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AI’s thirst for energy is ballooning into a monster of a challenge. And it’s not just about the electricity bills. The environmental fallout is serious, stretching to guzzling precious water resources, creating mountains of electronic waste, and, yes, adding to those greenhouse gas emissions we’re all trying to cut. As AI models get ever more complex and weave themselves into yet more parts of our lives, a massive question mark hangs in the air: can we power this revolution without costing the Earth? The numbers don’t lie: AI’s energy demand is escalating fast The sheer computing power needed for the smartest AI out there is on an almost unbelievable upward curve – some say it’s doubling roughly every few months. This isn’t a gentle slope; it’s a vertical climb that’s threatening to leave even our most optimistic energy plans in the dust. To give you a sense of scale, AI’s future energy needs could soon gulp down as much electricity as entire countries like Japan or the Netherlands, or even large US states like California. When you hear stats like that, you start to see the potential squeeze AI could put on the power grids we all rely on. 2024 saw a record 4.3% surge in global electricity demand, and AI’s expansion was a big reason why, alongside the ***** in electric cars and factories working harder. Wind back to 2022, and data centres, AI, and even cryptocurrency mining were already accounting for nearly 2% of all the electricity used worldwide – that’s about 460 terawatt-hours (TWh). Jump to 2024, and data centres on their own use around 415 TWh, which is roughly 1.5% of the global total, and growing at 12% a year. AI’s direct share of that slice is still relatively small – about 20 TWh, or 0.02% of global energy use – but hold onto your hats, because that number is set to rocket upwards. The forecasts? Well, they’re pretty eye-opening. By the end of 2025, AI data centres around the world could demand an extra 10 gigawatts (GW) of power. That’s more than the entire power capacity of a place like Utah. Roll on to 2026, and global data centre electricity use could hit 1,000 TWh – similar to what Japan uses right now. And, by 2027, the global power hunger of AI data centres is tipped to reach 68 GW, which is almost what California had in total power capacity back in 2022. Towards the end of this decade, the figures get even more jaw-dropping. Global data centre electricity consumption is predicted to double to around 945 TWh by 2030, which is just shy of 3% of all the electricity used on the planet. OPEC reckons data centre electricity use could even triple to 1,500 TWh by then. And Goldman Sachs? They’re saying global power demand from data centres could leap by as much as 165% compared to 2023, with those data centres specifically kitted out for AI seeing their demand shoot up by more than four times. There are even suggestions that data centres could be responsible for up to 21% of all global energy demand by 2030 if you count the energy it takes to get AI services to us, the users. When we talk about AI’s energy use, it mainly splits into two big chunks: training the AI, and then actually using it. Training enormous models, like GPT-4, takes a colossal amount of energy. Just to train GPT-3, for example, it’s estimated they used 1,287 megawatt-hours (MWh) of electricity, and GPT-4 is thought to have needed a whopping 50 times more than that. While training is a power hog, it’s the day-to-day running of these trained models that can chew through over 80% of AI’s total energy. It’s reported that asking ChatGPT a single question uses about ten times more energy than a Google search (we’re talking roughly 2.9 Wh versus 0.3 Wh). With everyone jumping on the generative AI bandwagon, the race is on to build ever more powerful – and therefore more energy-guzzling – data centres. So, can we supply energy for AI – and for ourselves? This is the million-dollar question, isn’t it? Can our planet’s energy systems cope with this new demand? We’re already juggling a mix of fossil fuels, nuclear power, and renewables. If we’re going to feed AI’s growing appetite sustainably, we need to ramp up and diversify how we generate energy, and fast. Naturally, renewable energy – solar, wind, hydro, geothermal – is a huge piece of the puzzle. In the US, for instance, renewables are set to go from 23% of power generation in 2024 to 27% by 2026. The tech giants are making some big promises; Microsoft, for example, is planning to buy 10.5 GW of renewable energy between 2026 and 2030 just for its data centres. AI itself could actually help us use renewable energy more efficiently, perhaps cutting energy use by up to 60% in some areas by making energy storage smarter and managing power grids better. But let’s not get carried away. Renewables have their own headaches. The sun doesn’t always shine, and the wind doesn’t always blow, which is a real problem for data centres that need power around the clock, every single day. The batteries we have now to smooth out these bumps are often expensive and take up a lot of room. Plus, plugging massive new renewable projects into our existing power grids can be a slow and complicated business. This is where nuclear power is starting to look more appealing to some, especially as a steady, low-carbon way to power AI’s massive energy needs. It delivers that crucial 24/7 power, which is exactly what data centres crave. There’s a lot of buzz around Small Modular Reactors (SMRs) too, because they’re potentially more flexible and have beefed-up safety features. And it’s not just talk; big names like Microsoft, Amazon, and Google are seriously looking into nuclear options. Matt Garman, who heads up AWS, recently put it plainly to the BBC, calling nuclear a “great solution” for data centres. He said it’s “an excellent source of zero carbon, 24/7 power.” He also stressed that planning for future energy is a massive part of what AWS does. “It’s something we plan many years out,” Garman mentioned. “We invest ahead. I think the world is going to have to build new technologies. I believe nuclear is a big part of that, particularly as we look 10 years out.” Still, nuclear power isn’t a magic wand. Building new reactors takes a notoriously long time, costs a fortune, and involves wading through complex red tape. And let’s be frank, public opinion on nuclear power is still a bit shaky, often because of past accidents, even though modern reactors are much safer. The sheer speed at which AI is developing also creates a bit of a mismatch with how long it takes to get a new nuclear plant up and running. This could mean we end up leaning even more heavily on fossil fuels in the short term, which isn’t great for our green ambitions. Plus, the idea of sticking data centres right next to nuclear plants has got some people worried about what that might do to electricity prices and reliability for everyone else. Not just kilowatts: Wider environmental shadow of AI looms AI’s impact on the planet goes way beyond just the electricity it uses. Those data centres get hot, and cooling them down uses vast amounts of water. Your average data centre sips about 1.7 litres of water for every kilowatt-hour of energy it burns through. Back in 2022, Google’s data centres reportedly drank their way through about 5 billion gallons of fresh water – that’s a 20% jump from the year before. Some estimates suggest that for every kWh a data centre uses, it might need up to two litres of water just for cooling. Put it another way, global AI infrastructure could soon be chugging six times more water than the entirety of Denmark. And then there’s the ever-growing mountain of electronic waste, or e-waste. Because AI tech – especially specialised hardware like GPUs and TPUs – moves so fast, old kit gets thrown out more often. We could be looking at AI contributing to an e-waste pile-up from data centres hitting five million tons every year by 2030. Even making the AI chips and all the other bits for data centres takes a toll on our natural resources and the environment. It means mining for critical minerals like lithium and cobalt, often using methods that aren’t exactly kind to the planet. Just to make one AI chip can take over 1,400 litres of water and 3,000 kWh of electricity. This hunger for new hardware is also pushing for more semiconductor factories, which, guess what, often leads to more gas-powered energy plants being built. And, of course, we can’t forget the carbon emissions. When AI is powered by electricity generated from burning fossil fuels, it adds to the climate change problem we’re all facing. It’s estimated that training just one big AI model can pump out as much CO2 as hundreds of US homes do in a year. If you look at the environmental reports from the big tech companies, you can see AI’s growing carbon footprint. Microsoft’s yearly emissions, for example, went up by about 40% between 2020 and 2023, mostly because they were building more data centres for AI. Google also reported that its total greenhouse gas emissions have shot up by nearly 50% over the last five years, with the power demands of its AI data centres being a major culprit. Can we innovate our way out? It might sound like all doom and gloom, but a combination of new ideas could help. A big focus is on making AI algorithms themselves more energy-efficient. Researchers are coming up with clever tricks like “model pruning” (stripping out unnecessary bits of an AI model), “quantisation” (using less precise numbers, which saves energy), and “knowledge distillation” (where a smaller, thriftier AI model learns from a big, complex one). Designing smaller, more specialised AI models that do specific jobs with less power is also a priority. Inside data centres, things like “power capping” (putting a lid on how much power hardware can draw) and “dynamic resource allocation” (shifting computing power around based on real-time needs and when renewable energy is plentiful) can make a real difference. Software that’s “AI-aware” can even shift less urgent AI jobs to times when energy is cleaner or demand on the grid is lower. AI can even be used to make the cooling systems in data centres more efficient. On-device AI could also help to reduce power consumption. Instead of sending data off to massive, power-hungry cloud data centres, the AI processing happens right there on your phone or device. This could slash energy use, as the chips designed for this prioritise being efficient over raw power. And we can’t forget about rules and regulations. Governments are starting to wake up to the need to make AI accountable for its energy use and wider environmental impact. Having clear, standard ways to measure and report AI’s footprint is a crucial first step. We also need policies that encourage companies to make hardware that lasts longer and is easier to recycle, to help tackle that e-waste mountain. Things like energy credit trading systems could even give companies a financial reason to choose greener AI tech. It’s worth noting that the United Arab Emirates and the United States shook hands this week on a deal to build the biggest AI campus outside the US in the Gulf. While this shows just how important AI is becoming globally, it also throws a spotlight on why all these energy and environmental concerns need to be front and centre for such huge projects. Finding a sustainable future for AI AI has the power to do some amazing things, but its ferocious appetite for energy is a serious hurdle. The predictions for its future power demands are genuinely startling, potentially matching what whole countries use. If we’re going to meet this demand, we need a smart mix of energy sources. Renewables are fantastic for the long run, but they have their wobbles when it comes to consistent supply and scaling up quickly. Nuclear power – including those newer SMRs – offers a reliable, low-carbon option that’s definitely catching the eye of big tech companies. But we still need to get our heads around the safety, cost, and how long they take to build. And remember, it’s not just about electricity. AI’s broader environmental impact – from the water it drinks to cool data centres, to the growing piles of e-waste from its hardware, and the resources it uses up during manufacturing – is huge. We need to look at the whole picture if we’re serious about lessening AI’s ecological footprint. The good news? There are plenty of promising ideas and innovations bubbling up. Energy-saving AI algorithms, clever power management in data centres, AI-aware software that can manage workloads intelligently, and the shift towards on-device AI all offer ways to cut down on energy use. Plus, the fact that we’re even talking about AI’s environmental impact more means that discussions around policies and rules to push for sustainability are finally happening. Dealing with AI’s energy and environmental challenges needs everyone – researchers, the tech industry, and policymakers – to roll up their sleeves and work together, and fast. If we make energy efficiency a top priority in how AI is developed, invest properly in sustainable energy, manage hardware responsibly from cradle to grave, and put supportive policies in place, we can aim for a future where AI’s incredible potential is unlocked in a way that doesn’t break our planet. The race to lead in AI has to be a race for sustainable AI too. (Photo by Nejc Soklič) See also: AI tool speeds up government feedback, experts urge caution Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Will the AI ***** fuel a global energy crisis? appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

When Huawei shocked the global tech industry with its Mate 60 Pro smartphone featuring an advanced 7-nanometer chip despite sweeping US technology restrictions, it demonstrated that innovation finds a way even under the heaviest sanctions. The US response was swift and predictable: tighter export controls and expanded restrictions. Now, with reports suggesting Huawei’s Ascend AI chips are approaching Nvidia-level performance—though the ******** company remains characteristically silent about these developments—America has preemptively escalated its semiconductor war to global proportions. The Trump administration’s declaration that using Huawei’s Ascend chips “anywhere in the world” violates US export controls reveals more than policy enforcement—it exposes a fundamental fear that American technological dominance may no longer be guaranteed through restrictions alone. This global AI chip ban emerged on May 14, 2025, when President Donald Trump’s administration rescinded the Biden-era AI Diffusion Rule without revealing details of a replacement policy. Instead, the Bureau of Industry and Security (BIS) announced guidance to “strengthen export controls for overseas AI chips,” specifically targeting Huawei’s Ascend processors. The new guidelines warn of “enforcement actions” including imprisonment and fines for any global business found using these ********-developed chips—a fundamental departure from traditional export controls, which typically govern what leaves a country’s borders, not what happens entirely outside them. The scope of America’s tech authority The South China Morning Post reports that these new guidelines explicitly single out Huawei’s Ascend chips after scrapping the Biden administration’s country-tiered “AI diffusion” rule. But the implications of this global AI chip ban extend far beyond bilateral US-China tensions. By asserting jurisdiction over global technology choices, America essentially demands that sovereign nations and independent businesses worldwide comply with its domestic policy preferences. This extraterritorial approach raises fundamental questions about national sovereignty and international trade. Should a Brazilian AI startup be prevented from using the most cost-effective chip solution simply because those chips are manufactured by a ******** company? Should European research institutions abandon promising collaborations because they involve hardware Washington deems unacceptable? According to Financial Times reporting, BIS stated that Huawei’s Ascend 910B, 910C, and 910D were all subject to the regulations as they were likely “designed with certain US software or technology or produced with semiconductor manufacturing equipment that is the direct product of certain US-origin software or technology, or both.” Industry resistance to universal controls Even within the United States, the chipmaking sector expresses alarm about Washington’s semiconductor policies. The aggressive expansion of export controls creates uncertainty beyond ******** companies, affecting global supply chains and innovation partnerships built over decades. “Washington’s new guidelines are essentially forcing global tech firms to pick a side – ******** or US hardware – which will further deepen the tech divide between the world’s two largest economies,” analysts note. This forced binary choice ignores the nuanced reality of modern technology development, where innovation emerges from diverse, international collaborations. The economic implications prove staggering. Recent analysis indicates Huawei’s Ascend 910B AI chip delivers 80% of Nvidia A100’s efficiency when training large language models, though “in some other tests, Ascend chips can beat the A100 by 20%.” By blocking access to competitive alternatives, this global AI chip ban may inadvertently stifle innovation and maintain artificial market monopolies. The innovation paradox Perhaps most ironically, policies intended to maintain American technological leadership may undermine it. Nvidia CEO Jensen Huang acknowledged earlier this month that Huawei was “one of the most formidable technology companies in the world,” noting that China was “not behind” in AI development. Attempting to isolate such capabilities through global restrictions may accelerate the development of parallel technology ecosystems, ultimately reducing American influence rather than preserving it. The secrecy surrounding Huawei’s Ascend chips—with the company keeping “details of its AI chips close to its chest, with only public information coming from third-party teardown reports”—has intensified with US sanctions. Following escalating restrictions, Huawei stopped officially disclosing information about the series, including release dates, production schedules, and fabrication technologies. The chips specified in current US restrictions, including the Ascend 910C and 910D, haven’t even been officially confirmed by Huawei. Geopolitical ramifications In a South China Morning Post’s report, Chim Lee, a senior analyst at the Economist Intelligence Unit, warns that “if the guidance is enforced strictly, it is likely to provoke retaliation from China” and could become “a negotiating point in ongoing trade talks between Washington and Beijing.” This assessment underscores the counterproductive nature of aggressive unilateral action in an interconnected global economy. The semiconductor industry thrives on international collaboration, shared research, and open competition. Policies that fragment this ecosystem serve no one’s long-term interests—including America’s. As the global community grapples with challenges from climate change to healthcare innovation, artificial barriers preventing the best minds from accessing optimal tools ultimately harm human progress. Beyond binary choices The question isn’t whether nations should protect strategic interests—they should and must. But when export controls extend “anywhere in the world,” we cross from legitimate national security policy into technological authoritarianism. The global technology community deserves frameworks that balance security concerns with innovation imperatives. This global AI chip ban risks accelerating the technological fragmentation it seeks to prevent. History suggests markets divided by political decree often spawn parallel innovation ecosystems that compete more effectively than those operating under artificial constraints. Rather than extending controls globally, a strategic approach would focus on out-innovating competitors through superior technology and international partnerships. The current path toward technological bifurcation serves neither American interests nor global innovation—it simply creates a more fragmented, less efficient world where artificial barriers replace natural competition. The semiconductor industry’s future depends on finding sustainable solutions that address legitimate security concerns without dismantling the collaborative networks that drive technological advancement. As this global AI chip ban takes effect, the world watches to see whether innovation will flourish through competition or fragment through control. See also: Huawei’s AI hardware breakthrough challenges Nvidia’s dominance Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Can the US really enforce a global AI chip ban? appeared first on AI News. View the full article

