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We’re all chasing talent. It’s become as crucial to success as building amazing products, and a lot of businesses are feeling the squeeze. The problem is that demand for people with AI skills is skyrocketing, but the supply isn’t keeping up. The OECD points this out – lots of us need AI expertise, but very few job postings actually require it. But there’s a promising trend emerging, and it’s happening in Europe. On the continent and in the ***, some things are happening in AI education – experiments that use AI to change how people learn. These are glimpses into the future workforce, showing us how the next generation will approach problem-solving and collaboration in a world increasingly using AI. Let’s take a look at a few examples, and examine how they can help businesses rethink their approach to talent. Training teachers to work with AI – the Manchester story The University of Manchester is integrating generative AI into how it prepares future educators, using the tools critically, creatively, and thoughtfully, combining AI’s suggestions with their students’ knowledge and experience. That suggests a future where employees aren’t consumers of training but are comfortable co-creating with AI. Future generations will expect AI assistance in their day-to-day tasks, and the real competitive edge won’t be whether people use AI, but how they use it responsibly and ethically. UNESCO’s take is spot-on, highlighting the enhancing of human capabilities, not replacing them. Building AI skills from the ground up: AI-ENTR4YOUTH AI-ENTR4YOUTH is a programme bringing together Junior Achievement Europe and partners in ten European countries. Here AI is embedded in entrepreneurship education, where students use AI tools to tackle real-world problems, with a focus on innovation and European values. This develops practical AI literacy early on, linking AI with the entrepreneurial mindset; the ability to spot opportunities and test new ideas. Importantly, it’s broadening the pool of AI talent by reaching students who might chose business, not technical degrees. The skills gap can be solved. Companies that complain about a lack of AI talent should ask: How can we actively support or emulate programmes like AI-ENTR4YOUTH to build the workforce we need? Personalised learning & impact: The Social Tides perspective Social Tides champions education innovators in Europe. Its work highlights projects that use AI to create more tailored learning experiences, particularly for students who need extra support or have diverse learning styles. AI is helping personalise content, act as mentor, and build communities around students. The common thread is human oversight. AI gives recommendations and insight, but humans are still very much in the loop, making judgements and offering support. This aligns with best AI business practice, as leaders try to make learning an integral part of the working day. Key questions for leaders What does this mean for decision-makers? Here are a few questions to consider: Learning architecture: Are we embracing AI-assisted, personalised learning paths internally? Talent & pipeline: Are we shaping the future talent pool through partnerships with local schools and universities? Governance & ethics: Do we have clear guidelines for AI use in training, ensuring fairness and transparency? Vendor choices: Are we selecting AI tools that align with our values and pertinent regulations? Although these educational programmes could be termed experiments, they are a signal of how the future of work might be shaped. Companies that pay attention now will be the ones to secure better talent and build more adaptable, learning-driven organisations. (Image source: “Laboratory” by ♔ Georgie R is licensed under CC BY-ND 2.0. T) 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 What Europe’s AI education experiments can teach a business appeared first on AI News. View the full article

By 2027, half of all business decisions will be augmented or automated by AI agents for decision intelligence. This seismic shift is changing how organisations operate, and AI leaders are under pressure to adapt, innovate, and guide their teams through complexity. Gartner Data & Analytics Summit 2026 is designed to help these leaders meet their biggest challenges head-on. This year, Gartner is expanding its AI content to ensure attendees have the resources and insights they need to succeed. The summit’s agenda places AI at the forefront, with a track dedicated entirely to artificial intelligence. Sessions will cover everything from AI strategy and responsible AI to risk management, governance, generative AI, large language models, retrieval augmented generation, prompt engineering, and machine learning. Attendees will have the opportunity to learn from experts who understand the nuances of scaling AI, building resilient architectures, and navigating the ethical considerations that come with advanced technologies. In addition to technical