ChatGPT

Microsoft believes it has a fix for AI prompts being given, the response missing the mark, and the cycle repeating. This inefficiency is a drain on resources. The “trial-and-error loop can feel unpredictable and discouraging,” turning what should be a productivity booster into a time sink. Knowledge workers often spend more time managing the interaction itself than understanding the material they hoped to learn. Microsoft has released Promptions (prompt + options), a UI framework designed to address this friction by replacing vague natural language requests with precise, dynamic interface controls. The open-source tool offers a method to standardise how workforces interact with large language models (LLMs), moving away from unstructured chat toward guided and reliable workflows. The comprehension bottleneck Public attention often centres on AI producing text or images, but a massive component of enterprise usage involves understanding—asking AI to explain, clarify, or teach. This distinction is vital for internal tooling. Consider a spreadsheet formula: one user may want a simple syntax breakdown, another a debugging guide, and another an explanation suitable for teaching colleagues. The same formula can require entirely different explanations depending on the user’s role, expertise, and goals. Current chat interfaces rarely capture this intent effectively. Users often find that the way they phrase a question doesn’t match the level of detail the AI needs. “Clarifying what they really want can require long, carefully worded prompts that are tiring to produce,” Microsoft explains. Promptions operates as a middleware layer to fix this familiar issue with AI prompts. Instead of forcing users to type lengthy specifications, the system analyses the intent and conversation history to generate clickable options – such as explanation length, tone, or specific focus areas – in real-time. Efficiency vs complexity Microsoft researchers tested this approach by comparing static controls against the new dynamic system. The findings offer a realistic view of how such tools function in a live environment. Participants consistently reported that dynamic controls made it easier to express the specifics of their tasks without repeatedly rephrasing their prompts. This reduced the effort of prompt engineering and allowed users to focus more on understanding content than managing the mechanics of phrasing. By surfacing options like “Learning Objective” and “Response Format,” the system prompted participants to think more deliberately about their goals. Yet, adoption brings trade-offs. Participants valued adaptability but also found the system more difficult to interpret. Some struggled to anticipate how a selected option would influence the response, noting that the controls seemed opaque because the effect became evident only after the output appeared. This highlights a balance to strike. Dynamic interfaces can streamline complex tasks but may introduce a learning curve where the connection between a checkbox and the final output requires user adaptation. Promptions: The solution to fix AI prompts? Promptions is designed to be lightweight, functioning as a middleware layer sitting between the user and the underlying language model. The architecture consists of two primary components: Option Module: Reviews the user’s prompt and conversation history to generate relevant UI elements. Chat Module: Incorporates these selections to produce the AI’s response. Of particular note for security teams, “there’s no need to store data between sessions, which keeps implementation simple.” This stateless design mitigates data governance concerns typically associated with complex AI overlays. Moving from “prompt engineering” to “prompt selection” offers a pathway to more consistent AI outputs across an organisation. By implementing UI frameworks that guide user intent, technology leaders can reduce the variability of AI responses and improve workforce efficiency. Success depends on calibration. Usability challenges remain regarding how dynamic options affect AI output and managing the complexity of multiple controls. Leaders should view this not as a complete solution to fix the results of AI prompts, but as a design pattern to test within their internal developer platforms and support tools. See also: Perplexity: AI agents are taking over complex enterprise tasks 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 Microsoft ‘Promptions’ fix AI prompts failing to deliver appeared first on AI News. View the full article

New adoption data from Perplexity reveals how AI agents are driving workflow efficiency gains by taking over complex enterprise tasks. For the past year, the technology sector has operated under the assumption that the next evolution of generative AI would advance beyond conversation into action. While Large Language Models (LLMs) serve as a reasoning engine, “agents” act as the hands, capable of executing complex, multi-step workflows with minimal supervision. Until now, however, visibility into how these tools are actually being utilised in the wild has been opaque, relying largely on speculative frameworks or limited surveys. New data released by Perplexity, analysing hundreds of millions of interactions with its Comet browser and assistant, provides a first large-scale field study of general-purpose AI agents. The data indicates that agentic AI is already being deployed by high-value knowledge workers to streamline productivity and research tasks. Understanding who is using these tools is essential for forecasting internal demand and identifying potential shadow IT vectors. The study reveals marked heterogeneity in adoption. Users in nations with higher GDP per capita and educational attainment are far more likely to engage with agentic tools. More telling for corporate planning is the occupational breakdown. Adoption is heavily concentrated in digital and knowledge-intensive sectors. The ‘Digital Technology’ cluster represents the largest share, accounting for 28 percent of adopters and 30 percent of queries. This is followed closely by academia, finance, marketing, and entrepreneurship. Collectively, these clusters account for over 70 percent of total adopters. This suggests that the individuals most likely to leverage agentic workflows are the most expensive assets within an organisation: software engineers, financial analysts, and market strategists. These early adopters are not dabbling; the data shows that “power users” (those with earlier access) make nine times as many agentic queries as average users, indicating that once integrated into a workflow, the technology becomes indispensable. AI agents: Partners for enterprise tasks, not butlers To advance beyond marketing narratives, enterprises must understand the utility these