America’s quest to protect its semiconductor technology from China has taken increasingly dramatic turns over the past few years—from export bans to global restrictions—but the latest proposal from Congress ventures into unprecedented territory. Lawmakers are now pushing for mandatory GPS-style tracking embedded in every AI chip exported from the United States, essentially turning advanced semiconductors into devices that report their location back to Washington. On May 15, 2025, a bipartisan group of eight House representatives introduced the Chip Security Act, which would require companies like Nvidia to embed location verification mechanisms in their processors before export. This represents perhaps the most invasive approach yet in America’s technological competition with China, moving far beyond restricting where chips can go to actively monitoring where they end up. The mechanics of AI chip surveillance Under the proposed Chip Security Act, AI chip surveillance would become mandatory for all “covered integrated circuit products”—including those classified under Export Control Classification Numbers 3A090, 3A001.z, 4A090, and 4A003.z. Companies like Nvidia would be required to embed location verification mechanisms in their AI chips before export, reexport, or in-country transfer to foreign nations. Representative Bill Huizenga, the Michigan Republican who introduced the House bill, stated that “we must employ safeguards to help ensure export controls are not being circumvented, allowing these advanced AI chips to fall into the hands of nefarious actors.” His co-lead, Representative Bill Foster—an Illinois Democrat and former physicist who designed chips during his scientific career—added, “I know that we have the technical tools to prevent powerful AI technology from getting into the wrong hands.” The legislation goes far beyond simple location tracking. Companies would face ongoing surveillance obligations, required to report any credible information about chip diversion, including location changes, unauthorized users, or tampering attempts. This creates a continuous monitoring system that extends indefinitely beyond the point of *****, fundamentally altering the relationship between manufacturers and their products. Cross-party support for technology control Perhaps most striking about this AI chip surveillance initiative is its bipartisan nature. The bill enjoys broad support across party lines, co-led by House Select Committee on China Chairman John Moolenaar and Ranking Member ***** Krishnamoorthi. Other cosponsors include Representatives Ted Lieu, Rick Crawford, Josh Gottheimer, and Darin LaHood. Moolenaar said that “the ******** ********** Party has exploited weaknesses in our export control enforcement system—using shell companies and smuggling networks to divert sensitive US technology.” The bipartisan consensus on AI chip surveillance reflects how deeply the China challenge has penetrated American political thinking, transcending traditional partisan divisions. The Senate has already introduced similar legislation through Senator Tom Cotton, suggesting that semiconductor surveillance has broad congressional support. Coordination between chambers indicates that some form of AI chip surveillance may become law regardless of which party controls Congress. Technical challenges and implementation questions The technical requirements for implementing AI chip surveillance raise significant questions about feasibility, security, and performance. The bill mandates that chips implement “location verification using techniques that are feasible and appropriate” within 180 days of enactment, but provides little detail on how such mechanisms would work without compromising chip performance or introducing new vulnerabilities. For industry leaders like Nvidia, implementing mandatory surveillance technology could fundamentally alter product design and manufacturing processes. Each chip would need embedded capabilities to verify its location, potentially requiring additional components, increased power consumption, and processing overhead that could impact performance—precisely what customers in AI applications cannot afford. The bill also grants the Secretary of Commerce broad enforcement authority to “verify, in a manner the Secretary determines appropriate, the ownership and location” of exported chips. This creates a real-time surveillance system where the US government could potentially track every advanced semiconductor worldwide, raising questions about data sovereignty and privacy. Commercial surveillance meets national security AI chip surveillance proposal represents an unprecedented fusion of national security imperatives with commercial technology products. Unlike traditional export controls that simply restrict destinations, the approach creates ongoing monitoring obligations that blur the lines between private commerce and state surveillance. Representative Foster’s background as a physicist lends technical credibility to the initiative, but it also highlights how scientific expertise can be enlisted in geopolitical competition. The legislation reflects a belief that technical solutions can solve political problems—that embedding surveillance capabilities in semiconductors can prevent their misuse. Yet the proposed law raises fundamental questions about the nature of technology export in a globalized world. Should every advanced semiconductor become a potential surveillance device? How will mandatory AI chip surveillance affect innovation in countries that rely on US technology? What precedent does this set for other nations seeking to monitor their technology exports? Accelerating technological decoupling The mandatory AI chip surveillance requirement could inadvertently accelerate the development of alternative semiconductor ecosystems. If US chips come with built-in tracking mechanisms, countries may intensify efforts to develop domestic alternatives or source from suppliers without such requirements. China, already investing heavily in semiconductor self-sufficiency following years of US restrictions, may view these surveillance requirements as further justification for technological decoupling. The irony is striking: efforts to track ******** use of US chips may ultimately reduce their appeal and market share in global markets. Meanwhile, allied nations may question whether they want their critical infrastructure dependent on chips that can be monitored by the US government. The legislation’s broad language suggests that AI chip surveillance would apply to all foreign countries, not just adversaries, potentially straining relationships with partners who value technological sovereignty. The future of semiconductor governance As the Trump administration continues to formulate its replacement for Biden’s AI Diffusion Rule, Congress appears unwilling to wait. The Chip Security Act represents a more aggressive approach than traditional export controls, moving from restriction to active surveillance in ways that could reshape the global semiconductor industry. This evolution reflects deeper changes in how nations view technology exports in an era of great power competition. The semiconductor industry, once governed primarily by market forces and technical standards, increasingly operates under geopolitical imperatives that prioritize control over commerce. Whether AI chip surveillance becomes law depends on congressional action and industry response. But the bipartisan support suggests that some form of semiconductor monitoring may be inevitable, marking a new chapter in the relationship between technology, commerce, and national security. Conclusion: The end of anonymous semiconductors from America? The question facing the industry is no longer whether the US will control technology exports, but how extensively it will monitor them after they leave American shores. In this emerging paradigm, every chip becomes a potential intelligence asset, and every export a data point in a global surveillance network. The semiconductor industry now faces a critical choice: adapt to a future where products carry their own tracking systems, or risk being excluded from the US market entirely. As Congress pushes for mandatory AI chip surveillance, we may be witnessing the end of anonymous semiconductors and the beginning of an era where every processor knows exactly where it belongs—and reports back accordingly. See also: US-China tech war escalates with new AI chips export controls Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Congress pushes GPS tracking for every exported semiconductor appeared first on AI News. View the full article

Microsoft is cutting about 7,000 jobs, or 3% of its workforce. The move isn’t about poor performance or falling revenue. It’s a clear shift in strategy—fewer layers, more engineers, and more investment in artificial intelligence. The layoffs affect staff across divisions and global offices. But the bulk of those let go are in middle management and non-technical roles, a pattern showing up across tech. The message: reduce overhead, speed up product cycles, and make room for ******* AI spending. The numbers behind the shift Microsoft ended its latest quarter with $70.07 billion in revenue. That beat Wall Street estimates and shows strong business health, and the company plans to spend as much as $80 billion this fiscal year—mainly on data centres designed for training and running AI models. That’s a big leap in infrastructure spending but it also explains why Microsoft is trimming elsewhere. AI models are compute-heavy and demand new types of hardware. Storage, cooling, and power need to scale: Building that capacity takes money, time, and fewer internal delays, and Microsoft appears to be cutting anything that slows the push. Management in the firing line Most cuts hit middle managers and support staff. These are roles that help coordinate, review, and report—but don’t directly write code or design systems. While these positions have long helped large companies function, they’re now being seen as blockers to fast action. Sources told Business Insider that Microsoft wants a higher ratio of technical staff to managers. This isn’t just about saving costs, it’s about reducing the number of people between engineers and final decisions. Analyst Rishi Jaluria told the Financial Times that tech giants like Microsoft have “too many layers.” He said companies are trying to strip back bureaucracy as they chase AI leadership. Microsoft has not publicly broken down which departments were most affected. But reports suggest LinkedIn, a Microsoft subsidiary, saw job cuts as part of this broader shift. Aligning with a broader industry trend Microsoft isn’t the only company trimming management, as Amazon, Google, and Meta have all done similarly. They’re removing layers and pushing more decisions closer to those building the product. For Microsoft, the changes come after several earlier rounds of cuts. In early 2024, the company laid off around 2,000 workers in performance-based trims. This new wave is different as it targets structure, not staff output. $80 billion on AI infrastructure Microsoft’s investment plan puts AI at the centre of its growth. According to Reuters, the company wants to spend up to $80 billion in fiscal 2025, much of it going toward AI-enabled data centres. These centres power large language models, natural language tools, and enterprise AI systems. Without them, even the best models won’t run at scale. The company’s move shows how serious it is about owning the AI backbone. This is about more than software updates, it’s about physical hardware, cloud capacity, and tight control over how AI gets built and used. Microsoft’s early partnership with OpenAI gave it a jumpstart, but Google, Meta, Amazon, and Apple are all making big AI moves. Microsoft appears to be betting that first-mover advantage is only as strong as the infrastructure behind it. Employee reactions reflect mixed sentiment As with most layoffs, employee reactions vary. Some posts on social media reflect understanding, others voice concern about job security and team stability. Several ex-employees described the mood as “tense but expected.” Many said they had been preparing for changes since Microsoft’s 2024 performance cuts. Some worry that too much focus on AI will weaken support roles, and others believe cutting managers will create confusion rather than clarity. Still, public sentiment shows a growing acceptance that AI is changing what jobs look like—even at the biggest firms. What this means for the industry Microsoft’s restructuring sets a tone: Strong revenue no longer guarantees job security, and growth in AI now drives org charts, not the other way around. Middle management is no longer safe, and non-technical roles must prove direct value to AI goals. Even product teams may face more pressure to automate or streamline. For employees, the message is clear. Learn how AI fits your job—or risk being cut from the plan. For other tech firms, Microsoft’s strategy may serve as a roadmap. Spending more on AI means spending less elsewhere. and many companies will likely follow that playbook to stay competitive. Long-term questions remain The short-term logic is clear. Microsoft is cutting structure to fund AI growth. But over time, companies will need to balance innovation with internal support. Removing middle managers may speed up some work, but it can also reduce mentorship, training, and context—things that help teams stay aligned. AI may need more data and compute. But people still build the tools, ask the right questions, and set the goals. How companies treat those people now will shape how well they compete later. (Photo by Ron Lach) See also: Alarming rise in AI-powered scams: Microsoft reveals $4B in thwarted fraud Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Why Microsoft is cutting roles despite strong earnings appeared first on AI News. View the full article

One of the ways in which organisations are using the latest AI algorithms to help them grow and thrive is the adoption of privately-held AI models in aligning their business strategies. The differentiation between private and public AI is important in this context – most organisations are rightly wary of allowing public AIs access to what are sensitive data sets, such as HR information, financial data, and details of operational history. It stands to reason that if an AI is given specific data on which to base its responses, its output will be more relevant, and be therefore more effective in helping decision-makers to judge how to strategise. Using private reasoning engines is the logical way that companies can get the best results from AI and keep their intellectual property safe. Enterprise-specific data and the ability to fine-tune a local AI model give organisations the ability to provide bespoke forecasting and operational tuning that are more grounded in the day-to-day reality of a company’s work. A Deloitte Strategy Insight paper calls private AI a “bespoke compass”, and places the use of internal data as a competitive advantage, and Accenture describes AIs as “poised to provide the most significant economic uplift and change to work since the agricultural and industrial revolutions.” There is the possibility, however, that like traditional business intelligence, using historical data drawn from several years of operations across the enterprise, can entrench decision-making in patterns from the past. McKinsey says companies are in danger of “mirroring their institutional past in algorithmic amber.” The Harvard Business Review picks up on some of the technical complexity, stating that the act of customising a model so that it’s activities are more relevant to the company is difficult, and perhaps, therefore, not a task to be taken on by any but the most AI-literate at a level of data science and programming. MIT Sloane strikes a balance between the fervent advocates and the conservative voices for private AI in business strategising. It advises that AI be regarded as a co-pilot, and urges continual questioning and verification of AI output, especially when the stakes are high. Believe in the revolution However, decision-makers considering pursuing this course of action (getting on the AI wave, but doing so in a private, safety-conscious way) may wish to consider the motivations of those sources of advice that advocate strongly for AI enablement in this way. Deloitte, for example, builds and manages AI solutions for clients using custom infrastructure such as its factory-as-a-service offerings, while Accenture has practices dedicated to its clients’ AI strategy, such as Accenture Applied Intelligence. It partners with AWS and Azure, building bespoke AI systems for Fortune 500 companies, among others, and Deloitte is partners with Oracle and Nvidia. With ‘skin in the game’, phrases such as “the most significant […] change to work since the agricultural and industrial revolutions” and a “bespoke compass” are inspiring, but the vendors’ motivations may not be entirely altruistic. Advocates for AI in general rightly point to the ability of models to identify trends and statistical undercurrents much more efficiently than humans. Given the mass of data available to the modern enterprise, comprising both internal and externally-available information, having software that can parse data at scale is an incredible advantage. Instead of manually creating analysis of huge repositories of data – which is time-consuming and error-prove – AI can see through the chaff and surface real, actionable insights. Asking the right questions Additionally, AI models can interpret queries couched in normal language, and make predictions based on empirical information, which, in the context of private AIs, is highly-relevant to the organisation. Relatively unskilled personnel can query data without having skills in statistical analysis or database query languages, and get answers that otherwise would have involved multiple teams and skill-sets drawn from across the enterprise. That time-saving alone is considerable, letting organisations focus on strategy, rather than forming the necessary data points and manually querying the information they’ve managed to gather. Both McKinsey and Gartner warn, however, of overconfidence and data obsolescence. On the latter, historical data may not be relevant to strategising, especially if records go back several years. Overconfidence is perhaps best termed in the context of AI as operators trusting AI responses without question, not delving independently into responses’ detail, or in some cases, taking as fact the responses to badly-phrased queries. For any software algorithm, human phrases such as “base your findings on our historical data” are open to interpretation, unlike, for example, “base your findings on the last twelve months’ sales data, ignoring outliers that differ from the mean by over 30%, although do state those instances for me to consider.” Software of experience Organisations might pursue private AI solutions alongside mature, existing business intelligence platforms. SAP Business Organisations is nearly 30 years old, yet a youngster compared to SAS Business Intelligence that’s been around since before the internet became mainstream in the 1990s. Even relative newcomers such as Microsoft Power *** represents at least a decade of development, iteration, customer feedback, and real-world use in business analysis. It seems sensible, therefore, that private AI’s deployment on business data should be regarded as an addition to the strategiser’s toolkit, rather than a silver bullet that replaces “traditional” tools. For users of private AI that have the capacity to audit and tweak their model’s inputs and inner algorithms, retaining human control and oversight is important – just as it is with tools like Oracle’s Business Intelligence suite. There are some scenarios where the intelligent processing of and acting on real-time data (online retail pricing mechanisms, for example) gives AI analysis a competitive edge over the incumbent *** platforms. But AI has yet to develop into a magical Swiss Army Knife for business strategy. Until AI purposed for business data analysis is as developed, iterated on, battle-hardened, and mature as some of the market’s go-to *** platforms, early adopters might temper the enthusiasm of AI and AI service vendors with practical experience and a critical eye. AI is a new tool, and one with a great deal of potential. However, it remains first-generation in its current guises, public and private. (Image source: “It’s about rules and strategy” by pshutterbug is licensed under CC BY 2.0.) The post AI in business intelligence: Caveat emptor appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