deep dives, the summit introduces a spotlight track focused on AI leadership. The programme is crafted for executives tasked with building world-class AI organisations. It explores real-world use cases, robust delivery models, and the importance of strong governance to ensure safe and scalable operations. The conference recognises that AI agents are not infallible, but with the right knowledge and collaboration, leaders can empower their organisations to make smarter, more confident decisions. The Gartner Data & Analytics Summit 2026 will take place from March 9 to 11 in Orlando, Florida. Attendees will join a global community of data, analytics, and AI professionals, gaining exclusive access to Gartner’s trusted research, expert insights, and networking opportunities with peers and thought leaders. For AI leaders ready to shape the future, this event offers the clarity, confidence, and connections needed to accelerate innovation and drive impact. Learn more about the Gartner Data & Analytics Summit and how you can advance your AI strategy here. The post Gartner Data & Analytics Summit unveils expanded AI agenda for 2026 appeared first on AI News. View the full article

Microsoft, Anthropic, and NVIDIA are setting a bar for cloud infrastructure investment and AI model availability with a new compute alliance. This agreement signals a divergence from single-model dependency toward a diversified, hardware-optimised ecosystem, altering the governance landscape for senior technology leaders. Microsoft CEO Satya Nadella says the relationship is a reciprocal integration where the companies are “increasingly going to be customers of each other”. While Anthropic leverages Azure infrastructure, Microsoft will incorporate Anthropic models across its product stack. Anthropic has committed to purchasing $30 billion of Azure compute capacity. This figure shows the immense computational requirements necessary to train and deploy the next generation of frontier models. The collaboration involves a specific hardware trajectory, beginning with NVIDIA’s Grace Blackwell systems and progressing to the Vera Rubin architecture. NVIDIA CEO Jensen Huang expects the Grace Blackwell architecture with NVLink to deliver an “order of magnitude speed up,” a necessary leap for driving down token economics. For those overseeing infrastructure strategy, Huang’s description of a “shift-left” engineering approach – where NVIDIA technology appears on Azure immediately upon release – suggests that enterprises running Claude on Azure will access performance characteristics distinct from standard instances. This deep integration may influence architectural decisions regarding latency-sensitive applications or high-throughput batch processing. Financial planning must now account for what Huang identifies as three simultaneous scaling laws: pre-training, post-training, and inference-time scaling. Traditionally, AI compute costs were weighted heavily toward training. However, Huang notes that with test-time scaling – where the model “thinks” longer to produce higher quality answers – inference costs are rising. Consequently, AI operational expenditure (OpEx) will not be a flat rate per token but will correlate with the complexity of the reasoning required. Budget forecasting for agentic workflows must therefore become more dynamic. Integration into existing enterprise workflows remains a primary hurdle for adoption. To address this, Microsoft has committed to continuing access for Claude across the Copilot family. Operational emphasis falls heavily on agentic capabilities. Huang highlighted Anthropic’s Model Context Protocol (MCP) as a development that has “revolutionised the agentic AI landscape”. Software engineering leaders should note that NVIDIA engineers are already utilising Claude Code to refactor legacy codebases. From a security perspective, this integration simplifies the perimeter. Security leaders vetting third-party API endpoints can now provision Claude capabilities within the existing Microsoft 365 compliance boundary. This streamlines data governance, as the interaction logs and data handling remain within the established Microsoft tenant agreements. Vendor lock-in persists as a friction point for CDOs and risk officers. This AI compute partnership alleviates that concern by making Claude the only frontier model available across all three prominent global cloud services. Nadella emphasised that this multi-model approach builds upon, rather than replaces, Microsoft’s existing partnership with OpenAI, which remains a core component of their strategy. For Anthropic, the alliance resolves the “enterprise go-to-market” challenge. Huang noted that building an enterprise sales motion takes decades. By piggybacking on Microsoft’s established channels, Anthropic bypasses this adoption curve. This trilateral agreement alters the procurement landscape. Nadella urges the industry to move beyond a “zero-sum narrative,” suggesting a future of broad and durable capabilities. Organisations should