agents provide. A common view suggests agents will primarily function as “digital concierges” for rote administrative chores. However, the data challenges this view: 57 percent of all agent activity focuses on cognitive work. Perplexity’s researchers developed a “hierarchical agentic taxonomy” to classify user intent, revealing the usage of AI agents is practical rather than experimental. The dominant use case is ‘Productivity & Workflow,’ which accounts for 36 percent of all agentic queries. This is followed by ‘Learning & Research’ at 21 percent. Specific anecdotes from the study illustrate how this translates to enterprise value. A procurement professional, for instance, used the assistant to scan customer case studies and identify relevant use cases before engaging with a vendor. Similarly, a finance worker delegated the tasks of filtering stock options and analysing investment information. In these scenarios, the agent handles the information gathering and initial synthesis autonomously to allow the human to focus on final judgment. This distribution provides a definite indication to operational leaders: the immediate ROI for agentic AI lies in scaling human capability rather than simply automating low-level friction. The study defines these agents as systems that “cycle automatically between three iterative phases to achieve the end goal: thinking, acting, and observing.” This capability allows them to support “deep cognitive work,” acting as a thinking partner rather than a simple butler. Stickiness and the cognitive migration A key insight for IT leaders is the “stickiness” of AI agents for enterprise workflows. The data shows that in the short term, users exhibit strong within-topic persistence. If a user engages an agent for a productivity task, their subsequent queries are highly likely to remain in that domain. However, the user journey often evolves. New users frequently “test the waters” with low-stakes queries, such as asking for movie recommendations or general trivia. Over time, a transition occurs. The study notes that while users may enter via various use cases, query shares tend to migrate toward cognitively oriented domains like productivity, learning, and career development. Once a user employs an agent to debug code or summarise a financial report, they rarely revert to lower-value tasks. The ‘Productivity’ and ‘Workflow’ categories demonstrate the highest retention rates. This behaviour implies that early pilot programmes should anticipate a learning curve where usage matures from simple information retrieval to complex task delegation. The “where” of agentic AI is just as important as the “what”. Perplexity’s study tracked the environments – specific websites and platforms – where these AI agents operate. The concentration of activity varies by task, but the top environments are staples of the modern enterprise stack. Google Docs is a primary environment for document and spreadsheet editing, while LinkedIn dominates professional networking tasks. For ‘Learning & Research,’ the activity is split between course platforms like Coursera and research repositories. For CISOs and compliance officers, this presents a new risk profile. AI agents are not just reading data; they are actively manipulating it within core enterprise applications. The study explicitly defines agentic queries as those involving “browser control” or actions on external applications via APIs. When an employee tasks an agent to “summarise these customer case studies,” the agent is interacting directly with proprietary data. The concentration of environments also highlights the potential for platform-specific optimisations. For instance, the top five environments account for 96 percent of queries in professional networking, primarily on LinkedIn. This high concentration suggests that businesses could see immediate efficiency gains by developing specific governance policies or API connectors for these high-traffic platforms. Business planning for agentic AI following Perplexity’s data The diffusion of capable AI agents invites new lines of inquiry for business planning. The data from Perplexity confirms that we have passed the speculative phase. Agents are currently being used to plan and execute multi-step actions, modifying their environments rather than just exchanging information. Operational leaders should consider three immediate actions: Audit the productivity and workflow friction points within high-value teams: The data shows this is where agents are naturally finding their foothold. If software engineers and financial analysts are already using these tools to edit documents or manage accounts, formalising these workflows could standardise efficiency gains. Prepare for the augmentation reality: The researchers note that while agents have autonomy, users often break tasks into smaller pieces, delegating only subtasks. This suggests that the immediate future of work is collaborative, requiring employees to be upskilled in how to effectively “manage” their AI counterparts. Address the infrastructure and security layer: With agents operating in “open-world web environments” and interacting with sites like GitHub and corporate email, the perimeter for data loss prevention expands. Policies must distinguish between a chatbot offering advice and an agent executing code or sending messages. As the market for agentic AI is projected to grow from $8 billion in 2025 to $199 billion by 2034, the early evidence from Perplexity serves as a bellwether. The transition to enterprise workflows led by AI agents is underway, driven by the most digitally capable segments of the workforce. The challenge for the enterprise is to harness this momentum without losing control of the governance required to scale it safely. See also: Accenture and Anthropic partner to boost enterprise AI integration 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 Perplexity: AI agents are taking over complex enterprise tasks appeared first on AI News. View the full article