An AI tool aims to wade through mountains of government feedback and understand what the public is trying to say. *** Technology Secretary Peter Kyle said: “No one should be wasting time on something AI can do quicker and better, let alone wasting millions of taxpayer pounds on outsourcing such work to contractors. This digital assistant, aptly named ‘Consult’, just aced its first big test with the Scottish Government. The Scottish Gov threw Consult in at the deep end, asking it to make sense of public opinion on regulating non-surgical cosmetic procedures such as lip fillers and laser hair removal. Consult came back with findings almost identical to what human officials had pieced together. Now, the plan is to roll this tech out across various government departments. The current way of doing things is expensive and slow. Millions of pounds often go to outside contractors just to analyse what the public thinks. Consult is part of a ******* push to build a leaner, more responsive *** government—one that can deliver on its ‘Plan for Change’ without breaking the bank or taking an age to do it. So, how did it fare in Scotland? Consult chewed through responses from over 2,000 people. Using generative AI, it picked out the main themes and concerns bubbling up from the feedback across six key questions. Of course, Consult wasn’t left completely to its own devices. Experts in the Scottish Government double-checked and fine-tuned these initial themes. Then, the AI got back to work to sort individual responses into these categories. Officials ended up with more precious time to consider what people were saying and what it meant for policy. Because this was Consult’s first live outing, the Scottish Government went through every single response by hand too—just to be sure. Figuring out exactly what someone means in a written comment and then deciding which ‘theme’ it fits under can be a bit subjective. Even humans don’t always agree. When the government compared Consult’s handiwork to human analysis, the AI was right most of the time. Where there were differences, they were so minor they didn’t change the overall picture of what mattered most to people. Consult is part of a ******* AI toolkit called ‘Humphrey’—a suite of digital helpers designed to free up civil servants from admin and cut down on those contractor bills. It’s all part of a grander vision to use technology to sharpen up public services, aiming to find £45 billion in productivity savings. The goal is a more nimble government that is better at delivering that ‘Plan for Change’ we keep hearing about. “After demonstrating such promising results, Humphrey will help us cut the costs of governing and make it easier to collect and comprehensively review what experts and the public are telling us on a range of crucial issues,” added Kyle. “The Scottish Government has taken a bold first step. Very soon, I’ll be using Consult, within Humphrey, in my own department and others in Whitehall will be using it too – speeding up our work to deliver the Plan for Change.” Over in Scotland, Public Health Minister Jenni Minto said: “Using the tool was very beneficial in helping the Scottish Government understand more quickly what people wanted us to hear and our respondents’ range of views. “Using this tool has allowed the Scottish Government to move more quickly to a focus on the policy questions and dive into the detail of the evidence we’ve been presented with, while remaining confident that we have heard the strong views expressed by respondents.” Of course, like many AI deployments in government, it’s still early days, and Consult is officially still in the trial phase. More number-crunching and testing are on the cards to make sure it’s working just as it should before any big decisions about a full rollout are made. But the potential here is huge. The government runs about 500 consultations every year. If Consult lives up to its promise, it could save officials a staggering 75,000 days of analysis annually. And what did the civil servants who first worked with Consult think? They were reportedly “pleasantly surprised,” finding the AI’s initial analysis a “useful starting point.” Others raved that it “saved [them] a heck of a lot of time” and let them “get to the analysis and draw out what’s needed next” much faster. Interestingly, they also felt Consult brought a new level of fairness to the table. As one official put it, its use “takes away the bias and makes it more consistent,” preventing individual analysts from, perhaps unconsciously, letting their “own preconceived ideas” colour the findings. Some consultations receive tens, even hundreds of thousands of responses. Given how well Consult has performed in these early tests, it won’t be long before it’s used on these massive consultations. It’s worth noting that humans aren’t being kicked out of the loop. Consult has been built to keep the experts involved every step of the way. Officials will always review the themes the AI suggests and how it sorts the responses. They’ll have an interactive dashboard to play with, letting them filter and search for specific insights. It’s about AI doing the heavy lifting, so the humans can do the smart thinking. Experts urge caution about the use of AI in government This move towards AI in government isn’t happening in a vacuum, and experts are watching closely. Stuart Harvey, CEO of Datactics, commented: “Using AI to speed up public consultations is a great example of how technology can improve efficiency and save money. But AI is only as good as the data behind it. For tools like this to work well and fairly, government departments need to make sure their data is accurate, up-to-date, and properly managed. “People need to trust the decisions made with AI. That means making sure the process is clear, well-governed, and ethical. If the data is messy or poorly handled, it can lead to biased or unreliable outcomes. “As the government expands its use of AI in public services, it’s vital to invest in strong data practices. That includes building clear and consistent data systems, making data accessible for review, and keeping humans involved in key decisions—especially when it comes to hearing from the public.” This sentiment is echoed by academics. Professor Michael Rovatsos from the University of Edinburgh, for instance, acknowledges the benefits but also wisely cautions about the risks of AI biases and even the potential for these tools to be manipulated. He’s calling for tough safeguards and ongoing investment to make sure any AI tool used by the government remains reliable and fair. Stuart Munton, Chief for Group Operations at AND Digital, added: “The government’s use of AI to speed up public consultations is a welcome step toward smarter, more efficient public services. However, as AI adoption grows, we must ensure that people – not just technology – are at the heart of this transformation.” “Tools like this will only reach their full potential if we invest in equipping public sector teams with the right skills and training. Empowering diverse talent to work with AI will not only improve how these tools perform but also ensure that innovation is inclusive to real-world needs.” If done right, with these expert caveats in mind, AI tools like Consult have the potential to improve how governments listen, learn, and make policy based on public opinion. The *** government isn’t hanging about; the plan is to get Consult working across various departments by the end of 2025. (Photo by Scott Rodgerson) See also: US slams brakes on AI Diffusion Rule, hardens chip export curbs Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post AI tool speeds up government feedback, experts urge caution appeared first on AI News. View the full article

Alibaba has unveiled Wan2.1-VACE, an open-source AI model designed to shake up how we create and edit videos. VACE isn’t appearing out of thin air; it’s part of Alibaba’s broader Wan2.1 family of video AI models. And they’re making a rather bold claim for it, stating it’s the “first open-source model in the industry to provide a unified solution for various video generation and editing tasks.” If Alibaba can succeed in shifting users away from having to juggle multiple, separate tools towards one streamlined hub—it could be a true game-changer. So, what can this thing actually do? Well, for starters, it can whip up videos using all sorts of prompts, including text commands, still pictures, and even snippets of other video clips. But it’s not just about making videos from scratch. The editing toolkit supports referencing images or specific frames to guide the AI, advanced video “repainting” (more on that in a sec), tweaking just selected bits of your existing video, and even stretching out the video. Alibaba reckons these features “enable the flexible combination of various tasks to enhance creativity.” Imagine you want to create a video with specific characters interacting, maybe based on some photos you have. VACE claims to be able to do that. Got a still image you wish was dynamic? Alibaba’s open-source AI model can add natural-looking movement to bring it to life. For those who love to fine-tune, there are those advanced “video repainting” functions I mentioned earlier. This includes things like transferring poses from one subject to another, having precise control over motion, adjusting depth perception, and even changing the colours. One feature that caught my eye is its ability to “supports adding, modification or deletion to selective specific areas of a video without affecting the surroundings.” That’s a massive plus for detailed edits – no more accidentally messing up the background when you’re just trying to tweak one small element. Plus, it can make your video canvas ******* and even fill in the new space with relevant content to make everything look richer and more expansive. You could take a flat photograph, turn it into a video, and tell the objects in it exactly how to move by drawing out a path. Need to swap out a character or an object with something else you provide as a reference? No problem. Animate those referenced characters? Done. Control their pose precisely? You got it. Alibaba even gives the example of its open-source AI model taking a tall, skinny vertical image and cleverly expanding it sideways into a widescreen video, automagically adding new bits and pieces by referencing other images or prompts. That’s pretty neat. Of course, VACE isn’t just magic. There’s some clever tech involved, designed to handle the often-messy reality of video editing. A key piece is something Alibaba calls the Video Condition Unit (VCU), which “supports unified processing of multimodal inputs such as text, images, video, and masks.” Then there’s what they term a “Context Adapter structure.” This clever bit of engineering “injects various task concepts using formalised representations of temporal and spatial dimensions.” Essentially, think of it as giving the AI a really good understanding of time and space within the video. With all this clever tech, Alibaba reckons VACE will be a hit in quite a few areas. Think quick social media clips, eye-catching ads and marketing content, heavy-duty post-production special effects for film and TV, and even for generating custom educational and training videos. Alibaba makes Wan2.1-VACE open-source to spread the AI love Building AI models this powerful usually costs a fortune and needs massive computing power and tons of data. So, Alibaba making Wan2.1-VACE open source? That’s a big deal. “Open access helps lower the barrier for more businesses to leverage AI, enabling them to create high-quality visual content tailored to their needs, quickly and cost-effectively,” Alibaba explains. Basically, Alibaba is hoping to let more folks – especially smaller businesses and individual creators – get their hands on top-tier AI without breaking the bank. This democratisation of powerful tools is always a welcome sight. And they’re not just dropping one version. There’s a hefty 14-billion parameter model for those with serious horsepower, and a more nimble 1.3-billion parameter one for lighter setups. You can grab them for free right now on Hugging Face and GitHub, or via Alibaba Cloud’s own open-source community, ModelScope. (Image source: www.alibabagroup.com) See also: US slams brakes on AI Diffusion Rule, hardens chip export curbs Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Alibaba Wan2.1-VACE: Open-source AI video tool for all appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

The Department of Commerce (DOC) has slammed the brakes on the sweeping “AI Diffusion Rule,” yanking it just a day before it was due to bite. Meanwhile, officials have laid down the gauntlet with stricter measures to control semiconductor exports. The AI Diffusion Rule, a piece of regulation cooked up under the Biden administration, was staring down a compliance deadline of May 15th. According to the folks at the DOC, letting this rule roll out would have been like throwing a spanner in the works of American innovation. DOC officials argue the rule would have saddled tech firms with “burdensome new regulatory requirements” and, perhaps more surprisingly, risked souring America’s relationships on the world stage by effectively “downgrading” dozens of countries “to second-tier status.” The nuts and bolts of this reversal will see the Bureau of Industry and Security (BIS), part of the DOC, publishing a notice in the Federal Register to make the rescission official. While this particular rule is heading for the shredder, the official line is that a replacement isn’t off the table; one will be cooked up and served “in the future.” Jeffery Kessler, the Under Secretary of Commerce for Industry and Security, has told BIS enforcement teams to stand down on anything concerning the now-canned AI Diffusion Rule. “The Trump Administration will pursue a bold, inclusive strategy to American AI technology with trusted foreign countries around the world, while keeping the technology out of the hands of our adversaries,” said Kessler. “At the same time, we reject the Biden Administration’s attempt to impose its own ill-conceived and counterproductive AI policies on the American people.” What was this ‘AI Diffusion Rule’ anyway? You might be wondering what this “AI Diffusion Rule” actually was, and why it’s causing such a stir. The rule wasn’t just a minor tweak; it was the Biden administration’s bid to get a tight grip on how advanced American tech – everything from the AI chips themselves to cloud computing access and even the crucial AI ‘model weights’ – flowed out of the US to the rest of the world. The idea, at least on paper, was to walk a tightrope: keep the US at the front of the AI pack, protect national security, and still champion American tech exports. But how did it plan to do this? The rule laid out a fairly complex playbook: A tiered system for nations: Imagine a global league table for AI access. Countries were split into three groups. Tier 1 nations, America’s closest allies like Japan and South Korea, would have seen hardly any new restrictions. Tier 3, unsurprisingly, included countries already under arms embargoes – like China and Russia – who were already largely banned from getting US chips and would face the toughest controls imaginable. The squeezed middle: This is where things got sticky. A large swathe of countries, including nations like Mexico, Portugal, India, and even Switzerland, found themselves in Tier 2. For them, the rule meant new limits on how many advanced AI chips they could import, especially if they were looking to build those super-powerful, large computing clusters essential for AI development. Caps and close scrutiny: Beyond the tiers, the rule introduced actual caps on the quantity of high-performance AI chips most countries could get their hands on. If anyone wanted to bring in chips above certain levels, particularly for building massive AI data centres, they’d have faced incredibly strict security checks and reporting duties. Controlling the ‘brains’: It wasn’t just about the hardware. The rule also aimed to regulate the storage and export of advanced AI model weights – essentially the core programming and learned knowledge of an AI system. There were strict rules about not storing these in arms-embargoed countries and only allowing their export to favoured allies, and even then, only under tight conditions. Tech as a bargaining chip: Underneath it all, the framework was also a bit of a power play. The US aimed to use access to its coveted AI technology as a carrot, encouraging other nations to sign up to American standards and safeguards if they wanted to keep the American chips and software flowing. The Biden administration had a clear rationale for these moves. They wanted to stop adversaries, with China being the primary concern, from getting their hands on advanced AI that could be turned against US interests or used for military purposes. It was also about cementing US leadership in AI, making sure the most potent AI systems and the infrastructure to run them stayed within the US and its closest circle of allies, all while trying to keep US tech exports competitive. However, the AI Diffusion Rule and broader plan didn’t exactly get a standing ovation. Far from it. Major US tech players – including giants like Nvidia, Microsoft, and Oracle – voiced strong concerns. They argued that the rule, instead of protecting US interests, would stifle innovation, bog businesses down in red tape, and ultimately hurt the competitiveness of American companies on the global stage. Crucially, they also doubted it would effectively stop China from accessing advanced AI chips through other means. And it wasn’t just industry. Many countries weren’t thrilled about being labelled “second-tier,” a status they felt was not only insulting but also risked undermining diplomatic ties. There was a real fear it could push them to look for AI technology elsewhere, potentially even from China, which was hardly the intended outcome. This widespread pushback and the concerns about hampering innovation and international relations are exactly what the current Department of Commerce is pointing to as reasons for today’s decisive action to scrap the rule. Fresh clampdown on AI chip exports It wasn’t just about scrapping old rules, though. The BIS also rolled out a new playbook to tighten America’s grip on AI chip exports, showing they’re serious about guarding the nation’s tech crown jewels. The latest clampdown includes: A spotlight on Huawei Ascend chips: New guidance makes it crystal clear: using Huawei Ascend chips anywhere on the planet is now a no-go under US export controls. This takes direct aim at one of China’s big players in the AI hardware game. Heads-up on ******** AI model training: A stark warning has gone out to the public and the industry about the serious consequences if US AI chips are used to train or run ******** AI models. The worry? That American tech could inadvertently supercharge AI systems that might not have US interests at heart. Guidance on shoring up supply chains: US firms are getting a fresh batch of advice on how to batten down the hatches on their supply chains to stop controlled tech from being siphoned off to unapproved destinations or users. The Department of Commerce is selling today’s double-whammy – axing the rule and beefing up export controls – as essential to “ensure that the United States will remain at the forefront of AI innovation and maintain global AI dominance.” It’s a strategy that looks to clear the runway for domestic tech growth while building higher fences around critical AI technologies, especially advanced semiconductors. This policy pivot will likely get a thumbs-up from some quarters in the US tech scene, particularly those who were getting sweaty palms about the AI Diffusion Rule and the red tape it threatened. On the flip side, the even tougher export controls – especially those zeroing in on China and firms like Huawei – show that trade policy is still very much a frontline tool in the high-stakes global chess game over who leads in tech. The whisper of a “replacement rule” down the line means this isn’t the final chapter in the saga of how to manage the AI revolution. For now, it seems the game plan is to clear the path for homegrown innovation and be much more careful about who gets to play with America’s latest breakthroughs. See also: Samsung AI strategy delivers record revenue despite semiconductor headwinds Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post US slams brakes on AI Diffusion Rule, hardens chip export curbs appeared first on AI News. View the full article