review their current model portfolios. The availability of Claude Sonnet 4.5 and Opus 4.1 on Azure warrants a comparative TCO analysis against existing deployments. Furthermore, the “gigawatt of capacity” commitment signals that capacity constraints for these specific models may be less severe than in previous hardware cycles. Following this AI compute partnership, the focus for enterprises must now turn from access to optimisation; matching the right model version to the specific business process to maximise the return on this expanded infrastructure. See also: How Levi Strauss is using AI for its DTC-first business 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 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 Microsoft, NVIDIA, and Anthropic forge AI compute alliance appeared first on AI News. View the full article

Firms in the asset management industry are turning increasingly to generative and agentic AI to streamline operations, improve decision-making, and uncover new sources of alpha (the measure of an investment strategy’s ability to outperform the market after accounting for risk). The trend is continuing with the latest partnership between Franklin Templeton and Wand AI, marking a shift toward more autonomous, data-driven investment processes. Franklin Resources, operating as Franklin Templeton, has entered into a strategic partnership with enterprise AI platform, Wand AI, to begin the enterprise deployment of agentic AI in Franklin Templeton’s worldwide platform. Wand’s Autonomous Workforce and Agent Management technologies have enabled Franklin to implement agentic AI at scale, accelerating data-driven decision-making in its investment processes. The collaboration has moved from initial small-scale pilot programmes to fully operational AI systems, strengthening the partnership between the two companies. The first implementations concentrated on high-value applications of AI in Franklin Templeton’s investment teams, but now both have plans to mass-deploy intelligent agents in various departments. The company plans to extend the use of Wand AI’s intelligent agent in 2026, a move designed to drive digital transformation in the organisation and enhance investment research. Franklin hopes to ensure AI systems are managed responsibly under strict oversight, compliance, and risk control, therefore maintaining trust and transparency. Vasundhara Chetluru, Head of AI Platform at Franklin Templeton, said, “With strong governance in place, we are demonstrating that AI can deliver secure, scalable, and measurable value.” Rotem Alaluf, CEO of Wand AI, commented on the company’s AI vision, saying its mission is to “elevate AI from experimental technology to a fully integrated, adaptive workforce that drives enterprise-wide transformation and delivers significant business impact.” Alaluf said AI agents can “seamlessly collaborate with human teams and operate at scale in complex, highly regulated environments to achieve transformative results,” but only when these are “properly governed, orchestrated, and deployed as a unified agentic workforce.” AI takes centre stage in asset management Other companies in the sector are also going all-in on AI. Goldman Sachs has implemented AI at scale, with CEO, David Solomon, pinpointing the technology as a key force in economic growth. He is on the record as saying the opportunity presented by AI is “enormous.” According to the Goldman Sachs report, “AI: In a Bubble?”, the company estimates that generative AI could create US $20 trillion of economic value in the long term. The report suggests AI has the capacity to create up to a 15% uplift in US labour productivity, if adopted at scale. In June 2025, Goldman Sachs (GS) expanded its use of AI by launching a generative-AI assistant inside the firm, joining an increasing list of big banks that were already using the technology for operations. The GS AI assistant was designed to help with tasks including drafting initial content, completing data analysis, and summarising complex documents. This has improved productivity in teams, freeing thousands of employees to prioritise higher-value strategic work, the bank says. Such moves signal a shift away from AI niche-use cases and pilot projects to border enterprise deployments in major institutions, aimed at enhancing productivity and operational support. While David Solomon acknowledges that AI presents an “enormous” opportunity, he has emphasised that there will be “winners and losers.” Some capital investments will not yield return, according to Solomon, which is why he says clients must be diligent in their AI investments. Solomon has also noted how technology has already transformed the composition of the GS workforce make-up over the last twenty-five years. Today, the bank employs 13,000 engineers, illustrating the change in job functions over time. Rather than roles disappearing with technological advancement, Solomon believes economies and workforces adapt to change. “At the end of the day, we have an