Many companies are still working out how to use AI in a steady and practical way, but a small group is already pulling ahead. New research from NTT DATA outlines a playbook that shows how these “AI leaders” set themselves apart through strong plans, firm decisions, and a disciplined approach to building and using AI across their organisations. The findings come from a survey of 2,567 senior executives in 35 countries and 15 industries. Only 15% of the organisations met the bar to be considered AI leaders. These companies share a few traits: clear direction on where AI fits into their business, a solid operating model, and consistent follow-through. They also reported higher revenue growth and stronger profit margins than everyone else in the study. Yutaka Sasaki, President and CEO of NTT DATA Group, put it simply: “AI accountability now belongs in the boardroom and demands an enterprise-wide agenda. Our research shows that a small group of AI leaders already are using AI to differentiate, grow and reinvent how humans and machines create value together.” The playbook behind strong AI plans One of the clearest differences between leaders and the rest is how they approach strategy. For these companies, AI is not a side project or a tool bolted onto existing work. They treat it as a core driver of growth and adjust their plans to match that view. A major advantage for these leaders is how closely they connect AI with their business goals. This alignment helps them move faster and stay focused, which in turn delivers stronger financial outcomes. They also zero in on a few high-value areas of the business rather than spreading resources too thin. By redesigning entire workflows around AI, they unlock more value than if they had only made small improvements in scattered parts of the organisation. The report describes this as a kind of flywheel: early investments bring early wins, which then encourage more investment. Over time, this cycle becomes self-reinforcing. Leaders also rebuild important applications with AI embedded inside them, instead of adding basic AI features on top of old systems. This approach helps them see deeper impact and prepares the organisation for long-term gains. How leaders put their plans to work A good plan only works when backed by strong execution. AI leaders stand out through the foundations they build, the way they support their people, and how they drive adoption across the entire organisation. These companies invest in secure and scalable systems that can support large AI workloads. In some cases, they shift or localise their infrastructure to support private or sovereign AI needs. They also work to remove system bottlenecks so teams can move without roadblocks. Rather than using AI as a replacement for workers, leaders use it to help experienced employees do higher-value work. This “expert-first” approach allows teams to use their judgment while letting AI handle complex or time-consuming tasks. AI leaders also focus on adoption as a long-term change effort. They treat it as a company-wide shift, supported by clear communication and structured change management. This helps reduce pushback and encourages steady use of AI at all levels. Governance is another major difference. Leading organisations centralise their AI oversight, give clear responsibility to senior roles such as Chief AI Officers, and build processes that help balance innovation with risk. These systems allow them to scale AI more confidently. Partnerships also play a major role. Top companies often bring in outside experts and are open to arrangements that tie outcomes to shared success. This helps them move faster while keeping their goals in view. Abhijit Dubey, CEO and CAIO of NTT DATA, Inc., summarised the path forward: “Once AI and business strategies are aligned, the single most effective move is to pick one or two domains that deliver disproportionate value and redesign them end-to-end with AI. Supporting this focused, end-to-end approach with strong governance, modern infrastructure and trusted partners is how today’s AI leaders are turning pilots into profit and pulling ahead of the market.” (Photo by Igor Omilaev) See also: OpenAI: Enterprise users swap AI pilots for deep integrations 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 Inside the playbook of companies winning with AI appeared first on AI News. View the full article

Accenture and Anthropic are setting out to boost enterprise AI integration with a newly-expanded partnership. While 2024 was defined by corporate curiosity regarding Large Language Models (LLMs), the current mandate for business leaders is operationalising these tools to achieve a return on investment. The new Accenture Anthropic Business Group combines Anthropic’s model capabilities with Accenture’s implementation machinery to industrialise the deployment of generative AI across regulated sectors. Industrialising the developer workflow A primary component of this collaboration focuses on software engineering. Coding assistance is often seen as the path of least resistance for AI adoption, yet integrating these tools into existing CI/CD pipelines remains complex. Accenture is positioning itself as a primary partner for Claude Code, Anthropic’s coding tool, which the company claims now holds over half of the AI coding market. The consultancy plans to train approximately 30,000 of its own professionals on Claude, creating one of the largest global ecosystems of practitioners familiar with the tool. The promise of deeper enterprise integration of AI coding tools is a complete restructuring of the development hierarchy. The joint offering suggests that junior developers can utilise these tools to produce senior-level code and complete integration tasks more quickly to reduce onboarding times from months to weeks. Senior developers can then concentrate on high-value architecture, validation, and oversight. Dario Amodei, CEO and Co-Founder of Anthropic, said: “AI is changing how almost everyone works, and enterprises need both cutting-edge AI and trusted expertise to deploy it at scale. Accenture brings deep enterprise transformation experience, and Anthropic brings the most capable models. “Our new partnership means that tens of thousands of Accenture developers will be using Claude Code, making this our largest ever deployment—and the new Accenture Anthropic Business Group will help enterprise clients use our smartest AI models to make major productivity gains.” Justifying AI inference costs and removing deployment barriers A persistent friction point for enterprise leaders seeking deeper AI integration is justifying the ongoing cost of inference against actual business value. To counter this, the partnership is launching a specific product designed to help CIOs measure value and drive adoption across engineering organisations. This offering attempts to provide a structured path for software design and maintenance, moving beyond the ad-hoc usage of coding assistants. It combines Claude Code with a framework for quantifying productivity gains and workflow redesigns tailored for AI-first development teams. For the enterprise, the goal is to translate individual developer efficiency into broader company impact; such as shorter development cycles and faster time-to-market for new products. However, the most substantial barrier to AI adoption in the Global 2000 remains compliance. Sectors such as financial services, healthcare, and the public sector face strict governance requirements that often stall AI initiatives. Accenture and Anthropic are developing industry-specific enterprise AI solutions to