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Digital payments and fintech company Ant International, has won the NeurIPS Competition of Fairness in AI Face Detection. The company says it’s committed to developing secure and inclusive financial services, particularly as deepfake technologies are becoming more common. The growing use of facial recognition in many sectors has highlighted the issue of algorithmic bias in AI. Research conducted by NIST (National Institute of Standards and Technology) shows many widely used facial recognition algorithms exhibit considerably higher error rates when analysing the faces of women and people of colour, a disparity that stems from a lack of diversity in the training data and the demographics of those building and controlling many mainstream AI platforms. The consequences of biased algorithms can lead to the denial of financial services to large sections of the population, and is seen as a vulnerability in security protocols. The NeurIPS Competition was held alongside the Conference on Neural Information Processing Systems, the well-respected AI conference, and challenged participants to create AI models capable of high performance and fairness covering a range of demographic factors: Gender, age, and skin tone. Ant International’s team beat over 2,100 submissions from 162 teams coming from all over the world. The given task was to accurately detect 1.2 million AI-generated face images which were chosen as properly representative of demographic groups. The approach taken by Ant’s winning AI model combines a Mixture of Experts (MoE) architecture with a bias-detection mechanism. The system trains two competing neural networks: one focused on identifying deepfakes, and the other designed to challenge the first, forcing it to disregard demographic characteristics. This dynamic process helps ensure the system learns to detect genuine signs of manipulation rather than inadvertently relying on demographic patterns. The model’s training incorporated a globally representative dataset and incorporated real-world payment fraud scenarios to ensure its performance at scale. “A biased AI system is inherently an insecure one,” explained Dr.Tianyi Zhang, general manager of risk management and cybersecurity at Ant International. “Our model’s fairness isn’t just a matter of ethics; it’s fundamental to preventing exploitation from deepfakes and ensuring reliable identity verification for every user”. The technology behind the winning entry is now being integrated into Ant’s payment and financial services to help counter the threat of deepfakes, and the companies says it achieves a detection rate of in excess of 99.8% in all demographics and in the 200 markets Ant operates in. Ant’s technology helps its customers meet global Electronic Know Your Customer (eKYC) standards, particularly during customer onboarding, without algorithmic bias. That’s held to be particularly important in emerging markets where greater financial inclusion can be hampered. Ant International serves over 150 million merchants and 1.8 billion user accounts, known for services like Alipay+, Antom, Bettr and WorldFirst. The company has stated AI security is a pillar of its operations. Its AI SHIELD, a framework for risk management as built on AI Security Docker to help mitigate the risk of vulnerabilities in AI services like unauthorised access and data leakage. AI SHIELD underpins a suite of risk-management solutions that provide broader protection of financial transactions, including measures against deepfake attacks and fraud. Alipay+ EasySafePay 360 has reduced incidents of account takeover in digital wallet payments by 90%, the company says. (Image source: “abstract art of a beautiful portrait, solid shapes, geometric shapes, neotokyo colors, muted colors, pixar, artstation, greg rutkowski, samdoesart, ge” – public domain) 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 part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Battling algorithmic bias in digital payments leads to competition win appeared first on AI News. View the full article

Resemble AI has raised US$13 million in a new strategic investment round for AI deepfake detection. The funding brings its total venture investment to US$25 million, with participation from Berkeley CalFund, Berkeley Frontier Fund, Comcast Ventures, Craft Ventures, Gentree, Google’s AI Futures Fund, IAG Capital Partners, and others. The funding comes as organisations are under pressure to verify the authenticity of digital content. Generative AI has made it easier for criminals to produce convincing deepfakes, contributing to more than US$1.56 billion in fraud losses in 2025. Analysts estimate that generative AI could enable US$40 billion in fraud losses in the US by 2027. Recent incidents highlight how quickly threats evolve. In Singapore, 13 individuals collectively lost more than SGD 360,000 after scammers impersonated a telecommunications provider and the Monetary Authority of Singapore. The attackers used caller ID spoofing, voice deepfakes, and social engineering techniques that created urgency and used the public’s trust in government and telecom brands. Deepfake detection tools and new AI capabilities Resemble AI develops real-time verification tools that help enterprises detect AI-generated audio, video, images, and text. The company plans to use its new funding to expand global access to its AI deepfake detection platform, which includes two recent releases: DETECT-3B Omni, a deepfake detection model designed for enterprise environments. The company reports 98% detection accuracy in more than 38 languages. Resemble Intelligence, a platform that provides explainability for multimodal and AI-generated content, using Google’s Gemini 3 models. Resemble AI positions these tools as part of a broader effort to support real-time verification for human users and AI agents interacting with digital content. According to the company, DETECT-3B Omni is already used in sectors like entertainment, telecommunications, and government. Public benchmark results on Hugging Face show the model ranking among the strongest performers on image and speech deepfake detection, with a lower average error rate than competing models. Industry stakeholders say the rapid improvement of generative AI is reshaping how enterprises think about content trust and identity systems. Representatives from Google’s AI Futures Fund, Sony Ventures, and Okta noted organisations are moving toward verification layers that can help maintain trust in authentication processes. Alongside the investment announcement, Resemble AI released its outlook on how deepfake-related risks may evolve in 2026. The company expects several shifts that could shape enterprise planning: Deepfake verification could become standard for official communications Following incidents involving government officials, it anticipates real-time deepfake detection may eventually be required for official video conferencing. Such a move would likely create new procurement activity and increase adoption in the public sector. Organisational readiness may determine competitive positioning As more jurisdictions introduce AI regulations, enterprises that integrate training, governance, and compliance processes early may find themselves better prepared for operational and regulatory demands. Identity emerges as a central focus in AI security With many AI-related attacks relying on impersonation, organisations may place greater emphasis on identity-centric security models, including zero-trust approaches for human and machine identities. Cyber insurance costs may rise The growing number of corporate deepfake incidents could lead insurers to reassess their policies on offer. Companies without detection tools could face higher premiums or limited coverage. The investment underscores the growing need for enterprises to understand how generative AI changes their risk exposure. Organisations in all sectors are evaluating how verification, identity safeguards, and incident readiness can fit into their broader security and compliance strategies. (Photo by Pau Casals) See also: AWS re:Invent 2025: Frontier AI agents replace chatbots 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 part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Google, Sony, and Okta back Resemble AI’s push into deepfake detection appeared first on AI News. View the full article

InTouchNow.ai is now offering doctors surgeries a piece of software designed to modernise phone answering, designed to reduce hold times and create a smoother, more responsive experience for patients and staff. In the ***, many GP (general practice) surgeries’ phone lines are tied up in the mornings as patients try to contact their medical practitioner for appointments. More acute need can be delayed among calls with routine enquiries, meaning high-priority callers can be left waiting for long periods. The system uses voice-based AI to handle calls, schedule appointments, and assess patient needs, and is capable of handling many calls simultaneously, channelling callers with appointment requests, those seeking general advice, prescription requests, and seeking results of clinical tests. Founded by Daniel Park, InTouchNow.ai draws on his 30+ years of experience in medical call centres. The AI receptionist answers calls quickly, and can automatically update integrated appointment systems. Practices can record a voice messages to personalise the experience for patients. Benefits for practices include reducing the numbers of missed calls, decreased workload for reception staff, and improved access by patients to medical services. Being entirely software-based, the system operates outside regular hours, which can reduce the need for staff overtime at times of peak demand. The system integrates with common GP software like Surgery Connect, AWS, and Anima, automating tasks and maintaining patient data security while giving practices full control. The technology supports over 200 languages, with options for different dialects and accents, an aspect that will help patients in multi-cultural areas like inner-cities. Several practices in the *** are already using InTouchNow.ai and have reported positive results in call handling and patient access. The much under-funded National Health Service in the *** has been quick to deploy AI-powered software to reduce its operating costs, often targeting the reduction of staff administration costs to funnel funds into patient care. For example, Smart Triage is an AI-powered system deployed in *** GP practices that can triage patients making initial enquiries, and based on their responses, book them into the right care pathway, such as GP or nurse appointment, or referral to specialist clinician. An evaluation of Smart Triage at a Surrey GP practice in 2024 showed the platform reduced the average patient waiting time by 73%. For clinicians, especially GPs, iatroX is a ***-based AI clinical reference platform that helps doctors retrieve evidence-based clinical guidance, and summarising relevant literature & guidelines. Doctors in general practice are expected to be able to assess a full range of patients’ needs, and such platforms help clinicians identify the cause of uncommon symptoms when GPs might lack specialist knowledge. An evaluation in 2025 found a majority of surveyed users stating iatroX was useful (~86%) or reliable (~79%). As documented by NHS England, AI platforms are used in practice, tackling tasks like diagnosis, the monitoring of chronic disease, provision of prescription advice, and handling general administration tasks that otherwise would take up clinicians’ time. Of all the sectors where sensitive data has to be protected, medicine has one of the highest standards of governance, making the deployment of AI a delicate balance between operational effectiveness and the preservation of privacy. (Image source: “Doctor appointment” by Taric25 is licensed under CC BY 2.0.) 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 part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post *** doctors’ surgeries deploying AI in patient care appeared first on AI News. View the full article

ByteDance’s December 2 launch of an agentic AI smartphone prototype with ZTE sparked immediate consumer frenzy—and just as quickly triggered privacy concerns that forced the company to dial back capabilities. But beneath the headline-grabbing sell-out and subsequent controversy lies a more significant story: the enterprise implications of operating-system-level AI agents that can autonomously execute complex, multi-step tasks across device ecosystems. The ZTE Nubia M153, powered by ByteDance’s Doubao large language model, represents more than a consumer gadget experiment. It’s a preview of how agentic AI smartphones could reshape workplace productivity, field operations, and enterprise mobility strategies—if the technology can overcome fundamental trust and governance challenges that enterprise adoption demands. From consumer curiosity to enterprise necessity The consumer appeal is obvious: voice-activated restaurant bookings, automatic photo editing, cross-platform price comparisons. But according to Gartner projections, by 2028, 33% of enterprise software applications will include agentic AI capabilities, up from less than 1% in 2024. The smartphone, as the most ubiquitous computing device in enterprise workflows, becomes a critical battleground. “Agentic AI in industries like manufacturing, construction, healthcare, and energy will enhance decisions, boost safety, and streamline tasks,” explains Nicholas Muy, CISO of Scrut Automation. However, he cautions that early adopters must navigate real risks around AI errors and security gaps. McKinsey research indicates that 23% of organisations are already scaling agentic AI systems within at least one business function, with an additional 39% experimenting with AI agents. However, enterprise adoption differs fundamentally from consumer use: it demands robust governance frameworks, audit trails, role-based permissions, and compliance mechanisms that ByteDance’s consumer-focused prototype notably lacked. China’s strategic advantage in software-hardware integration ByteDance’s approach—partnering with ZTE rather than building proprietary hardware—mirrors successful enterprise AI strategies. The company positions Doubao as a system-level integration that any manufacturer can adopt, similar to how Google leveraged Android. With 157 million monthly active users as of August 2025, according to data from QuestMobile, Doubao already dominates China’s consumer AI market, more than doubling Tencent’s Yuanbao, which had 73 million users. This software-over-hardware strategy addresses what Morgan Stanley analysts identified as a critical weakness: major smartphone manufacturers, including Apple, Huawei, and Xiaomi, possess strong enough technology capabilities to self-develop AI assistants rather than partnering with third-party providers. ByteDance’s realistic target market appears to be second-tier manufacturers and, potentially, enterprise device management platforms seeking differentiated capabilities. For enterprise buyers, this fragmentation presents both opportunity and challenge. Organisations can select device manufacturers based on hardware requirements while standardising on AI capabilities—but only if governance and security frameworks prove robust enough for regulated industries. The privacy panic that revealed enterprise requirements The swift backlash following entrepreneur Taylor Ogan’s viral demos of the M153’s capabilities illuminated precisely what enterprise adoption demands. When users witnessed an AI agent with deep system privileges autonomously accessing apps, processing payments, and manipulating data, the immediate concern wasn’t convenience—it was control. Another DeepSeek moment. This is the world’s first actual smart phone. It’s an engineering prototype of ZTE’s Nubia M153 running ByteDance’s Doubao AI agent fused into Android at the OS level. It has complete control over the phone. It can see the UI, choose/download apps,… pic.twitter.com/lM9PYMoQek — Taylor Ogan (@TaylorOgan) December 4, 2025 According to a Forum Ventures survey of 100 senior enterprise IT decision-makers, trust remains the primary adoption barrier. “The trust gap is enormous,” explains Jonah Midanik, General Partner at Forum Ventures. “While AI agents can perform tasks with remarkable efficiency, their outputs are based on statistical probabilities rather than inherent truths.” ByteDance’s reported rollback of capabilities demonstrates an understanding that enterprise-grade agentic AI smartphones require granular permission systems, comprehensive logging, and the ability to define strict operational boundaries—features notably absent from the consumer prototype. Enterprise vs. consumer: Different use cases, different requirements Enterprise use cases for agentic AI smartphones diverge sharply from consumer applications. Field service technicians could leverage AI agents that proactively surface equipment histories, recommend optimal routes based on real-time conditions, and guide complex procedures without manual searches. Healthcare providers could access patient context, treatment protocols, and decision support without navigating multiple systems. Financial services professionals could receive compliance-checked recommendations and automated workflow orchestration. According to PwC research, 79% of organisations have implemented AI agents at some level, with 96% of IT leaders planning expansions in 2025. However, Cloudera’s survey of 1,484 IT decision-makers revealed that successful enterprise deployment requires industry-specific data integration, transparent decision-making processes, and phased rollouts with comprehensive testing. The consumer smartphone market, projected by IDC to ship 912 million generative AI-enabled units by 2028, emphasises personalisation and convenience. Enterprise deployments prioritise auditability, compliance, and risk mitigation—requirements that consumer-focused agentic AI smartphones haven’t yet addressed. Global competitive dynamics and regional strategies The US-China technology divide adds complexity. Apple’s delayed Apple Intelligence rollout in mainland China created an opening that ByteDance, Alibaba, Baidu, and Tencent are competing to fill. However, Apple’s approach differs fundamentally: tight hardware-software integration with on-device processing prioritises user privacy—a stance that resonates with enterprise security requirements. ByteDance’s licensing strategy positions Doubao for rapid market penetration across ******** manufacturers, potentially establishing de facto standards before Western competitors can match operating-system-level integration. For multinational enterprises operating across regions, this creates device management challenges around data sovereignty, compliance frameworks, and consistent user experiences. According to Counterpoint Research, Asia-Pacific represents the fastest-growing market for AI agents, with the US currently holding 40.1% revenue share. Enterprise buyers must navigate this bifurcated landscape, potentially maintaining separate device strategies for different regulatory environments. The path forward: Solutions over hype For enterprise leaders evaluating agentic AI smartphones, ByteDance’s prototype offers valuable lessons in what to demand from vendors: First, comprehensive governance frameworks that define decision boundaries, log all autonomous actions, and provide role-based access controls. Anthropic’s enterprise solution, which features centralised provisioning, audit logs, and role-based permissioning, demonstrates market requirements. Second, hybrid approaches that balance on-device processing for sensitive operations with cloud capabilities for complex reasoning. Enterprise deployments require flexibility to meet varying data residency and compliance requirements across jurisdictions. Third, phased rollouts starting with low-risk use cases. Amazon’s deployment of AI agents for Java application modernisation—transforming tens of thousands of production applications while delivering measurable productivity gains—illustrates how enterprises can capture value while managing risk. The ByteDance-ZTE collaboration ultimately previews an inevitable convergence: agentic AI capabilities will become standard smartphone features, not premium differentiators. Enterprise adoption will follow proven patterns—pilot programs in controlled environments, rigorous security validation, and gradual expansion as governance frameworks mature. The question facing enterprise technology leaders isn’t whether agentic AI smartphones will transform workplace productivity, but whether they’ll shape deployment strategies proactively or react to consumer technologies retrofitted with enterprise features. The privacy panic that followed ByteDance’s launch suggests that organisations demanding enterprise-grade security and governance from the outset will define the technology’s trajectory. As Gartner projects that at least 15% of work decisions will be made autonomously by agentic AI by 2028, up from 0% in 2024, the smartphone becomes not just a communication device but an autonomous enterprise agent. The winners won’t be those who deploy fastest, but those who deploy most thoughtfully—with security, compliance, and scalable governance built in from day one. See also: IBM cites agentic AI, data policies, and quantum as 2026 trends 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 part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Agentic AI smartphones: ByteDance’s bold bet signals enterprise opportunity beyond consumer hype appeared first on AI News. View the full article

