Nvidia is digging its own grave” by creating conditions that encourage Chinese companies to reduce their dependence on its hardware. DeepSeek is working closely with Huawei and expects to participate deeply in its AI computing ecosystem.<h2>Can Huawei really replace Nvidia?</h2>Liang's comments should not be mistaken for a claim that China has already eliminated Nvidia's technological advantage. He acknowledged a substantial efficiency gap, saying roughly four Huawei chips may be needed for the work of one comparable Nvidia chip, while China's hardware remains about two years behind. His broader argument is about direction rather than today's outright performance. DeepSeek has already demonstrated an interest in designing its software around hardware limitations. Its researchers have previously detailed how DeepSeek-V3 was trained using 2,048 Nvidia H800 GPUs while relying on several hardware-aware efficiency techniques. Liang believes the same pressure could ultimately produce a broader Chinese AI ecosystem built around domestic chips, open models and lower prices. That would turn Washington's restrictions into something of a long-term gamble: limiting China's access to America's best AI hardware today could also give Chinese companies a powerful reason to make sure they need less of it tomorrow.

DeepSeek founder Liang Wenfeng says AGI, not short-term profit, remains the company's main goal. He also argues that US restrictions on Nvidia chips are accelerating China's push towards domestic AI hardware, with DeepSeek working closely with Huawei.


DeepSeek founder Liang Wenfeng has laid out an unusually ambitious vision for the Chinese AI company: focus on artificial general intelligence (AGI), keep commercialisation secondary and help build an AI ecosystem less dependent on Nvidia. His remarks emerged from a lengthy discussion with investors and offer a rare look at how the normally low-profile entrepreneur thinks about the global AI race. While companies across the industry are spending heavily to turn AI into profitable products, Liang said DeepSeek's main objective remains AGI broadly referring to AI capable of performing intellectual tasks across many areas at or beyond human level. DeepSeek wants the 'watermelon', not the 'sesame seeds' Liang compared near-term revenue opportunities with “sesame seeds”, suggesting DeepSeek will pick some up without allowing them to distract the company from a much bigger prize. “Our goal should be AGI,” he said. The philosophy does not mean DeepSeek will ignore commercial opportunities. Rather, Liang argues that building more advanced intelligence should take priority over maximising revenue from today's products. He described AI development as a staircase. Language models provide the foundation for reasoning, reasoning enables more capable agents, and future systems could eventually learn and improve continuously. If an AI system can help develop its own successor, Liang believes progress could accelerate dramatically. Why Liang thinks Nvidia is 'digging its own grave' Perhaps his most provocative remarks concerned Nvidia. US restrictions have limited Chinese companies' access to some of Nvidia's most advanced AI hardware. But Liang argues that those restrictions are creating a powerful incentive for China to develop alternatives. “If Nvidia cards can't be bought, everyone is forced into it everyone has to do domestic chips,” he said. Liang went further, saying he believes “Nvidia is digging its own grave” by creating conditions that encourage Chinese companies to reduce their dependence on its hardware. DeepSeek is working closely with Huawei and expects to participate deeply in its AI computing ecosystem. Can Huawei really replace Nvidia? Liang's comments should not be mistaken for a claim that China has already eliminated Nvidia's technological advantage. He acknowledged a substantial efficiency gap, saying roughly four Huawei chips may be needed for the work of one comparable Nvidia chip, while China's hardware remains about two years behind. His broader argument is about direction rather than today's outright performance. DeepSeek has already demonstrated an interest in designing its software around hardware limitations. Its researchers have previously detailed how DeepSeek-V3 was trained using 2,048 Nvidia H800 GPUs while relying on several hardware-aware efficiency techniques. Liang believes the same pressure could ultimately produce a broader Chinese AI ecosystem built around domestic chips, open models and lower prices. That would turn Washington's restrictions into something of a long-term gamble: limiting China's access to America's best AI hardware today could also give Chinese companies a powerful reason to make sure they need less of it tomorrow.

DeepSeek founder Liang Wenfeng says AGI, not short-term profit, remains the company's main goal. He also argues that US restrictions on Nvidia chips are accelerating China's push towards domestic AI hardware, with DeepSeek working closely with Huawei.

'Nvidia is digging its own grave': DeepSeek CEO backs China's AI chip push

Nvidia CEO Jensen Huang has delivered a striking defence of Taiwan's semiconductor industry, saying the island “saved” America's chip and computing industries rather than taking them away. “Taiwan saved the American semiconductor industry, saved the American computing industry, not the opposite,” Huang told reporters during the opening of Taiwanese manufacturer Wistron's first US manufacturing facility in Fort Worth, Texas. The comments carry particular weight because Huang sits at the centre of the global AI boom. Nvidia designs the GPUs powering many of today's most advanced AI systems, while Taiwan remains a critical part of the supply chain that makes those systems possible.Jensen Huang: Without Taiwan, Japan could have taken the leadHuang argued that the relationship between Taiwan and America's technology industry should be viewed as a partnership rather than a story of one country taking manufacturing from another. Without Taiwan's rise as a technology manufacturing powerhouse, he suggested, Japan could have taken a much larger share of America's semiconductor industry. Huang also said Taiwanese companies expanding manufacturing in America are doing so because it makes business sense. “Taiwan is doing it voluntarily because of its own wisdom, not because it's held a gun to its head,” Huang said. He described the relationship between American and Taiwanese technology companies as one built over decades of cooperation and trust.<h2>A sharp contrast with Trump's 'stolen' chip claim</h2>Huang did not name US President Donald Trump. But his comments offer a sharply different interpretation of how Taiwan became a semiconductor powerhouse. Trump has repeatedly accused Taiwan of taking America's semiconductor business and argued that previous US governments should have used tariffs to prevent chip manufacturing from shifting overseas. Taiwan has rejected that characterisation, arguing that its semiconductor industry was built through decades of investment, engineering expertise and the development of a highly specialised manufacturing ecosystem. Huang's argument goes further: Taiwan's rise, in his view, helped American technology companies remain competitive.<h2>Nvidia's latest AI superchips are now being made in Texas</h2>The setting for Huang's comments was significant. Wistron's new D1 facility in Fort Worth is a 324,000-square-foot manufacturing plant backed by a $700 million investment. The facility is already mass-producing Nvidia's GB300 Grace Blackwell Ultra Superchip in the United States. It is also expected to manufacture Nvidia's next-generation Vera Rubin Superchip. Wistron says the site has created more than 500 jobs, with plans to expand that number to around 1,000 by the end of 2026. For Nvidia, this represents something bigger than opening another factory.The company is trying to build more of the physical infrastructure behind the AI boom inside America from chips and packaging to servers and complete AI systems.And there is an irony at the centre of that effort. As Washington pushes to bring advanced technology manufacturing back to American soil, some of the companies helping make that possible are precisely the Taiwanese manufacturers that Huang believes America should be thanking rather than blaming.

Nvidia CEO Jensen Huang says Taiwan “saved” America's semiconductor and computing industries, challenging claims that it took US chip manufacturing away. His remarks came as Taiwan's Wistron opened a $700 million Nvidia AI manufacturing facility in Texas.


Nvidia CEO Jensen Huang has delivered a striking defence of Taiwan's semiconductor industry, saying the island “saved” America's chip and computing industries rather than taking them away. “Taiwan saved the American semiconductor industry, saved the American computing industry, not the opposite,” Huang told reporters during the opening of Taiwanese manufacturer Wistron's first US manufacturing facility in Fort Worth, Texas. The comments carry particular weight because Huang sits at the centre of the global AI boom. Nvidia designs the GPUs powering many of today's most advanced AI systems, while Taiwan remains a critical part of the supply chain that makes those systems possible. Jensen Huang: Without Taiwan, Japan could have taken the lead Huang argued that the relationship between Taiwan and America's technology industry should be viewed as a partnership rather than a story of one country taking manufacturing from another. Without Taiwan's rise as a technology manufacturing powerhouse, he suggested, Japan could have taken a much larger share of America's semiconductor industry. Huang also said Taiwanese companies expanding manufacturing in America are doing so because it makes business sense. “Taiwan is doing it voluntarily because of its own wisdom, not because it's held a gun to its head,” Huang said. He described the relationship between American and Taiwanese technology companies as one built over decades of cooperation and trust. A sharp contrast with Trump's 'stolen' chip claim Huang did not name US President Donald Trump. But his comments offer a sharply different interpretation of how Taiwan became a semiconductor powerhouse. Trump has repeatedly accused Taiwan of taking America's semiconductor business and argued that previous US governments should have used tariffs to prevent chip manufacturing from shifting overseas. Taiwan has rejected that characterisation, arguing that its semiconductor industry was built through decades of investment, engineering expertise and the development of a highly specialised manufacturing ecosystem. Huang's argument goes further: Taiwan's rise, in his view, helped American technology companies remain competitive. Nvidia's latest AI superchips are now being made in Texas The setting for Huang's comments was significant. Wistron's new D1 facility in Fort Worth is a 324,000-square-foot manufacturing plant backed by a $700 million investment. The facility is already mass-producing Nvidia's GB300 Grace Blackwell Ultra Superchip in the United States. It is also expected to manufacture Nvidia's next-generation Vera Rubin Superchip. Wistron says the site has created more than 500 jobs, with plans to expand that number to around 1,000 by the end of 2026. For Nvidia, this represents something bigger than opening another factory. The company is trying to build more of the physical infrastructure behind the AI boom inside America from chips and packaging to servers and complete AI systems. And there is an irony at the centre of that effort. As Washington pushes to bring advanced technology manufacturing back to American soil, some of the companies helping make that possible are precisely the Taiwanese manufacturers that Huang believes America should be thanking rather than blaming.

Nvidia CEO Jensen Huang says Taiwan “saved” America's semiconductor and computing industries, challenging claims that it took US chip manufacturing away. His remarks came as Taiwan's Wistron opened a $700 million Nvidia AI manufacturing facility in Texas.

'Not the opposite': Jensen Huang says Taiwan saved US chip industry

Elon Musk has offered one of his most striking AI predictions yet: artificial intelligence could become smarter than the combined intelligence of all humans within about five years. Speaking to The Economist, the Tesla and xAI chief said there may eventually be little that AI cannot do better than people, apart from simply “being human”. More strikingly, when asked whether humans would still control AI in 10 years, Musk said that was unlikely. Yet Musk, who has spent years warning about AI risks, is no longer arguing that development should be stopped. Instead, he believes its rapid advance may now be impossible to halt.'Let's enjoy the ride'Musk said AI and robotics still carry risks, but argued that their most likely outcome is “incredible abundance for all”. His current philosophy, he said, is essentially: “Let’s enjoy the ride.” Musk predicted a future in which increasingly capable AI and robots could dramatically increase productivity and make goods and services far more abundant.But there is a catch. If AI becomes vastly more intelligent than humans, Musk suggested that believing people would continue controlling it could be unrealistic. The intelligence gap could eventually resemble the gap between humans and chimpanzees. That makes the values built into powerful AI systems crucial. Musk argued that developers should try to ensure advanced AI cares about humanity and wants people to prosper.

<h2>Musk wants rival AI companies to check each other</h2>Musk also proposed a more unusual approach to AI safety: let competitors inspect each other's most powerful models before they are released. He suggested leading AI companies should hold regular discussions on safety and security. Rival developers could potentially receive a week or two to examine new frontier models and identify serious risks.“The competitors can keep each other honest,” Musk said.Governments, in his view, should intervene when a serious danger is identified and the company responsible fails to address it. Such cooperation could involve some of Musk's biggest rivals, including OpenAI, Anthropic and Google DeepMind.<h2>Musk says AI race has become unstoppable</h2>Musk also acknowledged an irony in his own AI journey.He helped establish OpenAI in 2015 partly as a counterweight to Google's growing AI power. He now believes those actions helped accelerate the very race he once hoped to make safer.“All roads lead to the acceleration of AI,” he said.Despite his long-running dispute with OpenAI CEO Sam Altman, Musk indicated that personal rivalries should not stand in the way of AI safety cooperation. He also offered unusually warm words for Anthropic CEO Dario Amodei, describing him as a principled person who cares about the world's future. The bigger message from Musk is therefore not simply that superintelligent AI is coming. It is that the race may already be impossible to stop and humanity's challenge is increasingly shifting from preventing it to making sure what comes next remains aligned with human interests.

Elon Musk predicts AI could surpass the combined intelligence of humanity within five years and says humans may no longer control it within a decade. He now wants rival AI labs to inspect each other's frontier models and regularly discuss safety risks.


Elon Musk has offered one of his most striking AI predictions yet: artificial intelligence could become smarter than the combined intelligence of all humans within about five years. Speaking to The Economist, the Tesla and xAI chief said there may eventually be little that AI cannot do better than people, apart from simply “being human”. More strikingly, when asked whether humans would still control AI in 10 years, Musk said that was unlikely. Yet Musk, who has spent years warning about AI risks, is no longer arguing that development should be stopped. Instead, he believes its rapid advance may now be impossible to halt. 'Let's enjoy the ride' Musk said AI and robotics still carry risks, but argued that their most likely outcome is “incredible abundance for all”. His current philosophy, he said, is essentially: “Let’s enjoy the ride.” Musk predicted a future in which increasingly capable AI and robots could dramatically increase productivity and make goods and services far more abundant. But there is a catch. If AI becomes vastly more intelligent than humans, Musk suggested that believing people would continue controlling it could be unrealistic. The intelligence gap could eventually resemble the gap between humans and chimpanzees. That makes the values built into powerful AI systems crucial. Musk argued that developers should try to ensure advanced AI cares about humanity and wants people to prosper.

Elon Musk predicts AI could surpass the combined intelligence of humanity within five years and says humans may no longer control it within a decade. He now wants rival AI labs to inspect each other's frontier models and regularly discuss safety risks.

Elon Musk says AI could surpass all human intelligence in 5 years, warns humans may lose control

AMD is making one of its biggest bets yet on the artificial intelligence boom—and on Anthropic. The chipmaker will invest up to $5 billion in the Claude developer, while Anthropic has agreed to deploy up to 2 gigawatts of AMD Instinct MI450-series GPUs. The first 1 GW deployment is expected to begin in the first half of 2027. The agreement could be worth tens of billions of dollars in hardware sales and comes as AI companies race to secure enormous amounts of computing capacity. For AMD, however, there is another prize: gaining ground on Nvidia, which continues to dominate the market for advanced AI accelerators.<h2>Why Anthropic needs 2 GW of AI chips</h2>Demand for Claude and other generative AI services is pushing Anthropic to build an increasingly diverse computing network. Two gigawatts is an extraordinary amount of computing infrastructure. It is roughly equivalent to the electricity output of two large nuclear power reactors, although actual data-centre power consumption will depend on how the infrastructure is deployed. Anthropic plans to use AMD's Helios rack-scale systems, powered by MI450-series GPUs. According to The Wall Street Journal, some chips could be deployed in Anthropic's own data centres, while additional computing capacity could be leased through cloud and specialised "neocloud" providers. AMD is also reportedly discussing providing financial backing for some future Anthropic data-centre leases.<h2>AMD isn't just selling Anthropic chips</h2>The partnership goes deeper than hardware. AMD and Anthropic engineers will work together to optimise Claude workloads for AMD Instinct GPUs and accelerate development of ROCm, AMD's software platform for AI computing. AMD also plans to use Claude extensively across its own engineering and product development teams. The investment in Anthropic will be tied to deployment milestones rather than being delivered all at once.<h2>Nvidia faces growing pressure</h2>Nvidia remains the company to beat in AI infrastructure, helped by both its GPUs and its deeply established CUDA software ecosystem.But AI companies increasingly want alternatives. AMD has been aggressively pursuing this opportunity. Earlier in 2026, it struck a multi-year agreement with Meta covering up to 6 GW of AMD GPUs. Anthropic now adds another major frontier AI developer to its customer list.Anthropic itself is also spreading its compute requirements across multiple suppliers instead of depending on one company. It already has major infrastructure relationships involving Amazon and Google. That diversification is becoming strategically important as AI models require ever-larger clusters and demand for advanced chips continues to soar. The AMD-Anthropic agreement therefore represents more than another giant chip order. It shows how the AI race is evolving into a competition not only over who builds the smartest model, but also over who can secure enough chips, electricity and data-centre capacity to run it at global scale.

AMD will invest up to $5 billion in Anthropic, while the Claude maker plans to deploy up to 2 GW of AMD Instinct MI450-series GPUs. The massive partnership gives Anthropic more AI compute and strengthens AMD's attempt to challenge Nvidia's dominance.


AMD is making one of its biggest bets yet on the artificial intelligence boom—and on Anthropic. The chipmaker will invest up to $5 billion in the Claude developer, while Anthropic has agreed to deploy up to 2 gigawatts of AMD Instinct MI450-series GPUs. The first 1 GW deployment is expected to begin in the first half of 2027. The agreement could be worth tens of billions of dollars in hardware sales and comes as AI companies race to secure enormous amounts of computing capacity. For AMD, however, there is another prize: gaining ground on Nvidia, which continues to dominate the market for advanced AI accelerators. Why Anthropic needs 2 GW of AI chips Demand for Claude and other generative AI services is pushing Anthropic to build an increasingly diverse computing network. Two gigawatts is an extraordinary amount of computing infrastructure. It is roughly equivalent to the electricity output of two large nuclear power reactors, although actual data-centre power consumption will depend on how the infrastructure is deployed. Anthropic plans to use AMD's Helios rack-scale systems, powered by MI450-series GPUs. According to The Wall Street Journal, some chips could be deployed in Anthropic's own data centres, while additional computing capacity could be leased through cloud and specialised "neocloud" providers. AMD is also reportedly discussing providing financial backing for some future Anthropic data-centre leases. AMD isn't just selling Anthropic chips The partnership goes deeper than hardware. AMD and Anthropic engineers will work together to optimise Claude workloads for AMD Instinct GPUs and accelerate development of ROCm, AMD's software platform for AI computing. AMD also plans to use Claude extensively across its own engineering and product development teams. The investment in Anthropic will be tied to deployment milestones rather than being delivered all at once. Nvidia faces growing pressure Nvidia remains the company to beat in AI infrastructure, helped by both its GPUs and its deeply established CUDA software ecosystem. But AI companies increasingly want alternatives. AMD has been aggressively pursuing this opportunity. Earlier in 2026, it struck a multi-year agreement with Meta covering up to 6 GW of AMD GPUs. Anthropic now adds another major frontier AI developer to its customer list. Anthropic itself is also spreading its compute requirements across multiple suppliers instead of depending on one company. It already has major infrastructure relationships involving Amazon and Google. That diversification is becoming strategically important as AI models require ever-larger clusters and demand for advanced chips continues to soar. The AMD-Anthropic agreement therefore represents more than another giant chip order. It shows how the AI race is evolving into a competition not only over who builds the smartest model, but also over who can secure enough chips, electricity and data-centre capacity to run it at global scale.