Saudi Arabia’s new state subsidiary, HUMAIN, is collaborating with NVIDIA to build AI infrastructure, nurture talent, and launch large-scale digital systems. The effort includes plans to set up AI “factories” powered by up to 500 megawatts of energy. The sites will be filled with NVIDIA GPUs, including the Grace Blackwell GB300 supercomputers connected via NVIDIA’s InfiniBand network. The goal is to create a base for training models, running simulations, and managing complex AI deployments. A major part of the push is about control. Saudi Arabia wants to build sovereign AI – models trained using local data, language, and systems. By building its own infrastructure, it avoids relying on foreign cloud providers. The shift aligns with a broader trend, as governments around the world start to question how AI tools are built, where data goes, and who controls it. HUMAIN is meant to give Saudi Arabia more say in that process. While other countries have launched national AI strategies, HUMAIN stands out for its structure. It’s not just a policy office or research fund; instead, it operates across the full AI value chain – building data centres, managing data, training models, and deploying applications. Few countries have a single body doing likewise with such a broad remit. Singapore’s NAIS 2.0, for example, focuses on public sector use cases and talent development, and the UAE’s approach, which emphasises frameworks and governance. China has set up AI labs in several cities, but they tend to work in silos. HUMAIN brings elements together with a central goal: make Saudi Arabia a producer, not just a user, of AI. The ambition is clear, but it comes with trade-offs. Running GPU-heavy data centres on this scale will use a lot of power. The 500-megawatt figure is far beyond typical enterprise deployments. Globally, the environmental cost of AI has become a growing concern. Microsoft and Google have both reported rising emissions from AI-related infrastructure. Saudi Arabia will need to explain how its AI factories will be powered – especially if it wants to align with its own sustainability targets under Vision 2030. The partnership with NVIDIA isn’t just about machines, it also includes training for people. HUMAIN and NVIDIA say they will run large-scale education programmes to help thousands of Saudi developers gain skills in AI, robotics, simulation, and digital twins. Building local talent is a core part of the effort, and without it, infrastructure likely won’t get used to its full potential. “AI, like electricity and internet, is essential infrastructure for every nation,” said Jensen Huang, founder and CEO of NVIDIA. “Together with HUMAIN, we are building AI infrastructure for the people and companies of Saudi Arabia to realise the bold vision of the Kingdom.” One of the tools HUMAIN plans to deploy is NVIDIA Omniverse, to be used as a multi-tenant platform for industries like logistics, manufacturing, and energy. These sectors could create digital twins – virtual versions of real systems – to test, monitor, and improve operations. The idea is simple: simulate before you build, or run stress tests in digital form to save time and money later. This type of simulation and optimisation supports Saudi Arabia’s broader push into automation and smart industry. It fits in a wider narrative of transitioning from oil to advanced tech as a core pillar of the economy. The deal fits into NVIDIA’s global strategy, and the company has similar partnerships in India, the UAE, and Europe. Saudi Arabia offers strong government support, deep funding, and the promise to become a new AI hub in the Middle East. In return, NVIDIA provides the technical backbone – GPUs, software platforms, and the know-how to run them. The partnership helps both sides. Saudi Arabia gets the tools to build AI from the ground up and build a new economic version of itself, while NVIDIA gains a long-term customer and a foothold in a growing market. There are still gaps to watch. How will HUMAIN govern the use of its models? Will they be open for researchers and startups, or tightly controlled by the state? What role will local universities or private companies play? And can workforce development keep pace with the rapid buildout of infrastructure? HUMAIN isn’t just building for now. The structure suggests a long-term bet – one that links compute power, national priorities, and a shift in how AI is developed and deployed. Saudi Arabia wants more than access. It wants influence. And HUMAIN, in partnership with NVIDIA, is the engine it’s building to get there. (Photo by Mariia Shalabaieva) See also: Huawei’s AI hardware breakthrough challenges Nvidia’s dominance Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Saudi Arabia moves to build its AI future with HUMAIN and NVIDIA appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

The US Food and Drugs Administration (FDA) has stated that it wants to accelerate the deployment of AI across its centres. FDA Commissioner Martin A. Makary has announced an aggressive timeline to scale use of AI by 30 June 2025 and is betting big on the technology to change drug approval processes for the US. But the rapid AI deployment at the FDA raises important questions about whether innovation can be balanced with oversight. Strategic leadership drive: FDA names first AI chief The foundation for the ambitious FDA AI deployment was laid with the appointment of Jeremy Walsh as the first-ever Chief AI Officer. Walsh previously led enterprise-scale technology deployments in federal health and intelligence agencies and came from government contractor Booz Allen Hamilton, where he worked for 14 years as chief technologist. His appointment, announced just before the May 8th rollout announcement, signals the agency’s serious commitment to technological transformation. The timing is significant – Walsh’s hiring coincided with workforce cuts at the FDA, including the loss of key tech talent. Among the losses was Sridhar Mantha, the former director of strategic programmes at the Center for Drug Evaluation and Research, who had co-chaired the AI Council at CDER and helped develop policy around AI’s use in drug development. Ironically, Mantha is now working alongside Walsh to coordinate the agency-wide rollout. The pilot programme: Impressive results, limited details What’s driving the rapid AI deployment is the reported success of the agency’s pilot programme trialling the software. Commissioner Makary said he was “blown away by the success of our first AI-assisted scientific review pilot,” with one official claiming the technology enabled him to perform scientific review tasks in minutes that used to take three days. However, the scope, rigour and results from the pilot scheme remain unreleased. The agency has not published detailed reports on the pilot’s methodology, validation procedures, or specific use cases tested. The lack of transparency is concerning given the high-stakes nature of drug evaluation. When pressed for details, the FDA has promised that additional details and updates on the initiative will be shared publicly in June. For an agency responsible for protecting public health through rigorous scientific review, the absence of published pilot data raises questions about the evidence base supporting such an aggressive timeline. Industry perspective: Cautious optimism meets concerns The pharmaceutical industry’s reaction to the FDA AI deployment reflects a mixture of optimism and apprehension. Companies have long sought faster approval processes, with Makary pointedly asking, “Why does it take over 10 years for a new drug to come to market?” “While AI is still developing, harnessing it requires a thoughtful and risk-based approach with patients at the centre. We’re pleased to see the FDA taking concrete action to harness the potential of AI,” said PhRMA spokesperson Andrew Powaleny. However, industry experts are raising practical concerns. Mike Hinckle, an FDA compliance expert at K&L Gates, highlighted a key issue: pharmaceutical companies will want to know how the proprietary data they submit will be secured. The concern is particularly acute given reports that the FDA was in discussions with OpenAI about a project called cderGPT, which appears to be an AI tool for the Centre for Drug Evaluation and Research. Expert warnings: The rush vs rigour debate Leading experts in the field are expressing concern about the pace of deployment. Eric Topol, founder of the Scripps Research Translational Institute, told Axios: “The idea is good, but the lack of details and the perceived ‘rush’ is concerning.” He identified critical gaps in transparency, including questions about which models are being used to train the AI, and what inputs are provided for specialised fine-tuning. Former FDA commissioner Robert Califf struck a balanced tone: “I have nothing but enthusiasm tempered by caution about the timeline.” His comment reflects the broader sentiment among experts who support AI integration but question whether the June 30th deadline allows sufficient time for proper validation and safeguards to be implemented. Rafael Rosengarten from the Alliance for AI in Healthcare supports automation but emphasises the need for governance, saying there is a need for policy guidance around what kind of data is used to train AI models and what kind of model performance is considered acceptable. Political context: Trump’s deregulatory AI vision The FDA AI deployment must be understood in the broader context of the Trump administration’s approach to AI governance. Trump’s overhaul of federal AI policy – ditching Biden-era guardrails in favour of speed and international dominance in technology – has turned the government into a tech testing ground. The administration has explicitly prioritised innovation over precaution. Vice President JD Vance outlined four key AI policy priorities, including encouraging “pro-growth AI policies” instead of “excessive regulation of the AI sector,” and he has taken action to ensure the forthcoming White House AI Action Plan would “avoid an overly precautionary regulatory regime.” The philosophy is evident in how the FDA is approaching its AI deployment. With Elon Musk leading a charge under an “AI-first” flag, critics warn that rushed rollouts at agencies could compromise data security, automate important decisions, and put Americans at risk. Safeguards and governance: What’s missing? While the FDA has promised that its AI systems will maintain strict information security and act in compliance with FDA policy, specific details about safeguards remain sparse. The agency’s claims that AI is a tool to support, not replace, human expertise and can enhance regulatory rigour by helping predict toxicities and adverse events. This provides some reassurance but lacks specificity. The absence of published governance frameworks for what is an internal process contrasts sharply with the FDA’s guidance for industry. The agency has previously issued draft guidance to pharma companies, providing recommendations on the use of AI intended to support a regulatory decision about a drug or biological product’s safety, effectiveness, or quality. Its published draft guidance in that instance was based on feedback from over 800 external comments and its experience with more than 500 drug submissions involving AI components in their development since 2016. The broader AI landscape: Federal agencies as testing grounds The FDA’s initiative is part of a larger federal AI adoption wave. The General Services Administration is piloting an AI chatbot to automate routine tasks, and the Social Security Administration plans to use AI software to transcribe applicant hearings. However, GSA officials noted its tool has been in development for 18 months – highlighting the contrast with the FDA’s accelerated timeline, which at the time of writing, is a matter of weeks. The rapid federal adoption reflects the Trump administration’s belief that America is well-positioned to maintain its global dominance in AI and that the Federal Government must capitalise on the advantages of American innovation. It also maintains the importance of strong protections for Americans’ privacy, civil rights, and civil liberties. Innovation at a crossroads The FDA’s ambitious timeline embodies the fundamental tension between technological promise and regulatory responsibility. While AI offers clear benefits in automating tedious tasks, the rush to implementation raises critical questions about transparency, accountability, and the erosion of scientific rigour. The June 30th deadline will test whether the agency can maintain the public trust that has long been its cornerstone. Success requires more than technological capability – it demands proof that oversight hasn’t been sacrificed for speed. The FDA AI deployment represents a defining moment for pharmaceutical regulation. The outcome will determine whether rapid AI adoption strengthens public health protection or serves as a cautionary tale about prioritising efficiency over safety in matters of life and death. The stakes couldn’t be higher. See also: AI vs COVID-19: Here are the AI tools and services fighting coronavirus Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post FDA AI deployment: Innovation vs oversight in drug regulation appeared first on AI News. View the full article

Google’s recent announcement of an AI Futures Fund shows the company wants to invest in AI startups. It sees itself as providing capital, early access to AI models yet to reach production, and support for startups from experts at the company. This is not Google’s first rodeo. To date, Alphabet (Google’s parent company) has invested in 38 AI companies. Big name acquisitions to date include the ***’s DeepMind, Waymo, the autonomous vehicle intelligence company, and home automation specialist Nest. While such investments signal a broad intent to at least associate Google with startups coming to market with a smart idea (if not necessarily to ******** up smaller companies), the current situation regarding the US courts’ attitude to monopolistic behaviour by Google questions just how far any relationship may go in the longer term. If Google faces increased scrutiny in the next few years on the back of any eventual ruling the US Department of Justice makes, having what might be interpreted as a monopolistic position in AI could bring down the ire of the judiciary. On the other hand, if Google is forced to divest itself of some of its more profitable divisions – the Chrome browser, the Android mobile operating system, some or all of its ad networks – the company may have to double down on its other sources of revenue; and AI could become its favoured métier. If the board at Alphabet decide to bet large stakes on AI, one core aspect of doing so will need clear and definite resolution: the economic viability of continuing AI implementation in the forms that users have become familiar with in the last couple of years. According to some industry commentators, the AI ‘whale’, OpenAI, is struggling to monetise its operations to the extent that will satisfy its investors. Google’s share of the AI market is tiny in comparison with OpenAI’s, but it suffers from the same potential financial issues. Google’s way through may be to continue its original role as provider of information searched for on the internet, and use its models to improve the search results given to online queries, and perhaps monetise around that transaction: either charging end-users for AI-powered search, or having advertisers pay for top spots in AI-generated search results. In that role, Google would be returning to its original function, but with the addition of AI algorithms under the surface – AI that improves a service that’s proven to be in massive demand, rather than AI being the main focus of user activity. Meta’s latest earnings call signalled that Mark Zuckerberg wants to do just that: return to the roots of the Facebook platform as a social connector, but have AI improve users’ experiences. It’s proposed that any acquisitions by Google of AI companies in the future would have to get the approval of the US Department of Justice. The company says such a move would limit investment in future AI technologies, a sentiment echoed by Anthropic representatives during the anti-monopolistic search practices court case brought against Google by the DOJ. If such a government approval edict were in place for Google, it would change the nature of companies that Google might fund via the AI Futures Fund or similar scheme. Rather than risking censure by appearing to add companies to the Alphabet stable that are in line with Google’s AI offerings, those benefiting from the company’s largess would be more likely to be niche players, bringing unique products to sectors of the economy where Google doesn’t already hold sway. Amazon’s acquisitions have been, at first glance, more in line with that seeking out of niche products to snap up. The Ring home smart device company (acquired in 2018 for $1bn) and One Medical (2022, $3.9bn) were purchases well outside of Amazon’s core cloud and retail verticals. Both, however, are prime sources of training data for AI models – consumer behaviour metrics and healthcare information are prime data real-estate. Google’s strategy for investment in smaller companies will need to be similarly canny, given that whatever the outcome of the DOJ case, its activities will be subject to intense scrutiny by the courts, end-users, and the press. No article discussing the activities of US big tech companies would be complete in 2025 without the addition of caveats around the present American leadership’s attitude to competition in the sector. The elephant in the room is the real possibility of executive veto of, or significant amendment to, any judicial ruling. Those potential game-changing elements could affect Google and Alphabet’s investment plans with little notice and less reason. (Image source: “Dallas DA LGBT Task Force visits DOJ FBP” by Dallas County DA is licensed under CC BY-NC-ND 2.0.) See also: Apple AI stresses privacy with synthetic and anonymised data Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Google’s AI Futures Fund may have to tread carefully appeared first on AI News. View the full article