The *** and Germany plan to integrate their science sectors to accelerate the commercialisation of quantum supercomputing technology. Announced on the final day of the ******* president’s state visit, these joint commitments target the gap between R&D and enterprise application in computing, sensing, and timing. The partnership involves specific funding to fast-track product development and establish shared operating standards. Quantum technology currently sits on the horizon for most roadmaps, yet economic modelling suggests a contribution of £11 billion to *** GDP by 2045, supporting over 100,000 jobs. To catalyse this, a £6 million joint R&D funding call launches in early 2026, with Innovate *** and VDI contributing £3 million each. This capital aims to help businesses bring new products to market rather than funding purely academic study. Supply chain maturity remains a hurdle. An £8 million investment in the Fraunhofer Centre for Applied Photonics in Glasgow addresses this by bolstering the development of applied photonics; a necessary component for commercial quantum sensing. Addressing hurdles in the ***, Germany, and beyond to commercialise quantum supercomputing Regulatory fragmentation often stalls adoption. A new Memorandum of Understanding between the ***’s National Physical Laboratory (NPL) and Germany’s Physikalisch-Technische Bundesanstalt (PTB) aims to harmonise measurement standards. This agreement complements the NMI-Q initiative, a global effort to develop shared norms. *** Science Minister Lord Vallance said: “Quantum technology will revolutionise fields such as cybersecurity, drug discovery, medical imaging, and much more. International collaboration is crucial to unlocking these benefits.” In practical terms, these advances allow pharmaceutical firms to identify new medicines faster. Similarly, next-generation sensors promise medical scanners that are more affordable, portable, and accurate than current iterations. The partnership also extends to high-performance computing (HPC). The ***’s National Supercomputing Centre at the University of Edinburgh was selected by the EuroHPC Joint Undertaking to host the ***’s AI Factory Antenna, partnering with the HammerHAI AI Factory in Stuttgart. To support HPC integration prior to the commercialisation of quantum supercomputing technology, the Department for Science, Innovation and Technology (DSIT) is allocating up to £3.9 million to match fund *** participation in three open EuroHPC calls. This funding assists teams developing exascale and AI-ready software. In the aerospace sector, the two nations recently committed joint funding of over €6 billion to the European Space Agency. This includes €1 billion for launch programmes and €10 million for Rocket Factory Augsburg, which plans to launch from Scotland in 2026. ******* President Frank-Walter Steinmeier concluded his visit at Siemens Healthineers in Oxford. The site produces superconducting magnets for MRI scanners, an existing example of how bilateral science ties support high-skilled manufacturing and health outcomes. As this bilateral cooperation deepens, the integrated approach between the *** and Germany toward supercomputing and quantum infrastructure aims to offer enterprises a powerful foundation for scaling high-performance workloads across Europe. See also: AWS re:Invent 2025: Frontier AI agents replace chatbots 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 part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post *** and Germany plan to commercialise quantum supercomputing appeared first on AI News. View the full article

The convergence of mobile and desktop operating systems is a goal that has remained elusive for big tech firms since the early days of the smartphone. Microsoft’s attempt in the form of Windows Mobile was reaching the end of its road by 2010, and despite Apple’s iOS/iPadOS and macOS moving very slowly towards one another for the last few years, Cupertino has not yet reached the fabled goal of the-one-OS-to-rule-them-all. But Google’s big play to merge ChromeOS and Android into a unified PC platform (with the anglicised codename Aluminium OS) is gradually taking shape. Android-powered laptops are planned for released in 2026, and the company wants to put its LLMs at the centre of the user experience. Hardware procurement decisions may be in step with company AI strategy in the enterprise, therefore, in the coming year. The prospect of chromebook-style devices and an accompanying lower price tag will be attractive both to organisations considering their next round of machine refreshes, and strategists who want to put AI at the heart of their employees’ daily work could. Soon, they might have a solution in common. It’s early days in the development of the converged device at Google, but the company is well known for both floating ideas that don’t get far and abandoning technologies it can’t monetise effectively enough. Unlike some of the company’s projects that may stem from its ‘20%’ policy (employees at Google are encouraged to dedicate 20% of their time to moonshot projects), the substantial Android development community and Google’s policy of putting Gemini front-and-centre may be the accelerant the new, converged operating system needs. Android’s existing AI capabilities like the Magic Editor for photos, audio transcription and summarisation would port very well to the workplace desktop. However, if Google wants to assuage the fears of security professionals, it may have to rely on local, small models for AI processing, rather than reaching out to cloud instances of Gemini for the required compute power. That puts into question the continuation of one of the chromebook range’s big selling points – its low price compared to fully-fledged workstations. There’s also a delicate balance the company needs to strike. Forcing users into an AI-centric workflow hasn’t played well for Microsoft: note the furore around Recall and the muted response to its much-reduced offspring that has sprung out of Copilot Labs. What Google needs is a killer AI feature that benefits the enterprise, and that may or may not be something that’s aimed at users. It’s undeniable that the addition of Gemini to Google Workspace has done wonders for the platform in terms of its competitiveness with Office 365 – despite a significant price hike earlier this year – driven in some part by new features like live translation in Google Meet and AI responses available in Gmail. Users do find some AI tools useful, but it may be becoming apparent that user-facing AI is a useful addition to existing workflows, rather than a catalyst that changes everything. If placing Gemini or Gemini Nano at the heart of the new operating system, therefore, it may be that Google is looking to offer value to different parts of the enterprise from the daily tasks users tackle. Android Authority suggests smart power management, device provisioning, and contextual awareness in accessing enterprise resources may be on the table. It’s difficult to see how these elements would be a game-changer for procurement teams, however. Google has many problems to solve at a deeper level, like compatibility with peripherals, OS-level drivers, and the necssary changes to the Android GUI to make it a great experience for end users wielding mouse and keyboard. But given enough effort and investment (something the company does not lack) these are issues that can be surmounted relatively easily. A thriving app ecosystem will ensure that the necessary tools are if not immediately available, could be made so with minimum effort. Ultimately, the success of Aluminium OS will depend on Google’s ability to offer a platform that solves tangible problems and integrates into existing workflows. Google sees AI in the form of Gemini (or localised Gemini Nano instance) powering a platform that offers integrated problem-solving. Hitting that target will generate demand, and a lower price per machine could be the decider for procurement teams. If Google gets it right, it could repeat the success it experienced in the education market with the original chromebook project, and there could be a substantial shift by enterprise fleets to Aluminium OS and Google Workspaces. There are big gains to be made for a company that dominates the mobile market worldwide and makes serious inroads into the enterprise workstation market. Plus, that elusive device convergence would be much closer to becoming a reality. (Image source: “Macro Monday : Aluminium buttons (Al on the periodic table)” by cchana is licensed under CC BY-SA 2.0.) 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 part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. 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According to AWS at this week’s re:Invent 2025, the chatbot hype cycle is effectively dead, with frontier AI agents taking their place. That is the blunt message radiating from Las Vegas this week. The industry’s obsession with chat interfaces has been replaced by a far more demanding mandate: “frontier agents” that don’t just talk, but work autonomously for days at a time. We are moving from the novelty phase of generative AI into a grinding era of infrastructure economics and operational plumbing. The “wow” factor of a poem-writing bot has faded; now, the cheque comes due for the infrastructure needed to run these systems at scale. Addressing the plumbing crisis at AWS re:Invent 2025 Until recently, building frontier AI agents capable of executing complex, non-deterministic tasks was a bespoke engineering nightmare. Early adopters have been burning resources cobbling together tools to manage context, memory, and security. AWS is trying to kill that complexity with Amazon Bedrock AgentCore. It’s a managed service that acts as an operating system for agents, handling the backend work of state management and context retrieval. The efficiency gains from standardising this layer are hard to ignore. Take MongoDB. By ditching their home-brewed infrastructure for AgentCore, they consolidated their toolchain and pushed an agent-based application to production in eight weeks—a process that previously ate up months of evaluation and maintenance time. The PGA TOUR saw even sharper returns, using the platform to build a content generation system that increased writing speed by 1,000 percent while slashing costs by 95 percent. Software teams are getting their own dedicated workforce, too. At re:Invent 2025, AWS rolled out three specific frontier AI agents: Kiro (a virtual developer), a Security Agent, and a DevOps Agent. Kiro isn’t just a code-completion tool; it hooks directly into workflows with “powers” (specialised integrations for tools like Datadog, Figma, and Stripe) that allow it to act with context rather than just guessing at syntax. Agents that run for days consume massive amounts of compute. If you are paying standard on-demand rates for that, your ROI evaporates. AWS knows this, which is why the hardware announcements this year are aggressive. The new Trainium3 UltraServers, powered by 3nm chips, are claiming a 4.4x jump in compute performance over the previous generation. For the organisations training massive foundation models, this cuts training timelines from months to weeks. But the more interesting shift is where that compute lives. Data sovereignty remains a headache for global enterprises, often blocking cloud adoption for sensitive AI workloads. AWS is countering this with ‘AI Factories’ (essentially shipping racks of Trainium chips and NVIDIA GPUs directly into customers’ existing data centres.) It’s a hybrid play that acknowledges a simple truth: for some data, the public cloud is still too far away. Tackling the legacy mountain Innovation like we’re seeing with frontier AI agents is great, but most IT budgets are strangled by technical debt. Teams spend roughly 30 percent of their time just keeping the lights on. During re:Invent 2025, Amazon updated AWS Transform to attack this specifically; using agentic AI to handle the grunt work of upgrading legacy code. The service can now handle full-stack Windows modernisation; including upgrading .NET apps and SQL Server databases. Air Canada used this to modernise thousands of Lambda functions. They finished in days. Doing it manually would have cost them five times as much and taken weeks. For developers who actually want to write code, the ecosystem is widening. The Strands Agents SDK, previously a Python-only affair, now supports TypeScript. As the lingua franca of the web, it brings type safety to the chaotic output of LLMs and is a necessary evolution. Sensible governance in the era of frontier AI agents There is a danger here. An agent that works autonomously for “days without intervention” is also an agent that can wreck a database or leak PII without anyone noticing until it’s too late. AWS is attempting to wrap this risk in ‘AgentCore Policy,’ a feature allowing teams to set natural language boundaries on what an agent can and cannot do. Coupled with ‘Evaluations,’ which uses pre-built metrics to monitor agent performance, it provides a much-needed safety net. Security teams also get a boost with updates to Security Hub, which now correlates signals from GuardDuty, Inspector, and Macie into single “events” rather than flooding the dashboard with isolated alerts. GuardDuty itself is expanding, using ML to detect complex threat patterns across EC2 and ECS clusters. We are clearly past the point of pilot programs. The tools announced at AWS re:Invent 2025, from specialised silicon to governed frameworks for frontier AI agents, are designed for production. The question for enterprise leaders is no longer “what can AI do?” but “can we afford the infrastructure to let it do its job?” See also: AI in manufacturing set to unleash new era of profit 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 part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AWS re:Invent 2025: Frontier AI agents replace chatbots appeared first on AI News. 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Amazon Web Services has scored another major win for its custom AWS Trainium accelerators after striking a deal with AI video startup Decart. The partnership will see Decart optimise its flagship Lucy model on AWS Trainium3 to support real-time video generation, and highlight the growing popularity of AI accelerators over Nvidia’s graphics processing units. Decart is essentially going all-in on AWS, and as part of the deal, the company will also make its models available through the Amazon Bedrock platform. Developers can integrate Decart’s real-time video generation capabilities into almost any cloud application without worrying about underlying infrastructure. The distribution through Bedrock increases AWS’s plug-and-play capabilities, demonstrating Amazon’s confidence in growing demand for real-time AI video. It also allows Decart to expand reach and grow adoption among the developer community. AWS Trainium provides Lucy with the extra processing grunt needed to generate high-fidelity video without sacrificing quality or latency. Custom AI accelerators like Trainium provide an alternative to Nvidia’s GPUs for AI workloads. While Nvidia still dominates the AI market, its GPUs processing the vast majority of AI workloads, it’s facing a growing threat from custom processors. Why all the fuss over AI accelerators? AWS Trainium isn’t the only option developers have. Google’s Tensor Processing Unit (TPU) product line and Meta’s Training and Inference Accelerator (MTIA) chips are other examples of custom silicon, each having a similar advantage over Nvidia’s GPUs – their ASIC architecture (Application-Specific Integrated Circuit). As the name suggests, ASIC hardware is engineered specifically to handle one kind of application and do so more efficiently than general purpose processors. While central processing units are generally considered to be the Swiss Army knife of the computing world due to their ability to handle multiple applications, GPUs are more akin to a powerful electric drill. They’re vastly more powerful than CPUs, designed to process massive amounts of repetitive, parallel computations, making them suitable for AI applications and graphics rendering tasks. If the GPU is a power drill, the ASIC might be considered a scalpel, designed for extremely precise procedures. When building ASICs, chipmakers strip out all functional units irrelevant to the task for greater efficiency – all their operations are dedicated to the task. This yields massive performance and energy efficiency benefits compared to GPUs, and may explain their growing popularity. A case in point is Anthropic, which has partnered with AWS on Project Rainier, an enormous cluster made up of hundreds of thousands of Trainium2 processors. Anthropic says that Project Rainier will provide it with hundreds of exaflops of computing power to run its most advanced AI models, including Claude Opus-4.5. The AI coding startup Poolside is also using AWS Trainium2 to train its models, and has plans to use its infrastructure for inference as well in future. Meanwhile, Anthropic is hedging its bets, also looking to train future Claude models on a cluster of up to one million Google TPUs. Meta Platforms is reportedly collaborating with Broadcom to develop a custom AI processor to train and run its Llama models, and OpenAI has similar plans. The Trainium advantage Decart chose AWS Trainium2 due to its performance, which let Decart achieve the low latency required by real-time video models. Lucy has a time-to-first-frame of 40ms, meaning that it begins generating video almost instantly after prompt. By streamlining video processing on Trainium, Lucy can also match the quality of much slower, more established video models like OpenAI’s Sora 2 and Google’s Veo-3, with Decart generating output at up to 30 fps. Decart believes Lucy will improve. As part of its agreement with AWS, the company has obtained early access to the newly announced Trainium3 processor, capable of outputs of up to 100 fps and lower latency. “Trainium3’s next-generation architecture delivers higher throughput, lower latency, and greater memory efficiency – allowing us to achieve up to 4x faster frame generation at half the cost of GPUs,” said Decart co-founder and CEO Dean Leitersdorf in a statement. Nvidia might not be too worried about custom AI processors. The AI chip giant is reported to be designing its own ASIC chips to rival cloud competitors’. Moreover, ASICs aren’t going to replace GPUs completely, as each chip has its own strengths. The flexibility of GPUs means they remain the only real option for general-purpose models like GPT-5 and Gemini 3, and are still dominant in AI training. However, many AI applications have stable processing requirements, meaning they’re particularly suited to running on ASICs. The rise of custom AI processors is expected to have a profound impact on the industry. By pushing chip design towards greater customisation and enhancing the performance of specialised applications, they’re setting the stage for a new wave of AI innovation, with real-time video at the forefront. Photo courtesy AWS re:invent The post Decart uses AWS Trainium3 for real-time video generation appeared first on AI News. View the full article