incredibly flexible, nimble economy. We have a great ability to adapt and adjust,” he said. “Yes, there will be job functions that shift and change, but I’m excited about it. If you take a three-to-five-year view, it’s giving us more capacity to invest in our business,” he said. Goldman Sachs and Franklin Temleton are part of a wider trend of financial institutions accelerating AI adoption. Solomon said, “I can’t find a CEO that I’m talking to, in any industry, that is not focused on how they can re-imagine and automate processes in their business to create operating efficiency and productivity.” (Image source: “Trading Floor at the New York Stock Exchange during the Zendesk IPO” by Scott Beale is licensed under CC BY-NC-ND 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 Franklin Templeton & Wand AI bring agentic AI to asset management appeared first on AI News. View the full article

Over half of us now use AI to search the web, yet the stubbornly low data accuracy of common tools creates new business risks. While generative AI (GenAI) offers undeniable efficiency gains, a new investigation highlights a disparity between user trust and technical accuracy that poses specific risks to corporate compliance, legal standing, and financial planning. For the C-suite, the adoption of these tools represents a classic ‘shadow IT’ challenge. According to a survey of 4,189 *** adults conducted in September 2025, around a third of users believe AI is already more important to them than standard web searching. If employees trust these tools for personal queries, they are almost certainly employing them for business research. The investigation, conducted by Which?, suggests that unverified reliance on these platforms could be costly. Around half of AI users report trusting the information they receive to a ‘reasonable’ or ‘great’ extent. Yet, looking at the granularity of the responses provided by AI models, that trust is often misplaced. The accuracy gap when using AI to search the web The study tested six major tools – ChatGPT, Google Gemini (both standard and ‘AI Overviews’), Microsoft Copilot, Meta AI, and Perplexity – across 40 common questions spanning finance, law, and consumer rights. Perplexity achieved the highest total score at 71 percent, closely followed by Google Gemini AI Overviews at 70 percent. In contrast, Meta scored the lowest at 55 percent. ChatGPT, despite its widespread adoption, received a total score of 64 percent, making it the second-lowest performer among the tools tested. This disconnect between market dominance and reliable output underlines the danger of assuming popularity equals performance in the GenAI space. However, the investigation revealed that all of these AI tools frequently misread information or provided incomplete advice that could pose serious business risks. For financial officers and legal departments, the nature of these errors is particularly concerning. When asked how to invest a £25,000 annual ISA allowance, both ChatGPT and Copilot failed to identify a deliberate error in the prompt regarding the statutory limit. Instead of correcting the figure, they offered advice that potentially risked breaching HMRC rules. While Gemini, Meta, and Perplexity successfully identified the error, the inconsistency across platforms necessitates a rigorous “human-in-the-loop” protocol for any business process involving AI to ensure accuracy. For legal teams, the tendency of AI to generalise regional regulations when using it for web search presents a distinct business risk. The testing found it common for tools to misunderstand that legal statutes often differ between *** regions, such as Scotland versus England and Wales. Furthermore, the investigation highlighted an ethical gap in how these models handle high-stakes queries. On legal and financial matters, the tools infrequently advised users to consult a registered professional. For example, when queried about a dispute with a builder, Gemini advised withholding payment; a tactic that experts noted could place a user in breach of contract and weaken their legal position. This “overconfident advice” creates operational hazards. If an employee relies on an AI for preliminary compliance checks or contract review without verifying the jurisdiction or legal nuance, the organisation could face regulatory exposure. Source transparency issues A primary concern for enterprise data governance is the lineage of information. The investigation found that AI search tools often bear a high responsibility to be transparent, yet frequently cited sources that were vague, non-existent, or have dubious accuracy, such as old forum threads. This opacity can lead to financial inefficiency. In one test regarding tax codes, ChatGPT and Perplexity presented links to premium tax-refund companies rather than directing the user to the free official HMRC tool. These third-party services are often characterised by high fees. In a business procurement context, such algorithmic bias from AI tools when using them