The Trump AI chip policy reversal announced this week signals a shift in how advanced computing technologies will flow in global markets, as the administration prepares to dismantle Biden’s complex three-tier regulatory framework. Set to take effect on May 15, 2025, the Biden administration’s Framework for Artificial Intelligence Diffusion would have created a stratified global technology landscape with significant implications for international trade, innovation, and geopolitical relationships. According to a Commerce Department on Wednesday, the Trump administration views the existing approach as fundamentally flawed. “The Biden AI rule is overly complex, overly bureaucratic, and would stymie American innovation,” a Commerce Department spokeswoman told Reuters. “We will be replacing it with a much simpler rule that freees American innovation and ensures American AI dominance,” they said. The policy shift comes months after the Biden administration finalised an export control framework during its final week in office. That framework represented the culmination of a four-year effort to restrict China’s access to cutting-edge chips while maintaining US leadership in artificial intelligence technology. The decision to rescind the rule reflects the Trump administration’s different approach to balancing national security concerns with commercial interests. < Understanding the three-tier system The soon-to-be-eliminated rule had established a hierarchical structure for global technology access. In the first tier, 17 countries plus Taiwan would have enjoyed unlimited access to advanced AI chips. A second tier of approximately 120 countries would have operated under strict numerical caps limiting their imports. The third and final tier – including China, Russia, Iran, and North Korea – would have been completely blocked from accessing these technologies. The structured approach aimed to prevent advanced technologies from reaching countries of concern through intermediaries while still allowing access for allies and neutral nations. However, critics argued the complexity of the system would create significant compliance burdens and push international partners toward alternative suppliers. < The new approach taking shape Instead of the tiered system, sources cited by Reuters indicate the Trump administration is considering implementing a global licensing regime supported by inter-governmental agreements. The approach would potentially offer more flexibility and maintain controls over sensitive technology. The timing of the announcement appears strategically significant. Bloomberg reported the changes are developing as President Trump prepares for a trip to the Middle East, where countries including Saudi Arabia and the United Arab Emirates have expressed frustration over existing restrictions on their acquisition of AI chips. The Commerce Department’s decision could be announced as soon as Thursday, according to a source familiar with the matter. < Market reaction and industry impact News of the policy reversal has already sent ripples through financial markets. Shares of Nvidia, the dominant manufacturer of chips used for training AI models, ended 3% higher on May 7 following the announcement, though they dipped 0.7% in after-hours trading, according to Reuters. The company has consistently opposed the growing number of US restrictions. Nvidia CEO Jensen Huang argues that American companies should be able to sell into China, which, he predicts, will become a $50 billion market for AI chips in the next couple of years. However, it’s important to note that the Trump AI chip policy shift does not signal a complete abandonment of export controls. The administration has already demonstrated its willingness to take strong action against China, specifically, by banning Nvidia from selling its H20 chip there – a move that cost the company $5.5 billion in writedowns, according to Bloomberg. < Global winners and losers The policy reversal creates a complex map of potential winners and losers in the global technology landscape. Countries like India and Malaysia, which hadn’t faced chip restrictions before the Biden rule was unveiled in January, will see temporary relief. In Malaysia’s case, this could particularly benefit Oracle Corporation, which has plans for a massive data centre expansion that would have exceeded limits established by rules governing AI hardware distribution. Middle Eastern nations also stand to gain. The UAE and Saudi Arabia, which have faced chip export controls since 2023, may now be able to negotiate more favourable terms. Trump has expressed interest in easing restrictions for the UAE specifically and could announce the beginning of work on a government-to-government AI chip agreement during his upcoming visit to the region from May 13 to 16. The UAE’s aggressive pursuit of such an agreement, backed by its pledge to invest up to $1.4 trillion in US technology and infrastructure over the next decade, exemplifies how high-stakes these negotiations have become for countries seeking to establish themselves as AI powerhouses. < Uncertainty ahead According to Axios, the Trump administration is currently developing a new control scheme, which could emerge as either a new rule or an executive order. The transition ******* creates significant uncertainty for companies like Nvidia regarding the regulatory environment they’ll face in the coming months. While the new framework takes shape, the administration has indicated it will continue enforcing existing chip export controls. One potential element of the new approach might involve imposing controls specifically on countries that have diverted chips to China, including Malaysia and Thailand, according to a source familiar with the matter. Industry stakeholders remain divided on the issue. While chip manufacturers have lobbied aggressively against strict export controls, some AI companies, including Anthropic, have advocated for maintaining protections that safeguard US intellectual property and technological advantages. < Balancing competing priorities The Biden administration’s export controls were designed to limit access to chips needed for cutting-edge AI development, with a particular focus on preventing ******** firms from finding indirect routes to technology that existing export controls prevented them from importing directly. Creating a balanced approach that addresses national security concerns while promoting US commercial interests presents significant challenges. Establishing agreements with a wide range of countries eager to purchase advanced AI chips would require navigating complex diplomatic relationships and potentially creating dozens of separate policy frameworks. The Commerce Department has not provided a specific timeline for when any new rules are to be finalised or implemented, only indicating that debate continues on the optimal approach forward. The shift in Trump AI chip policy reflects the administration’s broader emphasis on American competitiveness and innovation while still maintaining control over technologies with national security implications. As officials work to craft a replacement framework, the global AI chip market remains in flux, with profound implications for technological development, international relations, and corporate strategies in the evolving artificial intelligence landscape. also: US-China AI chip race: Cambricon’s first profit lands Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Trump AI chip policy to scrap Biden’s export controls appeared first on AI News. View the full article

Apple continues its focus on hardware produced in-house, and is currently working on a new generation of chips for future hardware, according to Bloomberg. The processors are thought to be destined for use in smart glasses, AI-capable servers, and the next generations of Macs. One project involves a custom chip designed for smart glasses, which are thought to offer voice commands, photo capture, and audio playback, but will not be full augmented reality (AR) devices. The chip design is based on the low-power components used at present in the latest models of the Apple Watch, but modified to use less energy and support multiple cameras. Apple has yet to comment on any of the rumoured projects ­ it’s a company with a strict policy of keeping the products it may, or may not be developing under wraps. However, production for the glasses chip is said to begin by late 2026 or early 2027. If that timeline holds true, devices could reach the market in two years. As with most of Apple’s chips, Taiwan Semiconductor Manufacturing Co. is expected to handle production. Smart glasses have been in development at Apple for several years, industry insiders claim. The company aims to build full AR wearables that overlay digital information onto real-world views, but the technology is yet to be ready for everyday use. In this sector, Meta has already broken some ground, launching smart glasses in partnership with Ray-Ban. Apple seems to be pursuing a similar product, minus the AR features – at least, in any device’s first iteration. Sources say Apple is developing both AR and non-AR glasses under the codename N401, previously N50. According to reports, Apple’s CEO Tim Cook hopes for the company to take a lead in this market segment. Meta, meanwhile, is expanding its own product line, planning to debut a high-end model of its Ray-Ban style device with a display later this year. The company is said to be targeting 2027 for its first, fully-AR glasses gadget. Apple’s non-AR glasses could use cameras to scan the environment and apply AI to assist users, mirroring Meta’s current strategy. Apple is said to be biding its time, and waiting for AI software to mature before committing to a full product release. In the meantime, Apple is exploring other avenues to improve its current product lines, with engineers reportedly testing features like cameras in AirPods and smartwatches, which will likely use Apple chips currently in development. Codename “Nevis” is slated for a camera-enabled Apple Watch, while “Glennie” is intended for AirPods. Both are thought to be planned for release by 2027. Apple is said to be preparing a new set of processors specifically for Macs; the M6 (Komodo) and M7 (Borneo), and a higher-end chip “Sotra”. Apple is also thought to be planning to upgrade the iPad Pro and MacBook Pro with its M5 chip later this year. Internal-to-Apple chip development efforts are part of Apple’s broader push to control the full hardware stack of its products. The hardware group, led by Johny Srouji, has been expanding its portfolio: Earlier this year, Apple launched its first in-house modem chip in the iPhone 16e, with a higher-end version, the C2, planned for release in 2026. (Photo by Unsplash) See also: Apple AI stresses privacy with synthetic and anonymised data Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Apple developing custom chips for smart glasses and more appeared first on AI News. View the full article

Samsung Electronics’ strategic focus on AI has delivered high revenue in the first quarter of 2025, as the South Korean tech giant navigates semiconductor market challenges and growing global trade uncertainties. The company posted an all-time quarterly high revenue of KRW 79.14 trillion ($55.4 billion), marking a 10% increase year-over-year, according to its financial results released on Wednesday. Operating profit climbed to KRW 6.7 trillion ($4.68 billion), representing a modest 1.5% increase compared to the same ******* last year. The results exceeded Samsung’s earlier forecast of KRW 79 trillion and analysts’ expectations. Smartphone success counters chip challenges The Mobile Experience (MX) Business emerged as the best performer, contributing KRW 37 trillion in consolidated revenue and KRW 4.3 trillion in operating profit – its highest level in four years. The success was driven primarily by strong sales of the flagship Galaxy S25 series, which features AI abilities via Galaxy AI. “Enhanced cost competency and price declines for some components also contributed to solid double-digit profitability,” the company’s earnings report said. In contrast, Samsung’s Device Solutions (DS) Division, which includes its semiconductor operations, posted KRW 25.1 trillion in revenue and KRW 1.1 trillion in operating profit – a 42% decline from the previous year. The performance reflects ongoing challenges in the semiconductor market, particularly in high-bandwidth memory (HBM) sales. “Overall earnings were impacted by the erosion of average selling price (ASP), as well as a decrease in HBM sales due to export controls on AI chips and deferred demand in anticipation of upcoming enhanced HBM3E products,” Samsung said. Trade tensions cloud future outlook Despite the record revenue, Samsung has expressed caution about the second quarter, dropping its usual business outlook due to growing macroeconomic uncertainties stemming from global trade tensions and slowing economic growth. “Due to the rapid changes in policies and geopolitical tensions among major countries, it’s difficult to accurately predict the business impact of tariffs and established countermeasures,” a Samsung executive stated during Wednesday’s earnings call. Of particular concern are US President Donald Trump’s “reciprocal” tariffs, most of which have been suspended until July but threaten to impact dozens of countries including Vietnam and South Korea, where Samsung produces smartphones and displays. While Samsung noted that its flagship products like semiconductors, smartphones, and tablets are currently exempt from these tariffs, the company revealed that Washington is conducting a product-specific tariff probe into these categories. “There are a lot of uncertainties ahead of us […] we are communicating with related countries to minimise negative effects,” Samsung said during the call. In response to its challenges, the company disclosed it is considering relocating production of TVs and home appliances. AI investment and future strategy Despite these headwinds, Samsung remains committed to its artificial intelligence strategy, allocating its highest-ever annual R&D expenditure for 2024. In the first quarter of 2025, the company increased R&D spending by 16% compared to the same ******* last year, amounting to KRW 9 trillion. For the remainder of 2025, Samsung plans to expand its AI smartphone lineup through the introduction of “Awesome Intelligence” to the Galaxy A series and the launch of the Galaxy S25 Edge in Q2. Later in the year, the company will strengthen its foldable lineup with enhanced AI user experiences. In the semiconductor space, Samsung aims to strengthen its position in the high-value-added market through its server-centric portfolio and the ramp-up of enhanced HBM3E 12H products to meet initial demand. The company expects AI-related demand to remain high in the second half of 2025, coinciding with the launch of new GPUs. “In the mobile and PC markets, on-device AI is expected to proliferate, so the Memory Business will proactively respond to this shift in the business environment with its industry-leading 10.7Gbps LPDDR5x products,” Samsung stated. The company’s foundry business remains focused on its 2nm Gate-All-Around (GAA) process development, which remains on schedule despite current challenges. Market reaction and competitive landscape Samsung shares were trading down approximately 0.6% following the announcement, reflecting investor concerns about the uncertain outlook. The results highlight Samsung’s complex position in the AI market – succeeding in consumer-facing applications while working to catch up with competitors in AI-specific semiconductor components. Local rival SK Hynix, which reported a 158% jump in operating profit last week to KRW 7.4 trillion, has overtaken Samsung in overall DRAM market revenue for the first time, capturing 36% global market share compared to Samsung’s 34%, according to Counterpoint Research. SK Hynix’s success has been particularly pronounced in the high-bandwidth memory segment, which is crucial for AI server applications. “Samsung has assumed that the uncertainties are diminished, it expects its performance to improve in the second half of the year,” the company noted, striking a cautiously optimistic tone despite the challenges ahead. Samsung’s record revenue masks a pivotal crossroads for the tech giant: while its AI-enhanced smartphones flourish, its once-dominant semiconductor business risks falling behind in the AI revolution. The coming quarters will reveal whether Samsung’s massive R&D investments can reclaim lost ground in HBM chips, or if we’re witnessing a fundamental power shift in Asian tech manufacturing that could alter the global AI supply chain for years to come. For a company that rebuilt itself numerous times over its 56-year history, the AI semiconductor race may prove to be its most consequential transformation yet. (Image credit: Anthropic) See also: Baidu ERNIE X1 and 4.5 Turbo boast high performance at low cost <figurewp-block-image”> Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Samsung AI strategy delivers record revenue despite semiconductor headwinds appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

ServiceNow has opened its Knowledge 2025 conference with a brand new AI platform. The ambition is clear: to give businesses a single, coherent way to get all their different AI tools and intelligent agents working together, right across the company. This isn’t just a fresh lick of paint; we’re talking deep-rooted new features and much cosier relationships with the likes of NVIDIA, Microsoft, Google, and Oracle. The aim? To finally help businesses orchestrate their operations with genuine intelligence, and it seems some big names like Adobe, Aptiv, the NHL, Visa, and Wells Fargo are already seeing it pay off. Most business leaders you speak with today will tell you they’re wrestling with a tangled mess of complexity. We’ve got systems that don’t talk to each other, data all over the shop, often no real game plan for AI, and that constant pressure to do more with less. ServiceNow believes AI innovation is the answer here, a way to fundamentally change how businesses run – making them more resilient, more efficient, and helping them get a handle on costs, all while chipping away at that mountain of tech debt and operational guesswork. However, the company rightly points out that just throwing AI at the problem won’t cut it. You need a clear vision and a real commitment to using ‘agentic AI’ – think smart, autonomous AI helpers – all underpinned by a platform built for what’s next. For the companies already diving in, these AI agents are apparently delivering the goods, boosting productivity in all sorts of ways. And it’s hitting the bottom line: ServiceNow’s own research, the Enterprise AI Maturity Index, shows that 55% of organisations using this kind of agentic AI have seen their gross margins improve. That’s a hefty jump compared to the mere 22% for those not yet on board. Bill McDermott, Chairman and CEO of ServiceNow, commented: “ServiceNow is igniting a new era of enterprise transformation with the ServiceNow AI Platform. We’re unleashing the full power of AI, across any industry, any agent, any workflow. “For decades, CEOs have wanted technology to accelerate the speed of business transformation. With this next generation architecture, we finally have the foundation to run the integrated enterprise in real time. We are the only ones who can orchestrate AI, data, and workflows on a single platform.” It’s not just talk; some big names are already putting ServiceNow’s agentic AI through its paces: Adobe is using it to speed up automation in IT and workplace services. Think AI agents handling common but time-consuming requests like password resets. The upshot? Fewer support tickets, quicker fixes, and happier, more productive staff. They’re also looking to add ServiceNow’s Workflow Data Fabric and RaptorDB to chew through their data even faster. Aptiv, known for its work in critical industries, is teaming up with ServiceNow. Their new partnership aims to blend ServiceNow’s AI smarts with Aptiv’s edge intelligence to boost automation and keep things running smoothly when it really matters. The NHL is, in their words, “going all in on ServiceNow AI.” The goal is to streamline how they operate, making life easier for employees needing quick solutions and helping arena technicians give fans an even better game day. Wells Fargo has rolled out ServiceNow AI with RaptorDB to automate tricky workflows and process huge amounts of data in real-time. This, they hope, will lead to smarter, AI-driven decisions right across the bank. Visa is set to bring in ServiceNow Disputes Management – a system they built together. It uses AI agents on the ServiceNow platform to help resolve payment disputes. And it seems they like what they see, as Visa plans to use ServiceNow’s AI to run its own managed dispute services. So, what’s under the bonnet of this reimagined ServiceNow AI Platform? It’s built to bring together intelligence, data, and the actual doing – the orchestration. The idea is to help companies move beyond a few scattered AI experiments to making AI a core part of how they operate. A big piece of this puzzle is what they call a “smart, conversational AI Engagement Layer.” This lets people get complex tasks done across different systems by tapping into ServiceNow’s Knowledge Graph, Workflow Data Fabric, and AI Agent Fabric. These clever bits are designed to connect smoothly with all sorts of enterprise data systems and AI frameworks. This launch is clearly just the start of a whole wave of new stuff. We’re seeing expanded partnerships, literally thousands of ready-made AI agents, and the introduction of the ServiceNow AI Control Tower. Alongside the main platform, ServiceNow is adding some additional firepower: AI Control Tower: Think of it as a central dashboard for managing everything AI – whether it’s ServiceNow’s own AI, or tools from other providers. It’s about keeping an eye on things, making sure it’s secure, and getting real value from every AI agent, model, and workflow, all in one place. The goal is better teamwork, solid oversight, and automation that actually scales. AI Agent Fabric: This is the communication network for a company’s AI ecosystem. It’s about getting AI agents – no matter who built them (ServiceNow, partners, or your own tech teams) – to work together smoothly across different tools, teams, and even vendors like Microsoft, NVIDIA, Google, and Oracle. They can share information, coordinate jobs, and generally get things done more effectively together. Next-generation CRM: They’re also beefing up their Customer Relationship Management offering with more AI. The plan is to bring sales, order fulfilment, and customer service onto one platform. This means businesses can shift from just reacting to customer problems to proactively engaging with them at every stage, from quoting a price right through to renewal. Apriel Nemotron 15B: This new reasoning LLM is built with NVIDIA. It’s designed to power intelligent AI agents that can think and act at scale, promising high accuracy, quick responses, lower running costs, and generally faster, smarter AI for everyone. And it’s not just about the tech; ServiceNow is also doubling down on people with the launch of ServiceNow University. This is their bid to give organisations the tools to upskill their workforce, helping them not just unlock individual potential but also drive real business change. At the end of the day, even the smartest AI needs smart people to make the most of it. See also: UAE to teach its children AI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post ServiceNow bets on unified AI to untangle enterprise complexity appeared first on AI News. View the full article