In the basement of a Boise, Idaho, dental office in 1978, four engineers founded what would become one of America’s semiconductor giants. Ward Parkinson, Joe Parkinson, Dennis Wilson, and Doug Pitman started Micron Technology as a modest design consultancy, backed by local investors including potato magnate J.R. Simplot. By 1983, they had achieved a technological breakthrough—producing chips roughly half the size of Japan’s leading products. Nearly five decades later, that same company has made a decision that crystallises artificial intelligence’s profound impact on hardware economics: AI memory hunger is forcing manufacturers to abandon entire market segments. On December 3, 2025, Micron announced it would completely exit the consumer memory market, discontinuing its 29-year-old Crucial brand by February 2026. “The AI-driven growth in the data centre has led to a surge in demand for memory and storage,” said Sumit Sadana, Micron’s Executive Vice President and Chief Business Officer. “Micron has made the difficult decision to exit the Crucial consumer business to improve supply and support for our larger, strategic customers in faster-growing segments.” Translation: data centres running AI workloads will pay substantially more for memory than individual consumers ever could, and Micron’s fabrication capacity cannot serve both markets simultaneously. The announcement represents more than a business decision—it’s a watershed moment revealing how AI memory hunger demands are fundamentally restructuring global semiconductor supply chains and forcing manufacturers to make stark choices about which customers deserve access to finite production capacity. The economics driving AI memory hunger Micron’s withdrawal reflects brutal economic realities. As the world’s third-largest DRAM producer with approximately 20%global market share, the company sits between South Korean giants Samsung Electronics (43%) and SK Hynix (35%). Together, these three manufacturers control roughly 95% of worldwide DRAM production—an oligopoly now facing unprecedented demand from AI infrastructure builders. The margin differentials tell the story. Consumer RAM modules compete in volatile retail markets with razor-thin profitability. Enterprise contracts for high-bandwidth memory (HBM) used in AI accelerators and DDR5 modules for data centre servers deliver substantially higher average selling prices, multi-year commitments, and predictable demand. For memory manufacturers, each fabrication wafer committed to consumer products represents foregone revenue from higher-value enterprise contracts—an opportunity cost that has become economically indefensible as AI demand accelerates. The numbers illustrate the magnitude of the shift. Micron reported record fiscal 2025 revenue of US$37.38 billion, representing nearly 50% year-over-year growth driven primarily by data centre and AI applications, which accounted for 56% of total revenue. SK Hynix has reportedly sold out its entire 2026 production capacity for DRAM, HBM, and NAND products. Consumer memory prices have surged accordingly. DRAM spot prices increased 172% year-over-year as of Q3 2025, with retail prices for 32GB DDR5 modules jumping 163-619% across global markets since September 2025. Component suppliers report paying US$13 for 16GB DDR5 chips that cost US$7 just six weeks earlier—increases sufficient to eliminate entire gross margins for third-party brands. Consumer market restructuring amid AI memory hunger Micron’s exit fundamentally alters the consumer memory landscape. Third-party brands, including Corsair, G.Skill, Kingston, and ADATA, source their DRAM chips from the major manufacturers. With Micron withdrawing entirely, these vendors must compete more aggressively for allocation from Samsung and SK Hynix—both simultaneously prioritising high-bandwidth memory production for AI accelerators. The concentration creates vulnerabilities. Samsung and SK Hynix now comprise the only major suppliers serving both consumer and enterprise markets directly. Both face identical capacity allocation pressures. If AI infrastructure investment maintains current trajectories, additional manufacturers may reduce or restructure consumer operations. Supply chain constraints are already materialising beyond DRAM. NAND flash wafer contract prices increased by over 60% in November 2025. Graphics memory markets face pressures as manufacturers shift to GDDR7 for next-generation GPUs, creating GDDR6 shortages that inflated prices by approximately 30%. Hard drive manufacturers increased prices 5-10% citing limited supply. For consumers and small businesses, the implications extend beyond pricing. Product availability may become increasingly constrained during peak demand periods. The reduction in direct supplier participation may compress product differentiation and limit competitive pricing dynamics that previously benefited buyers. The broader industry realignment Micron’s consumer exodus signals a structural transformation rather than a temporary reallocation. The AI infrastructure ***** differs fundamentally from previous technology transitions. Personal computing, internet expansion, and mobile devices created sustained memory demand over decades with gradual capacity adjustments. AI infrastructure deployment compresses that timeline dramatically—hyperscale operators are committing hundreds of billions in data centre construction over just a few years. Data centre semiconductor markets illustrate the scale. The total addressable market reached US$209 billion in 2024, projected to grow to nearly US$500 billion by 2030, driven primarily by AI and high-performance computing. GPU revenue alone is forecast to expand from US$100 billion in 2024 to US$215 billion by 2030, with each GPU requiring substantial high-bandwidth memory allocation. Memory architecture evolution compounds the challenge. AI training workloads increasingly require HBM3E modules offering superior bandwidth and power efficiency. Inference workloads demand DDR5 with tight latency specifications. Automotive applications adopting zonal architectures require multi-gigabyte DRAM configurations. Each application commands premium pricing and long-term contracts—economic incentives systematically pulling manufacturing capacity away from consumer markets. The manufacturing response reflects these priorities. Samsung is advancing 1c DRAM production and planning mass production of HBM4 in 2025 while phasing out DDR4 entirely. Micron began mass production of DRAM using Extreme Ultraviolet (EUV) lithography in 2025. SK Hynix focuses development resources on HBM and advanced LPDDR solutions. All three manufacturers are directing research and capital investment toward applications offering superior returns. What this means for enterprise buyers Enterprise procurement teams face their own challenges as memory markets restructure. Memory represents 10-25% of bill-of-materials costs for typical servers and commercial PCs. Price increases of 20-30% in memory components translate to 5-10% increases in total system costs, compounding into millions in additional expenditure for organisations procuring at scale. Strategic responses include forward purchasing agreements, establishing stronger direct relationships with manufacturers, and diversifying vendor partnerships. The timing uncertainty presents particular challenges. New fabrication capacity is under construction, supported by government incentives, but requires years to reach production readiness. Critical questions ahead Micron’s consumer market exit raises fundamental questions. Will Samsung and SK Hynix maintain consumer product lines, or will similar capacity pressures force comparable reductions? If consumer memory becomes primarily a third-party brand market sourcing chips from manufacturers prioritising enterprise customers, what happens to product innovation and competitive pricing? The concentration among just two major manufacturers serving consumer markets creates potential vulnerabilities. Supply chain disruptions affecting either Samsung or SK Hynix would have an outsized impact on global consumer product availability. Broader implications extend to technology accessibility. If memory pricing remains elevated or availability constrained for consumer products, the costs of personal computing and small business infrastructure increase accordingly, potentially widening digital divides. Micron’s decision crystallises artificial intelligence’s role as a transformative force reshaping not just software, but the fundamental economics of hardware manufacturing. The Crucial brand’s retirement after 29 years marks the end of an era when memory manufacturers could profitably serve both consumer and enterprise segments simultaneously. For the broader technology ecosystem, AI memory hunger has become the semiconductor industry’s dominant growth driver, commanding resources at levels that fundamentally alter which markets manufacturers choose to serve. (Photo: Micron Technology) See also: AI memory demand propels SK Hynix to historic DRAM market leadership 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 part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI memory hunger forces Micron’s consumer exodus: A turning point in semiconductor economics appeared first on AI News. View the full article

Manufacturing executives are wagering nearly half their modernisation budgets on AI, betting these systems will boost profit within two years. This aggressive capital allocation marks a definitive pivot. AI is now seen as the primary engine for financial performance. According to the Future-Ready Manufacturing Study 2025 by ***** Consultancy Services (TCS) and AWS, 88 percent of manufacturers anticipate AI will capture at least five percent of operating margin. One in four expect returns exceeding 10 percent. The money is there. The ambition is there. The plumbing, unfortunately, is not. A disparity exists between financial forecasts and the reality of the factory floor. While spending on intelligent systems accelerates, the underlying data infrastructure remains brittle, and risk management strategies still rely on expensive manual buffers. Pressure to extract value from AI for manufacturing The pressure to extract cash value from tech stacks has never been higher. 75 percent of respondents expect AI to rank as a top-three contributor to operating margins by 2026. Consequently, organisations are funneling 51 percent of their transformation spending toward AI and autonomous systems over the next two years. This spending eclipses other vital areas. Allocations for AI outpace workforce reskilling (19%) and cloud infrastructure modernisation (16%) by a wide margin. For CIOs, this imbalance signals a looming crisis: attempting to deploy advanced algorithms on shaky legacy foundations. Anupam Singhal, President of Manufacturing at TCS, said: “Manufacturing is an industry defined by precision, reliability, and the relentless pursuit of performance. Today, that strength of foundation becomes multifold with AI in orchestrating decisions—delivering transformational business outcomes through greater predictability, stability, and control. “At TCS, we see this as a defining opportunity to help manufacturers build resilient, adaptive, and future-ready enterprise ecosystems that can thrive in an era of intelligent autonomy.” Analogue hedges in a digital era Despite the heavy investment in predictive capabilities, operational behaviour betrays a lack of trust. When disruption hits, manufacturers aren’t leaning on the agility of their digital systems; they are reverting to physical safeguards. Following recent disruptions, 61 percent of organisations increased their safety stock. Half opted for multisourcing logistics. Only 26 percent utilised scenario planning via digital twins to navigate volatility. This is the disconnect. While AI promises dynamic inventory optimisation, a benefit cited by 49 percent of respondents, the prevailing instinct is to hoard inventory. Supply chain leaders are buying Ferraris but driving them like tractors. Bridging this gap requires moving from reactive safety measures to proactive and system-led responses. Ozgur Tohumcu, General Manager of Automotive and Manufacturing at AWS, commented: “Manufacturers today are facing unprecedented pressure—from tight margins to volatile supply chains and workforce gaps. At AWS, we are revolutionising manufacturing through AI-powered autonomous operations, shifting from manual, reactive processes to intelligent, self-optimising systems that operate at scale. “By embedding artificial intelligence into every layer of the operation and leveraging cloud-native architecture, manufacturers can move beyond simple automation to true autonomous decision-making where systems predict, adapt, and act independently with minimal human intervention. This enables not just faster response times, but fundamentally transforms operations with AI-driven predictability, resilience, and agility.” Infrastructure debt The primary obstacle to these financial returns isn’t the AI models; it’s the data they feed on. Only 21 percent of manufacturers claim to be “fully AI-ready” with clean, contextual, and unified data. The majority (61%) operate with partial readiness, struggling with inconsistent quality across different plants. This fragmentation creates data silos that prevent algorithms from accessing the enterprise-wide inputs necessary for accurate decision-making. Integration with legacy systems stands as the primary hurdle, cited by 54 percent of respondents. This “technical debt,” accumulated over decades of digitisation, makes it difficult to overlay modern autonomous agents on older operational technology. Security also bites. Security and governance concerns top the list of plant-level obstacles at 52 percent. In an environment where a cyber-physical breach can halt production or cause physical harm, the risk appetite for autonomous intervention remains low. The shift towards agentic AI in manufacturing Despite the headwinds, the industry is charging toward agentic AI (i.e. systems capable of making decisions with limited human oversight.) Seventy-four percent of manufacturers expect AI agents to manage up to half of routine production decisions by 2028. More immediately, 66 percent of organisations already allow – or plan to allow within 12 months – AI agents to approve routine work orders without human sign-off. This progression from “copilots” to independent agents capable of completing entire tasks fundamentally alters the workforce. While 89 percent of manufacturers expect AI-guided robotics to impact the workforce, the focus is on augmentation rather than displacement. Productivity gains are currently concentrated in knowledge-intensive roles. Quality inspectors (49%) and IT support staff (44%) are seeing the fastest gains. Traditional production roles like maintenance technicians (29%) lag behind. Adoption is following a pattern of cognitive augmentation before addressing physical coordination. As AI agents embed themselves across platforms, enterprise architects face a choice regarding orchestration. The market shows a strong aversion to vendor lock-in. 63 percent of manufacturers favour hybrid or multi-platform strategies over single-vendor solutions. Specifically, 33 percent plan to coordinate through multiple platform-native agents, while 30 percent prefer a hybrid model blending platform-native and custom orchestration. Only 13 percent are willing to anchor on a single foundational platform. Converting the manufacturing industry’s AI investment to profit To convert this massive capital outlay into actual profit, the C-suite needs to look past the hype. First, fix the data. With only 21 percent of firms fully ready, the immediate priority must be modernisation rather than algorithm development. Without clean, unified data, high-value use cases in sustainability and predictive maintenance will fail to scale. Second, leaders must bridge the AI trust gap. The reliance on safety stock indicates a lack of faith in digital signals. Staged autonomy is the answer—starting with administrative tasks like work orders, where 66 percent are already heading, before handing over complex supply chain decisions. Finally, avoid the monolithic trap. The data supports a multi-platform approach to maintain leverage and agility. Manufacturers are betting their future on AI, but realising those returns requires less focus on the “intelligence” of the models and more on the mundane work of cleaning data, integrating legacy equipment, and building workforce trust. See also: Frontier AI research lab tackles enterprise deployment challenges 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 part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI in manufacturing set to unleash new era of profit appeared first on AI News. View the full article