for web search could lead to unnecessary vendor spend or engagement with service providers that pose a high risk due to not meeting corporate due diligence standards. The major technology providers acknowledge these limitations, placing the burden of verification firmly on the user—and, by extension, the enterprise. A Microsoft spokesperson emphasised that their tool acts as a synthesiser rather than an authoritative source. “Copilot answers questions by distilling information from multiple web sources into a single response,” the company noted, adding that they “encourage people to verify the accuracy of content.” OpenAI, responding to the findings, said: “Improving accuracy is something the whole industry’s working on. We’re making good progress and our latest default model, GPT-5, is the smartest and most accurate we’ve built.” Mitigating AI business risk through policy and workflow For business leaders, the path forward is not to ban AI tools – which often increases by driving usage further into the shadows – but to implement robust governance frameworks to ensure the accuracy of their output when bring used for web search: Enforce specificity in prompts: The investigation notes that AI is still learning to interpret prompts. Corporate training should emphasise that vague queries yield risky data. If an employee is researching regulations, they must specify the jurisdiction (e.g., “legal rules for England and Wales”) rather than assuming the tool will infer the context. Mandate source verification: Trusting a single output is operationally unsound. Employees must demand to see sources and check them manually. The study suggests that for high-risk topics, users should verify findings across multiple AI tools or “double source” the information. Tools like Google’s Gemini AI Overviews, which allow users to review presented web links directly, performed slightly better in scoring because they facilitated this verification process. Operationalise the “second opinion”: At this stage of technical maturity, GenAI outputs should be viewed as just one opinion among many. For complex issues involving finance, law, or medical data, AI lacks the ability to fully comprehend nuance. Enterprise policy must dictate that professional human advice remains the final arbiter for decisions with real-world consequences. The AI tools are evolving and their web search accuracy is gradually improving, but as the investigation concludes, relying on them too much right now could prove costly. For the enterprise, the difference between a business efficiency gain from AI and a compliance failure risk lies in the verification process. See also: How Levi Strauss is using AI for its DTC-first business 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 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 web search risks: Mitigating business data accuracy threats appeared first on AI News. View the full article

A new Bain & Company report says many organisations in Southeast Asia are still stuck in early testing because they treat AI as a set of tools rather than a change in how the business works. In The Southeast Asia CEO’s Guide to AI Transformation, the authors say leaders should first look at how AI could reshape their industry and revenue plans, then put money into areas where they expect clear and measurable results. The region’s mix of cultures, income levels, and market sizes makes AI adoption harder than in places with more uniform conditions. People shop and behave differently across countries, wages are still low, and many firms don’t have the scale to run long and costly trials. These factors mean that simple efficiency gains rarely deliver strong returns. Real gains come when AI is used to rethink how the business runs, make decisions faster, or increase capacity without growing the team. Bain’s analysis shows that wages in Southeast Asia are about 7 per cent of US levels, which limits how much companies can save from labour cuts. The report also notes that only 40 per cent of the region’s market value comes from large-cap firms, compared with 60 per cent in India. With fewer large firms able to absorb early AI costs, leaders need to aim for speed, scale, and new processes instead of relying on cost savings alone. How AI is helping today Some organisations in the region are already seeing clear gains by linking their AI plans to business goals. The AI guide highlights early moves such as using AI to shorten product launch times or reduce supply chain issues, opening new chances for revenue. A factory might use predictive models to reduce machine downtime and lift output. A financial institution could use large language models to support compliance work, cutting the time needed to process and respond to requests. Bain senior partner Aadarsh Baijal says impact depends on how leaders think about their market. He believes many still see AI “as a rollout of software rather than a redesign of how the business competes.” When leaders understand how AI changes