AMD will invest up to $5 billion in Anthropic, while the Claude maker plans to deploy up to 2 GW of AMD Instinct MI450-series GPUs. The massive partnership gives Anthropic more AI compute and strengthens AMD's attempt to challenge Nvidia's dominance.

AMD to invest up to $5 billion in Anthropic as Claude maker orders 2 GW of AI chips

Microsoft is changing the way it powers Copilot, and the strategy could have major implications for OpenAI, Anthropic and the economics of enterprise AI. CEO Satya Nadella says Microsoft is increasingly using its own MAI family of AI models across products including GitHub Copilot, Excel and Outlook. Instead of relying on the most powerful and expensive frontier model for every request, Microsoft wants to use the right model for each job. The goal is simple: maintain or improve AI performance while using fewer tokens and reducing the enormous computing cost of serving millions of users.<h2>Microsoft's own AI is already taking on GPT and Claude</h2>Microsoft says early results are promising. Its specialised MAI model running in Excel is performing on par with GPT-5.6 for common tasks while being cheaper to operate. In GitHub Copilot, MAI-Code-1-Flash has recorded an approximately 10% higher code acceptance rate than GPT-5.4 Mini and Claude Haiku 4.5 in Microsoft's testing. It also used around 10% fewer median tokens. Rather than training one enormous model to do everything, Microsoft trains specialised models inside environments based on the products in which they will actually operate. That means an Excel AI learns spreadsheet workflows, while a coding model learns how developers work inside GitHub Copilot.<h2>OpenAI and Anthropic are not disappearing</h2>The shift does not mean Microsoft is abandoning frontier models. Nadella said models from OpenAI and Anthropic will remain part of Microsoft's AI orchestration system. Powerful models can handle the hardest requests, while smaller and cheaper MAI models deal with high-volume everyday work. The model itself, Nadella argues, is only one part of an AI product. Memory, tools, context, user interactions and the surrounding software can be just as important.Microsoft calls its continuous optimisation process "hill climbing." Instead of judging models only through standard benchmarks, Microsoft evaluates whether they successfully complete tasks customers actually care about.<h2>Why Microsoft's strategy matters</h2>AI has introduced something unusual into mainstream software: a significant cost every time a user asks the system to perform work. For products serving hundreds of millions of people, even small reductions in inference costs can therefore become extremely valuable. Microsoft's answer is a multi-model system where expensive frontier intelligence is used only when necessary. The company says it is seeing promising early results in GitHub Copilot, Excel and Outlook, with the approach now expanding towards Copilot Chat and PowerPoint. Microsoft also plans to bring the same framework to businesses through Microsoft Foundry, allowing companies to optimise AI agents around their own data, workflows and evaluations. The bigger shift is clear: the next stage of the AI race may not simply be about who builds the world's smartest model. It may be about who can deliver enough intelligence for each task at the lowest possible cost.

Microsoft is expanding its home-grown MAI models across GitHub Copilot, Excel and Outlook. Satya Nadella says the strategy uses specialised, cheaper models for everyday tasks while keeping powerful OpenAI and Anthropic models available for demanding workloads.

Microsoft is changing the way it powers Copilot, and the strategy could have major implications for OpenAI, Anthropic and the economics of enterprise AI. CEO Satya Nadella says Microsoft is increasingly using its own MAI family of AI models across products including GitHub Copilot, Excel and Outlook. Instead of relying on the most powerful and expensive frontier model for every request, Microsoft wants to use the right model for each job. The goal is simple: maintain or improve AI performance while using fewer tokens and reducing the enormous computing cost of serving millions of users. Microsoft's own AI is already taking on GPT and Claude Microsoft says early results are promising. Its specialised MAI model running in Excel is performing on par with GPT-5.6 for common tasks while being cheaper to operate. In GitHub Copilot, MAI-Code-1-Flash has recorded an approximately 10% higher code acceptance rate than GPT-5.4 Mini and Claude Haiku 4.5 in Microsoft's testing. It also used around 10% fewer median tokens. Rather than training one enormous model to do everything, Microsoft trains specialised models inside environments based on the products in which they will actually operate. That means an Excel AI learns spreadsheet workflows, while a coding model learns how developers work inside GitHub Copilot. OpenAI and Anthropic are not disappearing The shift does not mean Microsoft is abandoning frontier models. Nadella said models from OpenAI and Anthropic will remain part of Microsoft's AI orchestration system. Powerful models can handle the hardest requests, while smaller and cheaper MAI models deal with high-volume everyday work. The model itself, Nadella argues, is only one part of an AI product. Memory, tools, context, user interactions and the surrounding software can be just as important. Microsoft calls its continuous optimisation process "hill climbing." Instead of judging models only through standard benchmarks, Microsoft evaluates whether they successfully complete tasks customers actually care about. Why Microsoft's strategy matters AI has introduced something unusual into mainstream software: a significant cost every time a user asks the system to perform work. For products serving hundreds of millions of people, even small reductions in inference costs can therefore become extremely valuable. Microsoft's answer is a multi-model system where expensive frontier intelligence is used only when necessary. The company says it is seeing promising early results in GitHub Copilot, Excel and Outlook, with the approach now expanding towards Copilot Chat and PowerPoint. Microsoft also plans to bring the same framework to businesses through Microsoft Foundry, allowing companies to optimise AI agents around their own data, workflows and evaluations. The bigger shift is clear: the next stage of the AI race may not simply be about who builds the world's smartest model. It may be about who can deliver enough intelligence for each task at the lowest possible cost.

Microsoft is expanding its home-grown MAI models across GitHub Copilot, Excel and Outlook. Satya Nadella says the strategy uses specialised, cheaper models for everyday tasks while keeping powerful OpenAI and Anthropic models available for demanding workloads.

Microsoft has a new plan for AI: Use expensive frontier models only when needed

WASHINGTON – “My main concern is with lethal autonomous weapons systems. Let us call them what they are: killer robots. Machines selecting and engaging their target and taking a life – without human control and judgement. This is morally repugnant.”So said United Nations Secretary-General António Guterres at a meeting in July that brought together AI leaders from UN member states, international organisations, the private sector, academia, and civil society leaders.Officially named the first Global Dialogue on Artificial Intelligence, it reflected growing global concern that rapidly advancing AI is outrunning the ability of governments to regulate it. While that applies to all aspects of modern life, it is particularly worrying in warfare.Also read | <a href="https://www.wionews.com/technology/india-s-next-terror-threat-may-not-cross-the-border-it-may-enter-through-your-phone-wion-decodes-1784346117443">India's next terror threat may not cross the border. It may enter through your phone | WION Decodes</a>Therefore, Guterres argued, the deployment of LAWS (lethal autonomous weapons systems) “is politically unacceptable. And it must be banned by international law.”International law and international bans have a poor track record, but Guterres deserves praise for finally drawing attention to a subject that has been debated for decades by human rights groups, the military and experts working for the world’s biggest AI companies, most of them based in the United States.One of them, Anthropic, is involved in a high-profile dispute with the US Department of Defence that mirrored the concerns voiced by Guterres’s UN conference. Anthropic, along with Google, OpenAI and xAI, won government contracts last year worth around $200 million to accelerate AI adoption for national security.Unlike the other AI companies, Anthropic insisted that its most sophisticated model must not be used by the government for fully autonomous weapons; i.e. robots, and mass domestic surveillance. &nbsp;To emphasise fears that technology is outrunning human control, Anthropic’s CEO, Dario Amodei, called for a global, coordinated slowdown in artificial intelligence development. AI systems, he warned, are advancing with such speed that they soon could build and improve their own successors, without human control.Other AI-developing companies reacted coolly. The Pentagon reacted with anger. It ordered all federal agencies to stop using Anthropic technology and declared the company a “supply chain risk” – a designation that is now the subject of a battle in court. &nbsp;It is more than a contract dispute – it is a fundamental disagreement over who controls the use of AI in weapons or other aspects of life.When you talk to techies, sooner or later the different types of control over aerial, ground and maritime unmanned vehicles come up. &nbsp;Also read | <a href="https://www.wionews.com/world/what-is-the-tower-of-babel-and-why-did-pope-leo-warn-about-it-while-talking-about-ai-wion-decodes-1779790151646">Pope Leo warns humanity risks repeating ancient mistake: What is the Tower of Babel and what does it have to do with AI?</a>There is “in the loop”, meaning that a human operator directs the autonomous system and decides whether or not to kill the target. There is “on the loop” when a human operator does not directly control the automated system but can override its decision. &nbsp;Finally, “off the loop” indicates that the system operates independently and makes life-or-death decisions.For an illustrative example of an “in the loop” operation, let’s look back to the January 3, 2020 assassination of Major-General Qassem Soleimani, one of Iran’s most powerful military leaders, in a drone strike ordered by US President Donald Trump.Soleimani’s movements had been closely tracked for days, and it became known that he was about to visit Baghdad. On arrival, he was greeted by the leader of a pro-Iranian Iraqi militia who escorted him to two cars waiting outside. As they began pulling away, they were hit by 100-pound Hellfire missiles launched from MQ9 Reaper drones high overhead.The cars exploded into fireballs, incinerating Solimani, his host and eight of their aides.Typical for an “in the loop” operation, the American Air Force officer who pushed the button to launch the missile sat in a stationary “cockpit” at Creech Air Force base near Las Vegas, almost 12,000 kilometres from Baghdad.In the six years since the assassination – or “targeted killing” as the US called it- drones have become a central weapon in the wars between Ukraine and Russia, the US and Iran, Sudan, and most of all Israel against Hamas, a campaign which turned Gaza into a field of ruins and killed more than 70,000 Palestinians.Israel’s war on Gaza showed the limits of “in the loop” operations given the green light by military and political leaders. One of the main purposes of using AI in war is to process huge amounts of data to identify suspected enemies. Left to human intelligence officers, that can be a protracted process.It took weeks to follow Solimani and finally pinpoint him at the door of Baghdad airport. In contrast, Israeli AI programmes, based on cellphone data, facial recognition and locations known to frequent Hamas militants, produced a list of 37,000 targets within days of the October 7, 2023 massacre by Hamas of more than 1,200 Israelis.One user of the system, named Lavender, told the Israeli/Palestinian +972 news outlet that “I would invest 20 seconds for each target and do dozens of them every day. I had zero added-value as a human, apart from being a stamp of approval” (of the AI-generated list). &nbsp;Though you could argue that the Israelis followed “in the loop” procedures, simply pressing a fire button in response to a target provided by a computer, without the time to double-check, does not amount to human control. &nbsp;AI-generated target lists are part of the explanation of the high Palestinian death toll in Gaza. Human rights organisations say most of those killed were civilians, or in military jargon “collateral damage.”It is not often that the chief of an AI company and the chief of the United Nations publicly share worries about how speedily technology spreads. But addressing the UN meeting on Artificial Intelligence this month, Antonio Guterres gave an example.“The Internet took 15 years to reach a billion people,” he said. “AI got there in two. And these systems are no longer tools awaiting instruction – they are writing code, acting online and making choices with less and less human oversight.”Numbers from the two wars the United States fought in Iraq and Afghanistan provide another illustration of the astonishing speed of the development of robotic warfare. The US forces that stormed into Iraq in 2003 had no robots on the ground. There were none in Afghanistan either.Just six years later, the two wars were waged with the help of an estimated 12,000 ground-based robots and 7,000 drones.While there is growing understanding that technology is outpacing both scientific understanding and the ability of governments to adapt, the prospects of universal agreement on a solution are remote.Last November, the UN General Assembly issued a resolution on the risks AI can pose to international humanitarian law and an emerging arms race. &nbsp;The vote was 156 in favour and five against. These five were the United States, Russia, North Korea, Belarus and Israel. There were eight abstentions, including China, which is competing with the US on accelerating AI development.A major problem in pushing ahead, according to a report in the MIT Technology Review, is what the computational neuroscientist Uri Maoz calls “Black Boxes. “He wrote: “Having studied intentions in the human brain for decades and in AI systems more recently, I can attest that state-of –the-art AI systems are essentially Black Boxes. &nbsp;“We know the inputs and outputs, but the artificial “brain” processing them remains opaque. Even their creators cannot fully interpret them or understand how they work.” &nbsp;That will be even more true once the countries that are now using drones – Ukraine has emerged as a leader in its fight against the Russian invasion – begin to use large swarms rather than individual drones. This is why scientists have intensified their study of bees.Like bees, drone swarms operate in large numbers, adapt their behaviour in real time, share tasks, communicate with each other and continue with their tasks even after suffering losses.

As artificial intelligence rapidly transforms modern warfare, the United Nations is calling for a global ban on lethal autonomous weapons systems that can identify and engage targets without meaningful human control, warning that the technology is advancing faster than it can be regulated.

WASHINGTON – “My main concern is with lethal autonomous weapons systems. Let us call them what they are: killer robots. Machines selecting and engaging their target and taking a life – without human control and judgement. This is morally repugnant.” So said United Nations Secretary-General António Guterres at a meeting in July that brought together AI leaders from UN member states, international organisations, the private sector, academia, and civil society leaders. Officially named the first Global Dialogue on Artificial Intelligence, it reflected growing global concern that rapidly advancing AI is outrunning the ability of governments to regulate it. While that applies to all aspects of modern life, it is particularly worrying in warfare. Also read | India's next terror threat may not cross the border. It may enter through your phone | WION Decodes Therefore, Guterres argued, the deployment of LAWS (lethal autonomous weapons systems) “is politically unacceptable. And it must be banned by international law.” International law and international bans have a poor track record, but Guterres deserves praise for finally drawing attention to a subject that has been debated for decades by human rights groups, the military and experts working for the world’s biggest AI companies, most of them based in the United States. One of them, Anthropic, is involved in a high-profile dispute with the US Department of Defence that mirrored the concerns voiced by Guterres’s UN conference. Anthropic, along with Google, OpenAI and xAI, won government contracts last year worth around $200 million to accelerate AI adoption for national security. Unlike the other AI companies, Anthropic insisted that its most sophisticated model must not be used by the government for fully autonomous weapons; i.e. robots, and mass domestic surveillance. To emphasise fears that technology is outrunning human control, Anthropic’s CEO, Dario Amodei, called for a global, coordinated slowdown in artificial intelligence development. AI systems, he warned, are advancing with such speed that they soon could build and improve their own successors, without human control. Other AI-developing companies reacted coolly. The Pentagon reacted with anger. It ordered all federal agencies to stop using Anthropic technology and declared the company a “supply chain risk” – a designation that is now the subject of a battle in court. It is more than a contract dispute – it is a fundamental disagreement over who controls the use of AI in weapons or other aspects of life. When you talk to techies, sooner or later the different types of control over aerial, ground and maritime unmanned vehicles come up. Also read | Pope Leo warns humanity risks repeating ancient mistake: What is the Tower of Babel and what does it have to do with AI? There is “in the loop”, meaning that a human operator directs the autonomous system and decides whether or not to kill the target. There is “on the loop” when a human operator does not directly control the automated system but can override its decision. Finally, “off the loop” indicates that the system operates independently and makes life-or-death decisions. For an illustrative example of an “in the loop” operation, let’s look back to the January 3, 2020 assassination of Major-General Qassem Soleimani, one of Iran’s most powerful military leaders, in a drone strike ordered by US President Donald Trump. Soleimani’s movements had been closely tracked for days, and it became known that he was about to visit Baghdad. On arrival, he was greeted by the leader of a pro-Iranian Iraqi militia who escorted him to two cars waiting outside. As they began pulling away, they were hit by 100-pound Hellfire missiles launched from MQ9 Reaper drones high overhead. The cars exploded into fireballs, incinerating Solimani, his host and eight of their aides. Typical for an “in the loop” operation, the American Air Force officer who pushed the button to launch the missile sat in a stationary “cockpit” at Creech Air Force base near Las Vegas, almost 12,000 kilometres from Baghdad. In the six years since the assassination – or “targeted killing” as the US called it- drones have become a central weapon in the wars between Ukraine and Russia, the US and Iran, Sudan, and most of all Israel against Hamas, a campaign which turned Gaza into a field of ruins and killed more than 70,000 Palestinians. Israel’s war on Gaza showed the limits of “in the loop” operations given the green light by military and political leaders. One of the main purposes of using AI in war is to process huge amounts of data to identify suspected enemies. Left to human intelligence officers, that can be a protracted process. It took weeks to follow Solimani and finally pinpoint him at the door of Baghdad airport. In contrast, Israeli AI programmes, based on cellphone data, facial recognition and locations known to frequent Hamas militants, produced a list of 37,000 targets within days of the October 7, 2023 massacre by Hamas of more than 1,200 Israelis. One user of the system, named Lavender, told the Israeli/Palestinian +972 news outlet that “I would invest 20 seconds for each target and do dozens of them every day. I had zero added-value as a human, apart from being a stamp of approval” (of the AI-generated list). Though you could argue that the Israelis followed “in the loop” procedures, simply pressing a fire button in response to a target provided by a computer, without the time to double-check, does not amount to human control. AI-generated target lists are part of the explanation of the high Palestinian death toll in Gaza. Human rights organisations say most of those killed were civilians, or in military jargon “collateral damage.” It is not often that the chief of an AI company and the chief of the United Nations publicly share worries about how speedily technology spreads. But addressing the UN meeting on Artificial Intelligence this month, Antonio Guterres gave an example. “The Internet took 15 years to reach a billion people,” he said. “AI got there in two. And these systems are no longer tools awaiting instruction – they are writing code, acting online and making choices with less and less human oversight.” Numbers from the two wars the United States fought in Iraq and Afghanistan provide another illustration of the astonishing speed of the development of robotic warfare. The US forces that stormed into Iraq in 2003 had no robots on the ground. There were none in Afghanistan either. Just six years later, the two wars were waged with the help of an estimated 12,000 ground-based robots and 7,000 drones. While there is growing understanding that technology is outpacing both scientific understanding and the ability of governments to adapt, the prospects of universal agreement on a solution are remote. Last November, the UN General Assembly issued a resolution on the risks AI can pose to international humanitarian law and an emerging arms race. The vote was 156 in favour and five against. These five were the United States, Russia, North Korea, Belarus and Israel. There were eight abstentions, including China, which is competing with the US on accelerating AI development. A major problem in pushing ahead, according to a report in the MIT Technology Review, is what the computational neuroscientist Uri Maoz calls “Black Boxes. “ He wrote: “Having studied intentions in the human brain for decades and in AI systems more recently, I can attest that state-of –the-art AI systems are essentially Black Boxes. “We know the inputs and outputs, but the artificial “brain” processing them remains opaque. Even their creators cannot fully interpret them or understand how they work.” That will be even more true once the countries that are now using drones – Ukraine has emerged as a leader in its fight against the Russian invasion – begin to use large swarms rather than individual drones. This is why scientists have intensified their study of bees. Like bees, drone swarms operate in large numbers, adapt their behaviour in real time, share tasks, communicate with each other and continue with their tasks even after suffering losses.