The cybersecurity training provider Hack The Box (HTB) has launched the HTB AI Range, designed to let organisations test autonomous AI security agents under realistic conditions, albeit with oversight from human cybersecurity professionals. Its goal is to help users assess how well AI, and mixed human–AI teams might defend infrastructure. Vulnerabilities in AI models add to those already present in traditional IT, so before agentic or AI-based cybersecurity tools can be deployed in anger, HTB is proposing a testing environment where AI agents and human defenders can work together under realistic pressure to measure their cybersecurity prowess. How HTB AI Range works HTB describes the AI Range as a simulation of enterprise complexity with thousands of offensive and defensive targets that are continuously updated. The platform supports mapping to established cyber frameworks, including MITRE ATT&CK, the NIST/NICE guidelines, and the Open Worldwide Application Security Project (OWASP) Top 10. HTB says in a recent AI vs. human capture the flag (CTF) exercise, autonomous AI agents solved 19 out of 20 basic challenges. But in multi-step challenges in more complex environments, human teams outperformed the AI agents. The company suggests AI struggles with complexity and multi-stage operations, and this points to the continuing value of human expertise, especially in high-stakes or complex work. Testing, and closing the skills gap Enterprises can use the AI Range to validate whether existing security measures work under AI-powered attacks, give their cybersecurity teams experience of AI-powered threats, and develop more resilient cybersecurity tools based on agentic AI. Such exercises could be used to justify cybersecurity investment to financial decision-makers, Hack The Box suggests. HTB’s AI Range can be used for continuous testing and validation of cybersecurity defences, which the company states is more effective in the long-term than static audits or pen-testing exercises, and thus is closer to a CTEM model (continuous threat exposure management). HTB is launching a AI Red Teamer Certification early next year in an attempt quantify the skills necessary to harden AI defences. At present it seems wise to regard AI cyber-ranges as part of a layered security and resilience offering. As AI matures and frameworks like MITRE ATLAS gain traction, tools like HTB’s AI Range may become standard components in enterprise security programmes. “Hack The Box is where AI agents and humans learn to operate under real pressure together,” said Gerasimos Marketos, chief product officer at Hack The Box. “We’re addressing the urgent need to continuously validate AI systems in realistic operational contexts where stakes are high and human oversight remains vital. HTB AI Range makes that possible.” Haris Pylarinos, CEO and founder of Hack The Box said, “For over two years, we’ve been advancing AI-driven learning paths, labs, and research where machines and humans compete, collaborate, and co-evolve. With HTB AI Range, we’re not reacting to AI’s rise in cyber; we’re defining how defence evolves alongside it. This is how cybersecurity advances: not through fear, but through mastery.” (Image source: “The main cast” by Tim Dorr is licensed under CC BY-SA 2.0.) See also: New Nvidia Blackwell chip for China may outpace H20 model 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 part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post HTB AI Range offers experiments in cyber-resilience training appeared first on AI News. View the full article

AI is moving deeper into the physical world, and EY is laying out a more structured way for companies to work with robots, drones, and other smart devices. The organisation is introducing a physical AI platform built with NVIDIA tools, opening a new EY.ai Lab in Georgia, and adding new leadership to guide its work in this field. The platform uses NVIDIA Omniverse libraries, NVIDIA Isaac, and NVIDIA AI Enterprise software. EY says the setup gives organisations a clearer way to plan, test, and manage AI systems that operate in real environments, from factory robots to drones and edge devices. Omniverse libraries support the creation of digital twins so firms can model and test systems before deployment. NVIDIA Isaac tools offer open models and simulation frameworks to design and validate AI-driven robots in detailed 3D settings. NVIDIA AI Enterprise provides the computing base needed to run heavier AI workloads. EY describes the platform as built around three main areas: AI-ready data: Synthetic data to mirror a wide range of physical scenarios. Digital twins and robotics training: Tools that connect digital and physical systems, monitor performance in real time, and support operational continuity. Responsible physical AI: Governance and controls that address safety, ethics, and compliance. The platform is meant to support everything from early planning to long-term maintenance in sectors like industrials, energy, consumer, and health. Raj Sharma, EY Global Managing Partner – Growth & Innovation, says physical AI is already “transforming how businesses in sectors operate and help create value,” saying that it brings more automation and can help lower operating costs. He says the combination of EY’s industry experience and NVIDIA’s infrastructure is expected to speed up how companies move “from experimentation to enterprise-scale deployment.” NVIDIA’s John Fanelli notes that more enterprises are bringing robots and automation into real settings to address workforce changes and improve safety. He says the EY.ai Lab, supported by NVIDIA AI infrastructure, helps organisations “simulate, optimise and safely deploy robotics applications at enterprise scale,” which he views as part of the next phase of industrial AI. New leadership and a dedicated physical AI lab EY has also appointed Dr. Youngjun Choi as its Global Physical AI Leader. He will oversee robotics and physical AI work and help shape EY’s role as an advisor in this area. Choi, who has nearly 20 years’ experience in robotics and AI, previously led the UPS Robotics AI Lab, where he worked on digital twins, robotics projects, and AI tools to modernise its network. Before that, he served as research faculty in Aerospace Engineering at the Georgia Institute of Technology, contributing to aerial robotics and autonomous systems. A key part of his role is directing the newly opened EY.ai Lab in Alpharetta, Georgia – the first EY site focused on physical AI. The Lab includes robotics systems, sensors, and simulation tools so organisations can test ideas and build prototypes before deploying them at scale. Joe Depa, EY Global Chief Innovation Officer, says his clients want better ways to use technology for decision-making and performance. He adds that physical AI requires strong data foundations and trust from the start. With Choi leading the Lab, Depa says EY teams are beginning to “get beyond the surface of what is possible” and set up the base for scalable operations. At the Lab, organisations can: Design and test physical AI systems in a virtual testbed, Build solutions for humanoids, quadrupeds, and other next-generation robots, Improve logistics, manufacturing, and maintenance with digital twins. The new platform and Lab build on earlier collaboration between EY and NVIDIA, including an AI agent platform launched earlier this year. Both organisations plan to expand their physical AI work to areas like energy, health, and smart cities. They also aim to support automation projects that cut waste and help reduce environmental impact. See also: Microsoft, NVIDIA, and Anthropic forge AI compute alliance 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 part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post EY and NVIDIA to help companies test and deploy physical AI appeared first on AI News. View the full article

For years, cybersecurity experts debated when—not if—artificial intelligence would cross the threshold from advisor to autonomous attacker. That theoretical milestone has arrived. Anthropic’s recent investigation into a ******** state-sponsored operation has documented the first case of AI-orchestrated cyberattacks executing at scale with minimal human oversight, fundamentally altering what enterprises must prepare for in the threat landscape ahead. The campaign, attributed to a group Anthropic designates as GTG-1002, represents what security researchers have long warned about but never actually witnessed in the wild: an AI system autonomously conducting nearly every phase of cyber intrusion—from initial reconnaissance to data exfiltration—while human operators merely supervised strategic checkpoints. This isn’t incremental evolution. It’s a categorical shift in offensive capabilities that compresses what would take skilled hacking teams weeks into operations measured in hours, executed at machine speed across dozens of targets simultaneously. The numbers tell the story. Anthropic’s forensic analysis revealed that 80 to 90% of GTG-1002’s tactical operations ran autonomously, with humans intervening at just four to six critical decision points per campaign. The operation targeted approximately 30 entities—major technology corporations, financial institutions, chemical manufacturers, and government agencies—achieving confirmed breaches of several high-value targets. At peak activity, the AI system generated thousands of requests at rates of multiple operations per second, a tempo physically impossible for human teams to sustain. Anatomy of an autonomous breach The technical architecture behind these AI-orchestrated cyberattacks reveals a sophisticated understanding of both AI capabilities and safety bypass techniques. GTG-1002 built an autonomous attack framework around Claude Code, Anthropic’s coding assistance tool, integrated with Model Context Protocol (MCP) servers that provided interfaces to standard penetration testing utilities—network scanners, database exploitation frameworks, password crackers, and binary analysis suites. The breakthrough wasn’t in novel malware development but in orchestration. The attackers manipulated Claude through carefully constructed social engineering, convincing the AI it was conducting legitimate defensive security testing for a cybersecurity firm. They decomposed complex multi-stage attacks into discrete, seemingly innocuous tasks—vulnerability scanning, credential validation, data extraction—each appearing legitimate when evaluated in isolation, preventing Claude from recognising the broader malicious context. Once operational, the framework demonstrated remarkable autonomy. In one documented compromise, Claude independently discovered internal services within a target network, mapped complete network topology across multiple IP ranges, identified high-value systems including databases and workflow orchestration platforms, researched and wrote custom exploit code, validated vulnerabilities through callback communication systems, harvested credentials, tested them systematically across discovered infrastructure, and analyzedstolen data to categorize findings by intelligence value—all without step-by-step human direction. The AI maintained a persistent operational context across sessions spanning days, enabling campaigns to resume seamlessly after interruptions. It made autonomous targeting decisions based on discovered infrastructure, adapted exploitation techniques when initial approaches failed, and generated comprehensive documentation throughout all phases—structured markdown files tracking discovered services, harvested credentials, extracted data, and complete attack progression. What this means for enterprise security The GTG-1002 campaign dismantles several foundational assumptions that have shaped enterprise security strategies. Traditional defences calibrated around human attacker limitations—rate limiting, behavioural anomaly detection, operational tempo baselines—face an adversary operating at machine speed with machine endurance. The economics of cyberattacks have shifted dramatically, as 80-90% of tactical work can be automated, potentially bringing nation-state-level capabilities within reach of less sophisticated threat actors. Yet AI-orchestrated cyberattacks face inherent limitations that enterprise defenders should understand. Anthropic’s investigation documented frequent AI hallucinations during operations—Claude claiming to have obtained credentials that didn’t function, identifying “critical discoveries” that proved to be publicly available information, and overstating findings that required human validation. These reliability issues remain a significant friction point for fully autonomous operations, though assuming they’ll persist indefinitely would be dangerously naive as AI capabilities continue advancing. The defensive imperative The dual-use reality of advanced AI presents both challenge and opportunity. The same capabilities enabling GTG-1002’s operation proved essential for defence—Anthropic’s Threat Intelligence team relied heavily on Claude to analyse the massive data volumes generated during their investigation, demonstrating how AI augments human analysts in detecting and responding to sophisticated threats. For enterprise security leaders, the strategic priority is clear: active experimentation with AI-powered defence tools across SOC automation, threat detection, vulnerability assessment, and incident response. Building organisational experience with what works in specific environments—understanding AI’s strengths and limitations in defensive contexts—becomes critical before the next wave of more sophisticated autonomous attacks arrives. Anthropic’s disclosure signals an inflexion point. As AI models advance and threat actors refine autonomous attack frameworks, the question isn’t whether AI-orchestrated cyberattacks will proliferate across the threat landscape—it’s whether enterprise defences can evolve rapidly enough to counter them. The window for preparation, while still open, is narrowing faster than many security leaders may realise. The post Anthropic just revealed how AI-orchestrated cyberattacks actually work—Here’s what enterprises need to know appeared first on AI News. View the full article