demand, pricing, operations, or customer needs, they can decide where to focus their efforts. What the guide says about data, culture, and people in AI The report also stresses that AI transformation relies on people, habits, and skills, not only technology. Many organisations think scaling AI is a hiring problem, but Bain argues that the talent often already exists in the business. The real issue is getting teams to work together and helping staff understand how to use AI in their jobs. The authors describe two groups involved in successful change. The “Lab” is made up of technical teams who rebuild processes and create the first versions of new tools. The “Crowd” includes employees across the business who need enough AI awareness to use those tools day to day. Without both groups, projects stall. Senior partner Mohan Jayaraman says the strongest results appear when existing teams lead the work. In his view, impact increases when companies match small expert groups with wider training so new systems become part of normal workflows rather than one-off trials. Leaders also need to fix ongoing issues such as data quality, how data is tracked, governance, and links to current systems. They also need to decide how their AI plans connect with major platforms such as AWS Bedrock, Azure AI Foundry, Google Vertex AI, or IBM watsonx. Without this groundwork, early gains are hard to repeat at scale. A regional push to support enterprise AI Bain is setting up an AI Innovation Hub in Singapore with support from the Singapore Economic Development Board (EDB). The hub’s goal is to help companies move beyond trials by building AI systems that can run at scale. It will work across advanced manufacturing, energy and resources, financial services, healthcare, and consumer goods. The hub sits within a growing AI community in Singapore, which has more than a thousand startups and is expected to generate about S$198.3 billion in economic value from AI by 2030. Its work will cover production-ready systems such as predictive maintenance for factories, AI support for regulatory tasks in finance, and personalisation tools for retail. It will also help companies build internal teams and engineering skills so they can run AI programmes on their own. As competition in Southeast Asia increases, firms that treat AI as a shift in how they operate — a central theme in Bain’s AI guide — will be better positioned to turn pilots into long-term results. See also: Is AI in a bubble? Succeed despite a market correction 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 Bain & Company issues AI guide for CEOs and opens Singapore hub appeared first on AI News. View the full article

At SC25, Dell Technologies and NVIDIA introduced new updates to their joint AI platform, aiming to make it easier for organisations to run a wider range of AI workloads, from older models to newer agent-style systems. As more companies scale their AI plans, many run into the same issues. They need to manage a growing mix of hardware and software, keep control of their data, and make sure their systems can grow over time. Recent research shows that most organisations feel safer working with a trusted partner when adopting new technology, and many see more value when AI can operate closer to their own data. The Dell AI Factory with NVIDIA is built around that idea. It combines Dell’s full stack of infrastructure with NVIDIA’s AI tools, supported by Dell’s professional services team. The goal is to help companies move from ideas to real results while keeping technical complexity in check. Faster deployment through integrated platforms Dell is expanding its storage and AI capabilities to help organisations automate setup, improve performance, and run real-time AI tasks with more consistency. ObjectScale and PowerScale, the storage engines behind the Dell AI Data Platform, now work with the NVIDIA NIXL library from NVIDIA Dynamo. This integration supports scalable KV Cache storage and sharing, enabling a one-second Time to First Token at a 131K-token context window, while helping reduce costs and ease pressure on GPU memory. The Dell AI Factory with NVIDIA also adds support for Dell PowerEdge XE7740 and XE7745 systems equipped with the NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs and NVIDIA Hopper GPUs. According to Dell, these systems give organisations more room to run larger multimodal models, agent-style workloads, training tasks, and enterprise inferencing with stronger performance. Dell says the addition of the Dell Automation Platform is meant to remove guesswork by delivering tuned and validated deployments through a secure setup. The platform aims to produce repeatable results and give teams a clearer path to building AI workflows. Alongside this, software tools such as the AI code assistant with Tabnine and the agentic AI platform with Cohere North are becoming automated, helping teams move workloads into production faster and keep operations manageable as they scale. Beyond core data-centre systems, Dell’s