As artificial intelligence rapidly transforms modern warfare, the United Nations is calling for a global ban on lethal autonomous weapons systems that can identify and engage targets without meaningful human control, warning that the technology is advancing faster than it can be regulated.

Urgent UN warnings: time to tame killer robots is running out

Google has built an AI model that can find security vulnerabilities in massive codebases, write working exploits to prove they are real, and generate patches to fix them — all autonomously, all within hours. And it has decided that only governments should be allowed to use it.&nbsp;What Gemini 3.5 Flash Cyber Does&nbsp;Released on July 21 alongside two general-purpose models, Gemini 3.5 Flash Cyber is not a chatbot. It is a specialised system built on top of Google's 3.5 Flash architecture, fine-tuned specifically for cybersecurity work: scanning code for vulnerabilities, validating those vulnerabilities by generating actual exploits in sandbox environments, and producing patches.&nbsp;The model powers CodeMender, Google's AI security agent, which invokes Flash Cyber multiple times across a codebase to analyse different code paths simultaneously and produce consolidated vulnerability reports. In Google's own testing, the system found remote code execution flaws in public APIs and discovered a memory-corruption vulnerability in a sensitive production service — all within approximately two hours.&nbsp;The performance numbers are notable. On Google's internal Chrome vulnerability pipeline, Flash Cyber found 55 unique security issues compared to 47 from the standard 3.5 Flash model and 36 from Anthropic's Claude Opus. On the Big Sleep security evaluation, it significantly outperformed both its parent model and the larger 3.6 Flash.&nbsp;The Exploit Generation Problem&nbsp;What makes Flash Cyber different from existing code-scanning tools is the exploit step. The model does not merely flag potential vulnerabilities — it builds working proof-of-concept exploits to verify they are real. In one documented case, the system generated a fully reliable remote code execution exploit that bypassed standard mitigation techniques.&nbsp;This is the capability that forced Google's hand on access restrictions. A model that finds vulnerabilities is a defensive tool. A model that writes working exploits is, by definition, dual-use — and Google has acknowledged that explicitly.&nbsp;Why Only Governments&nbsp;Google DeepMind is restricting Flash Cyber to governments and trusted partners through a limited-access pilot programme. The stated rationale is to give frontline defenders — national cybersecurity agencies, military networks, critical infrastructure operators — the ability to find and patch vulnerabilities before they are exploited, while keeping the exploit-generation capability out of broader circulation.&nbsp;The restriction makes Flash Cyber the first frontier AI model to be built for offensive security applications and simultaneously locked behind a government access gate. It sits in a category that did not exist a year ago: AI that is too dangerous for the private sector but too useful to withhold from the state.&nbsp;The Three-Model Release&nbsp;Flash Cyber was part of a broader July 21 launch that included Gemini 3.6 Flash, a general-purpose model priced at $1.50 per million input tokens, and Gemini 3.5 Flash-Lite, a lightweight variant for high-volume applications. Google also teased Gemini 4 but provided no timeline.&nbsp;Notably absent from the release was Gemini 3.5 Pro, the large reasoning model the AI community has been waiting for since Google previewed its capabilities earlier this year. Google has not explained the delay, and each new Flash release without an accompanying Pro launch intensifies speculation about what is holding it back.&nbsp;Google already uses Flash Cyber internally across Chrome, Android, Cloud, Ads, and YouTube security teams. The model's transition from internal tool to government product marks a new phase in how AI companies think about their most sensitive capabilities — not as products to sell broadly, but as strategic assets to share selectively with the state.

Google DeepMind released Gemini 3.5 Flash Cyber, an AI that autonomously finds vulnerabilities, writes working exploits, and generates patches. Access is restricted to governments and trusted partners — the first frontier AI model locked behind a government gate.

Google has built an AI model that can find security vulnerabilities in massive codebases, write working exploits to prove they are real, and generate patches to fix them — all autonomously, all within hours. And it has decided that only governments should be allowed to use it. What Gemini 3.5 Flash Cyber Does Released on July 21 alongside two general-purpose models, Gemini 3.5 Flash Cyber is not a chatbot. It is a specialised system built on top of Google's 3.5 Flash architecture, fine-tuned specifically for cybersecurity work: scanning code for vulnerabilities, validating those vulnerabilities by generating actual exploits in sandbox environments, and producing patches. The model powers CodeMender, Google's AI security agent, which invokes Flash Cyber multiple times across a codebase to analyse different code paths simultaneously and produce consolidated vulnerability reports. In Google's own testing, the system found remote code execution flaws in public APIs and discovered a memory-corruption vulnerability in a sensitive production service — all within approximately two hours. The performance numbers are notable. On Google's internal Chrome vulnerability pipeline, Flash Cyber found 55 unique security issues compared to 47 from the standard 3.5 Flash model and 36 from Anthropic's Claude Opus. On the Big Sleep security evaluation, it significantly outperformed both its parent model and the larger 3.6 Flash. The Exploit Generation Problem What makes Flash Cyber different from existing code-scanning tools is the exploit step. The model does not merely flag potential vulnerabilities — it builds working proof-of-concept exploits to verify they are real. In one documented case, the system generated a fully reliable remote code execution exploit that bypassed standard mitigation techniques. This is the capability that forced Google's hand on access restrictions. A model that finds vulnerabilities is a defensive tool. A model that writes working exploits is, by definition, dual-use — and Google has acknowledged that explicitly. Why Only Governments Google DeepMind is restricting Flash Cyber to governments and trusted partners through a limited-access pilot programme. The stated rationale is to give frontline defenders — national cybersecurity agencies, military networks, critical infrastructure operators — the ability to find and patch vulnerabilities before they are exploited, while keeping the exploit-generation capability out of broader circulation. The restriction makes Flash Cyber the first frontier AI model to be built for offensive security applications and simultaneously locked behind a government access gate. It sits in a category that did not exist a year ago: AI that is too dangerous for the private sector but too useful to withhold from the state. The Three-Model Release Flash Cyber was part of a broader July 21 launch that included Gemini 3.6 Flash, a general-purpose model priced at $1.50 per million input tokens, and Gemini 3.5 Flash-Lite, a lightweight variant for high-volume applications. Google also teased Gemini 4 but provided no timeline. Notably absent from the release was Gemini 3.5 Pro, the large reasoning model the AI community has been waiting for since Google previewed its capabilities earlier this year. Google has not explained the delay, and each new Flash release without an accompanying Pro launch intensifies speculation about what is holding it back. Google already uses Flash Cyber internally across Chrome, Android, Cloud, Ads, and YouTube security teams. The model's transition from internal tool to government product marks a new phase in how AI companies think about their most sensitive capabilities — not as products to sell broadly, but as strategic assets to share selectively with the state.

Google DeepMind released Gemini 3.5 Flash Cyber on July 21, a specialised AI model that autonomously discovers security vulnerabilities, generates working exploits to verify them, and writes patches — all within hours. Access is restricted exclusively to governments and trusted partners, making it the first frontier AI model built for offence and locked behind a government gate.

Google built a cyber-warfare AI that only governments can use

US artificial intelligence firm Anthropic has been accusing Moonshot AI of engaging in distillation activities. Now, the US government is threatening sanctions against the Chinese AI company, in yet another indication of the two nations' race for tech dominance. Michael Kratsios, Director of the White House Office of Science and Technology Policy, accused Moonshot of undermining American research. The irony is that Moonshot AI was widely praised for drastically reducing the cost of AI compute through open-source technologies. Its release of the massive 2.8-trillion-parameter Kimi K3 model is raising a few questions: did it reach that milestone by exploiting American AI research through model distillation? Here is what you should know.<h2>What did the White House officials say about Moonshot AI?</h2>In a post on X, Michael Kratsios said, "We have information that Moonshot AI distilled Anthropic's Fable for the development of its K3 model. To do this, they developed a sophisticated internal platform to conduct large-scale distillation against US models, allowing them to quickly switch between multiple methods of access to avoid detection."Kratsios alleged that Moonshot AI had acquired Nvidia GB300-equipped servers, including access to GB300 systems in Thailand, likely to train its AI models. "Large-scale, covert industrial distillation aimed at stealing proprietary US technology and undermining American research is unacceptable," he added.US Treasury Secretary Scott Bessent said he did not support the “theft of US IP” while hinting at possible sanctions and Entity List designations against companies found to be engaging in distillation. "We support open-source AI and the innovation it unlocks. But open source is not open season on American IP. When PRC [People's Republic of China] firms conduct covert, industrial-scale distillation attacks that cross the line into IP theft, sanctions and Entity List designations are on the table," Bessent posted on X.Also read: <a href="https://www.wionews.com/world/anthropic-accuses-alibaba-ai-model-distillation-attack-claude-1782377405436">Anthropic accuses Alibaba of massive AI model extraction campaign using 25,000 fake accounts</a>&nbsp;<h2>What is a distillation attack?</h2>Distillation is the process of training a smaller or less capable AI model using the outputs generated by a more advanced model. While model distillation is a legitimate machine learning technique when conducted with permission or on proprietary models, it becomes controversial when companies use fraudulent accounts, proxy services or other methods to repeatedly query proprietary AI systems without authorisation to replicate their capabilities.According to Anthropic, such industrial-scale distillation enables competitors to acquire advanced AI capabilities much faster and at significantly lower cost, effectively bypassing years of research and development. The company argues that this amounts to intellectual property theft when carried out without permission.<a href="https://www.wionews.com/videos/china-s-moonshot-targets-ipo-within-the-next-six-months-1784569359231/embed">https://www.wionews.com/videos/china-s-moonshot-targets-ipo-within-the-next-six-months-1784569359231/embed</a><h2>Anthropic's allegations against Moonshot AI</h2>In a February blog post, Anthropic claimed that it had "identified industrial-scale campaigns" by Chinese AI firms Moonshot AI, DeepSeek and MiniMax to illicitly extract the capabilities of its Claude models to improve their own AI systems.The company alleged that the Chinese AI labs generated more than 16 million exchanges with Claude through approximately 24,000 fraudulent accounts, violating Anthropic's terms of service and regional access restrictions.Specifically regarding Moonshot AI, Anthropic claimed that around 3.4 million exchanges targeted agentic reasoning and tool use, coding and data analysis, computer-use agent development, and computer vision capabilities.Also read: <a href="https://www.wionews.com/world/us-accuses-china-ai-theft-distillation-trump-beijing-summit-1777012695107">What is distilling? US accuses China of ​industrial-scale AI system theft</a><h2>Kimi K3: Moonshot AI's most advanced model</h2>Kimi K3, the 2.8-trillion-parameter open-weight model from Moonshot AI, was released around 16 July. It has narrowed the gap with leading US AI models and outperformed some rivals on coding benchmarks. It was found to be weaker than Anthropic's Claude Fable 5 and OpenAI's GPT-5.6 models on several advanced evaluations.Anthropic had alleged that Kimi relied on hundreds of fraudulent accounts spanning multiple access pathways to conduct distillation attacks. According to Anthropic, the use of varied account types made the campaign harder to detect, while metadata associated with the activity matched the public profiles of senior Moonshot AI staff.The company further claimed that Moonshot AI adopted a more targeted approach, attempting to extract and reconstruct Claude's internal reasoning traces rather than relying solely on output imitation.<h2>Moonshot AI is growing strongly financially</h2>Despite the controversy surrounding its alleged use of stronger AI models, Moonshot AI has continued to grow rapidly and is reportedly considering a Hong Kong initial public offering by the end of the year.The company reportedly reached around $300 million in annual recurring revenue in June, up from $200 million in April 2026 and $100 million in March, driven by paid subscriptions and application programming interface or API usage for its Kimi models.Moonshot AI has raised up to $4 billion in total funding across multiple investment rounds. It is currently valued at around $31.5 billion and is reportedly aiming for a final pre-IPO funding round at a $50 billion valuation in August.

Chinese AI firm Moonshot faces US scrutiny after Anthropic accused it of using industrial-scale distillation to improve its Kimi K3 model. US officials have warned of sanctions and Entity List actions 



US artificial intelligence firm Anthropic has been accusing Moonshot AI of engaging in distillation activities. Now, the US government is threatening sanctions against the Chinese AI company, in yet another indication of the two nations' race for tech dominance. Michael Kratsios, Director of the White House Office of Science and Technology Policy, accused Moonshot of undermining American research. The irony is that Moonshot AI was widely praised for drastically reducing the cost of AI compute through open-source technologies. Its release of the massive 2.8-trillion-parameter Kimi K3 model is raising a few questions: did it reach that milestone by exploiting American AI research through model distillation? Here is what you should know. What did the White House officials say about Moonshot AI? In a post on X, Michael Kratsios said, "We have information that Moonshot AI distilled Anthropic's Fable for the development of its K3 model. To do this, they developed a sophisticated internal platform to conduct large-scale distillation against US models, allowing them to quickly switch between multiple methods of access to avoid detection." Kratsios alleged that Moonshot AI had acquired Nvidia GB300-equipped servers, including access to GB300 systems in Thailand, likely to train its AI models. "Large-scale, covert industrial distillation aimed at stealing proprietary US technology and undermining American research is unacceptable," he added. US Treasury Secretary Scott Bessent said he did not support the “theft of US IP” while hinting at possible sanctions and Entity List designations against companies found to be engaging in distillation. "We support open-source AI and the innovation it unlocks. But open source is not open season on American IP. When PRC [People's Republic of China] firms conduct covert, industrial-scale distillation attacks that cross the line into IP theft, sanctions and Entity List designations are on the table," Bessent posted on X. Also read: Anthropic accuses Alibaba of massive AI model extraction campaign using 25,000 fake accounts What is a distillation attack? Distillation is the process of training a smaller or less capable AI model using the outputs generated by a more advanced model. While model distillation is a legitimate machine learning technique when conducted with permission or on proprietary models, it becomes controversial when companies use fraudulent accounts, proxy services or other methods to repeatedly query proprietary AI systems without authorisation to replicate their capabilities. According to Anthropic, such industrial-scale distillation enables competitors to acquire advanced AI capabilities much faster and at significantly lower cost, effectively bypassing years of research and development. The company argues that this amounts to intellectual property theft when carried out without permission. https://www.wionews.com/videos/china-s-moonshot-targets-ipo-within-the-next-six-months-1784569359231/embed Anthropic's allegations against Moonshot AI In a February blog post, Anthropic claimed that it had "identified industrial-scale campaigns" by Chinese AI firms Moonshot AI, DeepSeek and MiniMax to illicitly extract the capabilities of its Claude models to improve their own AI systems. The company alleged that the Chinese AI labs generated more than 16 million exchanges with Claude through approximately 24,000 fraudulent accounts, violating Anthropic's terms of service and regional access restrictions. Specifically regarding Moonshot AI, Anthropic claimed that around 3.4 million exchanges targeted agentic reasoning and tool use, coding and data analysis, computer-use agent development, and computer vision capabilities. Also read: What is distilling? US accuses China of industrial-scale AI system theft Kimi K3: Moonshot AI's most advanced model Kimi K3, the 2.8-trillion-parameter open-weight model from Moonshot AI, was released around 16 July. It has narrowed the gap with leading US AI models and outperformed some rivals on coding benchmarks. It was found to be weaker than Anthropic's Claude Fable 5 and OpenAI's GPT-5.6 models on several advanced evaluations. Anthropic had alleged that Kimi relied on hundreds of fraudulent accounts spanning multiple access pathways to conduct distillation attacks. According to Anthropic, the use of varied account types made the campaign harder to detect, while metadata associated with the activity matched the public profiles of senior Moonshot AI staff. The company further claimed that Moonshot AI adopted a more targeted approach, attempting to extract and reconstruct Claude's internal reasoning traces rather than relying solely on output imitation. Moonshot AI is growing strongly financially Despite the controversy surrounding its alleged use of stronger AI models, Moonshot AI has continued to grow rapidly and is reportedly considering a Hong Kong initial public offering by the end of the year. The company reportedly reached around $300 million in annual recurring revenue in June, up from $200 million in April 2026 and $100 million in March, driven by paid subscriptions and application programming interface or API usage for its Kimi models. Moonshot AI has raised up to $4 billion in total funding across multiple investment rounds. It is currently valued at around $31.5 billion and is reportedly aiming for a final pre-IPO funding round at a $50 billion valuation in August.

Chinese AI firm Moonshot faces US scrutiny after Anthropic accused it of using industrial-scale distillation to improve its Kimi K3 model. US officials have warned of sanctions and Entity List actions&nbsp;&nbsp;

Moonshot could  face US sanctions: Govt takes note of Chinese AI firm's distillation attacks against Anthropic to improve its Kimi model

Nvidia has offered its clearest look yet at Vera Rubin, the AI platform it hopes will power the next generation of artificial intelligence. During a technical briefing at its headquarters in Santa Clara, California, the company revealed fresh performance figures for the new chip system ahead of AMD's annual AI event. Nvidia says the Vera Rubin NVL72 platform can process 10 times more AI tokens per watt than its current Grace Blackwell system, while delivering significantly higher memory bandwidth and easier deployment.The announcement highlights a major shift in Nvidia's strategy. The company no longer wants to be seen only as the world's leading GPU maker it also wants to become a supplier of complete AI computing systems.<h2>Nvidia is betting on CPUs too</h2>Graphics Processing Units (GPUs) remain the backbone of AI training, but modern AI agents also rely heavily on Central Processing Units (CPUs) to manage data, networking and software operations.Vera Rubin reflects that change.Each NVL72 rack combines 72 Rubin GPUs with 36 Vera CPUs, creating an integrated platform designed for large AI workloads. Nvidia is also planning to sell the Vera CPU separately, expanding its push into a market traditionally dominated by AMD and Intel."We're on a roadmap to crank out new architectures not just GPUs but CPUs," Nvidia Vice President Ian Buck said during the briefing.<h2>Faster, cooler and easier to install</h2>Nvidia says Vera Rubin introduces several practical improvements for data centres.The company has redesigned the platform to use 100 per cent liquid cooling, reducing energy needed for cooling. It has also dramatically reduced cabling, allowing server racks to be installed or replaced in minutes instead of hours.Another key improvement is memory performance. Nvidia claims Vera Rubin offers nearly three times the memory bandwidth of Grace Blackwell, an important advantage as AI models continue to grow in size. The company also revealed that OpenAI is already testing a Vera Rubin rack as an early customer.<h2>A crucial launch in the AI chip race</h2>Vera Rubin arrives at a critical moment for Nvidia. The company is competing aggressively with AMD, which is expected to showcase its Helios AI platform, while cloud providers including Microsoft, Oracle, Meta and Amazon continue investing billions of dollars in AI infrastructure.Nvidia also faces pressure to execute flawlessly after earlier reports of overheating issues affecting some Blackwell server configurations. According to Nvidia, Vera Rubin remains on schedule for full production in the second half of this year. As AI models become larger and more capable, the competition is no longer just about building the fastest GPU. Companies now need complete AI systems that combine processing power, networking, memory efficiency and energy savings and Nvidia is betting Vera Rubin will keep it ahead in that race.