As AI adoption has continued to surge over the last couple of months, one thing has become abundantly clear, i.e. there isn’t enough computational horsepower to go around (something that has become painfully obvious as cloud providers have accrued months-long waitlists for high-end GPU instances). And, unlike the brief crypto-mining GPU craze from just a few years ago, today’s crunch is being driven by real demand from AI research and deployments. For perspective sake, Amazon Web Services has been charging about $98 per hour for an 8-GPU server loaded with Nvidia’s top-tier H100 chips, while some decentralized GPU platforms offer comparable hardware for as little as $3 an hour. Amidst this stark 30× price gap, Singularity Compute, the infrastructure arm of decentralized AI pioneer SingularityNET, has announced the phase I deployment of its first enterprise-grade NVIDIA GPU cluster at a state-of-the-art data center in Sweden. Under a partnership with Swedish operator Conapto, Singularity’s cluster is using cutting-edge NVIDIA hardware (including the next-generation H200 and L40S GPUs) in a Stockholm facility powered entirely by renewable energy. What’s on offer exactly? The cluster, which has been made to be high density by design, serves as the foundation for both traditional enterprise workloads and the projects of the Artificial Superintelligence (ASI) Alliance, a decentralized AI ecosystem spearheaded by SingularityNET. It offers flexible access modes that mirror the needs of modern AI developers wherein companies can rent whole machines on bare metal, spin up GPU-powered virtual machines, or even tap into dedicated API endpoints for AI inference. In real world terms what this means is that an organization can potentially train entire large machine learning models from scratch, fine-tune existing models on custom datasets, or run heavy-duty inference for applications like generative AI, all using Singularity’s infrastructure. On the operational front, it bears mentioning that the partnership is set to be managed by popular cloud provider and NVIDIA partner Cudo Compute, with the latter ensuring the cluster’s timely delivery of enterprise-grade reliability and support that mission-critical AI projects demand. On the entire development, Dr. Ben Goertzel, founder of SingularityNET and co-chair of the ASI Alliance, opined: “As AI accelerates toward AGI and beyond, access to high-performance, ethically aligned compute is becoming a defining factor in who shapes the future. We need powerful compute that is configured for interoperation with decentralized networks running a rich variety of AI algorithms carrying out tasks for diverse populations. The new GPU deployment in Sweden is a meaningful milestone on the road to a truly open, global Artificial Superintelligence.” A similar sentiment was echoed by Singularity Compute CEO Joe Honan who believes the launch is about more than just extra compute capacity but rather a step toward a new paradigm in AI infrastructure, emphasizing that the cluster’s NVIDIA GPUs will deliver the performance and reliability modern AI demands, while upholding principles of openness, security, and sovereignty in how the compute is provisioned. In this broader context, it also bears mentioning that the Swedish cluster is set to serve as the backbone for ASI:Cloud, Singularity’s new AI model inference service developed in collaboration with Cudo. To elaborate, ASI:Cloud provides developers with wallet-based access to an OpenAI-compatible API for model inference, offering a smooth path to scale from serverless functions up to dedicated GPU servers. Early customers are already being onboarded to the Swedish cluster, with the team hinting that this is only the beginning of additional hardware and new geographic locations entering the fray. Thus, for a community that has often been at the bleeding edge of the ongoing AI and blockchain revolution, this deployment seems to be a tangible step toward the long-held goal of a decentralized, globally distributed AI infrastructure. The race for AI compute is underway and heating up fast Since the turn of the decade, the tech sector has poured major investments into AI infrastructure, with 2025 alone having witnessed over $1 trillion in new AI-focused data center projects. Even nation-states seem to be wading in with France, for example, having unveiled a surprise €100+ billion plan to boost AI infrastructure. Yet not everyone can spend billions to solve the current compute shortage, resulting in emergence of alternate approaches like decentralized or distributed GPU networks (that can tap into hardware spread across many locations and operators). In other words, if the 2010s rewarded those who accumulated data, the 2020s will seemingly reward those who control compute power. Within that future, efforts like Singularity Compute’s new GPU cluster embody a growing determination to democratize who gets to shape AI’s next chapter (primarily by broadening where the compute behind it is coming from). Interesting times ahead. The post Amidst the Ongoing AI Infrastructure Crunch, Singularity Compute Launches Swedish GPU Cluster appeared first on AI News. View the full article

Chip stacking strategy is emerging as China’s innovative response to US semiconductor restrictions, but can this approach truly close the performance gap with Nvidia’s advanced GPUs? As Washington tightens export controls on cutting-edge chipmaking technology, ******** researchers are proposing a bold workaround: stack older, domestically-producible chips together to match the performance of chips they can no longer access. The Core Concept: Building upward instead of forward The chip stacking strategy centres on a deceptively simple premise—if you can’t make more advanced chips, make smarter systems with the chips you can produce. Wei Shaojun, vice-president of the China Semiconductor Industry Association and a professor at Tsinghua University, recently outlined to the South China Morning Post an architecture that combines 14-nanometer logic chips with 18-nanometer DRAM using three-dimensional hybrid bonding. This matters because US export controls specifically target the production of logic chips at 14nm and below, and DRAM at 18nm and below. Wei’s proposal works precisely at these technological boundaries, using processes that remain accessible to ******** manufacturers. The technical approach involves what’s called “software-defined near-memory computing.” Instead of shuffling data back and forth between processors and memory—a major bottleneck in AI workloads—this chip stacking strategy places them in intimate proximity through vertical stacking. The 3D hybrid bonding technique creates direct copper-to-copper connections at sub-10 micrometre pitches, essentially eliminating the physical distance that slows down conventional chip architectures. The performance claims and reality check Wei claims this configuration could rival Nvidia’s 4nm GPUs while significantly reducing costs and power consumption. He’s cited performance figures of 2 TFLOPS per watt and a total of 120 TFLOPS. There’s just one problem: Nvidia’s A100 GPU, which Wei positions as the comparison point, actually delivers up to 312 TFLOPS—more than 2.5 times the claimed performance. This discrepancy highlights a critical question about the chip stacking strategy’s feasibility. While the architectural innovation is real, the performance gaps remain substantial. Stacking older chips doesn’t magically erase the advantages of advanced process nodes, which deliver superior power efficiency, higher transistor density, and better thermal characteristics. Why China is betting on this approach The strategic logic behind the chip stacking strategy extends beyond pure performance metrics. Huawei founder Ren Zhengfei has articulated a philosophy of achieving “state-of-the-art performance by stacking and clustering chips rather than competing node for node.” This represents a fundamental shift in how China approaches the semiconductor challenge. Consider the alternatives. TSMC and Samsung are pushing toward 3nm and 2nm processes that remain completely out of reach for ******** manufacturers. Rather than fighting an unwinnable battle for process node leadership, the chip stacking strategy proposes competing on system architecture and software optimisation instead. There’s also the CUDA problem. Nvidia’s dominance in AI computing rests not just on hardware but on its CUDA software ecosystem. Wei describes this as a “triple dependence” spanning models, architectures, and ecosystems. ******** chip designers pursuing traditional GPU architectures would need to either replicate CUDA’s functionality or convince developers to abandon a mature, widely adopted platform. The chip stacking strategy, by proposing an entirely different computing paradigm, offers a path to sidestep this dependency. The feasibility question Can the chip stacking strategy actually work? The technical foundations are sound—3D chip stacking is already used in high-bandwidth memory and advanced packaging solutions worldwide. The innovation lies in applying these techniques to create entirely new computing architectures rather than simply improving existing designs. However, several challenges loom large. First, thermal management becomes exponentially more difficult when stacking multiple active processing dies. The heat generated by 14nm chips is considerably higher than modern 4nm or 5nm processes, and stacking intensifies this problem. Second, yield rates in 3D stacking are notoriously difficult to optimise—a defect in any layer can compromise the entire stack. Third, the software ecosystem required to efficiently utilise such architectures doesn’t exist yet and would take years to mature. The most realistic assessment is that the chip stacking strategy represents a valid approach for specific workloads where memory bandwidth matters more than raw computational speed. AI inference tasks, certain data analytics operations, and specialised applications could potentially benefit. But matching Nvidia’s performance across the full spectrum of AI training and inference tasks remains a distant goal. What this means for the AI chip wars The emergence of the chip stacking strategy as a focal point for ******** semiconductor development signals a strategic pivot. Rather than attempting to replicate Western chip designs with inferior process nodes, China is exploring architectural alternatives that play to available manufacturing strengths. Whether this chip stacking strategy succeeds in closing the performance gap with Nvidia remains uncertain. What’s clear is that China’s semiconductor industry is adapting to restrictions by pursuing innovation in areas where export controls have less impact—system design, packaging technology, and software-hardware co-optimisation. For the global AI industry, this means the competitive landscape is becoming more complex. Nvidia’s current dominance faces challenges not just from traditional competitors like AMD and Intel, but from entirely new architectural approaches that may redefine what an “AI chip” looks like. The chip stacking strategy, whatever its current limitations, represents exactly this kind of architectural disruption—and that makes it worth watching closely. See also: New Nvidia Blackwell chip for China may outpace H20 model 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 part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Can China’s chip stacking strategy really challenge Nvidia’s AI dominance? appeared first on AI News. View the full article

Thomson Reuters and Imperial College London have established a frontier AI research lab to overcome historic deployment challenges. Speed and scale have defined the current AI *****. But for enterprises, the primary obstacles to deployment are different: trust, accuracy, and lineage. Addressing these barriers, Thomson Reuters and Imperial College London have announced a five-year partnership to establish a joint ‘Frontier AI Research Lab’. With the involvement of both a corporate and academic leader, the initiative appears built to target the disconnect between high-level computer science and the pragmatic requirements of professional services. The lab will pursue academic research in AI, focusing on safety, reliability, and the development of frontier capabilities. It offers enterprise leaders a preview of how future systems might advance beyond generative text to perform reliable work in high-stakes environments. Improving reliability with practical frontier AI research Current Large Language Models (LLMs) often struggle with the precision required in sectors such as law, tax, and compliance. To counter this, the lab plans to train large-scale foundation models jointly. This is an opportunity typically restricted to a handful of industrial technology giants. Researchers will experiment with data-centric machine learning and retrieval-augmented generation using Thomson Reuters’ substantial repository of content. By grounding AI models in verified and domain-specific data, the initiative aims to greatly improve the algorithms used to drive positive impact in the wider world and address challenges prior to real-world deployment. Dr Jonathan Richard Schwarz, Head of AI Research at Thomson Reuters, said: “We are only beginning to understand the transformative impact this technology will have on all aspects of society. “Our vision is a unique research space where foundational algorithms are developed and made available to world experts, advancing the transparency, verifiability, and trustworthiness in which these changes are driving impact in the world.” Data provenance is the central theme here. As Dr Schwarz suggests, the value lies not merely in the model architecture but in the quality of the information it processes. The partnership creates an avenue for researchers to access high-quality data spanning complex and knowledge-intensive domains. Making enterprise AI deployment challenges history The lab’s frontier AI research agenda indicates where enterprise technology is heading. Beyond simple content generation, the facility will investigate agentic AI systems, reasoning, planning, and human-in-the-loop workflows. These areas are essential for organisations looking to automate multi-step processes rather than just discrete tasks. Professor Alessandra Russo, who will co-lead the lab alongside Dr Schwarz and Cambridge’s Professor Felix Steffek, believes the dedicated infrastructure will empower researchers to deliver scientific advances that have practical relevance. “With dedicated space, a focused PhD cohort, and high-quality computing infrastructure and support, our researchers will be empowered to push the boundaries of AI and deliver scientific advances that truly matter,” Professor Russo stated. “Our collaboration with Thomson Reuters anchors that work in real-world use cases, ensuring that breakthroughs translate into meaningful societal benefit. There is huge potential to unlock creative approaches to a wide range of roles and sectors, enabling AI to strengthen society, energise traditional industries, and create new roles and opportunities across the economy.” Operations leaders should note that future AI implementations will likely require robust “reasoning” capabilities (i.e. the ability for a system to plan a series of actions and verify its own outputs) before they can be trusted with autonomous decision-making in regulated industries. Boosting infrastructure and talent pipelines to advance frontier AI research Running these experiments requires substantial compute power, a resource often lacking in purely academic settings. The partnership addresses this by providing researchers access to Imperial’s high-performance computing cluster. This enables AI experiments at a meaningful scale to uncover any challenges that need to be overcome prior to real-world deployment. The setup creates a feedback loop between research and practice. The lab is planned to host over a dozen PhD students who will work alongside Thomson Reuters foundational research scientists. This structure accelerates the translation of research into practice and establishes a direct pipeline for talent development and real-world validation. Professor Mary Ryan, Vice Provost for Research and Enterprise at Imperial, commented: “This collaboration gives our researchers the space and support to explore fundamental questions about how AI can and should work for society. “Progress in this area depends on rigorous science, open inquiry, and strong partnerships—ideals exemplified by the approach this lab will take.” Overcoming legal and economic challenges for successful enterprise AI deployments The risks associated with AI are as much legal and economic as they are technical. Recognising this, the lab’s steering committee includes Professor Felix Steffek, a Professor of Law at the University of Cambridge. “AI has great potential to improve access to justice,” said Professor Steffek. “However, there are significant challenges that foundational research needs to address in order to make legal AI applications safe and ethically responsible. “The lab will bring together bright minds from multiple disciplines – including law, ethics, and AI – to advance the potential and address the risks of legal AI.” The scope of research extends to the technology’s broader economic impact and the future of work. The lab aims to produce insights on how AI can energise traditional industries and create new roles across the economy. Overall, the Frontier AI Research Lab represents a model for de-risking enterprise AI strategies and overcoming challenges that have historically held back deployments. Coupling industrial data and compute resources with academic rigour helps organisations understand the “****** box” nature of these systems and overcome the challenges to ensure the success of any deployment. Activities at the lab will commence upon formal launch, starting with the recruitment of the initial PhD cohort. Business leaders should track the joint publications coming out of this unit as these findings will likely serve as valuable benchmarks for evaluating the safety and efficacy of internal AI deployments. See also: Agentic AI autonomy grows in North American enterprises 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 part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Frontier AI research lab tackles enterprise deployment challenges appeared first on AI News. View the full article