address these deployment challenges. In financial services, for instance, the focus is on automating compliance workflows and processing complex documents with the precision required for high-stakes decisions. Health and life sciences firms face a parallel demand. Here, the partnership aims to leverage Claude’s analytical capabilities to query proprietary datasets and streamline clinical trial processing. For the public sector, the utility lies in AI agents that assist citizens in navigating government services while adhering to statutory data privacy requirements. Julie Sweet, Chair and CEO of Accenture, commented: “With the powerful combination of Anthropic’s Claude capabilities and Accenture’s AI expertise and industry and function domain knowledge, organisations can embed AI everywhere responsibly and at speed – from software development to customer experience – to drive innovation, unlock new sources of growth, and build their confidence to lead in the age of AI.” How Accenture and Anthropic are mitigating risks to support enterprise AI integration To mitigate the risks associated with deploying non-deterministic models, the partnership emphasises “responsible AI.” This involves combining Anthropic’s “constitutional AI” principles – which embed safety rules directly into the model – with Accenture’s governance expertise. Practical implementation will occur through Accenture’s network of Innovation Hubs, which will serve as controlled environments or “sandboxes”. These hubs allow clients to prototype and validate solutions without exposing production systems or sensitive data to risk. The companies also plan to co-invest in a ‘Claude Center of Excellence’ to design bespoke AI offerings tailored to specific industry needs. This expanded partnership with Accenture follows Anthropic reporting a growth in its enterprise AI market share from 24 percent to 40 percent. For Accenture, establishing a dedicated business group with specific go-to-market focus reflects a long-term commitment to the platform. The era of standalone AI pilots is fading. The next phase for enterprise AI integration demands tight coupling between model capabilities, workforce training, and rigorous value measurement. See also: OpenAI targets AI skills gap with new certification standards 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 Accenture and Anthropic partner to boost enterprise AI integration appeared first on AI News. View the full article

Adoption of generative AI has outpaced workforce capability, prompting OpenAI to target the skills gap with new certification standards. While it’s safe to say OpenAI’s tools have reached mass adoption, organisations struggle to convert this usage into reliable output. To address this, OpenAI has announced ‘AI Foundations,’ a structured initiative designed to standardise how employees learn and apply the technology. OpenAI’s initiative marks a necessary evolution in the vendor ecosystem; indicating a departure from the “move fast” phase of experimental deployment toward a focus on verifiable competence. OpenAI explicitly states its intention to certify 10 million Americans by 2030. Workers and employers have an incentive to close the AI skills gap The economic case for AI training and certification is rooted in wage and productivity data. Workers possessing AI skills earn approximately 50 percent more than those without them. However, CIOs often find that productivity gains on paper fail to materialise in practice. OpenAI notes that gains “only materialise when people have the skills to use the technology.” Without guidance, widespread access can create operational risk. OpenAI admits the technology is “disruptive, leaving many people unsure which skills matter most.” By defining a standard curriculum, OpenAI aims to help organisations capture the efficiency gains promised by their software investments. The delivery method for AI Foundations differs from traditional corporate LMS (Learning Management System) modules. The course sits directly inside ChatGPT, allowing the platform to act as “tutor, the practice space, and the feedback loop” simultaneously. This integration allows learners to execute real tasks and receive context-aware corrections to help close the AI skills gap, rather than just watching passive video content. Completing the programme yields a badge verifying “job-ready AI skills”. This credential serves as a stepping stone toward a full OpenAI Certification. To ensure these badges carry weight in the labour market, OpenAI has engaged Coursera, ETS, and Credly by Pearson to validate the psychometric rigour and design of the assessments. Operational pilots for the AI certification and improving the hiring pipeline A consortium of large-scale employers and public-sector bodies will test the curriculum before a wider rollout. Pilot partners include Walmart, John Deere, Lowe’s, Boston Consulting Group, Russell Reynolds Associates, Upwork, Elevance Health, and Accenture. The Office of the Governor of Delaware is also participating, which shows interest from state-level administration. These partners span industries with heavy operational footprints (including retail, agriculture, and healthcare) suggesting the training targets core business functions rather than just technical roles. OpenAI plans to use the next few months to refine the course based on data from these pilots to ensure that it can effectively close the AI skills gap. OpenAI’s initiative extends into recruitment. The company is developing an ‘OpenAI Jobs Platform’ to connect certified workers with employers. Partnerships with Indeed and Upwork support this mechanism, aiming to make it easier for businesses to identify candidates with verified technical expertise. For hiring managers, this offers a potential solution to the difficulty of vetting AI literacy. A standardised AI certification could reduce the reliance on self-reported skills, providing “portable evidence” of a candidate’s development. Academic alignment to seed future AI talent While the enterprise focus is immediate, OpenAI is also seeding the future talent pipeline. A ‘ChatGPT Foundations for Teachers’ course has launched on Coursera. With three in five teachers already using AI tools to save time and personalise materials, this stream aims to formalise existing habits. Simultaneously, pilots with Arizona State University and the California State University system are creating pathways for students to certify their skills before entering the job market. This ensures that the next wave of graduates arrives with the “job-ready” verification that enterprise employers are beginning to demand. Organisations must now decide whether to rely on vendor-supplied certification or continue developing proprietary training. The involvement of firms like Boston Consulting Group and Accenture implies that major players see value in a standardised external benchmark. As OpenAI moves to certify millions of people and close the AI skills gap, the certification badge may become a baseline expectation for knowledge workers much like office suite proficiency in previous decades. See also: Instacart pilots agentic commerce by embedding in ChatGPT 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 OpenAI targets AI skills gap with new certification standards appeared first on AI News. View the full article