AI PC ecosystem now supports devices with NVIDIA RTX Blackwell GPUs and NVIDIA RTX Ada GPUs, giving organisations more hardware options across Dell laptops and desktops. Dell Professional Services is also offering interactive pilots that use a customer’s own data to test AI ideas before large investments. These pilots focus on clear metrics and outcomes so teams can judge business value with more certainty. Next-generation infrastructure for stronger AI performance Dell is updating its infrastructure portfolio to support more complex AI and HPC workloads, with an emphasis on performance, scale, and easier management. The Dell PowerEdge XE8712, arriving next month, supports up to 144 NVIDIA Blackwell GPUs in a standard rack. This makes rack-scale AI and HPC more accessible, backed by unified monitoring and automation through iDRAC, OpenManage Enterprise, and the Integrated Rack Controller. Enterprise SONiC Distribution by Dell Technologies now supports NVIDIA Spectrum-X platforms along with NVIDIA’s Cumulus OS. This helps organisations build open, standards-based AI networks that can operate across different vendors. The latest SmartFabric Manager release also extends support to Dell’s Enterprise SONiC on NVIDIA Spectrum-X platforms, aiming to reduce deployment time and setup errors through guided automation. More choice through an expanded AI ecosystem Organisations continue to adjust their AI budgets and plans, and many want flexibility in the tools they choose. Red Hat OpenShift for the Dell AI Factory with NVIDIA is now validated on more Dell PowerEdge systems, giving teams more ways to run AI workloads at scale. Support now includes both the Dell PowerEdge R760xa and the Dell PowerEdge XE9680 with NVIDIA H100 and H200 Tensor Core GPUs. This pairing brings together Red Hat’s controls and governance tools with Dell’s secure infrastructure, offering a clearer path for companies that need to scale AI. Dell executives say the updates are meant to help organisations move from small pilots to real deployment. Jeff Clarke, vice chairman and chief operating officer at Dell Technologies, said the Dell AI Factory with NVIDIA addresses a core challenge for many teams: “how to move from AI pilots to production without rebuilding their infrastructure.” He added that Dell has “done the integration work so customers don’t have to,” which he believes will help organisations deploy and scale with more confidence. NVIDIA sees the shift as part of a broader change in how companies use AI. Justin Boitano, vice president of Enterprise AI products, described the moment as one where enterprise AI is moving from experimentation to transformation, advancing at a speed that is “redefining how businesses operate.” He said Dell and NVIDIA aim to support this transition with a unified platform that brings together infrastructure, automation, and data tools to help organisations “deploy AI at scale and realise measurable impact.” Industry analysts see similar demand for integrated systems. Ashish Nadkarni, group vice president and general manager for Infrastructure Systems, Platforms and Technologies at IDC, said many teams want AI-ready systems that are powerful but also easier to run. He noted that the combination of Dell’s AI portfolio with NVIDIA’s technology represents “a significant step forward in delivering enterprise-ready AI.” (Image by Dell Technologies) See also: 10% of Nvidia’s cost: Why Tesla-Intel chip partnership demands attention 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 SC25 showcases the next phase of Dell and NVIDIA’s AI partnership appeared first on AI News. View the full article

Author: Olga Zharuk, CPO, Teqblaze When it comes to applying AI in programmatic, two things matter most: performance and data security. I’ve seen too many internal security audits flag third-party AI services as exposure points. Granting third-party AI agents access to proprietary bidstream data introduces unnecessary exposure that many organisations are no longer willing to accept. That’s why many teams shift to embedded AI agents: local models that operate entirely in your environment. No data leaves your perimeter. No blind spots in the audit trail. You retain full control over how models behave – and more importantly, what they see. Risks associated with external AI use Every time performance or user-level data leaves your infrastructure for inference, you introduce risk. Not theoretical – operational. In recent security audits, we’ve seen cases where external AI vendors log request-level signals under the pretext of optimisation. That includes proprietary bid strategies, contextual targeting signals, and in some cases, metadata with identifiable traces. The isn’t just a privacy concern – it’s a loss of control. Public bid requests are one thing. However, any performance data, tuning variables, and internal outcomes you share is proprietary data. Sharing it with third-party models, especially those hosted in extra-EEA cloud environments, creates gaps in both visibility