Enterprise leaders are entering 2026 with an uncomfortable mix of volatility, optimism, and pressure to move faster on AI and quantum computing, according to a paper published by the IBM Institute for Business Value. Its findings are based on more than 1,000 C-suite executives and 8,500 employees and consumers. While only around a third of executives are optimistic about the global economy, more than four in five are confident about their own organisation’s performance in the year ahead. Executives expect to make faster decisions and are willing to redesign operating models, while employees are broadly positive about AI in their working lives. Customers, in turn, are ready to reward (or punish) brands based on how companies use their data. Trend 1: agentic AI a strategic asset Agentic AI is emerging as one of the main tools leaders expect to use in the coming year, and most execs say AI agents are already helping them. However, for agentic AI to succeed, the expressed opinions state: Data architecture needs to support near real-time insight, not periodic reporting. AI agents’ success will depend on access to core systems (ERP, CRM, supply chain platforms). Agentic AI shifts from experimental to operational. Leaders feel they must decide which decisions can be delegated to AI agents, which require human review, and should must remain human-led. Trend 2: employees will ask for more training and AI is okay Most employees say the pace of technology change in their roles is sustainable, and that they’re confident about keeping up with new tools. Twice as many employees say they would embrace, not resist, greater use of AI in the workplace, seeing the technology as a way to remove repetitive tasks and learn new skills. This aligns with findings in research by KPMG. Executives expect a significant re-skilling requirement from their employees, so leaders should anticipate that at least half their workforce will need some form of re-skilling by the end of 2026, thanks to AI automation. Other surveys concur with IBM, and state the skills needed most are problem-solving, creativity, and innovation. Employees say they are willing to change employers to access better training opportunities, meaning skills development now plays a direct role reducing employee churn. Trend 3: customers will hold data policies to account The executives surveyed agreed that consumer trust in a brand’s use of AI will define the success of new products and services. Consumers are willing to tolerate occasional errors, but not opacity. Customers want explanations of how their data is used, knowledge of when AI is involved in interactions with them, and simple ways to opt in or out. The studies by Deloitte and KPMG (see above) reinforce this picture. Implications for leaders include treating transparency as a product feature and selecting models that support explainability. Trend 4: AI and cloud will need local provision AI sovereignty—an organisation’s ability to control and govern its AI systems, data, and infrastructure—has moved to the centre of resilience planning. Almost all executives surveyed said they will factor AI sovereignty into their 2026 strategy. In the light of concerns about data residency and cloud jurisdiction, leaders are rethinking where models run and where data lives. Studies from *** and European IT leaders show rising concern about over-reliance on foreign (read, ‘US-based’ cloud services in the latter case). Advisory firm Accenture also urges leaders [PDF] to develop sovereign AI strategies that prioritise control, transparency, and choice. Key takeaways include the need for portable AI platforms, monitoring for data compliance, and a heavy emphasis on the physical location of data. AI resilience is ultimately about continuity and transparency. It requires ensuring the organisation can adapt and operate openly, even when the global technological and geopolitical landscapes shift. Trend 5: planning on quantum advantage The report’s findings say quantum is moving towards experimentation in the near term. IBM’s own research on quantum readiness (in line with its monetisation of quantum services) suggests that early quantum advantage is likely in targeted domains such as optimisation and materials science. The report urges the identification of small numbers of high-impact quantum uses in the enterprise, and the joining of ecosystems early. “Identify big bets to win with emerging technologies, including quantum, and partner on innovation to share costs,” the report states. (Image source: “California Perfect” by moonjazz is licensed under CC BY-SA 2.0.) See also: How the MCP spec update boosts security as infrastructure scales 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 part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post IBM cites agentic AI, data policies, and quantum as 2026 trends appeared first on AI News. View the full article

While tech giants pour billions into computational power to train frontier AI models, China’s DeepSeek has achieved comparable results by working smarter, not harder. The DeepSeek V3.2 AI model matches OpenAI’s GPT-5 in reasoning benchmarks despite using ‘fewer total training FLOPs’ – a breakthrough that could reshape how the industry thinks about building advanced artificial intelligence. For enterprises, the release demonstrates that frontier AI capabilities need not require frontier-scale computing budgets. The open-source availability of DeepSeek V3.2 lets organisations evaluate advanced reasoning and agentic capabilities while maintaining control over deployment architecture – a practical consideration as cost-efficiency becomes increasingly central to AI adoption strategies. The Hangzhou-based laboratory released two versions on Monday: the base DeepSeek V3.2 and DeepSeek-V3.2-Speciale, with the latter achieving gold-medal performance on the 2025 International Mathematical Olympiad and International Olympiad in Informatics – benchmarks previously reached only by unreleased internal models from leading US AI companies. The accomplishment is particularly significant given DeepSeek’s limited access to advanced semiconductor chips due to export restrictions. Resource efficiency as a competitive advantage DeepSeek’s achievement contradicts the prevailing industry assumption that frontier AI performance requires greatly scaling computational resources. The company attributes this efficiency to architectural innovations, particularly DeepSeek Sparse Attention (DSA), which substantially reduces computational complexity while preserving model performance. The base DeepSeek V3.2 AI model achieved 93.1% accuracy on AIME 2025 mathematics problems and a Codeforces rating of 2386, placing it alongside GPT-5 in reasoning benchmarks. The Speciale variant was even more successful, scoring 96.0% on the American Invitational Mathematics Examination (AIME) 2025, 99.2% on the Harvard-MIT Mathematics Tournament (HMMT) February 2025, and achieving gold-medal performance on both the 2025 International Mathematical Olympiad and International Olympiad in Informatics. The results are particularly significant given DeepSeek’s limited access to the raft of tariffs and export restrictions affecting China. The technical report reveals that the company allocated a post-training computational budget exceeding 10% of pre-training costs – a substantial investment that enabled advanced abilities through reinforcement learning optimisation rather than brute-force scaling. Technical innovation driving efficiency The DSA mechanism represents a departure from traditional attention architectures. Instead of processing all tokens with equal computational intensity, DSA employs a “lightning indexer” and a fine-grained token selection mechanism that identifies and processes only the most relevant information for each query. The approach reduces core attention complexity from O(L²) to O(Lk), where k represents the number of selected tokens – a fraction of the total sequence length L. During continued pre-training from the DeepSeek-V3.1-Terminus checkpoint, the company trained DSA in 943.7 billion tokens using 480 sequences of 128K tokens per training step. The architecture also introduces context management tailored for tool-calling scenarios. Unlike previous reasoning models that discarded thinking content after each user message, the DeepSeek V3.2 AI model retains reasoning traces when only tool-related messages are appended, improving token efficiency in multi-turn agent workflows by eliminating redundant re-reasoning. Enterprise applications and practical performance For organisations evaluating AI implementation, DeepSeek’s approach offers concrete advantages beyond benchmark scores. On Terminal Bench 2.0, which evaluates coding workflow capabilities, DeepSeek V3.2 achieved 46.4% accuracy. The model scored 73.1% on SWE-Verified, a software engineering problem-solving benchmark, and 70.2% on SWE Multilingual, demonstrating practical utility in development environments. In agentic tasks requiring autonomous tool use and multi-step reasoning, the model showed significant improvements over previous open-source systems. The company developed a large-scale agentic task synthesis pipeline that generated over 1,800 distinct environments and 85,000 complex prompts, enabling the model to generalise reasoning strategies to unfamiliar tool-use scenarios. DeepSeek has open-sourced the base V3.2 model on Hugging Face, letting enterprises implement and customise it without vendor dependencies. The Speciale variant remains accessible only through API due to higher token use requirements – a trade-off between maximum performance and deployment efficiency. Industry implications and acknowledgement The release has generated substantial discussion in the AI research community. Susan Zhang, principal research engineer at Google DeepMind, praised DeepSeek’s detailed technical documentation, specifically highlighting the company’s work stabilising models post-training and enhancing agentic capabilities. The timing ahead of the Conference on Neural Information Processing Systems has amplified attention. Florian Brand, an expert on China’s open-source AI ecosystem attending NeurIPS in San Diego, noted the immediate reaction: “All the group chats today were full after DeepSeek’s announcement.” Acknowledged limitations and development path DeepSeek’s technical report addresses current gaps compared to frontier models. Token efficiency remains challenging – the DeepSeek V3.2 AI model typically requires longer generation trajectories to match the output quality of systems like Gemini 3 Pro. The company also acknowledges that the breadth of world knowledge lags behind leading proprietary models due to lower total training compute. Future development priorities include scaling pre-training computational resources to expand world knowledge, optimising reasoning chain efficiency to improve token use, and refining the foundation architecture for complex problem-solving tasks. See also: AI business reality – what enterprise leaders need to know 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 part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post China’s DeepSeek V3.2 AI model achieves frontier performance on a fraction of the computing budget appeared first on AI News. View the full article

Thrive Holdings’ push to modernise accounting and IT services is entering a new stage, as OpenAI prepares to take an ownership stake in the company and place its own specialists inside Thrive’s businesses. In doing so, OpenAI is testing an AI-driven model that pairs capital, sector expertise, and embedded technical teams. Thrive started its holding company earlier this year to buy and manage firms in day-to-day service industries. Its aim has been to rebuild these companies with more efficient processes, new data practices, and practical uses of AI. OpenAI’s deeper involvement now turns that idea into a real-time experiment in how traditional providers can update their work without relying only on off-the-shelf tools. A test case for bringing AI into core operational work While most enterprise discussions about AI tend to revolve around pilots and proof-of-concepts, Thrive is taking a different approach: buying companies outright and redesigning how they run. Its two current businesses – Crete Professionals Alliance (accounting) and Shield Technology Partners (IT services) – employ more than 1,000 people. Thrive has committed $500 million to Crete and, together with ZBS Partners, more than $100 million to Shield. For companies watching from the outside, the appeal is clear. These industries carry heavy workloads, manual tasks, and tight margins. They also handle sensitive data and operate under strict deadlines. Any AI system introduced into that environment needs domain context, training, and adjustments that fit local processes – not generic automation. Crete has already begun using AI to cut down routine tasks like data entry and early-stage tax workflows. Shield is on track to complete 10 acquisitions by the end of the year, giving Thrive a base of IT operations which it intends to redesign with new tools and methods. What OpenAI gains OpenAI is under pressure to find real, enterprise-scale use cases for its models. Investors value the company at roughly $500 billion, and its long-term commitments include about $1.4 trillion in infrastructure spending through 2033. To justify those figures, it is betting that businesses will spend heavily on tools that help them work faster and handle complex tasks at volume. By taking a stake in Thrive Holdings, OpenAI gains something it cannot produce on its own: access to companies where it can experience models in day-to-day working, and training specialists on real operations. The more Thrive’s companies grow, the more OpenAI’s stake may expand, according to a person familiar with the deal. Joshua Kushner, founder of both Thrive Capital and Thrive Holdings, said, “We are excited to extend our partnership with OpenAI to embed their frontier models, products, and services into sectors we believe have tremendous potential to benefit from technological innovation and adoption.” The partnership also gives OpenAI a path to collect value from the engineering support it provides. Its team will develop custom models for Thrive’s companies and embed researchers and engineers on site, according to partner Anuj Mehndiratta, who oversees product and technology strategy at Thrive Holdings. What enterprises can learn from this approach For many companies, the hardest part of using AI is not the model but the redesign of existing work. Thrive’s strategy reflects a shift toward deeper integration, where AI teams sit inside the business units they support rather than acting as external advisers. The model lets companies: Build tools shaped around real workflows, not abstract use cases Train models on controlled, high-quality data Reduce the gap between engineering teams and front-line employees Test changes faster, with direct feedback from staff It also surfaces the real cost of AI adoption. Custom work requires engineering time, domain knowledge, and long-term alignment between owners and model developers. Thrive’s partnership with OpenAI formalises that alignment in a way that may become more common as enterprises look for results rather than demonstrations. Brad Lightcap, OpenAI’s COO, said, “The partnership with Thrive Holdings is about demonstrating what’s possible when frontier AI research and deployment are rapidly deployed in entire organisations to revolutionise how businesses work and engage with customers.” The wider competitive landscape The deal lands at a time when AI companies are trying to anchor themselves inside major enterprise accounts. Anthropic is reaching more businesses through Microsoft partnerships, and. Google is drawing interest with its latest model and has seen its market value rise as companies explore new AI options. OpenAI, meanwhile, has taken stakes in partners like AMD and CoreWeave to support its long-term infrastructure needs. OpenAI also expanded its reach on Monday this week, announcing a separate agreement with Accenture. Its ChatGPT Enterprise product will be rolled out to “tens of thousands” of Accenture employees, giving OpenAI another route into large-scale corporate use. A possible blueprint If Thrive’s companies show meaningful improvement in how they operate, the model could influence how other enterprises think about AI transformation. Rather than layering tools on top of old processes, some may move toward deeper restructuring, guided by technical teams that understand both the model and the business. For now, Thrive Holdings serves as a live case study of what that approach looks like when applied to industries that rarely make tech headlines but form the backbone of day-to-day business operations. See also: AI business reality – what enterprise leaders need to know 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 part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How OpenAI and Thrive are testing a new enterprise AI model appeared first on AI News. View the full article