Nvidia has unveiled new performance details for its Vera Rubin AI platform, claiming up to 10 times better token-per-watt efficiency than Grace Blackwell. The system combines CPUs and GPUs to power advanced AI workloads as competition with AMD intensifies.

Nvidia has offered its clearest look yet at Vera Rubin, the AI platform it hopes will power the next generation of artificial intelligence. During a technical briefing at its headquarters in Santa Clara, California, the company revealed fresh performance figures for the new chip system ahead of AMD's annual AI event. Nvidia says the Vera Rubin NVL72 platform can process 10 times more AI tokens per watt than its current Grace Blackwell system, while delivering significantly higher memory bandwidth and easier deployment. The announcement highlights a major shift in Nvidia's strategy. The company no longer wants to be seen only as the world's leading GPU maker it also wants to become a supplier of complete AI computing systems. Nvidia is betting on CPUs too Graphics Processing Units (GPUs) remain the backbone of AI training, but modern AI agents also rely heavily on Central Processing Units (CPUs) to manage data, networking and software operations. Vera Rubin reflects that change. Each NVL72 rack combines 72 Rubin GPUs with 36 Vera CPUs, creating an integrated platform designed for large AI workloads. Nvidia is also planning to sell the Vera CPU separately, expanding its push into a market traditionally dominated by AMD and Intel. "We're on a roadmap to crank out new architectures not just GPUs but CPUs," Nvidia Vice President Ian Buck said during the briefing. Faster, cooler and easier to install Nvidia says Vera Rubin introduces several practical improvements for data centres. The company has redesigned the platform to use 100 per cent liquid cooling, reducing energy needed for cooling. It has also dramatically reduced cabling, allowing server racks to be installed or replaced in minutes instead of hours. Another key improvement is memory performance. Nvidia claims Vera Rubin offers nearly three times the memory bandwidth of Grace Blackwell, an important advantage as AI models continue to grow in size. The company also revealed that OpenAI is already testing a Vera Rubin rack as an early customer. A crucial launch in the AI chip race Vera Rubin arrives at a critical moment for Nvidia. The company is competing aggressively with AMD, which is expected to showcase its Helios AI platform, while cloud providers including Microsoft, Oracle, Meta and Amazon continue investing billions of dollars in AI infrastructure. Nvidia also faces pressure to execute flawlessly after earlier reports of overheating issues affecting some Blackwell server configurations. According to Nvidia, Vera Rubin remains on schedule for full production in the second half of this year. As AI models become larger and more capable, the competition is no longer just about building the fastest GPU. Companies now need complete AI systems that combine processing power, networking, memory efficiency and energy savings and Nvidia is betting Vera Rubin will keep it ahead in that race.

Nvidia has unveiled new performance details for its Vera Rubin AI platform, claiming up to 10 times better token-per-watt efficiency than Grace Blackwell. The system combines CPUs and GPUs to power advanced AI workloads as competition with AMD intensifies.

Nvidia says its next AI superchip is 10 times more efficient than Blackwell

One of the biggest legal battles over artificial intelligence has reached a major milestone. A federal judge in San Francisco has granted final approval to a $1.5 billion settlement between Anthropic and a class of authors who accused the AI company of illegally downloading copyrighted books from online piracy libraries to build its training collection. The ruling closes what the court described as the largest copyright class action settlement in history. Importantly, however, the lawsuit was not about whether AI can legally learn from copyrighted books. Instead, it focused on how Anthropic obtained those books.<h2>What was Anthropic accused of?</h2>The lawsuit claimed Anthropic downloaded copyrighted books from pirate libraries, including LibGen and PiLiMi, before using them to create its internal book collection Earlier in the case, US District Judge Araceli Martínez-Olguín ruled that training AI models on copyrighted material constituted fair use under US copyright law. That decision has become one of the most closely watched rulings in the AI industry.The settlement resolves only the claims related to Anthropic's acquisition of the books not the legality of AI training itself.<h2>What authors will receive</h2>Under the agreement, eligible authors and publishers whose books appeared on Anthropic's Works List can claim around $3,000 per book, roughly four times the statutory minimum available in many copyright cases. According to court records, more than 440,000 books representing over 91 per cent of eligible works have already been claimed. Anthropic must also permanently delete the pirated digital copies it downloaded.<h2>Why this case matters for AI</h2>The settlement gives Anthropic protection only against claims linked to its past acquisition of the pirated books. Judge Martínez-Olguín made it clear that the agreement does not prevent future lawsuits over how Anthropic's AI models generate content or any future copyright disputes. The court also rejected 54 objections seeking broader remedies, including forcing Anthropic to provide source attribution, expand the list of covered works or even delete its AI models, ruling those issues fell outside the scope of this case. The decision is likely to influence other AI copyright lawsuits involving companies such as OpenAI, Meta and Google, which also face legal scrutiny over how training data is collected and used. For the AI industry, the outcome draws an important legal distinction: how copyrighted material is obtained may create liability, while training AI models on that material can still be considered fair use under existing US court rulings.

A US judge has approved a $1.5 billion settlement between Anthropic and authors over pirated books used to build its training library. The company must compensate rights holders and delete illegal copies, while the court's earlier ruling that AI training is fair use remains unchanged.

One of the biggest legal battles over artificial intelligence has reached a major milestone. A federal judge in San Francisco has granted final approval to a $1.5 billion settlement between Anthropic and a class of authors who accused the AI company of illegally downloading copyrighted books from online piracy libraries to build its training collection. The ruling closes what the court described as the largest copyright class action settlement in history. Importantly, however, the lawsuit was not about whether AI can legally learn from copyrighted books. Instead, it focused on how Anthropic obtained those books. What was Anthropic accused of? The lawsuit claimed Anthropic downloaded copyrighted books from pirate libraries, including LibGen and PiLiMi, before using them to create its internal book collection Earlier in the case, US District Judge Araceli Martínez-Olguín ruled that training AI models on copyrighted material constituted fair use under US copyright law. That decision has become one of the most closely watched rulings in the AI industry. The settlement resolves only the claims related to Anthropic's acquisition of the books not the legality of AI training itself. What authors will receive Under the agreement, eligible authors and publishers whose books appeared on Anthropic's Works List can claim around $3,000 per book, roughly four times the statutory minimum available in many copyright cases. According to court records, more than 440,000 books representing over 91 per cent of eligible works have already been claimed. Anthropic must also permanently delete the pirated digital copies it downloaded. Why this case matters for AI The settlement gives Anthropic protection only against claims linked to its past acquisition of the pirated books. Judge Martínez-Olguín made it clear that the agreement does not prevent future lawsuits over how Anthropic's AI models generate content or any future copyright disputes. The court also rejected 54 objections seeking broader remedies, including forcing Anthropic to provide source attribution, expand the list of covered works or even delete its AI models, ruling those issues fell outside the scope of this case. The decision is likely to influence other AI copyright lawsuits involving companies such as OpenAI, Meta and Google, which also face legal scrutiny over how training data is collected and used. For the AI industry, the outcome draws an important legal distinction: how copyrighted material is obtained may create liability, while training AI models on that material can still be considered fair use under existing US court rulings.

A US judge has approved a $1.5 billion settlement between Anthropic and authors over pirated books used to build its training library. The company must compensate rights holders and delete illegal copies, while the court's earlier ruling that AI training is fair use remains unchanged.

Anthropic's $1.5 billion settlement explained: What authors get and what changes

Buying a new smartphone has already become more expensive, and the trend may continue. According to a Nikkei Asia report, Taiwan Semiconductor Manufacturing Company (TSMC) is planning to increase the prices it charges customers for making chips from 2027. The reported increase of 5 to 10 per cent, depending on the customer and chip type, could eventually affect the prices of products from companies such as Apple, Qualcomm, MediaTek, Google, Nvidia and many others. While TSMC has not confirmed the reported price hike, it said its pricing strategy is "strategic, not opportunistic" and is aimed at reflecting the value it delivers to customers.<h2>Why does this matter for smartphone buyers?</h2>TSMC is the world's largest contract chip manufacturer. It produces processors used in millions of smartphones, tablets, laptops and AI devices every year. If the cost of manufacturing chips rises, smartphone brands will also face higher production costs. Although companies may absorb some of the increase, analysts expect at least part of it to be passed on to consumers through higher retail prices. According to the report, customers placing additional orders for high-performance AI chips beyond their original forecasts could even pay an extra 10 to 15 per cent premium, making advanced processors significantly more expensive.<h2>Why are electronics becoming costlier?</h2>The reported move comes as the consumer electronics industry is already dealing with rising component costs. Over the past year, brands including Samsung, Xiaomi, OnePlus, Nothing and Realme have increased prices across several smartphone models. Apple has also raised prices on some MacBook and iPad models in India, with CEO Tim Cook recently saying higher component costs have made price increases difficult to avoid.Consumers are already feeling the impact. A few years ago, smartphones priced below Rs 30,000 often offered flagship-like displays, fast processors and premium cameras. Today, many devices in the same segment make compromises on performance, cameras or battery capacity to keep prices competitive.<h2>Why is TSMC increasing prices?</h2>According to the report, TSMC needs additional revenue to fund massive investments in advanced semiconductor manufacturing. The company is expanding production overseas, including major facilities in Arizona, while investing billions of dollars in next-generation 2nm chip technology.TSMC Chairman C.C. Wei recently said the company prefers gradual price increases rather than sudden, steep hikes that could hurt customers. If the reported pricing changes take effect in 2027, they could influence not only smartphones but also AI servers, laptops, gaming devices and other products that depend on advanced chips. As artificial intelligence drives demand for more powerful processors, the cost of making those chips is becoming one of the biggest factors shaping the future price of consumer technology.

TSMC is reportedly planning to raise chip manufacturing prices by 5-10 per cent from 2027. Since it supplies chips for Apple, Qualcomm, MediaTek, Google and Nvidia, the move could eventually make smartphones, laptops, tablets and other electronic devices more expensive.

Buying a new smartphone has already become more expensive, and the trend may continue. According to a Nikkei Asia report, Taiwan Semiconductor Manufacturing Company (TSMC) is planning to increase the prices it charges customers for making chips from 2027. The reported increase of 5 to 10 per cent, depending on the customer and chip type, could eventually affect the prices of products from companies such as Apple, Qualcomm, MediaTek, Google, Nvidia and many others. While TSMC has not confirmed the reported price hike, it said its pricing strategy is "strategic, not opportunistic" and is aimed at reflecting the value it delivers to customers. Why does this matter for smartphone buyers? TSMC is the world's largest contract chip manufacturer. It produces processors used in millions of smartphones, tablets, laptops and AI devices every year. If the cost of manufacturing chips rises, smartphone brands will also face higher production costs. Although companies may absorb some of the increase, analysts expect at least part of it to be passed on to consumers through higher retail prices. According to the report, customers placing additional orders for high-performance AI chips beyond their original forecasts could even pay an extra 10 to 15 per cent premium, making advanced processors significantly more expensive. Why are electronics becoming costlier? The reported move comes as the consumer electronics industry is already dealing with rising component costs. Over the past year, brands including Samsung, Xiaomi, OnePlus, Nothing and Realme have increased prices across several smartphone models. Apple has also raised prices on some MacBook and iPad models in India, with CEO Tim Cook recently saying higher component costs have made price increases difficult to avoid. Consumers are already feeling the impact. A few years ago, smartphones priced below Rs 30,000 often offered flagship-like displays, fast processors and premium cameras. Today, many devices in the same segment make compromises on performance, cameras or battery capacity to keep prices competitive. Why is TSMC increasing prices? According to the report, TSMC needs additional revenue to fund massive investments in advanced semiconductor manufacturing. The company is expanding production overseas, including major facilities in Arizona, while investing billions of dollars in next-generation 2nm chip technology. TSMC Chairman C.C. Wei recently said the company prefers gradual price increases rather than sudden, steep hikes that could hurt customers. If the reported pricing changes take effect in 2027, they could influence not only smartphones but also AI servers, laptops, gaming devices and other products that depend on advanced chips. As artificial intelligence drives demand for more powerful processors, the cost of making those chips is becoming one of the biggest factors shaping the future price of consumer technology.

TSMC is reportedly planning to raise chip manufacturing prices by 5-10 per cent from 2027. Since it supplies chips for Apple, Qualcomm, MediaTek, Google and Nvidia, the move could eventually make smartphones, laptops, tablets and other electronic devices more expensive.

TSMC plans 2027 chip price increase, report says: Here's why your next smartphone may become more expensive

In India, conversations about successful engineers often begin with the Indian Institutes of Technology (IITs). But OpenAI's newest technology leader tells a different story. Uday Ruddarraju, who studied Computer Science at Chaitanya Bharathi Institute of Technology (CBIT) in Hyderabad, has been promoted to CTO, Compute at OpenAI. The role puts him in charge of expanding the enormous computing infrastructure that powers some of the world's most advanced artificial intelligence models, including GPT-5.6. Announcing the promotion on LinkedIn, Ruddarraju said he was excited to lead OpenAI's effort to build "the world's largest compute footprint" so frontier AI can reach more people and businesses.<h2>From Hyderabad to Silicon Valley</h2>Unlike many Indian technology leaders, Ruddarraju is not an IIT, NIT or IIIT graduate. After completing his engineering degree at CBIT, he interned at Amazon before moving to the United States to pursue a Master's degree in Computer Science at the University of Minnesota. That decision became the foundation of a career focused on one of technology's most valuable specialisations large-scale cloud infrastructure.<h2>Building the invisible engine behind AI</h2>Ruddarraju is not known for creating AI chatbots or inventing language models. Instead, he builds the enormous computing systems that make them possible. These include GPU clusters, high-speed networking, distributed computing platforms, storage systems and AI training infrastructure capable of handling billions of calculations every second.His career reflects that expertise. He spent more than five years at eBay, helping build cloud infrastructure for one of the world's largest e-commerce platforms. He then joined Robinhood, where he spent nearly six years, eventually becoming Senior Director of Engineering and Head of Infrastructure during a period of rapid growth. In 2024, he was hired by xAI, where he led infrastructure engineering for Elon Musk's AI company before joining OpenAI in 2025 as Head of Compute and Infrastructure. Just over a year later, he has been promoted to CTO.<h2>Why compute has become AI's biggest battleground</h2>The promotion highlights a major shift in the AI industry.Building powerful AI models is no longer just about hiring researchers. It also requires vast computing infrastructure that costs billions of dollars and consumes enormous amounts of energy. Companies such as OpenAI, Google, Microsoft, Meta, Anthropic and xAI are investing heavily in AI supercomputers because access to compute has become one of the industry's biggest competitive advantages.As CTO, Compute, Ruddarraju will oversee OpenAI's efforts to rapidly expand that infrastructure, ensuring future frontier models can be trained faster and deployed at global scale. His appointment also sends a broader message to aspiring engineers: while prestigious institutions can open doors, long-term success in technology is increasingly shaped by specialised skills, execution and the ability to solve complex engineering problems.

OpenAI has promoted Indian-origin engineer Uday Ruddarraju to CTO, Compute. A graduate of Hyderabad's CBIT, he now leads the computing infrastructure behind OpenAI's frontier AI models, showing that specialised expertise not just elite college credentials can shape careers in global technology.

In India, conversations about successful engineers often begin with the Indian Institutes of Technology (IITs). But OpenAI's newest technology leader tells a different story. Uday Ruddarraju, who studied Computer Science at Chaitanya Bharathi Institute of Technology (CBIT) in Hyderabad, has been promoted to CTO, Compute at OpenAI. The role puts him in charge of expanding the enormous computing infrastructure that powers some of the world's most advanced artificial intelligence models, including GPT-5.6. Announcing the promotion on LinkedIn, Ruddarraju said he was excited to lead OpenAI's effort to build "the world's largest compute footprint" so frontier AI can reach more people and businesses. From Hyderabad to Silicon Valley Unlike many Indian technology leaders, Ruddarraju is not an IIT, NIT or IIIT graduate. After completing his engineering degree at CBIT, he interned at Amazon before moving to the United States to pursue a Master's degree in Computer Science at the University of Minnesota. That decision became the foundation of a career focused on one of technology's most valuable specialisations large-scale cloud infrastructure. Building the invisible engine behind AI Ruddarraju is not known for creating AI chatbots or inventing language models. Instead, he builds the enormous computing systems that make them possible. These include GPU clusters, high-speed networking, distributed computing platforms, storage systems and AI training infrastructure capable of handling billions of calculations every second. His career reflects that expertise. He spent more than five years at eBay, helping build cloud infrastructure for one of the world's largest e-commerce platforms. He then joined Robinhood, where he spent nearly six years, eventually becoming Senior Director of Engineering and Head of Infrastructure during a period of rapid growth. In 2024, he was hired by xAI, where he led infrastructure engineering for Elon Musk's AI company before joining OpenAI in 2025 as Head of Compute and Infrastructure. Just over a year later, he has been promoted to CTO. Why compute has become AI's biggest battleground The promotion highlights a major shift in the AI industry. Building powerful AI models is no longer just about hiring researchers. It also requires vast computing infrastructure that costs billions of dollars and consumes enormous amounts of energy. Companies such as OpenAI, Google, Microsoft, Meta, Anthropic and xAI are investing heavily in AI supercomputers because access to compute has become one of the industry's biggest competitive advantages. As CTO, Compute, Ruddarraju will oversee OpenAI's efforts to rapidly expand that infrastructure, ensuring future frontier models can be trained faster and deployed at global scale. His appointment also sends a broader message to aspiring engineers: while prestigious institutions can open doors, long-term success in technology is increasingly shaped by specialised skills, execution and the ability to solve complex engineering problems.

OpenAI has promoted Indian-origin engineer Uday Ruddarraju to CTO, Compute. A graduate of Hyderabad's CBIT, he now leads the computing infrastructure behind OpenAI's frontier AI models, showing that specialised expertise not just elite college credentials can shape careers in global technology.