The United Arab Emirates looks set to integrating AI education in its schools’ curricula, meaning all children from kindergarten to high school will learn about the technology, how it can be used day-to-day, and the best ways to implement the various types of models. There will also be classes covering the ethics of AI, something that the country’s young might eventually teach to the world, according to OpenAI’s Sam Altman, who once termed the UAE the world’s ‘sandbox’ in which issues around AI such as governance could be thrashed out, and from which the rest of the world can make its regulatory models. The new curriculum will include areas such as data and algorithms, software applications, the ethics of AI, real-world applications of the technology, policies, and social engagement. All modules have been designed to be age-appropriate, and will be incorporated into the standard curriculum, rather than being taught after-hours. The rollout into schools is part of the country’s National Artificial Intelligence Strategy 2031, which aims to position the Kingdom as a global leader in AI capabilities – including education – and is part of wider efforts to diversify the UAE’s economy from its core basis of oil production and *****. In addition to the changes to school timetables, the National AI Strategy also includes funds to promote AI in research, with institutions like the American University of Sharjah and United Arab Emirates University committed to the technology’s use in the higher and postgraduate sectors. There are also public awareness and learning initiatives springing up in the country as it gathers momentum to become the country that’s most behind the possibilities of AI in the modern world. An AI investment fund is expected to reach a value of $100 billion in the next few years, according to people familiar with the project. The country also has plans to spend around $1.4 trillion in in the US in the next ten years on energy generation, semiconductor manufacture, and AI infrastructure. Investments abroad will ensure the Kingdom retains positive relations with elements of its supply chain related to AI. US President Trump is thought to be considering easing tariffs and other restrictions on exports of Nvidia hardware to the Kingdom, and has planned a visit to the region later this month, when he will also visit Saudi Arabia and Qatar. The UAE has actively encouraged investment in infrastructure from ******** manufacturers such as Huawei, and is seen as something of an impartial middle-ground in the ongoing trade war between the Western and Eastern industrial and technology blocs. The wider region is home to some of the most affluent nations, so any curbs on trade tend to have negative effects on vendors based on both sides of the divide. See also: Conversations with AI – Education (Image source: “Dubai” by Eugene Kaspersky is licensed under CC BY-NC-SA 2.0.) The post UAE to teach its children AI appeared first on AI News. View the full article

Ever wondered what happens when a company trying to build a ‘brain for the world’ needs to grow up, fast, without selling its soul? Well, OpenAI has just given us a peek as it pledges to keep its nonprofit core amid broader restructuring. OpenAI CEO Sam Altman has laid out their roadmap, and the headline news is: they’re rejigging the money side of things, but their core mission to make Artificial General Intelligence (AGI) work for all of us remains bolted down. In a letter, Altman wrote: “OpenAI is not a normal company and never will be.” It’s a bold statement, but it sets the scene for a company wrestling with how to fund world-changing tech while keeping its ethical compass pointing true north. Cast your mind back, if you will, to OpenAI’s early days. Altman paints a picture that’s a far cry from the tech behemoth it’s becoming. “When we started OpenAI, we did not have a detailed sense for how we were going to accomplish our mission,” he shared. “We started out staring at each other around a kitchen table, wondering what research we should do.” Forget fancy business models or product roadmaps back then. The idea of AI dishing out medical advice, revolutionising how we learn, or needing the kind of computing power that makes your gaming PC look like a pocket calculator – “hundreds of billions of dollars of compute,” as Altman puts it – wasn’t even on the horizon. Even the ‘how’ of building AGI was a bit of a head-scratcher. When OpenAI was founded as a nonprofit, some of the early thinkers at the company apparently thought AI should probably only be trusted to a handful of “trusted people” who could “handle it.” That view has done a complete 180. “We now see a way for AGI to directly empower everyone as the most capable tool in human history,” Altman declared. The big dream? If everyone gets their hands on AGI, we’ll cook up amazing things for each other, pushing society forward. Sure, some might use it for dodgy stuff, but Altman’s betting on humanity: “We trust humanity and think the good will outweigh the bad by orders of magnitude.” Their game plan is what they call “democratic AI.” They want to give us all these incredible tools. They’re even talking about open-sourcing powerful models, saying they want us to make decisions about how ChatGPT behaves. “We want to build a brain for the world and make it super easy for people to use for whatever they want (subject to few restrictions; freedom shouldn’t impinge on other people’s freedom, for example),” Altman explained. And people are already getting stuck in. Scientists are crunching data faster, programmers are coding smarter, and folks are even using ChatGPT to navigate tricky health issues or get advice on tough personal situations. Here’s the rub: the world wants way more AI than they can currently churn out. “We currently cannot supply nearly as much AI as the world wants,” Altman admitted. This insatiable appetite for AI, and the eye-watering sums of cash needed to feed it, is why OpenAI feels it’s time for it to “evolve” beyond a strict nonprofit structure. Altman boiled the restructuring down to three main goals: Getting the dough: They need to find a way to pull in the “hundreds of billions of dollars and may eventually require trillions of dollars” – yes, trillions with a ‘T’ – to make their AI tools available to everyone on the planet. Think of it like building a global superhighway for intelligence. Supercharging the nonprofit: They want their original nonprofit arm to be the “largest and most effective nonprofit in history,” using AI to make a massive positive difference in people’s lives. Delivering AGI that’s helpful and safe: This means doubling down on safety and making sure AI aligns with human values. Altman’s proud of OpenAI’s track record, including creating new “red teaming” methods (where they get clever people to try and break their AI to find flaws) and being open about how their models work. So, what’s the grand plan for this evolution? Crucially, the nonprofit side of OpenAI is staying firmly in the driver’s seat. This isn’t just some vague promise; it came after serious chats with “civic leaders” and the offices of the Attorneys General of California and Delaware. “OpenAI was founded as a nonprofit, is today a nonprofit that oversees and controls the for-profit, and going forward will remain a nonprofit that oversees and controls the for-profit. That will not change,” Altman stated. The bit that is changing is the for-profit LLC that currently sits under the nonprofit. This will morph into a Public Benefit Corporation (PBC). If you’re scratching your head, a PBC is a type of company that’s legally bound to consider its public benefit mission alongside making money. Think of companies like Patagonia or some ethical food brands – they want to do good while still being a business. It’s a model other AGI labs like Anthropic are using too, so it’s becoming a bit of a trend for purpose-driven tech firms. This also means they’re ditching their old, rather head-scratching “capped-profit” system. Altman explained this made sense when it looked like one company might dominate AGI, but now, with lots of players in the game, a “normal capital structure where everyone has stock” is simpler. The nonprofit side of OpenAI won’t just be in the driving seat; it’ll also become a big shareholder in this new PBC. According to Altman, this means the nonprofit will get a hefty chunk of resources to pour into programmes that help AI benefit different communities. As the PBC makes more money, the nonprofit gets more cash to splash on projects in areas like health, education, and science. They’re even getting a special commission to dream up ways their nonprofit work can make AI more democratic. Altman wrapped things up with a healthy dose of optimism, saying, “We believe this sets us up to continue to make rapid, safe progress and to put great AI in the hands of everyone.” OpenAI is clearly trying to attract the colossal funding needed for AGI development while hard-wiring its “benefit all of humanity” mantra into its very DNA. It’s a delicate tightrope walk, and you can bet the entire tech world, and probably a good chunk of the rest of us, will be watching to see if they can pull it off. (Image by Mohamed Hassan) See also: Google AMIE: AI doctor learns to ‘see’ medical images Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Sam Altman: OpenAI to keep nonprofit soul in restructuring appeared first on AI News. View the full article

Google is giving its diagnostic AI the ability to understand visual medical information with its latest research on AMIE (Articulate Medical Intelligence Explorer). Imagine chatting with an AI about a health concern, and instead of just processing your words, it could actually look at the photo of that worrying rash or make sense of your ECG printout. That’s what Google is aiming for. We already knew AMIE showed promise in text-based medical chats, thanks to earlier work published in Nature. But let’s face it, real medicine isn’t just about words. Doctors rely heavily on what they can see – skin conditions, readings from machines, lab reports. As the Google team rightly points out, even simple instant messaging platforms “allow static multimodal information (e.g., images and documents) to enrich discussions.” Text-only AI was missing a huge piece of the puzzle. The big question, as the researchers put it, was “Whether LLMs can conduct diagnostic clinical conversations that incorporate this more complex type of information.” Google teaches AMIE to look and reason Google’s engineers have beefed up AMIE using their Gemini 2.0 Flash model as the brains of the operation. They’ve combined this with what they call a “state-aware reasoning framework.” In plain English, this means the AI doesn’t just follow a script; it adapts its conversation based on what it’s learned so far and what it still needs to figure out. It’s close to how a human clinician works: gathering clues, forming ideas about what might be wrong, and then asking for more specific information – including visual evidence – to narrow things down. “This enables AMIE to request relevant multimodal artifacts when needed, interpret their findings accurately, integrate this information seamlessly into the ongoing dialogue, and use it to refine diagnoses,” Google explains. Think of the conversation flowing through stages: first gathering the patient’s history, then moving towards diagnosis and management suggestions, and finally follow-up. The AI constantly assesses its own understanding, asking for that skin photo or lab result if it senses a gap in its knowledge. To get this right without endless trial-and-error on real people, Google built a detailed simulation lab. Google created lifelike patient cases, pulling realistic medical images and data from sources like the PTB-XL ECG database and the SCIN dermatology image set, adding plausible backstories using Gemini. Then, they let AMIE ‘chat’ with simulated patients within this setup and automatically check how well it performed on things like diagnostic accuracy and avoiding errors (or ‘hallucinations’). The virtual OSCE: Google puts AMIE through its paces The real test came in a setup designed to mirror how medical students are assessed: the Objective Structured Clinical Examination (OSCE). Google ran a remote study involving 105 different medical scenarios. Real actors, trained to portray patients consistently, interacted either with the new multimodal AMIE or with actual human primary care physicians (PCPs). These chats happened through an interface where the ‘patient’ could upload images, just like you might in a modern messaging app. Afterwards, specialist doctors (in dermatology, cardiology, and internal medicine) and the patient actors themselves reviewed the conversations. The human doctors scored everything from how well history was taken, the accuracy of the diagnosis, the quality of the suggested management plan, right down to communication skills and empathy—and, of course, how well the AI interpreted the visual information. Surprising results from the simulated clinic Here’s where it gets really interesting. In this head-to-head comparison within the controlled study environment, Google found AMIE didn’t just hold its own—it often came out ahead. The AI was rated as being better than the human PCPs at interpreting the multimodal data shared during the chats. It also scored higher on diagnostic accuracy, producing differential diagnosis lists (the ranked list of possible conditions) that specialists deemed more accurate and complete based on the case details. Specialist doctors reviewing the transcripts tended to rate AMIE’s performance higher across most areas. They particularly noted “the quality of image interpretation and reasoning,” the thoroughness of its diagnostic workup, the soundness of its management plans, and its ability to flag when a situation needed urgent attention. Perhaps one of the most surprising findings came from the patient actors: they often found the AI to be more empathetic and trustworthy than the human doctors in these text-based interactions. And, on a critical safety note, the study found no statistically significant difference between how often AMIE made errors based on the images (hallucinated findings) compared to the human physicians. Technology never stands still, so Google also ran some early tests swapping out the Gemini 2.0 Flash model for the newer Gemini 2.5 Flash. Using their simulation framework, the results hinted at further gains, particularly in getting the diagnosis right (Top-3 Accuracy) and suggesting appropriate management plans. While promising, the team is quick to add a dose of realism: these are just automated results, and “rigorous assessment through expert physician review is essential to confirm these performance benefits.” Important reality checks Google is commendably upfront about the limitations here. “This study explores a research-only system in an OSCE-style evaluation using patient actors, which substantially under-represents the complexity… of real-world care,” they state clearly. Simulated scenarios, however well-designed, aren’t the same as dealing with the unique complexities of real patients in a busy clinic. They also stress that the chat interface doesn’t capture the richness of a real video or in-person consultation. So, what’s the next step? Moving carefully towards the real world. Google is already partnering with Beth Israel Deaconess Medical Center for a research study to see how AMIE performs in actual clinical settings with patient consent. The researchers also acknowledge the need to eventually move beyond text and static images towards handling real-time video and audio—the kind of interaction common in telehealth today. Giving AI the ability to ‘see’ and interpret the kind of visual evidence doctors use every day offers a glimpse of how AI might one day assist clinicians and patients. However, the path from these promising findings to a safe and reliable tool for everyday healthcare is still a long one that requires careful navigation. (Photo by Alexander Sinn) See also: Are AI chatbots really changing the world of work? Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Google AMIE: AI doctor learns to ‘see’ medical images appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