For the past year, we’ve been told that artificial intelligence is revolutionising productivity—helping us write emails, generate code, and summarise documents. But what if the reality of how people actually use AI is completely different from what we’ve been led to believe? A data-driven study by OpenRouter has just pulled back the curtain on real-world AI usage by analysing over 100 trillion tokens—essentially billions upon billions of conversations and interactions with large language models like ChatGPT, Claude, and dozens of others. The findings challenge many assumptions about the AI revolution. ​​OpenRouter is a multi-model AI inference platform that routes requests across more than 300 models from over 60 providers—from OpenAI and Anthropic to open-source alternatives like DeepSeek and Meta’s LLaMA. With over 50% of its usage originating outside the United States and serving millions of developers globally, the platform offers a unique cross-section of how AI is actually deployed across different geographies, use cases, and user types. Importantly, the study analysed metadata from billions of interactions without accessing the actual text of conversations, preserving user privacy while revealing behavioural patterns. Open-source AI models have grown to capture approximately one-third of total usage by late 2025, with notable spikes following major releases. The roleplay revolution nobody saw coming Perhaps the most surprising discovery: more than half of all open-source AI model usage isn’t for productivity at all. It’s for roleplay and creative storytelling. Yes, you read that right. While tech executives tout AI’s potential to transform business, users are spending the majority of their time engaging in character-driven conversations, interactive fiction, and gaming scenarios. Over 50% of open-source model interactions fall into this category, dwarfing even programming assistance. “This counters an assumption that LLMs are mostly used for writing code, emails, or summaries,” the report states. “In reality, many users engage with these models for companionship or exploration.” This isn’t just casual chatting. The data shows users treat AI models as structured roleplaying engines, with 60% of roleplay tokens falling under specific gaming scenarios and creative writing contexts. It’s a massive, largely invisible use case that’s reshaping how AI companies think about their products. Programming’s meteoric rise While roleplay dominates open-source usage, programming has become the fastest-growing category across all AI models. At the start of 2025, coding-related queries accounted for just 11% of total AI usage. By the end of the year, that figure had exploded to over 50%. This growth reflects AI’s deepening integration into software development. Average prompt lengths for programming tasks have grown fourfold, from around 1,500 tokens to over 6,000, with some code-related requests exceeding 20,000 tokens—roughly equivalent to feeding an entire codebase into an AI model for analysis. For context, programming queries now generate some of the longest and most complex interactions in the entire AI ecosystem. Developers aren’t just asking for simple code snippets anymore; they’re conducting sophisticated debugging sessions, architectural reviews, and multi-step problem solving. Anthropic’s Claude models dominate this space, capturing over 60% of programming-related usage for most of 2025, though competition is intensifying as Google, OpenAI, and open-source alternatives gain ground. Programming-related queries exploded from 11% of total AI usage in early 2025 to over 50% by year’s end. The ******** AI surge Another major revelation: ******** AI models now account for approximately 30% of global usage—nearly triple their 13% share at the start of 2025. Models from DeepSeek, Qwen (Alibaba), and Moonshot AI have rapidly gained traction, with DeepSeek alone processing 14.37 trillion tokens during the study *******. This represents a fundamental shift in the global AI landscape, where Western companies no longer hold unchallenged dominance. Simplified ******** is now the second-most common language for AI interactions globally at 5% of total usage, behind only English at 83%. Asia’s overall share of AI spending more than doubled from 13% to 31%, with Singapore emerging as the second-largest country by usage after the United States. The rise of “Agentic” AI The study introduces a concept that will define AI’s next phase: agentic inference. This means AI models are no longer just answering single questions—they’re executing multi-step tasks, calling external tools, and reasoning across extended conversations. The share of AI interactions classified as “reasoning-optimised” jumped from nearly zero in early 2025 to over 50% by year’s end. This reflects a fundamental shift from AI as a text generator to AI as an autonomous agent capable of planning and execution. “The median LLM request is no longer a simple question or isolated instruction,” the researchers explain. “Instead, it is part of a structured, agent-like loop, invoking external tools, reasoning over state, and persisting across longer contexts.” Think of it this way: instead of asking AI to “write a function,” you’re now asking it to “debug this codebase, identify the performance bottleneck, and implement a solution”—and it can actually do it. The “Glass Slipper Effect” One of the study’s most fascinating insights relates to user retention. Researchers discovered what they call the Cinderella “Glass Slipper” effect—a phenomenon where AI models that are “first to solve” a critical problem create lasting user loyalty. When a newly released model perfectly matches a previously unmet need—the metaphorical “glass slipper”—those early users stick around far longer than later adopters. For example, the June 2025 cohort of Google’s Gemini 2.5 Pro retained approximately 40% of users at month five, substantially higher than later cohorts. This challenges conventional wisdom about AI competition. Being first matters, but specifically being first to solve a high-value problem creates a durable competitive advantage. Users embed these models into their workflows, making switching costly both technically and behaviorally. Cost doesn’t matter (as much as you’d think) Perhaps