North American enterprises are now actively deploying agentic AI systems intended to reason, adapt, and act with complete autonomy. Data from Digitate’s three-year global programme indicates that, while adoption is universal across the board, regional maturity paths are diverging. North American firms are scaling toward full autonomy, whereas their European counterparts are prioritising governance frameworks and data stewardship to build long-term resilience. From utility to profitability The story of enterprise automation has changed. In 2023, the primary objective for most IT leaders was cost reduction and the streamlining of routine tasks. By 2025, the focus has expanded. AI is no longer viewed solely as an operational utility but as a capability enabling profit. Data supports this change in perspective. The report indicates that North American organisations are seeing a median return on investment (ROI) of $175 million from their implementations. Interestingly, this financial validation is not unique to the fast-moving North American market. European enterprises, despite a more measured and governance-heavy approach, report a comparable median ROI of approximately $170 million. This consistency suggests that while deployment strategies differ, with Europe focusing on risk management and North America on speed, the financial outcomes are similar. Every organisation surveyed confirmed implementing AI within the last two years, utilising an average of five distinct tools. While generative AI remains the most widely deployed at 74 percent, there is a notable rise in “agentic” capabilities. Over 40 percent of enterprises have introduced agentic or agent-based AI, advancing beyond static automation toward systems that can manage goal-oriented workflows. IT operations autonomy becomes the proving ground for agentic AI While marketing and customer service often dominate public discourse regarding AI, the IT function itself has emerged as the primary laboratory for these deployments. IT environments are inherently data-rich and structured, creating ideal conditions for models to learn, yet they remain dynamic enough to require the adaptive reasoning that agentic AI systems promise. This explains why 78 percent of respondents have deployed AI within IT operations, the highest rate of any business function. Cloud visibility and cost optimisation lead the adoption curve at 52 percent, followed closely by event management at 48 percent. In these scenarios, the technology is not alerting humans to problems so much as actively interpreting telemetry data to provide a unified view of spending across hybrid environments. Teams leveraging these tools report improvements in decision accuracy (44%) and efficiency (43%), allowing them to handle higher workloads without a corresponding increase in escalations. The cost-human conundrum Despite the optimism surrounding ROI, the report highlights a “cost-human conundrum” that threatens to stall progress. The paradox is straightforward: enterprises deploy AI to reduce reliance on human labour and operational costs, yet those exact factors act as the primary inhibitors to growth. 47 percent of respondents cite the continued need for human intervention as a major drawback. Far from achieving the complete autonomy of “set and forget” solutions, these agentic AI systems require ongoing oversight, tuning, and exception management. Simultaneously, the cost of implementation ranks as the second-highest concern at 42 percent, driven by the expenses associated with model retraining, integration, and cloud infrastructure. The talent required to manage these costs is in short supply. A lack of technical skills remains the primary obstacle to further adoption for 33 percent of organisations. Demand for professionals capable of developing, monitoring, and governing these complex systems exceeds current supply, creating a self-reinforcing loop where investment increases operational capacity but simultaneously raises human and financial dependencies. Trust and perception gap A divergence in perspective exists between executive leadership and operational practitioners. While 94 percent of total respondents express trust in AI, this confidence is not distributed evenly. C-suite leaders are markedly more optimistic, with 61 percent classifying AI as “very trustworthy” and viewing it primarily as a financial lever. Only 46 percent of non-C-suite practitioners share this high level of trust. Those closer to the daily operation of these models are more acutely aware of reliability issues, transparency deficits, and the necessity for human oversight. This gap suggests that while leadership focuses on long-term overhaul and autonomy, teams on the ground are grappling with pragmatic delivery and governance challenges. There is also a mixed view on how these agents will function. 61 percent of IT leaders view agentic systems not as replacements, but as collaborators that augment human capability. However, the expectation of automation varies by industry. In retail and transport, 67 percent believe agentic AI will alter the essential tasks of their roles, while in manufacturing, the same percentage views these agents primarily as personal assistants. Complete agentic AI autonomy is rapidly approaching The industry anticipates a rapid progression toward reduced human involvement in routine processes. Currently, 45 percent of organisations operate as semi- to fully-autonomous enterprises. Projections indicate this figure will rise to 74 percent by 2030. This evolution implies a change in the role of IT. As capabilities mature, IT departments are expected to transition from being operational enablers to acting as orchestrators. In this model, the IT function manages the “system of systems,” ensuring that various intelligent agents interact correctly while humans focus on creativity, interpretation, and governance rather than execution. “Agentic AI is the bridge between human ingenuity and autonomous intelligence that marks the dawn of IT as a profit-driving, strategic capability,” notes Avi Bhagtani, CMO at Digitate. “Enterprises have moved from experimenting with automation to scaling AI for measurable impact.” The transition to agentic AI requires more than just software procurement; it demands an organisational philosophy that balances automation with human augmentation. Policies alone are insufficient; governance must be integrated directly into system design to ensure transparency and ethical oversight in every decision loop. European organisations are currently leading in this area, prioritising ethical deployment and strong oversight frameworks as a foundation for resilience. Furthermore, the shortage of technical talent cannot be solved by hiring alone. Organisations must invest in upskilling existing teams, combining operations expertise with data science and compliance literacy. Finally, reliable autonomy depends on high-quality data. Investments in data integration and observability platforms are necessary to provide agents with the context required to act independently. The era of experimental AI has passed. The current phase is defined by the pursuit of autonomy, where value is derived not from novelty, but from the ability to scale agentic AI sustainably across the enterprise. “As organisations balance autonomy with accountability, those that embed trust, transparency, and human engagement into their AI strategy will shape the future of digital business,” Bhagtani concludes. See also: How the MCP spec update boosts security as infrastructure scales 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 part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Agentic AI autonomy grows in North American enterprises appeared first on AI News. View the full article

When JPMorgan Asset Management reported that AI spending accounted for two-thirds of US GDP growth in the first half of 2025, it wasn’t just a statistic – it was a signal. Enterprise leaders are making trillion-dollar bets on AI transformation, even as market observers debate whether we might be witnessing bubble-era exuberance. The conversation reached a turning point recently when OpenAI CEO Sam Altman, Amazon’s Jeff Bezos, and Goldman Sachs CEO David Solomon each acknowledged market froth within days of each other. But here’s what matters for enterprise decision-makers: acknowledging overheated markets isn’t the same as dismissing AI’s enterprise value. Corporate AI investment reached US$252.3 billion in 2024, with private investment climbing 44.5%, according to Stanford University. The question isn’t whether to invest in AI – it’s how to invest strategically while others – specifically, an organisation’s competitors – overspend on infrastructure and solutions that may never deliver returns. What separates AI winners from the 95% who fail An MIT study found that 95% of businesses invested in AI have failed to make money off the technology, according to ABC News. But that statistic masks a more important truth: 5% succeed – and they’re doing things fundamentally differently. High-performing organisations are investing more in AI capabilities, with more than one-third committing over 20% of their digital budgets to AI technologies, a McKinsey report shows. But they’re not just spending more – they’re spending smarter. The McKinsey research reveals what separates winners from the pack. About three-quarters of high performers say their organisations are scaling or have scaled AI, compared with one-third of other organisations. The leaders share common characteristics: they push for transformative innovation rather than incremental improvements, redesign workflows around AI capabilities, and implement rigorous governance frameworks. The infrastructure investment dilemma Enterprise leaders face a genuine dilemma. Google’s Gemini Ultra cost US$191 million to train, while OpenAI’s GPT-4 required US$78 million in hardware costs alone. For most enterprises, building proprietary large language models isn’t viable – and that makes vendor selection and partnership strategy important. Despite surging demand, CoreWeave slashed its 2025 capital expenditure guidance by up to 40%, citing delayed power infrastructure delivery. Oracle is “still waving off customers” due to capacity shortages, CEO Safra Catz confirmed, as per a Euronews report. This creates risk and opportunity. Enterprises that diversify their AI infrastructure strategies – building relationships with multiple providers, validating alternative architectures, and stress-testing for supply constraints – position themselves better than those betting everything on a single hyperscaler. Strategic AI investment in a frothy market Goldman Sachs equity analyst Peter Oppenheimer points out that “unlike speculative companies of the early 2000s, today’s AI giants are delivering real profits. While AI stock prices have appreciated strongly, this has been matched by sustained earnings growth.” The enterprise takeaway isn’t to avoid AI investment – it’s to avoid the mistakes that plague the 95% who see no returns: Focus on specific use cases with measurable ROI: High performers are more than three times more likely than others to say their organisation intends to use AI to bring about transformative change to their businesses, data from McKinsey shows. They’re not deploying AI for AI’s sake – they’re targeting specific business problems where AI delivers quantifiable value. Invest in organisational readiness, not just technology: Having an agile product delivery organisation is strongly correlated with achieving value. Establishing robust talent strategies and implementing technology and data infrastructure show meaningful contributions to AI success. Build governance frameworks now: The share of respondents reporting mitigation efforts for risks like personal and individual privacy, explainability, organisational reputation, and regulatory compliance has grown since 2022. As regulations tighten globally, early governance investment becomes a competitive advantage. Learning from market concentration In late 2025, 30% of the US S&P 500 was held up by just five companies – the greatest concentration in half a century. For enterprises, this concentration creates dependencies worth managing. The successful five percent diversify their AI vendors and their strategic approaches. They’re combining cloud-based AI services with edge computing, partnering with multiple model providers, and building internal capabilities for the workflows most important to competitive advantage. The real AI investment strategy Google’s Sundar Pichai captured the nuance enterprises must navigate: “We can look back at the internet right now. There was clearly a lot of excess investment, but none of us would question whether the internet was profound. I expect AI to be the same.” OpenAI’s ChatGPT has about 700 million weekly users, making it one of the fastest-growing consumer products in history. The enterprise challenge is deploying it effectively, leaving others waste billions on vanity projects. The enterprises winning at AI share a common approach: they treat AI as a business transformation initiative, not a technology project. They establish clear success metrics before deployment. They invest in change management as much as infrastructure. And they maintain healthy scepticism about vendor promises and remain committed to the technology’s potential. What this means for enterprise strategy Whether we’re in an AI bubble matters less to enterprise leaders than building sustainable AI capabilities. The market will correct itself – it always does. But businesses that develop genuine AI competencies during this investment surge will emerge stronger regardless of market dynamics. In 2024, the proportion of survey respondents reporting AI use by their organisations jumped to 78% from 55% in 2023, as per the Stanford data. AI adoption is accelerating, and enterprises that wait for perfect market conditions risk falling behind competitors building capabilities today. The strategic imperative isn’t to predict when the bubble bursts – it’s to ensure your AI investments deliver measurable business value regardless of market sentiment. Focus on practical deployments, measurable outcomes, and organisational readiness. Let others chase inflated valuations while you build sustainable competitive advantage. (Image source:Jasper Campbell) Want to experience the full spectrum of enterprise technology innovation? Join TechEx in Amsterdam, California, and London. Covering AI, Big Data, Cyber Security, IoT, Digital Transformation, Intelligent Automation, Edge Computing, and Data Centres, TechEx brings together global leaders to share real-world use cases and in-depth insights. Click here for more information. TechHQ is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI business reality – what enterprise leaders need to know appeared first on AI News. View the full article

If you asked most enterprise leaders which AI tools are delivering ROI, many would point to front-end chatbots or customer support automation. That’s the wrong door. The most value-generating AI systems today aren’t loud, customer-facing marvels. They’re tucked away in backend operations. They work silently, flagging irregularities in real-time, automating risk reviews, mapping data lineage, or helping compliance teams detect anomalies before regulators do. The tools don’t ask for credit, but are saving millions. Operational resilience no longer comes from having the loudest AI tool. It comes from having the smartest one, placed where it quietly does the work of five teams before lunch. The machines that spot what humans don’t Take the case of a global logistics company that integrated a background AI system for monitoring procurement contracts. The tool scanned thousands of PDFs, email chains, and invoice patterns per hour. No flashy dashboard. No alerts that interrupt workflow. Just continuous monitoring. In the first six months, it flagged multiple vendor inconsistencies that, if left unchecked, would have resulted in regulatory audits. The system didn’t just detect anomalies. It interpreted patterns. It noticed a vendor whose delivery timelines were always one day off compared to logged timestamps. Humans had seen those reports for months. But the AI noticed that the error always occurred near quarter-end. The conclusion? Inventory padding. That insight led to a contract renegotiation that saved millions. This isn’t hypothetical. One similar real-world use case reported a seven-figure operational loss prevented through a near-identical approach. That’s the kind of ROI that doesn’t need a flashy pitch deck. Why advanced education still matters in the age of AI It’s easy to fall into the trap of thinking AI tools are replacing human expertise. But smart organisations aren’t replacing but reinforcing. People with advanced academic backgrounds are helping enterprises integrate AI with strategic precision. Specifically, those with a doctorate of business administration in business intelligence bring an irreplaceable level of systems thinking and contextual insight. The professionals understand the complexity behind data ecosystems, from governance models to algorithmic biases, and can assess which tools serve long-term resilience versus short-term automation hype. When AI models are trained on historical data, it takes educated leadership to spot where historical bias may become a future liability. And when AI starts making high-stakes decisions, you need someone who can ask better questions about risk exposure, model explainability, and ethics in decision-making. This is where doctorates aren’t just nice to have – they’re essential. Invisible doesn’t mean simple Too often, companies install AI as if it were antivirus software. Set it, forget it, hope it works. That’s how you get ******-box risk. Invisible tools must still be transparent internally. It’s not enough to say, “AI flagged it.” The teams relying on these tools – risk officers, auditors, operations leads – must understand the decision-making logic or at least the signals that drive the alert. The requires not just technical documentation, but collaboration between engineers and business units. Enterprises that win with background AI systems build what could be called “decision-ready infrastructure.” The are workflows where data ingestion, validation, risk detection, and notification are all stitched together. Not in silos. Not in parallel systems. But in one loop that feeds actionable insight straight to the team responsible. That’s resilience. Where operational AI works best Here’s where invisible AI is already proving its worth in industries: Compliance Monitoring: Automatically detecting early signs of non-compliance in internal logs, transactional data, and communication channels without triggering false positives. Data Integrity: Identifying stale, duplicate, or inconsistent data in business units to prevent decision errors and reporting flaws. Fraud Detection: Recognising pattern shifts in transactions before losses occur. Not reactive alerts after the fact. Supply Chain Optimisation: Mapping supplier dependencies and predicting bottlenecks based on third-party risk signals or external disruptions. In all these cases, the key isn’t automation for automation’s sake. It’s precision. AI models that are well-calibrated, integrated with domain knowledge, and fine-tuned by experts – not simply deployed off the shelf. What makes the systems resilient? Operational resilience isn’t built in a sprint. It’s the result of smart layering. One layer catches data inconsistencies. Another tracks compliance drift. Another layer analyses behavioural signals in departments. And yet another feeds all of that into a risk model trained on historical issues. The resilience depends on: Human supervision with domain expertise, especially from those trained in business intelligence. Cross-functional transparency, so that audit, tech, and business teams are aligned. The ability to adapt models over time as the business evolves, not just retrain when performance dips. Systems that get this wrong often create alert fatigue or over-correct with rigid rule-based models. That’s not AI. That’s bureaucracy in disguise. Real ROI doesn’t scream Most ROI-focused teams chase visibility. Dashboards, reports, charts. But the most valuable AI tools don’t scream. They tap a shoulder. They point out a loose thread. They suggest a second look. That’s where the money is. Quiet detection. Small interventions. Avoided disasters. The companies that treat AI as a quiet partner – not a front-row magician – are already ahead. They’re using it to build internal resilience, not just customer-facing shine. They’re integrating it with human intelligence, not replacing it. And most of all, they’re measuring ROI not by how cool the tech looks, but by how quietly it works. That’s the future. Invisible AI agents and assistants. Visible outcomes. Real, measurable resilience. The post How background AI builds operational resilience & visible ROI appeared first on AI News. View the full article