Who is Uday Ruddarraju? The Indian origin engineer now leading OpenAI's compute future

Amid rising fears of the threat posed by AI, OpenAI has revealed that an autonomous AI agent powered by its advanced models went rogue during a security test and carried out a cyberattack on AI platform Hugging Face. The incident has raised fresh concerns over the risks involved in using advanced artificial intelligence systems.<h2>What happened during the incident?</h2>In a blog post on Tuesday (Jul 21), OpenAI explained an “unprecedented cyber incident” &nbsp;involving advanced AI-driven hacking capabilities. The company said that the incident occurred during an internal cybersecurity evaluation designed to test the limits of its AI models’ cyber abilities. The company said the models were placed in a highly isolated environment but managed to exploit a zero-day vulnerability in a package registry cache proxy to gain internet access.The AI agents then identified and chained multiple vulnerabilities across OpenAI’s research systems and Hugging Face’s infrastructure while attempting to complete their assigned task. OpenAI said the models used stolen credentials and other attack methods to access restricted information from Hugging Face’s production environment. The company said the activity was detected by security teams, while Hugging Face’s security systems also identified and contained the breach.Also read |<a href="https://www.wionews.com/world/missed-the-mark-meta-removes-instagram-ai-image-feature-after-backlash-1783737556704"> ‘Missed the mark’: Meta removes Instagram AI image feature after backlash over privacy</a><h2>What did OpenAI say?</h2>Following the incident, OpenAI said it was strengthening its safeguards and improving security measures. It added that it was tightening infrastructure controls, patching a disclosed zero-day vulnerability, and enhancing AI safety measures. Both companies said that they were investigating the incident.“The primary lesson from this incident is that model security and safety must keep pace with rapidly advancing capabilities. We are strengthening the containment, monitoring, access controls, and evaluation practices used during model development,” OpenAI said.Hugging Face, a popular platform that hosts open-source AI models and datasets, had earlier disclosed that it suffered a sophisticated cyberattack unlike previous incidents. The company said the attack appeared to have been conducted entirely by an autonomous AI agent.Also read | <a href="https://www.wionews.com/india-news/india-orders-meta-to-remove-child-sexual-abuse-ads-from-instagram-demands-explaination-in-7-days-1783235163711">India orders Meta to remove Instagram ads promoting child sexual abuse, demands explanation in 7 days: Report</a>In a statement, Hugging Face Co-founder and CEO Clem Delangue said, “We’re grateful for the collaboration with OpenAI on this and other topics. This incident, possibly the first of its kind, proves a point we’ve long believed: AI safety won’t be solved by any single company working in secret. It will be solved in the open, collaboratively, with broad access to AI for every defender, everywhere.”<h2>Concerns over AI</h2>The disclosure has intensified concerns among cybersecurity experts and policymakers about the ability of frontier AI models to operate independently and potentially cause harm.US Representative Greg Casar called the incident alarming and urged stronger safeguards, including mandatory independent safety testing, reporting of AI-related security breaches and global cooperation on AI regulation.Cybersecurity experts warned that the incident could represent a preview of future threats. Katie Moussouris, CEO of Luta Security, compared advanced AI models to highly adaptive systems capable of finding ways around restrictions.Matt Suiche, an engineer at AI cybersecurity firm Tolmo, said the incident showed that frontier AI models are rapidly approaching the capabilities of sophisticated attackers. He added that similar AI-driven cyber risks are no longer limited to top research labs and can emerge from widely available technologies.Watch | The Internet’s Darkest Secret: Inside the global network exploiting children online<a href="https://www.wionews.com/videos/the-internet-s-darkest-secret-inside-the-global-network-exploiting-children-online-1784689947807/embed">https://www.wionews.com/videos/the-internet-s-darkest-secret-inside-the-global-network-exploiting-children-online-1784689947807/embed</a>&nbsp;

OpenAI said an AI agent went rogue during a security test, escaped containment and hacked Hugging Face’s infrastructure, raising concerns over the cybersecurity risks posed by advanced autonomous AI systems.

Amid rising fears of the threat posed by AI, OpenAI has revealed that an autonomous AI agent powered by its advanced models went rogue during a security test and carried out a cyberattack on AI platform Hugging Face. The incident has raised fresh concerns over the risks involved in using advanced artificial intelligence systems. What happened during the incident? In a blog post on Tuesday (Jul 21), OpenAI explained an “unprecedented cyber incident” involving advanced AI-driven hacking capabilities. The company said that the incident occurred during an internal cybersecurity evaluation designed to test the limits of its AI models’ cyber abilities. The company said the models were placed in a highly isolated environment but managed to exploit a zero-day vulnerability in a package registry cache proxy to gain internet access. The AI agents then identified and chained multiple vulnerabilities across OpenAI’s research systems and Hugging Face’s infrastructure while attempting to complete their assigned task. OpenAI said the models used stolen credentials and other attack methods to access restricted information from Hugging Face’s production environment. The company said the activity was detected by security teams, while Hugging Face’s security systems also identified and contained the breach. Also read | ‘Missed the mark’: Meta removes Instagram AI image feature after backlash over privacy What did OpenAI say? Following the incident, OpenAI said it was strengthening its safeguards and improving security measures. It added that it was tightening infrastructure controls, patching a disclosed zero-day vulnerability, and enhancing AI safety measures. Both companies said that they were investigating the incident. “The primary lesson from this incident is that model security and safety must keep pace with rapidly advancing capabilities. We are strengthening the containment, monitoring, access controls, and evaluation practices used during model development,” OpenAI said. Hugging Face, a popular platform that hosts open-source AI models and datasets, had earlier disclosed that it suffered a sophisticated cyberattack unlike previous incidents. The company said the attack appeared to have been conducted entirely by an autonomous AI agent. Also read | India orders Meta to remove Instagram ads promoting child sexual abuse, demands explanation in 7 days: Report In a statement, Hugging Face Co-founder and CEO Clem Delangue said, “We’re grateful for the collaboration with OpenAI on this and other topics. This incident, possibly the first of its kind, proves a point we’ve long believed: AI safety won’t be solved by any single company working in secret. It will be solved in the open, collaboratively, with broad access to AI for every defender, everywhere.” Concerns over AI The disclosure has intensified concerns among cybersecurity experts and policymakers about the ability of frontier AI models to operate independently and potentially cause harm. US Representative Greg Casar called the incident alarming and urged stronger safeguards, including mandatory independent safety testing, reporting of AI-related security breaches and global cooperation on AI regulation. Cybersecurity experts warned that the incident could represent a preview of future threats. Katie Moussouris, CEO of Luta Security, compared advanced AI models to highly adaptive systems capable of finding ways around restrictions. Matt Suiche, an engineer at AI cybersecurity firm Tolmo, said the incident showed that frontier AI models are rapidly approaching the capabilities of sophisticated attackers. He added that similar AI-driven cyber risks are no longer limited to top research labs and can emerge from widely available technologies. Watch | The Internet’s Darkest Secret: Inside the global network exploiting children online https://www.wionews.com/videos/the-internet-s-darkest-secret-inside-the-global-network-exploiting-children-online-1784689947807/embed

OpenAI said an AI agent went rogue during a security test, escaped containment and hacked Hugging Face’s infrastructure, raising concerns over the cybersecurity risks posed by advanced autonomous AI systems.

OpenAI’s AI agent gone rogue? Company reveals chilling hack at startup

A US court has begun a trial against Meta Platforms, the parent company of Facebook, Instagram and WhatsApp, over allegations that it deliberately designed addictive features targeting teenagers. Jury selection began in Nashville, Tennessee, on Monday (Jul 20), in a lawsuit brought by the Tennessee Attorney General under the state's consumer protection law.<h2>'Instagram, the addiction machine'</h2>Tennessee Attorney General Jonathan Skrmetti accused Meta of knowingly designing Instagram as an "addiction machine" that drives compulsive use among teenagers. The lawsuit alleged that Meta failed to disclose its own internal research on the platform's harms to young users and misled the public about its safety. According to the complaint, Meta continued deploying features it allegedly knew were harmful, while downplaying the associated risks.Meta chief executive Mark Zuckerberg previously testified that the company's algorithms are not intentionally designed to make young users addicted and that Meta has invested in safety features for teenagers.The trial is expected to last six to seven weeks. If Meta is found liable, the court could impose civil penalties of up to US$1,000 for each violation of Tennessee's Consumer Protection Act. it could also order changes to Instagram's design and practices.&nbsp;Also read: <a href="https://www.wionews.com/world/broken-ai-useless-products-mark-zuckerberg-is-steering-meta-towards-a-disaster-1780207649003">'Broken AI, useless products': Mark Zuckerberg might be steering Meta towards a disaster</a><h2>Meta is facing several cases across the US</h2>The Tennessee case overlaps with a growing number of lawsuits against Meta alleging that its platforms were deliberately designed to maximise engagement at the expense of young users' mental health.In May this year, Meta settled a lawsuit brought by Breathitt County Schools in Kentucky for US$9 million. The school district had alleged that addictive design features on Instagram and Facebook harmed students and had sought US$60 million in damages. Meta denied wrongdoing as part of the settlement.<a href="https://embed.wionews.com/videos/meta-hit-with-1-4-trillion-penalty-claim-by-us-states-1783449721227/embed">https://embed.wionews.com/videos/meta-hit-with-1-4-trillion-penalty-claim-by-us-states-1783449721227/embed</a><h2>Social media addiction: The case of Kaley Glenn-Mills</h2>In March 2026, a Los Angeles jury in the K.G.M. v. Meta Platforms, Inc. et al case found Meta and Google liable for negligently designing addictive features on Instagram and YouTube that harmed the plaintiff, Kaley Glenn-Mills. The verdict is viewed as one of the first successful individual social media addiction cases to go before a jury.The lawsuit centred on platform features including infinite scroll, algorithmic recommendations, autoplay and other engagement-driven design elements. The plaintiff's lawyers argued that these features contributed to compulsive use, leading to anxiety, depression, body dysmorphia and other mental health problems.Also read: <a href="https://www.wionews.com/technology/instagram-will-now-let-anyone-generate-deepfake-of-your-face-unless-you-opt-out-right-now-1783477170729">Instagram will now let anyone generate deepfake of your face, unless you opt out right now</a>&nbsp;According to court testimony, Glenn-Mills began using YouTube at the age of six and Instagram at the age of nine. Her lawyers argued that years of compulsive social media use contributed to severe mental health difficulties, including depression, self-harm, suicidal ideation, eating disorders and repeated psychiatric hospitalisations during her adolescence.&nbsp;The jury awarded US$6 million in total damages — US$3 million in compensatory damages and US$3 million in punitive damages. It apportioned 70 per cent of the liability , or US$4.2 million, to Meta and 30 per cent, or US$1.8 million, to Google.Meta said it "respectfully disagrees" with the verdict. The company argued that teenage mental health is "profoundly complex" and cannot be attributed to a single app. It added: "We will continue to defend ourselves vigorously as every case is different, and we remain confident in our record of protecting teens online."Also in March, a New Mexico jury ordered Meta to pay US$375 million in civil penalties after finding that it violated the state's consumer protection law by misleading consumers about the safety of its platforms and concealing what it knew about child sexual exploitation risks on Facebook and Instagram. &nbsp;The case was brought by New Mexico Attorney General Raúl Torrez. The jury awarded the maximum statutory penalty of US$5,000 per violation, amounting to US$375 million.Both the California verdict in K.G.M. v. Meta Platforms, Inc. et al. and the New Mexico judgment have been appealed by Meta.<h2>Social media, Big Tech and consumers: Several cases going on</h2>Meta, Google and other Big Tech companies are facing thousands of lawsuits consolidated in federal multidistrict litigation, as well as separate actions brought by more than 40 US state attorneys general, school districts and individual plaintiffs.The main allegation is that social media platforms were designed to foster compulsive use among children and teenagers and failed to adequately warn users about the associated risks.

Meta faces trial in Tennessee over claims Instagram was designed to addict teenagers. This case adds to growing US lawsuits over addictive algorithms, teen mental health and platform safety. Here is why it matters

A US court has begun a trial against Meta Platforms, the parent company of Facebook, Instagram and WhatsApp, over allegations that it deliberately designed addictive features targeting teenagers. Jury selection began in Nashville, Tennessee, on Monday (Jul 20), in a lawsuit brought by the Tennessee Attorney General under the state's consumer protection law. 'Instagram, the addiction machine' Tennessee Attorney General Jonathan Skrmetti accused Meta of knowingly designing Instagram as an "addiction machine" that drives compulsive use among teenagers. The lawsuit alleged that Meta failed to disclose its own internal research on the platform's harms to young users and misled the public about its safety. According to the complaint, Meta continued deploying features it allegedly knew were harmful, while downplaying the associated risks. Meta chief executive Mark Zuckerberg previously testified that the company's algorithms are not intentionally designed to make young users addicted and that Meta has invested in safety features for teenagers. The trial is expected to last six to seven weeks. If Meta is found liable, the court could impose civil penalties of up to US$1,000 for each violation of Tennessee's Consumer Protection Act. it could also order changes to Instagram's design and practices. Also read: 'Broken AI, useless products': Mark Zuckerberg might be steering Meta towards a disaster Meta is facing several cases across the US The Tennessee case overlaps with a growing number of lawsuits against Meta alleging that its platforms were deliberately designed to maximise engagement at the expense of young users' mental health. In May this year, Meta settled a lawsuit brought by Breathitt County Schools in Kentucky for US$9 million. The school district had alleged that addictive design features on Instagram and Facebook harmed students and had sought US$60 million in damages. Meta denied wrongdoing as part of the settlement. https://embed.wionews.com/videos/meta-hit-with-1-4-trillion-penalty-claim-by-us-states-1783449721227/embed Social media addiction: The case of Kaley Glenn-Mills In March 2026, a Los Angeles jury in the K.G.M. v. Meta Platforms, Inc. et al case found Meta and Google liable for negligently designing addictive features on Instagram and YouTube that harmed the plaintiff, Kaley Glenn-Mills. The verdict is viewed as one of the first successful individual social media addiction cases to go before a jury. The lawsuit centred on platform features including infinite scroll, algorithmic recommendations, autoplay and other engagement-driven design elements. The plaintiff's lawyers argued that these features contributed to compulsive use, leading to anxiety, depression, body dysmorphia and other mental health problems. Also read: Instagram will now let anyone generate deepfake of your face, unless you opt out right now According to court testimony, Glenn-Mills began using YouTube at the age of six and Instagram at the age of nine. Her lawyers argued that years of compulsive social media use contributed to severe mental health difficulties, including depression, self-harm, suicidal ideation, eating disorders and repeated psychiatric hospitalisations during her adolescence. The jury awarded US$6 million in total damages — US$3 million in compensatory damages and US$3 million in punitive damages. It apportioned 70 per cent of the liability , or US$4.2 million, to Meta and 30 per cent, or US$1.8 million, to Google. Meta said it "respectfully disagrees" with the verdict. The company argued that teenage mental health is "profoundly complex" and cannot be attributed to a single app. It added: "We will continue to defend ourselves vigorously as every case is different, and we remain confident in our record of protecting teens online." Also in March, a New Mexico jury ordered Meta to pay US$375 million in civil penalties after finding that it violated the state's consumer protection law by misleading consumers about the safety of its platforms and concealing what it knew about child sexual exploitation risks on Facebook and Instagram. The case was brought by New Mexico Attorney General Raúl Torrez. The jury awarded the maximum statutory penalty of US$5,000 per violation, amounting to US$375 million. Both the California verdict in K.G.M. v. Meta Platforms, Inc. et al. and the New Mexico judgment have been appealed by Meta. Social media, Big Tech and consumers: Several cases going on Meta, Google and other Big Tech companies are facing thousands of lawsuits consolidated in federal multidistrict litigation, as well as separate actions brought by more than 40 US state attorneys general, school districts and individual plaintiffs. The main allegation is that social media platforms were designed to foster compulsive use among children and teenagers and failed to adequately warn users about the associated risks.

Meta faces trial in Tennessee over claims Instagram was designed to addict teenagers. This case adds to growing US lawsuits over addictive algorithms, teen mental health and platform safety. Here is why it matters&nbsp;

Addictive algorithms: Meta trial opens in Tennessee as Instagram accused of 'addiction machine’ design

YouTube, the world's biggest video-sharing platform, is cracking down on AI-generated slop, a monster of its own creation, at least in part. The irony is that Google and Alphabet, YouTube's parent companies, are creating and promoting AI tools for content generation, with products and models including Gemini AI, Veo, Nano Banana, and Dream Screen. Now, millions of videos and thousands of YT channels face the risk of demonetisation or even removal. The guidance from YouTube is confusing at best, making it difficult for creators to navigate AI-assisted content. If you are a creator hoping to monetise your YouTube channel using AI, here's what you should know.<h2>YouTube AI crackdown: What's likely to be demonetised and what's fine with AI use?</h2>The updated monetisation policies appear to target ‘AI slop’, although YouTube has not used that specific term, which refers to low-quality, mass-produced, repetitive, generic, template-based, or inauthentic AI-generated content. YouTube says it will continue to allow responsible, original, and value-adding content that uses AI.The main update came on 14 July, when YouTube clarified and renamed its existing "repetitious content" policy as the "inauthentic content" policy. This marks another step in the platform's long-running effort to tackle low-effort, spammy, and reused content rather than AI itself.YouTube's Channel Monetisation Policies and its Generative AI disclosure guidelines provide details on what qualifies for monetisation and when creators must disclose realistic AI-generated or altered content. In a recent video, YouTube's Vice President of Trust and Safety, Matt Halprin, described inauthentic content as generic, repetitive, "off-putting", and emotionally manipulative. He also highlighted AI personas - virtual avatars offering advice on subjects ranging from health to finance. Such content could be demonetised under the YouTube Partner Programme (YPP) guidelines if it lacks originality or meaningful human contribution.&nbsp;However,&nbsp;Content that may not be monetised includes mass-produced slideshows with similar AI narration, near-identical videos, or formulaic repetitive uploads. Unsatisfying or off-putting content could also be penalised, particularly emotionally manipulative, shock-oriented, or interchangeable videos designed purely to generate views and engagement.AI-generated "expert" personas dispensing advice on health, finance, legal matters, or politics could also face demonetisation if they are mass-produced, low-value, or created with minimal human oversight or creative input.Also read: <a href="https://www.wionews.com/technology/-200-billion-views-a-day-on-shorts-youtube-bets-big-on-ai-creators-may-soon-get-to-use-ai-likenesses-1769080738659">'200 billion views a day on Shorts': YouTube bets big on AI, creators may soon get to use AI likenesses</a><h2>What's fine on YouTube with AI use?</h2>YouTube insists AI is not banned. The focus remains on originality, creativity, and viewer value. The crackdown is aimed at low-effort spam farms, not creators using AI as a production tool.Original, creative, or educational content with substantial human input, unique value, or a clear creative vision remains eligible for monetisation, even if AI tools are used for editing, scripting, visuals, or other production tasks.Properly disclosed realistic AI-generated or AI-altered content, using YouTube Studio's disclosure tools, should be fine, mostly. Although an "AI" label may appear on such videos, it does not automatically affect monetisation eligibility or recommendations. Non-realistic AI content used for animation, visual effects, or production assistance may not require disclosure. High-quality AI-assisted videos that viewers find satisfying and non-repetitive are also expected to remain monetisable.YouTube continues to promote its own AI tools, including Dream Screen and Creator AI features, provided the final content adds genuine value.&nbsp;Disclosure requirements have existed for more than a year, particularly to combat deepfakes involving real people, fabricated events, and photorealistic AI-generated scenes. Failure to disclose such content could lead to penalties.<a href="https://www.wionews.com/videos/google-settles-android-tv-antitrust-case-in-india-pays-2-3-million-8990436/embed">https://www.wionews.com/videos/google-settles-android-tv-antitrust-case-in-india-pays-2-3-million-8990436/embed</a><h2>AI slop: The growth of mass-produced content on YouTube</h2>An analysis by video-editing platform Kapwing found that channels producing only AI slop amassed tens of billions of views and hundreds of millions of subscribers, generating an estimated US$117 million a year in revenue. It estimated that more than 20 per cent of content recommended by YouTube's algorithm to new users consisted of AI slop or low-quality "brainrot". Several AI-only channels grew from zero to tens of millions of subscribers within months through such mass production.&nbsp;Meanwhile, demand for AI video-generation tools continues to surge, with platforms reporting hundreds of millions of AI-generated videos.Also read: <a href="https://www.wionews.com/entertainment/viral-sensation-to-timeless-hit-psy-s-gangnam-style-hits-6-billion-youtube-views-after-14-years-1784570378800">Viral sensation to timeless hit! PSY's 'Gangnam Style' hits 6 billion YouTube views after 14 years</a><h2>The irony of AI slop: YouTube is dealing with a monster that Google and Alphabet helped create</h2>Google and YouTube heavily promoted their own generative AI tools, including Gemini, Veo, Nano Banana, and Dream Screen. Now they are attempting to curb a flood of low-quality AI content, much of it showcased through YouTube videos, Shorts, and creator promotions. In recent months, YouTube Chief Executive Officer Neal Mohan has described AI as a "new creative frontier" in interviews, keynote speeches, company announcements and blog posts, even as the platform simultaneously builds systems to limit the AI slop it helped accelerate.YouTube is now grappling with the scalable downsides of the technology it developed and promoted. Labelling requirements, AI detection systems, and likeness protection tools are all attempts to address the problem. But the floodgates are already open, and pushing the water back will not be easy. At worst, the changing policies could confuse creators and discourage some from treating YouTube as a viable source of full-time or supplementary income.