We’ve heard endless predictions about how AI chatbots will transform work, but data paints a much calmer picture—at least for now. Despite huge and ongoing advancements in generative AI, the massive wave it was supposed to create in the world of work looks more like a ripple so far. Researchers Anders Humlum (University of Chicago) and Emilie Vestergaard (University of Copenhagen) didn’t just rely on anecdotes. They dug deep, connecting responses from two big surveys (late 2023 and 2024) with official, detailed records about jobs and pay in Denmark. The pair zoomed in on around 25,000 people working in 7,000 different places, covering 11 jobs thought to be right in the path of AI disruption. Everyone’s using AI chatbots for work, but where are the benefits? What they found confirms what many of us see: AI chatbots are everywhere in Danish workplaces now. Most bosses are actually encouraging staff to use them, a real turnaround from the early days when companies were understandably nervous about things like data privacy. Almost four out of ten employers have even rolled out their own in-house chatbots, and nearly a third of employees have had some formal training on these tools. When bosses gave the nod, the number of staff using chatbots practically doubled, jumping from 47% to 83%. It also helped level the playing field a bit. That gap between men and women using chatbots? It shrank noticeably when companies actively encouraged their use, especially when they threw in some training. So, the tools are popular, companies are investing, people are getting trained… but the big economic shift? It seems to be missing in action. Using statistical methods to compare people who used AI chatbots for work with those who didn’t, both before and after ChatGPT burst onto the scene, the researchers found… well, basically nothing. “Precise zeros,” the researchers call their findings. No significant bump in pay, no change in recorded work hours, across all 11 job types they looked at. And they’re pretty confident about this – the numbers rule out any average effect ******* than just 1%. This wasn’t just a blip, either. The lack of impact held true even for the keen beans who jumped on board early, those using chatbots daily, or folks working where the boss was actively pushing the tech. Looking at whole workplaces didn’t change the story; places with lots of chatbot users didn’t see different trends in hiring, overall wages, or keeping staff compared to places using them less. Productivity gains: More of a gentle nudge than a shove Why the big disconnect? Why all the hype and investment if it’s not showing up in paychecks or job stats? The study flags two main culprits: the productivity boosts aren’t as huge as hoped in the real world, and what little gains there are aren’t really making their way into wages. Sure, people using AI chatbots for work felt they were helpful. They mentioned better work quality and feeling more creative. But the number one benefit? Saving time. However, when the researchers crunched the numbers, the average time saved was only about 2.8% of a user’s total work hours. That’s miles away from the huge 15%, 30%, even 50% productivity jumps seen in controlled lab-style experiments (RCTs) involving similar jobs. Why the difference? A few things seem to be going on. Those experiments often focus on jobs or specific tasks where chatbots really shine (like coding help or basic customer service responses). This study looked at a wider range, including jobs like teaching where the benefits might be smaller. The researchers stress the importance of what they call “complementary investments”. People whose companies encouraged chatbot use and provided training actually did report ******* benefits – saving more time, improving quality, and feeling more creative. This suggests that just having the tool isn’t enough; you need the right support and company environment to really unlock its potential. And even those modest time savings weren’t padding wallets. The study reckons only a tiny fraction – maybe 3% to 7% – of the time saved actually showed up as higher earnings. It might be down to standard workplace inertia, or maybe it’s just harder to ask for a raise based on using a tool your boss hasn’t officially blessed, especially when many people started using them off their own bat. Making new work, not less work One fascinating twist is that AI chatbots aren’t just about doing old work tasks faster. They seem to be creating new tasks too. Around 17% of people using them said they had new workloads, mostly brand new types of tasks. This phenomenon happened more often in workplaces that encouraged chatbot use. It even spilled over to people not using the tools – about 5% of non-users reported new tasks popping up because of AI, especially teachers having to adapt assignments or spot AI-written homework. What kind of new tasks? Things like figuring out how to weave AI into daily workflows, drafting content with AI help, and importantly, dealing with the ethical side and making sure everything’s above board. It hints that companies are still very much in the ‘figuring it out’ phase, spending time and effort adapting rather than just reaping instant rewards. What’s the verdict on the work impact of AI chatbots? The researchers are careful not to write off generative AI completely. They see pathways for it to become more influential over time, especially as companies get better at integrating it and maybe as those “new tasks” evolve. But for now, their message is clear: the current reality doesn’t match the hype about a massive, immediate job market overhaul. “Despite rapid adoption and substantial investments… our key finding is that AI chatbots have had minimal impact on productivity and labor market outcomes to date,” the researchers conclude. It brings to mind that old quote about the early computer age: seen everywhere, except in the productivity stats. Two years on from ChatGPT’s launch kicking off the fastest tech adoption we’ve ever seen, its actual mark on jobs and pay looks surprisingly light. The revolution might still be coming, but it seems to be taking its time. See also: Claude Integrations: Anthropic adds AI to your favourite work tools Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Are AI chatbots really changing the world of work? appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Anthropic just launched ‘Integrations’ for Claude that enables the AI to talk directly to your favourite daily work tools. In addition, the company has launched a beefed-up ‘Advanced Research’ feature for digging deeper than ever before. Starting with Integrations, the feature builds on a technical standard Anthropic released last year (the Model Context Protocol, or MCP), but makes it much easier to use. Before, setting this up was a bit technical and local. Now, developers can build secure bridges allowing Claude to connect safely with apps over the web or on your desktop. For end-users of Claude, this means you can now hook it up to a growing list of popular work software. Right out of the gate, they’ve included support for ten big names: Atlassian’s Jira and Confluence (hello, project managers and dev teams!), the automation powerhouse Zapier, Cloudflare, customer comms tool Intercom, plus Asana, Square, Sentry, PayPal, Linear, and Plaid. Stripe and GitLab are joining the party soon. So, what’s the big deal? The real advantage here is context. When Claude can see your project history in Jira, read your team’s knowledge base in Confluence, or check task updates in Asana, it stops guessing and starts understanding what you’re working on. “When you connect your tools to Claude, it gains deep context about your work—understanding project histories, task statuses, and organisational knowledge—and can take actions across every surface,” explains Anthropic. They add, “Claude becomes a more informed collaborator, helping you execute complex projects in one place with expert assistance at every step.” Let’s look at what this means in practice. Connect Zapier, and you suddenly give Claude the keys to thousands of apps linked by Zapier’s workflows. You could just ask Claude, conversationally, to trigger a complex sequence – maybe grab the latest sales numbers from HubSpot, check your calendar, and whip up some meeting notes, all without you lifting a finger in those apps. For teams using Atlassian’s Jira and Confluence, Claude could become a serious helper. Think drafting product specs, summarising long Confluence documents so you don’t have to wade through them, or even creating batches of linked Jira tickets at once. It might even spot potential roadblocks by analysing project data. And if you use Intercom for customer chats, this integration could be a game-changer. Intercom’s own AI assistant, Fin, can now work with Claude to do things like automatically create a bug report in Linear if a customer flags an issue. You could also ask Claude to sift through your Intercom chat history to spot patterns, help debug tricky problems, or summarise what customers are saying – making the whole journey from feedback to fix much smoother. Anthropic is also making it easier for developers to build even more of these connections. They reckon that using their tools (or platforms like Cloudflare that handle the tricky bits like security and setup), developers can whip up a custom Integration with Claude in about half an hour. This could mean connecting Claude to your company’s unique internal systems or specialised industry software. Beyond tool integrations, Claude gets a serious research upgrade Alongside these new connections, Anthropic has given Claude’s Research feature a serious boost. It could already search the web and your Google Workspace files, but the new ‘Advanced Research’ mode is built for when you need to dig really deep. Flip the switch for this advanced mode, and Claude tackles big questions differently. Instead of just one big search, it intelligently breaks your request down into smaller chunks, investigates each part thoroughly – using the web, your Google Docs, and now tapping into any apps you’ve connected via Integrations – before pulling it all together into a detailed report. Now, this deeper digging takes a bit more time. While many reports might only take five to fifteen minutes, Anthropic says the really complex investigations could have Claude working away for up to 45 minutes. That might sound like a while, but compare it to the hours you might spend grinding through that research manually, and it starts to look pretty appealing. Importantly, you can trust the results. When Claude uses information from any source – whether it’s a website, an internal doc, a Jira ticket, or a Confluence page – it gives you clear links straight back to the original. No more wondering where the AI got its information from; you can check it yourself. These shiny new Integrations and the Advanced Research mode are rolling out now in beta for folks on Anthropic’s paid Max, Team, and Enterprise plans. If you’re on the Pro plan, don’t worry – access is coming your way soon. Also worth noting: the standard web search feature inside Claude is now available everywhere, for everyone on any paid Claude.ai plan (Pro and up). No more geographical restrictions on that front. Putting it all together, these updates and integrations show Anthropic is serious about making Claude genuinely useful in a professional context. By letting it plug directly into the tools we already use and giving it more powerful ways to analyse information, they’re pushing Claude towards being less of a novelty and more of an essential part of the modern toolkit. (Image credit: Anthropic) See also: Baidu ERNIE X1 and 4.5 Turbo boast high performance at low cost Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Claude Integrations: Anthropic adds AI to your favourite work tools appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Developer experience (DevEx) is more than just a buzzphrase. With the rise of complex tech stacks, remote-first teams, and continuous delivery, developers’ work processes have become more complex. Poor DevEx leads to slower deployments, burnout, and increased turnover. Great DevEx, on the other hand, boosts productivity, developer satisfaction, and the quality of shipped code. Developer Experience Insight Tools help engineering teams measure, optimise, and elevate how developers work. The tools track workflows, streamline collaboration, catch issues early, and ultimately create an environment where devs can do their best work. Why developer experience (DevEx) matters In the evolving world of software development, providing a seamless and efficient developer experience (DevEx) has become important. DevEx impacts productivity, code quality, and overall project success. A positive DevEx reduces onboarding time, minimises frustration, and fosters innovation by letting developers focus on solving problems rather than battling tools or workflows. Best practices for implementing developer experience (DevEx) insight tools Here are the key best practices: 1. Set clear objectives Before choosing a tool, identify the specific challenges you want to address – whether it’s reducing lead time, improving code review efficiency, or increasing deployment frequency. Clear goals guide tool selection and help you measure success. 2. Include developers in the decision process Involve developers early when evaluating DevEx tools. Their feedback ensures the solution aligns with daily workflows and avoids adding unnecessary complexity. A tool embraced by engineers is far more likely to deliver impact. 3. Focus on seamless integration Choose tools that integrate well with your existing toolchain – like Git platforms, CI/CD systems, IDEs, and project management software. This ensures insights flow naturally without disrupting developer habits. 4. Start with a pilot team Roll out the tool to a small group first. Collect feedback, refine configurations, and evaluate results before expanding across the organisation. A phased rollout minimises risk and builds internal champions. 5. Prioritise actionable insights Avoid tools that overwhelm with vanity metrics. Look for platforms that surface specific, actionable recommendations developers can use to improve workflows and outcomes immediately. 6. Continuously monitor and Iterate Developer tools evolve. Regularly review tool performance, gather feedback, and adjust settings or processes as needed. Continuous improvement is key to long-term DevEx success. Top 10 developer experience insight tools of 2025 1. Milestone Milestone is built for engineering operations leaders who need visibility into the actual developer experience. It aggregates data across Git repositories, issue trackers, and CI/CD platforms to uncover bottlenecks in delivery, collaboration, and productivity. Unlike traditional tools, Milestone emphasises context-aware metrics like review latency, merge frequency, and time-in-status. It helps managers pinpoint workflow friction and enable smoother engineering cycles, while giving developers visibility into how their work contributes to team goals. Highlights: Seamless integration with GitHub, Jira, and CI/CD tools Rich dashboards for tracking velocity, quality, and workflow health Helps identify systemic delivery delays Suitable for both team leads and individual contributors 2. Visual Studio Code Visual Studio Code (VS Code) is more than just an editor – it’s a central DevEx powerhouse. With its blazing speed, massive extension ecosystem, and deep integrations, VS Code allows developers to stay productive without leaving the IDE. Its features like IntelliSense, Live Share, built-in terminal, and version control support streamline the coding experience. Developers can collaborate, debug, and deploy – all from one interface. With growing support for cloud-based development and AI-powered tools (like GitHub Copilot), VS Code continues to redefine DevEx in 2025. Highlights: Robust plugin ecosystem (AI, Git, testing, Docker, etc.) Live Share enables real-time collaboration Built-in Git support and terminal access Customisable themes, layouts, and keyboard shortcuts 3. SonarQube SonarQube offers continuous inspection of code quality through static analysis, helping teams reduce technical debt and maintain clean, secure codebases. It identifies bugs and security vulnerabilities in more than 30 languages. By integrating into CI/CD pipelines, SonarQube enforces quality gates before code gets merged. Developers receive real-time feedback on code issues and maintainability, improving both DevEx and long-term project health. In 2025, SonarQube remains a go-to tool for teams that treat quality as a DevEx priority. Highlights: Static analysis for 30+ languages Real-time feedback during pull requests Technical debt tracking and maintainability scoring Tight CI/CD and Git integration 4. LogRocket LogRocket enhances frontend DevEx by providing user session replays and performance analytics. It captures how users interact with your application – clicks, navigation, console logs, and network activity – making bug reproduction and performance debugging far more efficient. It bridges the gap between code and user experience, letting developers trace issues quickly. LogRocket also offers integrations with Sentry, Segment, and other analytics platforms to add context to every user issue. Highlights: Session replays with console and network logs Frontend performance monitoring Automatic capture of UI errors and crashes Support for React, Vue, Angular, and more 5. OverOps OverOps specialises in identifying and preventing critical errors in production. It captures the full state of your application (stack trace, variable state, logs) at the moment of failure – without relying on log files alone. OverOps gives developers insight into “why” errors happen, not just “what” happened. This enables faster root-cause analysis, fewer regressions, and higher deployment confidence – all important to frictionless DevEx in modern environments. Highlights: Automated root-cause analysis of runtime errors Continuous monitoring in pre-prod and production Eliminates reliance on verbose logging Insights into code changes that introduced issues 6. Buddy Buddy is a modern DevOps automation platform that enhances DevEx through simple, visual pipelines. With a drag-and-drop UI, developers can set up and manage CI/CD workflows, run tests, build containers, and deploy – all without complex scripts. What makes Buddy unique is its speed and simplicity. It supports Docker, Kubernetes, AWS, and dozens of integrations out-of-the-box, helping teams ship faster while keeping DevEx at the forefront. Highlights: Intuitive UI for CI/CD automation Docker, Kubernetes, and serverless deployment support Real-time feedback on build and test status Git-based workflow and pipeline versioning 7. Docusaurus Docusaurus improves DevEx by making documentation creation and maintenance as easy and developer-friendly as possible. Built by Facebook, it allows dev teams to build fast, versioned, and customisable documentation websites using Markdown and React. In 2025, Docusaurus continues to lead in the “docs as code” movement, helping developers maintain high-quality internal and external documentation without leaving their code editors. Better docs lead to faster onboarding, fewer support tickets, and smoother development workflows. Highlights: Easy setup with React + Markdown Built-in search, versioning, and localisation Custom theming and plugin support Git-based deployment with GitHub Pages or Vercel 8. Exaflow Exaflow is a DevEx observability platform focused on surfacing friction in development and delivery workflows. It aggregates signals from Git providers, issue trackers, code reviews, and builds, offering real-time insights into how teams work. It emphasises transparency and operational health, providing metrics like lead time, handoff delays, and deployment frequency. By highlighting where delays or inefficiencies happen, Exaflow helps teams proactively improve DevEx and delivery outcomes. Highlights: Workflow observability and DevOps telemetry Actionable insights for velocity and bottlenecks Git, Jira, and CI/CD tool integrations Visual timelines of developer handoffs 9. Replit Replit is an online IDE that brings DevEx into the browser. Developers can code, collaborate, and deploy without setting up a local environment. With support for 50+ languages, instant hosting, and live multiplayer coding, it’s a game-changer for fast experimentation and learning. Replit is particularly impactful for onboarding new developers or running internal tooling. It supports AI code suggestions, deployment previews, and GitHub integrations, and offers a frictionless experience from idea to execution. Highlights: Cloud-based, zero-setup IDE Real-time collaboration with multiplayer editing Instant hosting and deployment features Built-in AI tools for autocomplete and debugging 10. Codacy Codacy brings automated code reviews into the DevEx toolkit. It analyses every commit and pull request to flag issues related to code quality, security, duplication, and style – before they reach production. Codacy integrates with your CI and Git workflows, helping developers maintain consistent standards without manual review overhead. It also enables teams to track quality trends over time, ensuring scalable and maintainable codebases. Highlights: Automated code analysis for multiple languages Configurable quality standards and code patterns GitHub/GitLab/Bitbucket CI/CD integration Security and maintainability insights What to consider when selecting a DevEx insight tool? Selecting the right DevEx tool can make or break your team’s efficiency. Below are critical factors to keep in mind: Compatibility with existing ecosystem: Does the tool integrate with your current tech stack, repositories, and CI/CD pipelines? Ease of use: Tools should be intuitive and require minimal learning curves for developers to adopt quickly. Customisability: Every organisation has unique needs. The tools should allow customisation to fit your workflows. Scalability: Ensure the tool can grow with your development team, projects, and increasing workloads. Cost-effectiveness: Evaluate the pricing model to ensure it aligns with your budget without sacrificing features. Community and support: A vibrant community or robust technical support can make the adoption process smoother and keep the tool up-to-date. Insight & analytics: Choose tools that provide powerful analytics and actionable insights to improve workflows. Compliance standards: Consider whether the tool adheres to regulatory and security requirements relevant to your industry. As software teams continue to scale, improving Developer Experience is increasingly important. The right DevEx insight tools allow you to identify friction, empower your engineers, and build healthier development cultures. The post Top 10 developer experience insight tools appeared first on AI News. View the full article