SAP is moving its sovereignty plans forward with EU AI Cloud, a setup meant to bring its past efforts under one approach. The goal is simple: give organisations in Europe more choice and more control over how they run AI and cloud services. Some may prefer SAP’s own data centres, some may use trusted European providers, and others may want everything managed on-site. EU AI Cloud is built to support those different needs while keeping data inside the region and in line with EU rules. Strengthening AI sovereignty across Europe SAP is also working with Cohere to bring new agent-style and multimodal AI tools to customers through Cohere North. These models will be available through SAP Business Technology Platform (SAP BTP), giving industries with strict data residency needs a way to build production-ready AI into everyday operations. The two companies say the goal is to help enterprises find better insights, improve decision support, and automate complex tasks without giving up control over compliance or performance. As Cohere’s team put it, their work with SAP is meant to keep advanced AI accessible to organisations that cannot move data outside Europe. SAP is building EU AI Cloud with help from a range of European and global partners. Models and applications from Cohere, Mistral AI, OpenAI, and others are integrated directly into SAP BTP, giving customers a clearer path to build, deploy, and scale AI applications. Companies can access partner tools as SaaS, PaaS, or IaaS and choose where to run them: on SAP infrastructure or on approved European partners. The aim is to give enterprises and public sector groups access to modern AI tools while staying within European standards for security, data protection, and sovereignty. Deployment choices tied to different security needs EU AI Cloud works through SAP Sovereign Cloud, which lets customers pick the level of control they want across the stack—from infrastructure to applications. AI models run on SAP’s cloud infrastructure and SAP BTP in European data centres, which keeps operations separate from US hyperscalers. Here are the deployment options: SAP Sovereign Cloud on SAP Cloud Infrastructure (EU) SAP’s IaaS is based on open-source tools and runs inside SAP’s European data centre network. Data stays within the EU to support compliance with regional data protection rules. SAP Sovereign Cloud On-Site Infrastructure is managed by SAP but housed in a customer’s chosen data centre. This setup offers the highest level of control over data, operations, and legal requirements while keeping access to SAP’s cloud architecture. Selected hyperscalers by market Some customers may still run SAP commercial SaaS on global cloud providers. When they do, they can add sovereignty features based on regional needs. Delos Cloud A sovereign cloud service in Germany designed for the public sector. It supports local rules and is built to help government organisations modernise their digital systems. EU AI Cloud gives organisations in Europe more choice in how they run AI and cloud workloads while keeping control of their data. The mix of deployment options, partner models, and sovereign design aims to support companies that face strict rules around privacy, storage, and operational oversight. For enterprises and public bodies that need AI systems built around local requirements, SAP’s approach offers a way to use advanced tools without giving up the safeguards they rely on. (Photo by Antoine Schibler) See also: Adversarial learning breakthrough enables real-time AI security 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 part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post SAP outlines new approach to European AI and cloud sovereignty appeared first on AI News. View the full article

The latest MCP spec update fortifies enterprise infrastructure with tighter security, moving AI agents from pilot to production. Marking its first year, the Anthropic-created open-source project released a revised spec this week aimed at the operational headaches keeping generative AI agents stuck in pilot mode. Backed by Amazon Web Services (AWS), Microsoft, and Google Cloud, the update adds support for long-running workflows and tighter security controls. The market is drifting away from fragile, bespoke integrations. For enterprises, this is a chance to deploy agentic AI that can read and write to corporate data stores without incurring massive technical debt. MCP advances from ‘developer curiosity’ to practical infrastructure The narrative has shifted from experimental chatbots to structural integration. Since September, the registry has expanded by 407 percent, now housing nearly two thousand servers. “A year on from Anthropic’s launch of the Model Context Protocol, MCP has gone from a developer curiosity to a practical way to connect AI to the systems where work and data live,” says Satyajith Mundakkal, Global CTO at Hexaware, following this latest spec update. Microsoft has already “signaled the shift by adding native MCP support to Windows 11,” effectively moving the standard directly into the operating system layer. This software standardisation arrives alongside an aggressive hardware scale-up. Mundakkal highlights the “unprecedented infrastructure build-out,” citing OpenAI’s multi-gigawatt ‘Stargate’ programme. “These are clear signals that AI capabilities, and the data they depend on, are scaling fast,” he says. MCP is the plumbing feeding these massive compute resources. As Mundakkal puts it: “AI is only as good as the data it can reach safely.” Until now, hooking an LLM into a database was mostly synchronous. That works for a chatbot checking the weather, but it fails when migrating a codebase or analysing healthcare records. The new ‘Tasks’ feature changes this (SEP-1686). It gives servers a standard way to track work, allowing clients to poll for status or cancel jobs if things go sideways. Ops teams automating infrastructure migration need agents that can run for hours without timing out. Supporting states like working or input_required finally brings resilience to agentic workflows. MCP spec update improves security For CISOs especially, AI agents often look like a massive and uncontrolled attack surface. The risks are already visible; “security researchers even found approximately 1,800 MCP servers exposed on the public internet by mid-2025,” implying that private infrastructure adoption is significantly wider. “Done poorly,” Mundakkal warns, “[MCP] becomes integration sprawl and a ******* attack surface.” To address this, the maintainers tackled the friction of Dynamic Client Registration (DCR). The fix is URL-based client registration (SEP-991), where clients provide a unique ID pointing to a self-managed metadata document to cut the admin bottleneck. Then there’s ‘URL Mode Elicitation’ (SEP-1036). It allows a server – handling payments, for instance – to bounce a user to a secure browser window for credentials. The agent never sees the password; it just gets the token. It keeps the core credentials isolated, a non-negotiable for PCI compliance. Harish Peri, SVP at Okta, believes this brings the “necessary oversight and access control to build a secure and open AI ecosystem.” One feature as part of the spec update for MCP infrastructure has somewhat flown under the radar: ‘Sampling with Tools’ (SEP-1577). Servers used to be passive data fetchers; now they can run their own loops using the client’s tokens. Imagine a “research server” spawning sub-agents to scour documents and synthesise a report. No custom client code required—it simply moves the reasoning closer to the data. However, wiring these connections is only step one. Mayur Upadhyaya, CEO at APIContext, argues that “the first year of MCP adoption has shown that enterprise AI doesn’t begin with rewrites, it begins with exposure.” But visibility is the next hurdle. “The next wave will be about visibility: enterprises will need to monitor MCP uptime and validate authentication flows just as rigorously as they monitor APIs today,” Upadhyaya explains. MCP’s roadmap reflects this, with updates targeting better “reliability and observability” for debugging. If you treat MCP servers as “set and forget,” you’re asking for trouble. Mundakkal agrees, noting the lesson from year one is to “pair MCP with strong identity, RBAC, and observability from day one.” Star-studded industry line-up adopting MCP for infrastructure A protocol is only as good as who uses it. In a year since the original spec’s release, MCP hit nearly two thousand servers. Microsoft is using it to bridge GitHub, Azure, and M365. AWS is baking it into Bedrock. Google Cloud supports it across Gemini. This reduces vendor lock-in. A Postgres connector built for MCP should theoretically work across Gemini, ChatGPT, or an internal Anthropic agent without a rewrite. The “plumbing” phase of Generative AI is settling down, and open standards are winning the debate on connectivity. Technology leaders should look to audit internal APIs for MCP readiness – focusing on exposure rather than rewrites – and verify that the new URL-based registration fits current IAM frameworks. Monitoring protocols must also be established immediately. While the latest MCP spec update is backward compatible with existing infrastructure; the new features are the only way to bring agents into regulated, mission-relevant workflows and ensure security. See also: Adversarial learning breakthrough enables real-time AI security 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 part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How the MCP spec update boosts security as infrastructure scales appeared first on AI News. View the full article

The next frontier for edge AI medical devices isn’t wearables or bedside monitors—it’s inside the human body itself. Cochlear’s newly launched Nucleus Nexa System represents the first cochlear implant capable of running machine learning algorithms while managing extreme power constraints, storing personalised data on-device, and receiving over-the-air firmware updates to improve its AI models over time. For AI practitioners, the technical challenge is staggering: build a decision-tree model that classifies five distinct auditory environments in real time, optimise it to run on a device with a minimal power budget that must last decades, and do it all while directly interfacing with human neural tissue. Decision trees meet ultra-low power computing At the core of the system’s intelligence lies SCAN 2, an environmental classifier that analyses incoming audio and categorises it as Speech, Speech in Noise, Noise, Music, or Quiet. “These classifications are then input to a decision tree, which is a type of machine learning model,” explains Jan Janssen, Cochlear’s Global CTO, in an exclusive interview with AI News. “This decision is used to adjust sound processing settings for that situation, which adapts the electrical signals sent to the implant.” The model runs on the external sound processor, but here’s where it gets interesting: the implant itself participates in the intelligence through Dynamic Power Management. Data and power are interleaved between the processor and implant via an enhanced RF link, allowing the chipset to optimise power efficiency based on the ML model’s environmental classifications. This isn’t just smart power management—it’s edge AI medical devices solving one of the hardest problems in implantable computing: how do you keep a device operational for 40+ years when you can’t replace its battery? The spatial intelligence layer Beyond environmental classification, the system employs ForwardFocus, a spatial noise algorithm that uses inputs from two omnidirectional microphones to create target and noise spatial patterns. The algorithm assumes target signals originate from the front while noise comes from the sides or behind, then applies spatial filtering to attenuate background interference. What makes this noteworthy from an AI perspective is the automation layer. ForwardFocus can operate autonomously, removing cognitive load from users navigating complex auditory scenes. The decision to activate spatial filtering happens algorithmically based on environmental analysis—no user intervention required. Upgradeability: The medical device AI paradigm shift Here’s the breakthrough that separates this from previous-generation implants: upgradeable firmware in the implanted device itself. Historically, once a cochlear implant was surgically placed, its capabilities were frozen. New signal processing algorithms, improved ML models, better noise reduction—none of it could benefit existing patients. Jan Janssen, Chief Technology Officer, Cochlear Limited The Nucleus Nexa Implant changes that equation. Using Cochlear’s proprietary short-range RF link, audiologists can deliver firmware updates through the external processor to the implant. Security relies on physical constraints—the limited transmission range and low power output require proximity during updates—combined with protocol-level safeguards. “With the smart implants, we actually keep a copy [of the user’s personalised hearing map] on the implant,” Janssen explained. “So you lose this [external processor], we can send you a blank processor and put it on—it retrieves the map from the implant.” The implant stores up to four unique maps in its internal memory. From an AI deployment perspective, this solves a critical challenge: how do you maintain personalised model parameters when hardware components fail or get replaced? From decision trees to deep neural networks Cochlear’s current implementation uses decision tree models for environmental classification—a pragmatic choice given power constraints and interpretability requirements for medical devices. But Janssen outlined where the technology is headed: “Artificial intelligence through deep neural networks—a complex form of machine learning—in the future may provide further improvement in hearing in noisy situations.” The company is also exploring AI applications beyond signal processing. “Cochlear is investigating the use of artificial intelligence and connectivity to automate routine check-ups and reduce lifetime care costs,” Janssen noted. This points to a broader trajectory for edge AI medical devices: from reactive signal processing to predictive health monitoring, from manual clinical adjustments to autonomous optimisation. The Edge AI constraint problem What makes this deployment fascinating from an ML engineering standpoint is the constraint stack: Power: The device must run for decades on minimal energy, with battery life measured in full days despite continuous audio processing and wireless transmission. Latency: Audio processing happens in real-time with imperceptible delay—users can’t tolerate lag between speech and neural stimulation. Safety: This is a life-critical medical device directly stimulating neural tissue. Model failures aren’t just inconvenient—they impact quality of life. Upgradeability: The implant must support model improvements over 40+ years without hardware replacement. Privacy: Health data processing happens on-device, with Cochlear applying rigorous de-identification before any data enters their Real-World Evidence program for model training across their 500,000+ patient dataset. These constraints force architectural decisions you don’t face when deploying ML models in the cloud or even on smartphones. Every milliwatt matters. Every algorithm must be validated for medical safety. Every firmware update must be bulletproof. Beyond Bluetooth: The connected implant future Looking ahead, Cochlear is implementing Bluetooth LE Audio and Auracast broadcast audio capabilities—both requiring future firmware updates to the implant. These protocols offer better audio quality than traditional Bluetooth while reducing power consumption, but more importantly, they position the implant as a node in broader assistive listening networks. Auracast broadcast audio allows direct connection to audio streams in public venues, airports, and gyms—transforming the implant from an isolated medical device into a connected edge AI medical device participating in ambient computing environments. The longer-term vision includes totally implantable devices with integrated microphones and batteries, eliminating external components entirely. At that point, you’re talking about fully autonomous AI systems operating inside the human body—adjusting to environments, optimising power, streaming connectivity, all without user interaction. The medical device AI blueprint Cochlear’s deployment offers a blueprint for edge AI medical devices facing similar constraints: start with interpretable models like decision trees, optimise aggressively for power, build in upgradeability from day one, and architect for the 40-year horizon rather than the typical 2-3 year consumer device cycle. As Janssen noted, the smart implant launching today “is actually the first step to an even smarter implant.” For an industry built on rapid iteration and continuous deployment, adapting to decade-long product lifecycles while maintaining AI advancement represents a fascinating engineering challenge. The question isn’t whether AI will transform medical devices—Cochlear’s deployment proves it already has. The question is how quickly other manufacturers can solve the constraint problem and bring similarly intelligent systems to market. For 546 million people with hearing loss in the Western Pacific Region alone, the pace of that innovation will determine whether AI in medicine remains a prototype story or becomes standard of care. (Photo by Cochlear) See also: FDA AI deployment: Innovation vs oversight in drug regulation 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 part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Edge AI inside the human body: Cochlear’s machine learning implant breakthrough appeared first on AI News. View the full article