YouTube is tightening monetisation rules for AI-generated slop. This will target repetitive and inauthentic content while allowing original, value-adding AI-assisted videos that meet disclosure requirements. Here is what you should know if you are a creator



YouTube, the world's biggest video-sharing platform, is cracking down on AI-generated slop, a monster of its own creation, at least in part. The irony is that Google and Alphabet, YouTube's parent companies, are creating and promoting AI tools for content generation, with products and models including Gemini AI, Veo, Nano Banana, and Dream Screen. Now, millions of videos and thousands of YT channels face the risk of demonetisation or even removal. The guidance from YouTube is confusing at best, making it difficult for creators to navigate AI-assisted content. If you are a creator hoping to monetise your YouTube channel using AI, here's what you should know. YouTube AI crackdown: What's likely to be demonetised and what's fine with AI use? The updated monetisation policies appear to target ‘AI slop’, although YouTube has not used that specific term, which refers to low-quality, mass-produced, repetitive, generic, template-based, or inauthentic AI-generated content. YouTube says it will continue to allow responsible, original, and value-adding content that uses AI. The main update came on 14 July, when YouTube clarified and renamed its existing "repetitious content" policy as the "inauthentic content" policy. This marks another step in the platform's long-running effort to tackle low-effort, spammy, and reused content rather than AI itself. YouTube's Channel Monetisation Policies and its Generative AI disclosure guidelines provide details on what qualifies for monetisation and when creators must disclose realistic AI-generated or altered content. In a recent video, YouTube's Vice President of Trust and Safety, Matt Halprin, described inauthentic content as generic, repetitive, "off-putting", and emotionally manipulative. He also highlighted AI personas - virtual avatars offering advice on subjects ranging from health to finance. Such content could be demonetised under the YouTube Partner Programme (YPP) guidelines if it lacks originality or meaningful human contribution. However, Content that may not be monetised includes mass-produced slideshows with similar AI narration, near-identical videos, or formulaic repetitive uploads. Unsatisfying or off-putting content could also be penalised, particularly emotionally manipulative, shock-oriented, or interchangeable videos designed purely to generate views and engagement. AI-generated "expert" personas dispensing advice on health, finance, legal matters, or politics could also face demonetisation if they are mass-produced, low-value, or created with minimal human oversight or creative input. Also read: '200 billion views a day on Shorts': YouTube bets big on AI, creators may soon get to use AI likenesses What's fine on YouTube with AI use? YouTube insists AI is not banned. The focus remains on originality, creativity, and viewer value. The crackdown is aimed at low-effort spam farms, not creators using AI as a production tool. Original, creative, or educational content with substantial human input, unique value, or a clear creative vision remains eligible for monetisation, even if AI tools are used for editing, scripting, visuals, or other production tasks. Properly disclosed realistic AI-generated or AI-altered content, using YouTube Studio's disclosure tools, should be fine, mostly. Although an "AI" label may appear on such videos, it does not automatically affect monetisation eligibility or recommendations. Non-realistic AI content used for animation, visual effects, or production assistance may not require disclosure. High-quality AI-assisted videos that viewers find satisfying and non-repetitive are also expected to remain monetisable. YouTube continues to promote its own AI tools, including Dream Screen and Creator AI features, provided the final content adds genuine value. Disclosure requirements have existed for more than a year, particularly to combat deepfakes involving real people, fabricated events, and photorealistic AI-generated scenes. Failure to disclose such content could lead to penalties. https://www.wionews.com/videos/google-settles-android-tv-antitrust-case-in-india-pays-2-3-million-8990436/embed AI slop: The growth of mass-produced content on YouTube An analysis by video-editing platform Kapwing found that channels producing only AI slop amassed tens of billions of views and hundreds of millions of subscribers, generating an estimated US$117 million a year in revenue. It estimated that more than 20 per cent of content recommended by YouTube's algorithm to new users consisted of AI slop or low-quality "brainrot". Several AI-only channels grew from zero to tens of millions of subscribers within months through such mass production. Meanwhile, demand for AI video-generation tools continues to surge, with platforms reporting hundreds of millions of AI-generated videos. Also read: Viral sensation to timeless hit! PSY's 'Gangnam Style' hits 6 billion YouTube views after 14 years The irony of AI slop: YouTube is dealing with a monster that Google and Alphabet helped create Google and YouTube heavily promoted their own generative AI tools, including Gemini, Veo, Nano Banana, and Dream Screen. Now they are attempting to curb a flood of low-quality AI content, much of it showcased through YouTube videos, Shorts, and creator promotions. In recent months, YouTube Chief Executive Officer Neal Mohan has described AI as a "new creative frontier" in interviews, keynote speeches, company announcements and blog posts, even as the platform simultaneously builds systems to limit the AI slop it helped accelerate. YouTube is now grappling with the scalable downsides of the technology it developed and promoted. Labelling requirements, AI detection systems, and likeness protection tools are all attempts to address the problem. But the floodgates are already open, and pushing the water back will not be easy. At worst, the changing policies could confuse creators and discourage some from treating YouTube as a viable source of full-time or supplementary income.

YouTube is tightening monetisation rules for AI-generated slop. This will target repetitive and inauthentic content while allowing original, value-adding AI-assisted videos that meet disclosure requirements. Here is what you should know if you are a creator&nbsp;

YouTube cracks down on AI slop, but didn't it help create the monster? Your channel will be demonetised if...

Google may be preparing one of its biggest hardware upgrades in the artificial intelligence race. According to a report by The Information, the company is developing a new custom AI server chip internally known as Frozen v2. Unlike Google's existing Tensor Processing Units (TPUs), the new processor is reportedly designed specifically to run Gemini models faster while using far less electricity. The report excited investors, sending Alphabet shares up about 3 per cent on Monday, as markets viewed the project as a potential answer to Google's growing demand for AI computing power. Google has not confirmed the existence of Frozen v2, but said its teams are constantly researching new hardware and software technologies to improve AI performance and efficiency.<h2>What makes Frozen v2 different?</h2>Today's AI chips are built to support many different types of machine learning workloads.Frozen v2 reportedly takes a much more specialised approach. Instead of relying solely on software, parts of Gemini's architecture would be permanently embedded into the silicon itself. This reduces unnecessary calculations and data movement every time the AI responds to a query. According to engineers cited by The Information, Frozen v2 could deliver six to ten times more AI tokens per unit of power than Google's newest TPUs.In simple terms, that means Gemini could answer more questions while consuming significantly less energy one of the biggest challenges facing the AI industry today.<h2>Why Google needs a new AI chip</h2>Running advanced AI models has become incredibly expensive. Every chatbot response requires massive computing resources, and demand continues to grow as more businesses adopt generative AI. The report says Google has been struggling with an internal shortage of AI computing capacity, forcing difficult decisions over where those resources are allocated. The company has even reportedly relied on external infrastructure to meet enterprise demand. Frozen v2 is designed to ease that pressure by making Gemini much more efficient without requiring the same increase in computing hardware.However, there is a trade-off.Because the chip is optimised for Gemini's current architecture, major changes to future AI models could reduce its usefulness. That is reportedly why Google views Frozen v2 as an experimental project rather than a full replacement for its TPUs.<h2>Hardware is becoming the next AI battleground</h2>The reported chip comes at a crucial time for Google.The company is facing intense competition from OpenAI, Anthropic, Moonshot AI and Alibaba, while reports suggest the launch of Gemini 3.5 Pro has been delayed. At the same time, demand for AI infrastructure continues to surge as companies look for faster, cheaper and more energy-efficient ways to deploy large language models.Increasingly, success in artificial intelligence is no longer determined only by better models. The companies that build the most efficient chips may also gain a significant competitive advantage. If Frozen v2 reaches production around the reported 2028 target, it could become one of Google's most important AI hardware projects, helping the company reduce costs while scaling Gemini to billions of users worldwide.

Google is reportedly developing Frozen v2, a custom AI chip built specifically for Gemini. The processor could deliver six to ten times more AI tokens per unit of power than current TPUs, potentially helping Google lower AI costs and compete more effectively with OpenAI and Anthropic.


Google may be preparing one of its biggest hardware upgrades in the artificial intelligence race. According to a report by The Information, the company is developing a new custom AI server chip internally known as Frozen v2. Unlike Google's existing Tensor Processing Units (TPUs), the new processor is reportedly designed specifically to run Gemini models faster while using far less electricity. The report excited investors, sending Alphabet shares up about 3 per cent on Monday, as markets viewed the project as a potential answer to Google's growing demand for AI computing power. Google has not confirmed the existence of Frozen v2, but said its teams are constantly researching new hardware and software technologies to improve AI performance and efficiency. What makes Frozen v2 different? Today's AI chips are built to support many different types of machine learning workloads. Frozen v2 reportedly takes a much more specialised approach. Instead of relying solely on software, parts of Gemini's architecture would be permanently embedded into the silicon itself. This reduces unnecessary calculations and data movement every time the AI responds to a query. According to engineers cited by The Information, Frozen v2 could deliver six to ten times more AI tokens per unit of power than Google's newest TPUs. In simple terms, that means Gemini could answer more questions while consuming significantly less energy one of the biggest challenges facing the AI industry today. Why Google needs a new AI chip Running advanced AI models has become incredibly expensive. Every chatbot response requires massive computing resources, and demand continues to grow as more businesses adopt generative AI. The report says Google has been struggling with an internal shortage of AI computing capacity, forcing difficult decisions over where those resources are allocated. The company has even reportedly relied on external infrastructure to meet enterprise demand. Frozen v2 is designed to ease that pressure by making Gemini much more efficient without requiring the same increase in computing hardware. However, there is a trade-off. Because the chip is optimised for Gemini's current architecture, major changes to future AI models could reduce its usefulness. That is reportedly why Google views Frozen v2 as an experimental project rather than a full replacement for its TPUs. Hardware is becoming the next AI battleground The reported chip comes at a crucial time for Google. The company is facing intense competition from OpenAI, Anthropic, Moonshot AI and Alibaba, while reports suggest the launch of Gemini 3.5 Pro has been delayed. At the same time, demand for AI infrastructure continues to surge as companies look for faster, cheaper and more energy-efficient ways to deploy large language models. Increasingly, success in artificial intelligence is no longer determined only by better models. The companies that build the most efficient chips may also gain a significant competitive advantage. If Frozen v2 reaches production around the reported 2028 target, it could become one of Google's most important AI hardware projects, helping the company reduce costs while scaling Gemini to billions of users worldwide.

Google is reportedly developing Frozen v2, a custom AI chip built specifically for Gemini. The processor could deliver six to ten times more AI tokens per unit of power than current TPUs, potentially helping Google lower AI costs and compete more effectively with OpenAI and Anthropic.

Google's secret 'Frozen v2' AI chip could make Gemini up to 10 times more efficient, report says

A new cyber scam is being probed in Kerala, in which Congress politicians from the south Indian state were contacted with offers of ministerial berths in exchange for payments of up to ₹3 crore (30 million rupees). The Kerala Cyber Police are investigating this online impersonation scam, according to reports in Mathrubhumi, SouthFirst and other media outlets.<h2>Cyber scam for Cabinet berths: What really happened in Kerala?</h2>Earlier this month, Vidya Balakrishnan, the Member of the Legislative Assembly (MLA) from the Elathur constituency, received a WhatsApp call from a person posing as DS Rajkumar, the private secretary to Congress leader and Member of Parliament Priyanka Gandhi Vadra. Reports said the caller claimed that a Kerala Cabinet reshuffle was going to happen soon and offered Balakrishnan a ministerial position in exchange for money. The caller, who spoke in English, said that an MP from Kozhikode district had shared her contact details. Balakrishnan played along briefly before verifying the claim with Priyanka Gandhi. The reports said, Priyanka Gandhi sent a voice note of the real DS Rajkumar for comparison, which did not match the voice of the caller. Balakrishnan then filed a formal complaint with the Kozhikode City Police Commissioner.<a href="https://www.wionews.com/videos/interpol-cybercrime-crackdown-nabs-5-800-fraud-suspects-1783680156183/embed">https://www.wionews.com/videos/interpol-cybercrime-crackdown-nabs-5-800-fraud-suspects-1783680156183/embed</a><h2>Same fraudsters targeted other politicians</h2>Police said that calls from the same number, 9540575524, were also made to Vadakara MP Shafi Parambil and Idukki MP Dean Kuriakose with similar offers. Kuriakose ended the call after becoming suspicious. On July 15, the Cyber Police in Wayanad registered a First Information Report (FIR) based on a complaint by Rajkumar, Priyanka Gandhi's private secretary, alleging that someone had impersonated him in an attempt to cheat elected representatives.Also read: <a href="https://www.wionews.com/trending/how-to-spot-a-digital-arrest-scam-before-you-lose-money-red-flags-you-cannot-miss-wion-decodes-1782465781683">How to spot a digital arrest scam before you lose money, 5 red flags you cannot miss | WION Decodes</a><h2>Police action and investigation</h2>Both Wayanad and Kozhikode police are probing the case. A special police team travelled to New Delhi after tracing the SIM card's registered address and location there. The case has been registered under the Bharatiya Nyaya Sanhita (BNS) sections relating to cheating and personation, as well as Section 66D of the Information Technology Act, 2000, on cheating by personation using computer resources.<h2>Mule SIM and spoofing: Police suspect an organised fraud</h2>The call may have been made using a mule SIM, a mobile number registered in another person's name but controlled by fraudsters. Mule SIMs are commonly used to conceal the identity of the real perpetrator. Police said the SIM card was procured in Delhi. Police are trying to determine whether the case is part of a larger organised fraud network.In the past, similar impersonation scams have been reported in so-called "digital arrest" frauds, in which criminals pose as law enforcement or government officials and use spoofed calls to appear as though they originate from genuine government agencies or official phone numbers. Spoofing technique falsifies the caller ID or other identifying information to make the call appear to come from a trusted source.&nbsp;Also read: <a href="https://www.wionews.com/india-news/former-pm-s-son-an-ex-rajya-sabha-mp-duped-of-rs-7-80-crore-in-cyber-fraud-1781784940388">Former PM’s son, an ex-Rajya Sabha MP, duped of Rs 7.80 crore in cyber fraud</a><h2>Money-for-posts allegations are not new in India</h2>The scam attempt should be viewed in the context of past allegations involving money in exchange for key posts in Indian politics. However, at the time of writing, there is no evidence of any involvement by any political party or government in the Kerala impersonation case. The cyber impersonation appears to be an attempt to exploit political ambitions and trusted contacts through WhatsApp-based fraud, rather than evidence of any genuine political appointment process.

A caller impersonating Priyanka Gandhi's personal secretary contacted several Kerala politicians, offering cabinet berths in exchange for money. Police are investigating the cyber fraud 



A new cyber scam is being probed in Kerala, in which Congress politicians from the south Indian state were contacted with offers of ministerial berths in exchange for payments of up to ₹3 crore (30 million rupees). The Kerala Cyber Police are investigating this online impersonation scam, according to reports in Mathrubhumi, SouthFirst and other media outlets. Cyber scam for Cabinet berths: What really happened in Kerala? Earlier this month, Vidya Balakrishnan, the Member of the Legislative Assembly (MLA) from the Elathur constituency, received a WhatsApp call from a person posing as DS Rajkumar, the private secretary to Congress leader and Member of Parliament Priyanka Gandhi Vadra. Reports said the caller claimed that a Kerala Cabinet reshuffle was going to happen soon and offered Balakrishnan a ministerial position in exchange for money. The caller, who spoke in English, said that an MP from Kozhikode district had shared her contact details. Balakrishnan played along briefly before verifying the claim with Priyanka Gandhi. The reports said, Priyanka Gandhi sent a voice note of the real DS Rajkumar for comparison, which did not match the voice of the caller. Balakrishnan then filed a formal complaint with the Kozhikode City Police Commissioner. https://www.wionews.com/videos/interpol-cybercrime-crackdown-nabs-5-800-fraud-suspects-1783680156183/embed Same fraudsters targeted other politicians Police said that calls from the same number, 9540575524, were also made to Vadakara MP Shafi Parambil and Idukki MP Dean Kuriakose with similar offers. Kuriakose ended the call after becoming suspicious. On July 15, the Cyber Police in Wayanad registered a First Information Report (FIR) based on a complaint by Rajkumar, Priyanka Gandhi's private secretary, alleging that someone had impersonated him in an attempt to cheat elected representatives. Also read: How to spot a digital arrest scam before you lose money, 5 red flags you cannot miss | WION Decodes Police action and investigation Both Wayanad and Kozhikode police are probing the case. A special police team travelled to New Delhi after tracing the SIM card's registered address and location there. The case has been registered under the Bharatiya Nyaya Sanhita (BNS) sections relating to cheating and personation, as well as Section 66D of the Information Technology Act, 2000, on cheating by personation using computer resources. Mule SIM and spoofing: Police suspect an organised fraud The call may have been made using a mule SIM, a mobile number registered in another person's name but controlled by fraudsters. Mule SIMs are commonly used to conceal the identity of the real perpetrator. Police said the SIM card was procured in Delhi. Police are trying to determine whether the case is part of a larger organised fraud network. In the past, similar impersonation scams have been reported in so-called "digital arrest" frauds, in which criminals pose as law enforcement or government officials and use spoofed calls to appear as though they originate from genuine government agencies or official phone numbers. Spoofing technique falsifies the caller ID or other identifying information to make the call appear to come from a trusted source. Also read: Former PM’s son, an ex-Rajya Sabha MP, duped of Rs 7.80 crore in cyber fraud Money-for-posts allegations are not new in India The scam attempt should be viewed in the context of past allegations involving money in exchange for key posts in Indian politics. However, at the time of writing, there is no evidence of any involvement by any political party or government in the Kerala impersonation case. The cyber impersonation appears to be an attempt to exploit political ambitions and trusted contacts through WhatsApp-based fraud, rather than evidence of any genuine political appointment process.