counterintuitively, the study reveals that AI usage is relatively price-inelastic. A 10% decrease in price corresponds to only about a 0.5-0.7% increase in usage. Premium models from Anthropic and OpenAI command $2-35 per million tokens while maintaining high usage, while budget options like DeepSeek and Google’s Gemini Flash achieve similar scale at under $0.40 per million tokens. Both coexist successfully. “The LLM market does not seem to behave like a commodity just yet,” the report concludes. “Users balance cost with reasoning quality, reliability, and breadth of capability.” This means AI hasn’t become a race to the bottom on pricing. Quality, reliability, and capability still command premiums—at least for now. What this means going forward The OpenRouter study paints a picture of real-world AI usage that’s far more nuanced than industry narratives suggest. Yes, AI is transforming programming and professional work. But it’s also creating entirely new categories of human-computer interaction through roleplay and creative applications. The market is diversifying geographically, with China emerging as a major force. The technology is evolving from simple text generation to complex, multi-step reasoning. And user loyalty depends less on being first to market than on being first to truly solve a problem. As the report notes, “ways in which people use LLMs do not always align with expectations and vary significantly country by country, state by state, use case by use case.” Understanding these real-world patterns—not just benchmark scores or marketing claims—will be crucial as AI becomes further embedded in daily life. The gap between how we think AI is used and how it’s actually used is wider than most realise. This study helps close that gap. See also: Deep Cogito v2: Open-source AI that hones its reasoning skills 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 people really use AI: The surprising truth from analysing billions of interactions appeared first on AI News. View the full article

Artificial intelligence is transforming the way information is created, summarised, and delivered. For publishers, the shift is already visible. Search engines provide AI-generated overviews, users get answers without clicking, and content is scraped by large language models that train on decades of journalism. In this environment one question remains: How does a publisher survive when the traditional rules of distribution fall apart? Dev Pragad, the CEO of Newsweek, is offering one of the clearest answers. Pragad’s strategy begins with an acknowledgement of reality. In his view, publishers need to accept the search-driven traffic model that defined the digital era is no longer dependable. AI-powered answer engines are restructuring the way users interact with information. A user might ask a question, receive a summary generated by an LLM, and never visit the publisher’s website. Page views become unpredictable, programmatic advertising becomes unstable, and legacy structures become vulnerable. Rather than respond with fear, Dev Pragad has taken a proactive approach grounded in three core areas. Redesign the brand so that it remains visually strong in any context. Diversify revenue so the business is not tied to a single distribution mechanism. Expand those content formats that are less dependent on search engines and more aligned with the new habits of audiences. In September 2025 Newsweek unveiled its redesigned identity under the tagline ‘A World Drawn Closer’. This redesign, created with 2×4, introduced a refined wordmark, a bold ‘N’ icon, and a unified visual system used for print, digital, video and international editions. For the AI era such a coherence matters. An AI summary might reference Newsweek visually, a feed might show a thumbnail with minimal space, and a social clip might require brand clarity in a fraction of a second. The new design prepares Newsweek for the new reality by making the brand easy to identify. The editorial shift under Dev Pragad is also significant. Newsmakers, the series that features cultural leaders (Spike Lee, Liam Neeson, and Clark Hunt, for example), is available free on YouTube and digital platforms. The decision to make the series accessible at no cost is strategic. Video that travels across platforms is harder for AI summaries to replace. It is more immersive, and it reaches audiences directly, plus it builds brand equity and cultural relevance beyond search traffic. In interviews Pragad has said Newsmakers represents the future of journalism, blending storytelling, accessibility and platform fluency. Each episode is supported by a companion article and a collectable cover, creating a cross media footprint that is not reliant on one format or algorithm. In addition to editorial innovation, Newsweek is evolving its business architecture to withstand AI driven disruption. While digital advertising remains part of the company’s revenue model, Pragad has expanded the title into events, direct advertising relationships, data driven rankings, and verticals such as healthcare. This approach creates multiple revenue streams that do not depend on unpredictable traffic patterns. Another factor shaping Newsweek’s AI strategy is the way large language models scrape content. Newsweek monitors this activity through systems like TollBit which track bot behaviour and provide insight into how often AI engines attempt to access the site. Pragad has turned down licensing deals that undervalued the worth of Newsweek’s archives and has advocated for fair compensation for the use of publisher content. He believes publishers must negotiate collectively and maintain leverage rather than rush into agreements that minimise the value of their intellectual property. The redesign is also in response to the challenge of brand recognition in a world dominated by fast-moving feeds and AI-driven surfaces. Clear typography, concise visual hierarchy, and a distinct colour palette support recognition across AI-generated snippets, smart devices, social networks, and search previews. This is a design built for the realities of the modern information economy. Newsweek’s growth reflects the strength of these choices. The publication has been recognised as one of the fastest-rising digital news destinations in the US, and global audience numbers continue to climb. Although the company continues to evolve its revenue structure, its editorial mission remains grounded in fairness and trust. The new tagline reflects that commitment. Journalism brings the world closer when it is clear, accessible, and human-centred. The AI revolution has placed publishers in a difficult position, yet it has also opened an opportunity. Those willing to rethink design, editorial formats, AI licensing, distribution, and revenue have the chance to define what comes next. Under Dev Pragad Newsweek is doing exactly that. The company is no longer relying on assumptions about how audiences discover information. It’s building a future in which journalism can coexist with AI, not be erased by it. Dev Pragad has created a blueprint that demonstrates how a legacy publisher can reinvent itself for the AI age. Through design clarity, accessible cultural storytelling, diversified business models, and a firm stance on content value, he is positioning Newsweek not only to survive, but to lead in a world where information flows faster and more unpredictably than before. The result is a modern media entity built for a new era of intelligence, creativity, and connection. The post Newsweek: Building AI-resilience for the next era of information appeared first on AI News. View the full article