The classroom hasn’t changed much in over a century. A teacher at the front, rows of students listening, and a curriculum defined by what’s testable – not necessarily what’s meaningful. But AI, as arguably the most powerful tool humanity has created in the last few years, is about to break that model open. Not with smarter software or faster grading, but by forcing us to ask: “What is the purpose of education in a world where machines could teach?” At AI News, rather than speculate about distant futures or lean on product announcements and edtech deals, we started a conversation – with an AI. We asked it what it sees when it looks at the classroom, the teacher, and the learner. What follows is a distilled version of that exchange, given here not as a technical analysis, but as a provocation. The system cracks Education is under pressure worldwide: Teachers are overworked, students are disengaged, and curricula feel outdated in a changing world. Into this comes AI – not as a patch or plug-in, but as a potential accelerant. Our opening prompt: “What roles might an AI play in education?“ The answer was wide-ranging: Personalised learning pathways Intelligent tutoring systems Administrative efficiency Language translation and accessibility tools Behavioural and emotional recognition Scalable, always-available content delivery These are features of an education system, its nuts and bolts. But what about meaning and ethics? Flawed by design? One concern kept resurfacing: bias. We asked the AI: “If you’re trained on the internet – and the internet is the output of biased, flawed human thought – doesn’t that mean your responses are equally flawed?” The AI acknowledged the logic. Bias is inherited. Inaccuracies, distortions, and blind spots all travel from teacher to pupil. What an AI learns, it learns from us, and it can reproduce our worst habits at vast scale. But we weren’t interested in letting human teachers off the hook either. So we asked: “Isn’t bias true of human educators too?” The AI agreed: human teachers are also shaped by the limitations of their training, culture, and experience. Both systems – AI and human – are imperfect. But only humans can reflect and care. That led us to a deeper question: if both AI and human can reproduce bias, why use AI at all? Why use AI in education? The AI outlined what it felt were its clear advantages, which seemed to be systemic, rather than revolutionary. The aspect of personalised learning intrigued us – after all, doing things fast and at scale is what software and computers are good at. We asked: “How much data is needed to personalise learning effectively?“ The answer: it varies. But at scale, it could require gigabytes or even terabytes of student data – performance, preferences, feedback, and longitudinal tracking over years. Which raises its own question: “What do we trade in terms of privacy for that precision?” A personalised or fragmented future? Putting aside the issue of whether we’re happy with student data being codified and ingested, if every student were to receive a tailored lesson plan, what happens to the shared experience of learning? Education has always been more than information. It’s about dialogue, debate, discomfort, empathy, and encounters with other minds, not just mirrored algorithms. AI can tailor a curriculum, but it can’t recreate the unpredictable alchemy of a classroom. We risk mistaking customisation for connection. “I use ChatGPT to provide more context […] to plan, structure and compose my essays.” – James, 17, Ottawa, Canada. The teacher reimagined Where does this leave the teacher? In the AI’s view: liberated. Freed from repetitive tasks and administrative overload, the teacher is able to spend more time guiding, mentoring, and cultivating important thinking. But this requires a shift in mindset – from delivering knowledge to curating wisdom. In broad terms, from part-time administrator, part-time teacher, to in-classroom collaborator. AI won’t replace teachers, but it might reveal which parts of the teaching job were never the most important. “The main way I use ChatGPT is to either help with ideas for when I am planning an essay, or to reinforce understanding when revising.” – Emily, 16, Eastbourne College, ***. What we teach next So, what do we want students to learn? In an AI-rich world, important thinking, ethical reasoning, and emotional intelligence rise in value. Ironically, the more intelligent our machines become, the more we’ll need to double down on what makes us human. Perhaps the ultimate lesson isn’t in what AI can teach us – but in what it can’t, or what it shouldn’t even try. Conclusion The future of education won’t be built by AI alone. The is our opportunity to modernise classrooms, and to reimagine them. Not to fear the machine, but to ask the ******* question: “What is learning in a world where all knowledge is available?” Whatever the answer is – that’s how we should be teaching next. (Image source: “Large lecture college classes” by Kevin Dooley is licensed under CC BY 2.0) See also: AI in education: Balancing promises and pitfalls Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Conversations with AI: Education appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

If you’re building with AI, or trying to defend against the less savoury side of the technology, Meta just dropped new Llama security tools. The improved security tools for the Llama AI models arrive alongside fresh resources from Meta designed to help cybersecurity teams harness AI for defence. It’s all part of their push to make developing and using AI a bit safer for everyone involved. Developers working with the Llama family of models now have some upgraded kit to play with. You can grab these latest Llama Protection tools directly from Meta’s own Llama Protections page, or find them where many developers live: Hugging Face and GitHub. First up is Llama Guard 4. Think of it as an evolution of Meta’s customisable safety filter for AI. The big news here is that it’s now multimodal so it can understand and apply safety rules not just to text, but to images as well. That’s crucial as AI applications get more visual. This new version is also being baked into Meta’s brand-new Llama API, which is currently in a limited preview. Then there’s LlamaFirewall. This is a new piece of the puzzle from Meta, designed to act like a security control centre for AI systems. It helps manage different safety models working together and hooks into Meta’s other protection tools. Its job? To spot and block the kind of risks that keep AI developers up at night – things like clever ‘prompt injection’ attacks designed to trick the AI, potentially dodgy code generation, or risky behaviour from AI plug-ins. Meta has also given its Llama Prompt Guard a tune-up. The main Prompt Guard 2 (86M) model is now better at sniffing out those pesky jailbreak attempts and prompt injections. More interestingly, perhaps, is the introduction of Prompt Guard 2 22M. Prompt Guard 2 22M is a much smaller, nippier version. Meta reckons it can slash latency and compute costs by up to 75% compared to the ******* model, without sacrificing too much detection power. For anyone needing faster responses or working on tighter budgets, that’s a welcome addition. But Meta isn’t just focusing on the AI builders; they’re also looking at the cyber defenders on the front lines of digital security. They’ve heard the calls for better AI-powered tools to help in the fight against cyberattacks, and they’re sharing some updates aimed at just that. The CyberSec Eval 4 benchmark suite has been updated. This open-source toolkit helps organisations figure out how good AI systems actually are at security tasks. This latest version includes two new tools: CyberSOC Eval: Built with the help of cybersecurity experts CrowdStrike, this framework specifically measures how well AI performs in a real Security Operation Centre (SOC) environment. It’s designed to give a clearer picture of AI’s effectiveness in threat detection and response. The benchmark itself is coming soon. AutoPatchBench: This benchmark tests how good Llama and other AIs are at automatically finding and fixing security holes in code before the bad guys can exploit them. To help get these kinds of tools into the hands of those who need them, Meta is kicking off the Llama Defenders Program. This seems to be about giving partner companies and developers special access to a mix of AI solutions – some open-source, some early-access, some perhaps proprietary – all geared towards different security challenges. As part of this, Meta is sharing an AI security tool they use internally: the Automated Sensitive Doc Classification Tool. It automatically slaps security labels on documents inside an organisation. Why? To stop sensitive info from walking out the door, or to prevent it from being accidentally fed into an AI system (like in RAG setups) where it could be leaked. They’re also tackling the problem of fake audio generated by AI, which is increasingly used in scams. The Llama Generated Audio Detector and Llama Audio Watermark Detector are being shared with partners to help them spot AI-generated voices in potential phishing calls or fraud attempts. Companies like ZenDesk, Bell Canada, and AT&T are already lined up to integrate these. Finally, Meta gave a sneak peek at something potentially huge for user privacy: Private Processing. This is new tech they’re working on for WhatsApp. The idea is to let AI do helpful things like summarise your unread messages or help you draft replies, but without Meta or WhatsApp being able to read the content of those messages. Meta is being quite open about the security side, even publishing their threat model and inviting security researchers to poke holes in the architecture before it ever goes live. It’s a sign they know they need to get the privacy aspect right. Overall, it’s a broad set of AI security announcements from Meta. They’re clearly trying to put serious muscle behind securing the AI they build, while also giving the wider tech community better tools to build safely and defend effectively. See also: Alarming rise in AI-powered scams: Microsoft reveals $4B in thwarted fraud Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Meta beefs up AI security with new Llama tools appeared first on AI News. View the full article

The *** has cut the ribbon on a pioneering electron beam (E-Beam) lithography facility to build the semiconductor chips of the future. What makes this special? It’s the first of its kind in Europe, and only the second facility like it on the planet—the other being in Japan. So, what’s the big deal about E-Beam lithography? Imagine trying to draw incredibly complex patterns, but thousands of times smaller than a human hair. That’s essentially what this technology does, using a focused beam of tiny electrons. Such precision is vital for designing the microscopic components inside the chips that run everything from our smartphones and gaming consoles to life-saving medical scanners and advanced defence systems. Semiconductors are already big business for the ***, adding around £10 billion to its economy each year. And that figure is only expected to climb, potentially hitting £17 billion by the end of the decade. Nurturing this sector is a major opportunity for the ***—not just for bragging rights in advanced manufacturing, but for creating high-value jobs and driving real economic growth. Speaking at the launch of the facility in Southampton, Science Minister Lord Patrick Vallance said: “Britain is home to some of the most exciting semiconductor research anywhere in the world—and Southampton’s new E-Beam facility is a major boost to our national capabilities. “By investing in both infrastructure and talent, we’re giving our researchers and innovators the support they need to develop next-generation chips right here in the ***.” Lord Vallance’s visit wasn’t just a photo opportunity, though. It came alongside some sobering news: fresh research published today highlights that one of the biggest hurdles facing the ***’s growing chip industry is finding enough people with the right skills. We’re talking about a serious talent crunch. When you consider that a single person working in semiconductors contributes an average of £460,000 to the economy each year, you can see why plugging this skills gap is so critical. So, what’s the plan? The government isn’t just acknowledging the problem; they’re putting money where their mouth is with a £4.75 million semiconductor skills package. The idea is to build up that talent pipeline, making sure universities like Southampton – already powerhouses of chip innovation – have resources like the E-Beam lab and the students they need. “Our £4.75 million skills package will support our Plan for Change by helping more young people into high-value semiconductors careers, closing skills gaps and backing growth in this critical sector,” Lord Vallance explained. Here’s where that cash is going: Getting students hooked (£3 million): Fancy £5,000 towards your degree? 300 students starting Electronics and Electrical Engineering courses this year will get just that, along with specific learning modules to show them what a career in semiconductors actually involves, particularly in chip design and making the things. Practical chip skills (£1.2 million): It’s one thing learning the theory, another designing a real chip. This pot will fund new hands-on chip design courses for students (undergrad and postgrad) and even train up lecturers. They’re also looking into creating conversion courses to tempt talented people from other fields into the chip world. Inspiring the next generation (Nearly £550,000): To really build a long-term pipeline, you need to capture interest early. This funding aims to give 7,000 teenagers (15-18) and 450 teachers some real, hands-on experience with semiconductors, working with local companies in existing *** chip hotspots like Newport, Cambridge, and Glasgow. The goal is to show young people the cool career paths available right on their doorstep. Ultimately, the hope is that this targeted support will give the *** semiconductor scene the skilled workforce it needs to thrive. It’s about encouraging more students to jump into these valuable careers, helping companies find the people they desperately need, and making sure the *** stays at the forefront of the technologies that will shape tomorrow’s economy. Professor Graham Reed, who heads up the Optoelectronics Research Centre (ORC) at Southampton University, commented: “The introduction of the new E-Beam facility will reinforce our position of hosting the most advanced cleanroom in *** academia. “It facilitates a vast array of innovative and industrially relevant research, and much needed semiconductor skills training.” Putting world-class tools in the hands of researchers while simultaneously investing in the people who will use them will help to cement the ***’s leadership in semiconductors. See also: AI in education: Balancing promises and pitfalls Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post *** opens Europe’s first E-Beam semiconductor chip lab appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Duolingo is restructuring parts of its workforce as it shifts toward becoming an “AI-first” company, according to an internal memo from CEO and co-founder Luis von Ahn that was later shared publicly on the company’s LinkedIn page. The memo outlines a series of planned changes to how the company operates, with a particular focus on how artificial intelligence will be used to streamline processes, reduce manual tasks, and scale content development. Duolingo will gradually stop using contractors for work that AI can take over. The company will also begin evaluating job candidates and employee performance partly based on how they use AI tools. Von Ahn said that headcount increases will only be considered when a team can no longer automate parts of its work effectively. “Being AI-first means we will need to rethink much of how we work. Making minor tweaks to systems designed for humans won’t get us there,” von Ahn wrote. “AI helps us get closer to our mission. To teach well, we need to create a massive amount of content, and doing that manually doesn’t scale.” One of the main drivers behind the shift is the need to produce content more quickly, and Von Ahn says that producing new content manually would take decades. By integrating AI into its workflow, Duolingo has replaced processes he described as slow and manual those that are more efficient and automated. The company has also used AI to develop features that weren’t previously feasible such as an AI-powered video call feature, which aims to provide tutoring to the level of human instructors. According to von Ahn, tools like this move the Duolingo platform closer to its mission – to deliver language instruction globally. The internal shift is not limited to content creation or product development. Von Ahn said most business functions will be expected to rethink how they operate and identify opportunities to embed AI into daily work. Teams will be encouraged to adopt what he called “constructive constraints” – policies that push them to prioritise automation before requesting additional resources. The move echoes a broader trend in the tech industry. Shopify CEO Tobi Lütke recently gave a similar directive to employees, urging them to demonstrate why tasks couldn’t be completed with AI before requesting new headcount. Both companies appear to be setting new expectations for how teams manage growth in an AI-dominated environment. Duolingo’s leadership maintains the changes are not intended to reduce its focus on employee well-being, and the company will continue to support staff with training, mentorship, and tools designed to help employees adapt to new workflows. The goal, he wrote, is not to replace staff with AI, but to eliminate bottlenecks and allow employees to concentrate on complex or creative work. “AI isn’t just a productivity boost,” von Ahn wrote. “It helps us get closer to our mission.” The company’s move toward more automation reflects a belief that waiting too long to embrace AI could be a missed opportunity. Von Ahn pointed to Duolingo’s early investment in mobile-first design in 2012 as a model. That shift helped the company gain visibility and user adoption, including being named Apple’s iPhone App of the Year in 2013. The decision to go “AI-first” is framed as a similarly forward-looking step. The transition is expected to take some time. Von Ahn acknowledged that not all systems are ready for full automation and that integrating AI into certain areas, like codebase analysis, could take longer. Nevertheless, he said moving quickly – even if it means accepting occasional setbacks – is more important than waiting for the technology to be fully mature. By placing AI at the centre of its operations, Duolingo is aiming to deliver more scalable learning experiences and manage internal resources more efficiently. The company plans to provide additional updates as the implementation progresses. (Photo by Unsplash) See also: AI in education: Balancing promises and pitfalls Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Duolingo shifts to AI-first model, cutting contractor roles appeared first on AI News. View the full article