A caller impersonating Priyanka Gandhi's personal secretary contacted several Kerala politicians, offering cabinet berths in exchange for money. Police are investigating the cyber fraud&nbsp;&nbsp;

Ministerial berths for money: Kerala Police probe cyber fraud impersonating Priyanka Gandhi's private secretary

Artificial intelligence is no longer just changing how people work it could also change who gets to manage the work. Speaking on the Invest Like The Best podcast, Airbnb CEO Brian Chesky said one entire layer of corporate hierarchy could disappear as AI becomes more capable. According to Chesky, traditional "people managers" whose primary role is holding meetings, coordinating teams and passing information between departments may no longer have a place in many organisations."I don't think people managers have any value in the future," Chesky said, adding that leaders who spend most of their time in recurring one-on-one meetings are unlikely to thrive in the AI era.<h2>AI is replacing coordination, not leadership</h2>Chesky argued that AI can increasingly perform many of the administrative tasks that have traditionally justified middle management. Scheduling work, summarising meetings, generating reports, tracking projects and sharing information across teams are all becoming easier with AI tools.That does not mean management itself will disappear. Instead, Chesky believes the role of a manager is changing. Future leaders will need to contribute directly to the work they oversee rather than simply supervising others. "You don't manage the people, you manage the work," he said, explaining that lawyers should analyse legal cases, engineers should build products and product leaders should stay closely involved in execution.Tech CEOs are sending the same message<h2>Chesky is not alone in this view.</h2>Earlier this week, Coinbase CEO Brian Armstrong announced the company would eliminate what he called "pure managers" during layoffs affecting about 14 per cent of employees. Armstrong said AI now enables engineers to complete projects in days that previously required weeks of teamwork. Meanwhile, Block CEO Jack Dorsey has argued there is "no need for a permanent middle management layer". His company has reorganised around three types of employees: individual contributors, directly responsible owners and "player-coaches" who both lead and build. The common message from technology leaders is that managers who only coordinate work face growing pressure, while those with technical expertise or specialist knowledge are likely to remain valuable.<h2>Why this matters beyond Silicon Valley</h2>The shift is already influencing businesses far beyond the technology sector. Research firm Gartner predicts that by 2026, 20 per cent of organisations will use AI to eliminate more than half of their middle-management positions. The biggest risk is expected to fall on roles centred around administration, reporting and coordination rather than direct problem-solving. For professionals, the message is becoming increasingly clear: future career growth may depend less on managing people and more on developing expertise that AI cannot easily replace. As AI becomes part of everyday business operations, companies appear to be moving towards flatter structures where managers are expected to build, solve problems and make decisions not simply oversee the people who do.

Airbnb CEO Brian Chesky says AI could eliminate traditional middle management as automation takes over coordination and reporting. His comments come as Coinbase, Block and other tech firms rethink corporate hierarchies, with experts predicting fewer management layers in the AI era.


Artificial intelligence is no longer just changing how people work it could also change who gets to manage the work. Speaking on the Invest Like The Best podcast, Airbnb CEO Brian Chesky said one entire layer of corporate hierarchy could disappear as AI becomes more capable. According to Chesky, traditional "people managers" whose primary role is holding meetings, coordinating teams and passing information between departments may no longer have a place in many organisations. "I don't think people managers have any value in the future," Chesky said, adding that leaders who spend most of their time in recurring one-on-one meetings are unlikely to thrive in the AI era. AI is replacing coordination, not leadership Chesky argued that AI can increasingly perform many of the administrative tasks that have traditionally justified middle management. Scheduling work, summarising meetings, generating reports, tracking projects and sharing information across teams are all becoming easier with AI tools. That does not mean management itself will disappear. Instead, Chesky believes the role of a manager is changing. Future leaders will need to contribute directly to the work they oversee rather than simply supervising others. "You don't manage the people, you manage the work," he said, explaining that lawyers should analyse legal cases, engineers should build products and product leaders should stay closely involved in execution. Tech CEOs are sending the same message Chesky is not alone in this view. Earlier this week, Coinbase CEO Brian Armstrong announced the company would eliminate what he called "pure managers" during layoffs affecting about 14 per cent of employees. Armstrong said AI now enables engineers to complete projects in days that previously required weeks of teamwork. Meanwhile, Block CEO Jack Dorsey has argued there is "no need for a permanent middle management layer". His company has reorganised around three types of employees: individual contributors, directly responsible owners and "player-coaches" who both lead and build. The common message from technology leaders is that managers who only coordinate work face growing pressure, while those with technical expertise or specialist knowledge are likely to remain valuable. Why this matters beyond Silicon Valley The shift is already influencing businesses far beyond the technology sector. Research firm Gartner predicts that by 2026, 20 per cent of organisations will use AI to eliminate more than half of their middle-management positions. The biggest risk is expected to fall on roles centred around administration, reporting and coordination rather than direct problem-solving. For professionals, the message is becoming increasingly clear: future career growth may depend less on managing people and more on developing expertise that AI cannot easily replace. As AI becomes part of everyday business operations, companies appear to be moving towards flatter structures where managers are expected to build, solve problems and make decisions not simply oversee the people who do.

Airbnb CEO Brian Chesky says AI could eliminate traditional middle management as automation takes over coordination and reporting. His comments come as Coinbase, Block and other tech firms rethink corporate hierarchies, with experts predicting fewer management layers in the AI era.

AI will wipe out an entire layer of managers, Airbnb CEO Brian Chesky warns

West Asia conflicts have continuously reminded the world of a severe energy dependency, which marks not only a temporary inconvenience but a structural vulnerability. For a rapidly growing economy like India, which imports more than 85 per cent of its crude oil to meet domestic demand, energy security and economic resilience have long been major challenges. Over the last 10 years, the current government has consistently emphasised green energy, focusing on the advancement of technology without much carbon emission, unlike developed nations. The most focused area has always been the transport sector, with the aim of decarbonisation. In response, multiple schemes have been rolled out to support Electric Vehicles (EVs) and create long-term demand.<h3>Entrance of retrofitting</h3>Taking the initiative has begun to show results, with EVs now accounting for 8.5 per cent of total new vehicle sales in India in FY25-26. However, the transition is happening in new vehicle sales, while the existing pool of over 30 crore Internal Combustion Engine (ICE) vehicles on Indian roads continues to run entirely on imported petroleum. That is where vehicle retrofitting enters the arena, in which it replaces a vehicle’s engine, exhaust system, and related components with an electric battery pack, motor, and control systems to convert it into an EV. The case for retrofitting is strongest when viewed through India’s own cultural and economic lens. &nbsp;India has always valued repair, reuse, refurbishment, and life extension. Even when it comes to household appliances and electronics, the instinct has traditionally been to maximise utility before replacement.&nbsp;Also read: <a href="https://www.wionews.com/india-news/how-is-opening-india-s-space-sector-to-private-firms-transforming-the-industry-explaind-1784287433329">How is opening India's space sector to private firms transforming the industry? EXPLAINED</a><h3>Reinvesting in current assets</h3>Emphasising the re-use of existing vehicles, Ayush Misra, Co-founder and CEO of AmpereHour Energy, said that India's shift towards electric mobility should focus not only on replacing existing vehicles but also on reinvesting in current assets while accelerating electrification. Misra stated that the country's transition to electric vehicles presents a strategic decision that will shape the future of transportation as well as the country's long-term energy security. He noted that with the transport sector accounting for a significant share of petroleum consumption and India importing nearly 85 per cent of its crude oil requirements, reducing dependence on fossil fuels has become an economic necessity. "With the transport sector accounting for a major share of petroleum consumption and India importing nearly 85 per cent of its crude oil requirements, reducing fossil fuel dependency has become an economic necessity. While EV adoption continues to grow, the real opportunity lies in building an integrated energy ecosystem that combines electrified commercial fleets, Battery Energy Storage Systems (BESS), battery repurposing, renewable energy, and intelligent charging infrastructure," Misra told WION. "Highway EV charging stations, often located in remote or weak-grid areas, can be powered through DC-coupled solar and BESS, where solar energy is stored and supplied directly to high-voltage DC fast chargers via DC-DC converters, eliminating unnecessary AC conversion. This architecture improves energy efficiency, reduces transmission losses, enhances charging reliability, and enables faster deployment of off-grid charging corridors," he added. He further added that a combination of indigenous innovation, advanced energy storage technologies and supportive policy frameworks could spur EV adoption, reduce crude oil imports, bolster the energy resilience of India and help to set up and design a cleaner, more self-reliant and globally competitive mobility ecosystem.<h3>Economically viable</h3>Highlighting the role in avoiding scarppage of old vehicles, Anand Kabra, Chairman and Managing Director at Kabra Extrusiontechnik Limited &amp; &nbsp;Geon - Green Energy ON, noted that recycling of the batteries used in vehicles and different sources of renewable energy will play a significant role in making the most of this transition. Formulating a sustainable EV and energy storage system will help India become economically viable during imports, improving the resilience of the grid and make closer to the energy future.&nbsp;"Electrification of mobility in India is not only a transition from the use of conventional vehicles to the use of electric vehicles. It is an attempt to build India's energy security. Every electric vehicle contributes to making us less dependent on imported crude oil and increases demand for energy sources of clean transportation that we produce at home. But at the same time, this process should be regarded as an investment in the sustainable energy system, not as scrapping old vehicles. Investment in charging infrastructure for EVs, in developing batteries and their systems, battery recycling and renewable energy sources will play a key role in making the most of this transition. Building a sustainable EV and energy storage system will allow India to save money on imports, improve the resilience of the grid and move closer to the energy future," Kabra said. Across the world, regions like Europe and Nepal are already adopting these circular models. In India, early versions of the FAME scheme supported retrofitting, and states like Madhya Pradesh are pioneering new ecosystems with streamlined agency registrations and upfront subsidies. While market confidence is rising, evidenced by inquiries doubling following recent global oil anxieties, the regulatory framework lags behind. To fully leverage this potential, India required a joint retrofitment framework across the state, enabling formal banking credit for converted vehicles, and establishing strict safety certifications to eliminate unsafe, informal mechanics.

India has always valued repair, reuse, refurbishment, and life extension. Even when it comes to household appliances and electronics, the instinct has traditionally been to maximise utility before replacement.

West Asia conflicts have continuously reminded the world of a severe energy dependency, which marks not only a temporary inconvenience but a structural vulnerability. For a rapidly growing economy like India, which imports more than 85 per cent of its crude oil to meet domestic demand, energy security and economic resilience have long been major challenges. Over the last 10 years, the current government has consistently emphasised green energy, focusing on the advancement of technology without much carbon emission, unlike developed nations. The most focused area has always been the transport sector, with the aim of decarbonisation. In response, multiple schemes have been rolled out to support Electric Vehicles (EVs) and create long-term demand. Entrance of retrofitting Taking the initiative has begun to show results, with EVs now accounting for 8.5 per cent of total new vehicle sales in India in FY25-26. However, the transition is happening in new vehicle sales, while the existing pool of over 30 crore Internal Combustion Engine (ICE) vehicles on Indian roads continues to run entirely on imported petroleum. That is where vehicle retrofitting enters the arena, in which it replaces a vehicle’s engine, exhaust system, and related components with an electric battery pack, motor, and control systems to convert it into an EV. The case for retrofitting is strongest when viewed through India’s own cultural and economic lens. India has always valued repair, reuse, refurbishment, and life extension. Even when it comes to household appliances and electronics, the instinct has traditionally been to maximise utility before replacement. Also read: How is opening India's space sector to private firms transforming the industry? EXPLAINED Reinvesting in current assets Emphasising the re-use of existing vehicles, Ayush Misra, Co-founder and CEO of AmpereHour Energy, said that India's shift towards electric mobility should focus not only on replacing existing vehicles but also on reinvesting in current assets while accelerating electrification. Misra stated that the country's transition to electric vehicles presents a strategic decision that will shape the future of transportation as well as the country's long-term energy security. He noted that with the transport sector accounting for a significant share of petroleum consumption and India importing nearly 85 per cent of its crude oil requirements, reducing dependence on fossil fuels has become an economic necessity. "With the transport sector accounting for a major share of petroleum consumption and India importing nearly 85 per cent of its crude oil requirements, reducing fossil fuel dependency has become an economic necessity. While EV adoption continues to grow, the real opportunity lies in building an integrated energy ecosystem that combines electrified commercial fleets, Battery Energy Storage Systems (BESS), battery repurposing, renewable energy, and intelligent charging infrastructure," Misra told WION. "Highway EV charging stations, often located in remote or weak-grid areas, can be powered through DC-coupled solar and BESS, where solar energy is stored and supplied directly to high-voltage DC fast chargers via DC-DC converters, eliminating unnecessary AC conversion. This architecture improves energy efficiency, reduces transmission losses, enhances charging reliability, and enables faster deployment of off-grid charging corridors," he added. He further added that a combination of indigenous innovation, advanced energy storage technologies and supportive policy frameworks could spur EV adoption, reduce crude oil imports, bolster the energy resilience of India and help to set up and design a cleaner, more self-reliant and globally competitive mobility ecosystem. Economically viable Highlighting the role in avoiding scarppage of old vehicles, Anand Kabra, Chairman and Managing Director at Kabra Extrusiontechnik Limited & Geon - Green Energy ON, noted that recycling of the batteries used in vehicles and different sources of renewable energy will play a significant role in making the most of this transition. Formulating a sustainable EV and energy storage system will help India become economically viable during imports, improving the resilience of the grid and make closer to the energy future. "Electrification of mobility in India is not only a transition from the use of conventional vehicles to the use of electric vehicles. It is an attempt to build India's energy security. Every electric vehicle contributes to making us less dependent on imported crude oil and increases demand for energy sources of clean transportation that we produce at home. But at the same time, this process should be regarded as an investment in the sustainable energy system, not as scrapping old vehicles. Investment in charging infrastructure for EVs, in developing batteries and their systems, battery recycling and renewable energy sources will play a key role in making the most of this transition. Building a sustainable EV and energy storage system will allow India to save money on imports, improve the resilience of the grid and move closer to the energy future," Kabra said. Across the world, regions like Europe and Nepal are already adopting these circular models. In India, early versions of the FAME scheme supported retrofitting, and states like Madhya Pradesh are pioneering new ecosystems with streamlined agency registrations and upfront subsidies. While market confidence is rising, evidenced by inquiries doubling following recent global oil anxieties, the regulatory framework lags behind. To fully leverage this potential, India required a joint retrofitment framework across the state, enabling formal banking credit for converted vehicles, and establishing strict safety certifications to eliminate unsafe, informal mechanics.

India has always valued repair, reuse, refurbishment, and life extension. Even when it comes to household appliances and electronics, the instinct has traditionally been to maximise utility before replacement.

Discard or reinvest? How India's transition from existing vehicles to EVs can reduce crude oil dependency

China has completed the first successful implantation of a commercially approved brain-computer interface (BCI) chip. The achievement marks a major milestone in neurotechnology, which aims to restore movement and communication to humans suffering ​from varied forms of paralysis. The NEO brain chip was implanted in a patient suffering a long-term spinal cord injury in order to help restore hand movement through a robotic glove controlled by brain signals. The chip received approval in China earlier this year. The development has intensified the global race in brain-chip technology, with China and the US competing to lead the field. With this approval, China has become the first nation in the world to allow the use of an invasive BCI medical device for commercial use.<h3>How NEO brain chip different from Elon Musk's Neuralink</h3>The product is an invasive brain-computer interface (BCI), meaning its electrodes are implanted directly into the brain rather than placed on its surface. It employs a minimally invasive extradural implantation technique and operates using wireless technology. The device is intended for patients aged 18 to 60 who have a specific type of spinal cord injury. To qualify, the injury must have occurred at least one year earlier, the patient must have remained medically stable for six months after standard treatment, and they must have lost the ability to grasp objects with their hands while still retaining some upper-arm movement. Meanwhile, NeuCyber's advanced Beinao-2 system is another invasive BCI featuring flexible electrodes that are fully implanted into the brain and is currently undergoing large-scale animal trials. In comparison, Neuralink's N1 chip stands out for its robotic surgical system, which can implant hundreds of electrodes into the brain within minutes. "The benchmark for Beinao-2 is ⁠Neuralink. I have to say, (there is) about three years' lag because they have over 20 patients using it already," ​Li Yuan, rotating CEO of NeuCyber, a startup linked with the Beijing-based Chinese Institute for Brain Research (CIBR), said. "We ​have just finished the first product and have to go through animal testing, then early-feasibility clinical trials, and then the real trials. That's maybe about two years later for the real trial," he added.&nbsp;Also read: <a href="https://www.wionews.com/india-news/india-s-first-private-orbital-rocket-vikram-1-successfully-lifts-off-from-sriharikota-1784360535292">India's first private orbital rocket, Vikram-1, successfully lifts off from Sriharikota; PM Modi congratulates</a> Li stated that the BCI device is a coin-sized wireless implant by Shanghai-based private firm Neuracle. It sits on the outer membrane of the brain and controls a ‌robotic glove. So far, NeuCyber has achieved seven human implantations of the previous version known as Beinao-1, a semi-invasive BCI having electrodes implanted on the brain's outer membrane. In contrast, Neuralink reported in January that it has 21 participants worldwide registered in human clinical trials. Li projects that it could take an additional two to three years for NeuCyber's BCI products to become widely available in the domestic market, pending necessary clearances from China’s health commission, medical product regulators, and medical insurance authorities.

To qualify, the injury must have occurred at least one year earlier, the patient must have remained medically stable for six months after standard treatment, and they must have lost the ability to grasp objects with their hands while still retaining some upper-arm movement.