Instacart has deployed an embedded checkout experience within ChatGPT through the emerging Agentic Commerce Protocol. With the deployment, the company is the first partner to launch an app on ChatGPT that offers a complete shopping cycle – from query to payment – without requiring the user to leave the conversation interface. Operationalising agentic commerce The integration fixes a broken link in conversational commerce: the “handoff”. Historically, AI models could suggest products or generate meal plans, but the execution phase required deep-linking out to a separate application or website, often resulting in cart abandonment. Under this new deployment, users can interact with the AI for meal planning and have the system build a cart based on local retailer inventory. The differentiator here is the checkout process. By leveraging the Agentic Commerce Protocol, the transaction is processed directly within the chat interface using a credit card flow powered by Stripe. According to Nick Turley, VP and Head of ChatGPT, the objective is to connect AI suggestions directly to real-world services. “With the Instacart app directly in ChatGPT, users can go from meal planning to checkout in a single, seamless conversation,” Turley said. “It’s another step toward bringing our vision to life—where AI delivers helpful suggestions and connects directly to real-world services, saving people time and effort in their everyday lives.” This integration goes deeper than standard API consumption. Instacart served as an early contributor to the OpenAI Operator research preview, providing feedback to ensure the technology could navigate real-world constraints while adhering to established norms. This “preview” involvement suggests that Instacart’s complex data environment – involving tens of thousands of SKUs and dynamic stock levels – served as a testing ground for OpenAI’s agentic capabilities. Rather than simply adopting the tool, Instacart helped define the parameters of how an AI agent interacts with external fulfilment logistics. The Instacart deployment underscores why structured, real-time data matters when integrating with large language models (LLMs). An AI agent is only as effective as the data it can access; hallucinations in a commercial context – such as selling out-of-stock items – carry financial and reputational risk. Anirban Kundu, CTO at Instacart, notes that powering shopping inside an AI agent requires technology capable of interpreting highly local and constantly fluctuating inventory. Instacart attempts to mitigate the “hallucination” risk by grounding the AI’s responses in its massive dataset, which covers more than 1.8 billion product instances across 100,000 stores. “Instacart and ChatGPT are redefining what’s possible in AI-powered shopping,” said Kundu. “Built on Agentic Commerce Protocol, this experience brings intelligent, real-time support to one of the most essential parts of daily life: getting groceries to feed your family. “Together, we’re creating a seamless and secure way for people to turn simple conversations into real-world action—helping customers go from inspiration to a full cart delivered from the store to their door with ease.” Dual adoption: Customer-facing and internal efficiency While the embedded checkout grabs headlines, Instacart’s broader plan involves extensive internal deployment. The company utilises ChatGPT Enterprise to streamline internal workflows, aimed at accelerating the development of customer experiences. Furthermore, they have deployed OpenAI’s Codex to power an internal coding agent. This dual approach – using AI to sell (Agentic Commerce) and AI to build (Codex) – offers a model for operations. It moves beyond isolated pilots into a holistic stance where generative models drive both revenue and R&D efficiency. The deployment points to a change in how brands view digital storefronts. Instacart’s approach appears to accept that consumer entry points are fragmenting. Rather than forcing all traffic through a proprietary app, the company is positioning its infrastructure as the backend fulfilment layer for third-party AI platforms. The company has explicitly stated its intention to bridge AI inspiration with real-world fulfilment, acting as a primary partner for major AI players including OpenAI, Google, and Microsoft. By embedding its service into these broad-reach platforms, Instacart aims to capture incremental demand that originates outside its native ecosystem. Implementation and availability of Instacart in ChatGPT The experience is currently active for users on desktop and mobile web platforms, while native mobile availability for iOS and Android applications is rolling out shortly. To access the feature, users must invoke the specific Instacart application within the ChatGPT interface (for example, by prompting “Instacart, can you help me shop for apple pie ingredients?”) and link their accounts. This opt-in mechanism ensures that data sharing is consensual, a requisite governance step for enterprises deploying consumer-facing AI agents. This integration serves as a case study of agentic AI for commerce. For retail and technology execs, the Instacart model demonstrates that the next phase of digital adoption involves preparing API structures and data pipelines to serve “non-human” customers (AI agents) as reliably as human ones. The focus must remain on data accuracy and real-time availability; without these foundations, agentic workflows will fail to deliver return on investment. See also: OpenAI: Enterprise users swap AI pilots for deep integrations 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 Instacart pilots agentic commerce by embedding in ChatGPT appeared first on AI News. View the full article

According to OpenAI, enterprise AI has graduated from the sandbox and is now being used for daily operations with deep workflow integrations. New data from the company shows that firms are now assigning complex and multi-step workflows to models rather than simply asking for text summaries. The figures illustrate a hard change in how organisations deploy generative models. With OpenAI’s platform now serving over 800 million users weekly, a “flywheel” effect is driving consumer familiarity into professional environments. The company’s latest report notes that over a million business customers now use these tools, and the goal is now even deeper integration. This evolution presents two realities for decision-makers: productivity gains are concrete, but a growing divide between “frontier” adopters and the median enterprise suggests that value depends heavily on usage intensity. From chatbots to deep reasoning The best metric for corporate deployment maturity is not seat count, but task complexity OpenAI reports that ChatGPT message volume has grown eightfold year-over-year, but a better indicator for enterprise architects is the consumption of API reasoning tokens which suggests deeper integrations are taking place. This figure has increased by nearly 320 times per organisation—evidence that companies are systematically wiring more intelligent models into their products to handle logic rather than basic queries. The rise of configurable interfaces supports this view. Weekly users of Custom GPTs and Projects (tools that allow workers to instruct models with specific institutional knowledge) have increased approximately 19x this year. Roughly 20 percent of all enterprise messages are now processed via these customised environments, indicating that standardisation is now a prerequisite for professional use. For enterprise leaders auditing the ROI of AI seats, the data offers evidence on time savings. On average, users attribute between 40-60 minutes of time saved per active day to the technology. The impact varies by function: data science, engineering, and communication professionals report higher savings (averaging 60-80 minutes daily.) Beyond efficiency, the software is altering role boundaries. There is a specific effect on technical capability, particularly regarding code generation. Among enterprise users, OpenAI says that coding-related messages have risen across all business functions. Outside of engineering, IT, and research roles, coding queries have grown by an average of 36 percent over the past six months. Non-technical teams are using the tools to perform analysis that previously required specialised developers. Operational improvements extend across departments. Survey data shows 87 percent of IT workers report faster issue resolution, while 75 percent of HR professionals see improved employee engagement. Widening enterprise AI competence gap OpenAI’s data suggests that a split is forming between organisations that simply provide access to tools and those in which integrations are being deeply embedded into their operating models. The report identifies a “frontier” class of workers – those in the 95th percentile of adoption intensity – who generate six times more messages than the median worker. This disparity is stark at the organisational level. Frontier firms generate approximately twice as many messages per seat as the median enterprise and seven times more messages to custom GPTs. Leading firms are not just using the tools more frequently; they are investing in the infrastructure and standardisation required to make AI a persistent part of operations. Users who engage across a wider variety of tasks (roughly seven distinct types) report saving five times more time than those who limit their usage to three or four basic functions. Benefits correlate directly with the depth of use, implying that a “light touch” deployment plan may fail to deliver the anticipated ROI. While the professional services, finance, and technology sectors were early adopters and maintain the largest scale of usage, other industries are sprinting to catch up. The technology sector leads with 11x year-over-year growth, but healthcare and manufacturing follow closely with 8x and 7x growth respectively. Global adoption patterns also challenge the notion that this is solely a US-centric phenomenon. International usage is surging, with markets such as Australia, Brazil, the Netherlands, and France showing business customer growth rates exceeding 140 percent year-over-year. Japan has also surfaced as a key market, holding the largest number of corporate API customers outside of the US. OpenAI: Deep AI integrations accelerate enterprise workflows Examples of deployment highlight how these tools influence key business metrics. Retailer Lowe’s deployed an associate-facing tool to over 1,700 stores, resulting in a customer satisfaction score increase of 200 basis points when associates used the system. Furthermore, when online customers engaged with the retailer’s AI tool, conversion rates more than doubled. In the pharmaceutical sector, Moderna used enterprise AI to speed up the drafting of Target Product Profiles (TPPs), a process that typically involves weeks of cross-functional effort. By automating the extraction of key facts from massive evidence packs, the company reduced core analytical steps from weeks to hours. Financial services firm BBVA leveraged the technology to fix a bottleneck in legal validation for corporate signatory authority. By building a generative AI solution to handle standard legal queries, the bank automated over 9,000 queries annually, effectively freeing up the equivalent of three full-time employees for higher-value tasks. However, the transition to production-grade AI requires more than software procurement; it necessitates organisational readiness. The primary blockers for many organisations are no longer model capabilities, but implementation and internal structures. Leading firms consistently enable deep system integration by “turning on” connectors that give models secure access to company data. Yet, roughly one in four enterprises has not taken this step, limiting their models to generic knowledge rather than specific organisational context. Successful deployment relies on executive sponsorship that sets explicit mandates and encourages the codification of institutional knowledge into reusable assets. As the technology continues to evolve, organisations must adjust their approach. OpenAI’s data suggests that success now depends on delegating complex workflows with deep integrations rather than just asking for outputs, treating AI as a primary engine for enterprise revenue growth. 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 OpenAI: Enterprise users swap AI pilots for deep integrations appeared first on AI News. View the full article

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. The post Aluminium OS is the AI-powered successor to ChromeOS appeared first on AI News. View the full article

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