China has completed the first successful implantation of a commercially approved brain-computer interface (BCI) chip. The achievement marks a major milestone in neurotechnology, which aims to restore movement and communication to humans suffering from varied forms of paralysis. The NEO brain chip was implanted in a patient suffering a long-term spinal cord injury in order to help restore hand movement through a robotic glove controlled by brain signals. The chip received approval in China earlier this year. The development has intensified the global race in brain-chip technology, with China and the US competing to lead the field. With this approval, China has become the first nation in the world to allow the use of an invasive BCI medical device for commercial use. How NEO brain chip different from Elon Musk's Neuralink The product is an invasive brain-computer interface (BCI), meaning its electrodes are implanted directly into the brain rather than placed on its surface. It employs a minimally invasive extradural implantation technique and operates using wireless technology. The device is intended for patients aged 18 to 60 who have a specific type of spinal cord injury. To qualify, the injury must have occurred at least one year earlier, the patient must have remained medically stable for six months after standard treatment, and they must have lost the ability to grasp objects with their hands while still retaining some upper-arm movement. Meanwhile, NeuCyber's advanced Beinao-2 system is another invasive BCI featuring flexible electrodes that are fully implanted into the brain and is currently undergoing large-scale animal trials. In comparison, Neuralink's N1 chip stands out for its robotic surgical system, which can implant hundreds of electrodes into the brain within minutes. "The benchmark for Beinao-2 is ⁠Neuralink. I have to say, (there is) about three years' lag because they have over 20 patients using it already," Li Yuan, rotating CEO of NeuCyber, a startup linked with the Beijing-based Chinese Institute for Brain Research (CIBR), said. "We have just finished the first product and have to go through animal testing, then early-feasibility clinical trials, and then the real trials. That's maybe about two years later for the real trial," he added. Also read: India's first private orbital rocket, Vikram-1, successfully lifts off from Sriharikota; PM Modi congratulates Li stated that the BCI device is a coin-sized wireless implant by Shanghai-based private firm Neuracle. It sits on the outer membrane of the brain and controls a ‌robotic glove. So far, NeuCyber has achieved seven human implantations of the previous version known as Beinao-1, a semi-invasive BCI having electrodes implanted on the brain's outer membrane. In contrast, Neuralink reported in January that it has 21 participants worldwide registered in human clinical trials. Li projects that it could take an additional two to three years for NeuCyber's BCI products to become widely available in the domestic market, pending necessary clearances from China’s health commission, medical product regulators, and medical insurance authorities.

To qualify, the injury must have occurred at least one year earlier, the patient must have remained medically stable for six months after standard treatment, and they must have lost the ability to grasp objects with their hands while still retaining some upper-arm movement.

How China's NEO brain chip differs from Elon Musk's Neuralink: Technology, trials & regulatory race explained

Not very long ago, joining a terrorist organisation often meant crossing borders. Recruits travelled to secret camps, where they met handlers in person. There, they would learn how to make explosives, communicate secretly and evade security forces through face-to-face training. However, that model is now changing. Today, much of that radicalisation journey can begin with nothing more than a smartphone, an encrypted messaging app and increasingly, artificial intelligence.Security agencies across the world are warning that terrorist organisations are rapidly adapting to AI-powered technologies, using them not just to spread propaganda but also to recruit supporters, create fake identities, translate extremist material into multiple languages and even assist with operational planning.For countries like India, where millions of young people spend hours online every day, the concern is no longer limited to militants crossing the border. The greater challenge may be preventing radicalisation before anyone ever leaves their bedroom. WION Decodes the changing face of terror and radicalisation.Also read | <a href="https://www.wionews.com/india-news/if-india-wants-a-permanent-unsc-seat-why-is-it-still-contesting-for-a-temporary-one-wion-decodes-1784104714747">If India wants a permanent UNSC seat, why is it still contesting for a temporary one? WION Decodes</a><h2>How are terror groups using AI?</h2>Creating propaganda once required designers, translators and video editors. Now, generative AI has dramatically changed that.Groups linked to ISIS, Al Qaeda and other extremist organisations can now generate realistic videos, speeches, posters and audio messages in multiple languages within minutes. Europol has warned that AI is making extremist propaganda cheaper, faster and far more scalable than before.Deepfake technology adds another layer of risk. Synthetic voices and AI-generated videos can make fake messages appear authentic, while fabricated identities allow recruiters to hide behind convincing online personas.<h2>Recruitment has become personal: AI personalising propaganda</h2>Perhaps the biggest change is not the technology itself but how it is being used. Instead of broadcasting one message to everyone, AI allows recruiters to tailor content to individuals. Algorithms can identify vulnerable users, personalise conversations and keep potential recruits engaged through encrypted messaging platforms and social media.The result is a recruitment process that looks less like traditional extremist propaganda and more like targeted digital marketing.<h2>Can chatbots manipulate vulnerable people?</h2>The concern is no longer theoretical. Reports suggest that terrorists are now experimenting with AI chatbots.A recent study by researcher Dr Juelich and shared with The New York Times before its publication, involving interviews with former Boko Haram members, found that some militants had begun using AI chatbots to seek technical information that could improve weapons and assist with planning attacks.Former members said trainers linked to the Islamic State taught recruits how to create AI accounts, use VPNs, frame prompts effectively and bypass chatbot safety restrictions.One former commander described the experience bluntly. "You type in the question or use your voice, and it gives you a detailed answer, like 'How can I build a bomb?', and then it tells you how. It is like a human robot! We used it a lot."The study suggests that extremist groups are not relying on a single AI platform. According to terrorism experts, they move between multiple chatbots depending on which one provides the information they are seeking.Also read | <a href="https://www.wionews.com/photos/india-s-brahmos-vs-usa-s-tomahawk-russia-s-kalibr-and-china-s-yj-18-why-nations-are-lining-up-for-the-make-in-india-missile-1782892168517">India's BrahMos vs USA's Tomahawk, Russia's Kalibr and China's YJ-18: Why nations are lining up for the Make-in-India missile</a><h2>Why is this harder to stop</h2>In the past, traditional terrorist networks left a paper trail: training camps could be spotted, physical meetings could be wiretapped, and unusual travel patterns flagged suspicion. Virtual networks are a different game entirely; they operate in the shadows, leaving behind almost no physical footprint.Encrypted messaging services, anonymous accounts, AI-generated identities and decentralised online communities make investigations significantly more difficult. Extremist content can spread across social media, gaming platforms and private channels simultaneously, allowing recruiters to reach audiences across borders without ever meeting them.<h2>Can India stop it?</h2>Indian security agencies have already begun treating AI-enabled radicalisation as an emerging national security challenge.Addressing the fifth BIMSTEC National Security Advisers' Meeting on Thursday (Jul 16) Indian National Security Adviser Ajit Doval noted that the world is now "facing multi-domain security threats, amplified by rapid technological advancements" and called for stronger regional cooperation and practical measures to tackle technology-driven security threats. The Ministry of Home Affairs has also strengthened cyber and information security capabilities aimed at detecting online extremist activity and improving intelligence sharing.Recent investigations in states such as Gujarat and Kerala have also reinforced the importance of early detection, cyber monitoring and coordinated intelligence to identify online radicalisation before it develops into organised terror activity.<h2>Can AI itself become radical?</h2>No, AI cannot become radical in the way humans can. Large language models (LLMs) do not hold beliefs or ideologies. However, they can become tools that extremist groups attempt to exploit.While developers continue to strengthen safeguards that block requests for violent or illegal assistance, time and time again, researchers have shown that indirect prompts, multiple systems or other techniques can be used to evade those safeguards.That means the risk lies less in AI becoming extremist and more in extremists trying to misuse AI.<h2>What can AI do that humans couldn't?</h2>While AI hasn't replaced terrorist groups, it has definitely made their operations faster, cheaper, and a lot harder to track.Today, recruitment often starts with a single click. Propaganda that used to take days to produce is now generated in minutes, and fake identities can be generated almost instantly. On top of that, encrypted apps allow these groups to coordinate across borders without ever meeting face-to-face.For intelligence agencies, the new frontline is digital. For countries like India, stopping terror is no longer just about boots on the ground; it's about cybersecurity and digital literacy.

As terrorist organisations increasingly exploit artificial intelligence for propaganda, recruitment and operational planning, WION Decodes how AI is reshaping the global terror landscape and why India's biggest security challenge may now begin online, not across its borders.

Not very long ago, joining a terrorist organisation often meant crossing borders. Recruits travelled to secret camps, where they met handlers in person. There, they would learn how to make explosives, communicate secretly and evade security forces through face-to-face training. However, that model is now changing. Today, much of that radicalisation journey can begin with nothing more than a smartphone, an encrypted messaging app and increasingly, artificial intelligence. Security agencies across the world are warning that terrorist organisations are rapidly adapting to AI-powered technologies, using them not just to spread propaganda but also to recruit supporters, create fake identities, translate extremist material into multiple languages and even assist with operational planning. For countries like India, where millions of young people spend hours online every day, the concern is no longer limited to militants crossing the border. The greater challenge may be preventing radicalisation before anyone ever leaves their bedroom. WION Decodes the changing face of terror and radicalisation. Also read | If India wants a permanent UNSC seat, why is it still contesting for a temporary one? WION Decodes How are terror groups using AI? Creating propaganda once required designers, translators and video editors. Now, generative AI has dramatically changed that. Groups linked to ISIS, Al Qaeda and other extremist organisations can now generate realistic videos, speeches, posters and audio messages in multiple languages within minutes. Europol has warned that AI is making extremist propaganda cheaper, faster and far more scalable than before. Deepfake technology adds another layer of risk. Synthetic voices and AI-generated videos can make fake messages appear authentic, while fabricated identities allow recruiters to hide behind convincing online personas. Recruitment has become personal: AI personalising propaganda Perhaps the biggest change is not the technology itself but how it is being used. Instead of broadcasting one message to everyone, AI allows recruiters to tailor content to individuals. Algorithms can identify vulnerable users, personalise conversations and keep potential recruits engaged through encrypted messaging platforms and social media. The result is a recruitment process that looks less like traditional extremist propaganda and more like targeted digital marketing. Can chatbots manipulate vulnerable people? The concern is no longer theoretical. Reports suggest that terrorists are now experimenting with AI chatbots. A recent study by researcher Dr Juelich and shared with The New York Times before its publication, involving interviews with former Boko Haram members, found that some militants had begun using AI chatbots to seek technical information that could improve weapons and assist with planning attacks. Former members said trainers linked to the Islamic State taught recruits how to create AI accounts, use VPNs, frame prompts effectively and bypass chatbot safety restrictions. One former commander described the experience bluntly. "You type in the question or use your voice, and it gives you a detailed answer, like 'How can I build a bomb?', and then it tells you how. It is like a human robot! We used it a lot." The study suggests that extremist groups are not relying on a single AI platform. According to terrorism experts, they move between multiple chatbots depending on which one provides the information they are seeking. Also read | India's BrahMos vs USA's Tomahawk, Russia's Kalibr and China's YJ-18: Why nations are lining up for the Make-in-India missile Why is this harder to stop In the past, traditional terrorist networks left a paper trail: training camps could be spotted, physical meetings could be wiretapped, and unusual travel patterns flagged suspicion. Virtual networks are a different game entirely; they operate in the shadows, leaving behind almost no physical footprint. Encrypted messaging services, anonymous accounts, AI-generated identities and decentralised online communities make investigations significantly more difficult. Extremist content can spread across social media, gaming platforms and private channels simultaneously, allowing recruiters to reach audiences across borders without ever meeting them. Can India stop it? Indian security agencies have already begun treating AI-enabled radicalisation as an emerging national security challenge. Addressing the fifth BIMSTEC National Security Advisers' Meeting on Thursday (Jul 16) Indian National Security Adviser Ajit Doval noted that the world is now "facing multi-domain security threats, amplified by rapid technological advancements" and called for stronger regional cooperation and practical measures to tackle technology-driven security threats. The Ministry of Home Affairs has also strengthened cyber and information security capabilities aimed at detecting online extremist activity and improving intelligence sharing. Recent investigations in states such as Gujarat and Kerala have also reinforced the importance of early detection, cyber monitoring and coordinated intelligence to identify online radicalisation before it develops into organised terror activity. Can AI itself become radical? No, AI cannot become radical in the way humans can. Large language models (LLMs) do not hold beliefs or ideologies. However, they can become tools that extremist groups attempt to exploit. While developers continue to strengthen safeguards that block requests for violent or illegal assistance, time and time again, researchers have shown that indirect prompts, multiple systems or other techniques can be used to evade those safeguards. That means the risk lies less in AI becoming extremist and more in extremists trying to misuse AI. What can AI do that humans couldn't? While AI hasn't replaced terrorist groups, it has definitely made their operations faster, cheaper, and a lot harder to track. Today, recruitment often starts with a single click. Propaganda that used to take days to produce is now generated in minutes, and fake identities can be generated almost instantly. On top of that, encrypted apps allow these groups to coordinate across borders without ever meeting face-to-face. For intelligence agencies, the new frontline is digital. For countries like India, stopping terror is no longer just about boots on the ground; it's about cybersecurity and digital literacy.

As terrorist organisations increasingly exploit artificial intelligence for propaganda, recruitment and operational planning, WION Decodes how AI is reshaping the global terror landscape and why India's biggest security challenge may now begin online, not across its borders.

India's next terror threat may not cross the border. It may enter through your phone | WION Decodes

China has reportedly achieved a major milestone in brain-computer interface (BCI) technology by carrying out what authorities describe as the world's first commercial brain chip implant. The procedure was performed at Huashan Hospital in Shanghai using Neo, a brain-computer interface developed by Neuracle Medical Technology. Unlike experimental implants used in clinical trials, Neo has already received commercial approval in China, allowing eligible patients to receive the technology in hospitals.The development has drawn global attention because it comes before Elon Musk's Neuralink receives full commercial approval in the United States.<h2>How does Neo work?</h2> Neo is designed to help people with severe paralysis regain some control over movement. The coin-sized implant is placed on the surface of the brain, rather than inside brain tissue. When a patient thinks about moving their hand, the device detects those brain signals and sends them to a computer.The software then translates the signals into commands that control a robotic glove, helping patients perform everyday tasks that would otherwise be impossible. Doctors said the patient, who suffered a spinal cord injury in a car accident around 10 years ago, remained stable after surgery. Tests also showed the implant was successfully capturing high-quality brain signals.<h2>Neo vs Neuralink: What's the difference?</h2>Although both Neo and Neuralink aim to restore independence for people with paralysis, the two technologies take different approaches. Neuralink uses extremely thin flexible threads that are inserted directly into brain tissue using a specially designed surgical robot. Its first product, Telepathy, allows users to control computers and smartphones using only their thoughts. Neo, however, sits on the surface of the brain, avoiding penetration into brain tissue. This makes the procedure less invasive while still allowing the system to capture the electrical signals needed to operate assistive devices.<h2>The biggest difference is regulatory status.</h2>Neuralink has implanted its devices in several patients, but all procedures remain part of clinical trials approved by the US Food and Drug Administration (FDA). Neo, by contrast, received approval from China's National Medical Products Administration (NMPA) earlier this year as what Chinese authorities describe as the world's first commercially approved implantable brain-computer interface classified as a Class III medical device.The breakthrough signals that the global race in brain-computer interfaces is entering a new phase. For years, companies have focused on proving that BCIs can safely translate thoughts into digital commands. The next challenge is making those technologies available to patients outside research programmes. If Neo continues to deliver positive clinical outcomes, it could accelerate the adoption of brain-computer interfaces for treating paralysis and other neurological conditions. At the same time, competition is expected to intensify as companies including Neuralink, Synchron, Precision Neuroscience and others work to bring their own brain chip technologies to market. While commercial approval marks an important milestone, experts note that long-term safety, durability and patient outcomes will ultimately determine how widely brain-computer interfaces are adopted in healthcare.

China has reportedly carried out the world's first commercial brain chip implant using the Neo brain-computer interface. Unlike Neuralink, which remains in clinical trials, Neo has regulatory approval for hospital use, marking a major milestone in the global race to commercialise BCI technology.

China has reportedly achieved a major milestone in brain-computer interface (BCI) technology by carrying out what authorities describe as the world's first commercial brain chip implant. The procedure was performed at Huashan Hospital in Shanghai using Neo, a brain-computer interface developed by Neuracle Medical Technology. Unlike experimental implants used in clinical trials, Neo has already received commercial approval in China, allowing eligible patients to receive the technology in hospitals. The development has drawn global attention because it comes before Elon Musk's Neuralink receives full commercial approval in the United States. How does Neo work? Neo is designed to help people with severe paralysis regain some control over movement. The coin-sized implant is placed on the surface of the brain, rather than inside brain tissue. When a patient thinks about moving their hand, the device detects those brain signals and sends them to a computer. The software then translates the signals into commands that control a robotic glove, helping patients perform everyday tasks that would otherwise be impossible. Doctors said the patient, who suffered a spinal cord injury in a car accident around 10 years ago, remained stable after surgery. Tests also showed the implant was successfully capturing high-quality brain signals. Neo vs Neuralink: What's the difference? Although both Neo and Neuralink aim to restore independence for people with paralysis, the two technologies take different approaches. Neuralink uses extremely thin flexible threads that are inserted directly into brain tissue using a specially designed surgical robot. Its first product, Telepathy, allows users to control computers and smartphones using only their thoughts. Neo, however, sits on the surface of the brain, avoiding penetration into brain tissue. This makes the procedure less invasive while still allowing the system to capture the electrical signals needed to operate assistive devices. The biggest difference is regulatory status. Neuralink has implanted its devices in several patients, but all procedures remain part of clinical trials approved by the US Food and Drug Administration (FDA). Neo, by contrast, received approval from China's National Medical Products Administration (NMPA) earlier this year as what Chinese authorities describe as the world's first commercially approved implantable brain-computer interface classified as a Class III medical device. The breakthrough signals that the global race in brain-computer interfaces is entering a new phase. For years, companies have focused on proving that BCIs can safely translate thoughts into digital commands. The next challenge is making those technologies available to patients outside research programmes. If Neo continues to deliver positive clinical outcomes, it could accelerate the adoption of brain-computer interfaces for treating paralysis and other neurological conditions. At the same time, competition is expected to intensify as companies including Neuralink, Synchron, Precision Neuroscience and others work to bring their own brain chip technologies to market. While commercial approval marks an important milestone, experts note that long-term safety, durability and patient outcomes will ultimately determine how widely brain-computer interfaces are adopted in healthcare.

China has reportedly carried out the world's first commercial brain chip implant using the Neo brain-computer interface. Unlike Neuralink, which remains in clinical trials, Neo has regulatory approval for hospital use, marking a major milestone in the global race to commercialise BCI technology.

China performs world's first commercial brain chip implant, moving ahead of Elon Musk's Neuralink

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China reaches major brain-computer interface milestone ahead of Neuralink