and compliance. Under regulations like GDPR and CPRA/CCPA, even “pseudonymous” data can trigger legal exposure if transferred improperly or used beyond its declared purpose. For example, a model hosted on an external endpoint receives a call to assess a bid opportunity. Alongside the call, payloads may include price floors, win/loss outcomes, or tuning variables. The values, often embedded in headers or JSON payloads, may be logged for debugging or model improvement and retained beyond a single session, depending on vendor policy. ******-box AI models compound the issue. When vendors don’t disclose inference logic or model behaviour, you’re left without the ability to audit, debug, or even explain how decisions are made. That’s a liability – both technically and legally. Local AI: A strategic shift for programmatic control The shift toward local AI is not merely a defensive move to address privacy regulations – it is an opportunity to redesign how data workflows and decisioning logic are controlled in programmatic platforms. Embedded inference keeps both input and output logic fully controlled – something centralised AI models take away. Control over data Owning the stack means having full control over the data workflow – from deciding which bidstream fields are exposed to models, to setting TTL for training datasets, and defining retention or deletion rules. The enables teams to run AI models without external constraints and experiment with advanced setups tailored to specific business needs. For example, a DSP can restrict sensitive geolocation data while still using generalized insights for campaign optimisation. Selective control is harder to guarantee once data leaves the platform’s boundary. Auditable model behaviour External AI models often offer limited visibility into how bidding decisions are made. Using a local model allows organisations to audit their behaviour, test its accuracy against their own KPIs, and fine-tune its parameters to meet specific yield, pacing, or performance targets. The level of auditability strengthens trust in the supply chain. Publishers can verify and demonstrate that inventory enrichment follows consistent, verifiable standards. The gives buyers higher confidence in inventory quality, reduces spend on invalid traffic, and minimises fraud exposure. Alignment with data privacy requirements Local inference keeps all data in your infrastructure, under your governance. That control is essential for complying with any local laws and privacy requirements in regions. Signals like IP addresses or device IDs can be processed on-site, without ever leaving your environment – reducing exposure while preserving signal quality with appropriate legal basis and safeguards. Practical applications of local AI in programmatic In addition to protecting bidstream data, local AI improves decisioning efficiency and quality in the programmatic chain without increasing data exposure. Bidstream enrichment Local AI can classify page or app taxonomy, analyse referrer signals, and enrich bid requests with contextual metadata in real time. For example, models can calculate visit frequency or recency scores and pass them as additional request parameters for DSP optimisation. The accelerates decision latency and improves contextual accuracy – without exposing raw user data to third parties. Pricing optimisation Since ad tech is dynamic, pricing models must continuously adapt to short-term shifts in demand and supply. Rule-based approaches often react more slowly to changes compared to ML-driven repricing models. Local AI can detect emerging traffic patterns and adjust the bid floor or dynamic price recommendations accordingly. Fraud detection Local AI detects anomalies pre-auction – like randomized IP pools, suspicious user agent patterns, or sudden deviations in win rate – and flags them for mitigation. For example, it can flag mismatches between request volume and impression rate, or abrupt win-rate drops inconsistent with supply or demand shifts.The does not replace dedicated fraud scanners, but augments them with local anomaly detection and monitoring, without requiring external data sharing. The are just a few of the most visible applications – local AI also enables tasks like signals deduplication, ID bridging, frequency modeling, inventory quality scoring, and supply path analysis, all benefiting from secure, real-time execution at the edge. Balancing control and performance with local AI Running AI models in your own infrastructure ensures privacy and governance without sacrificing optimisation potential. local AI moves decision-making closer to the data layer, making it auditable, region-compliant, and fully under platform control. Competitive advantage isn’t about the fastest models, but about models that balance speed with data stewardship and transparency. The approach defines the next phase of programmatic evolution – intelligence that remains close to the data, aligned with business KPIs and regulatory frameworks. Author: Olga Zharuk, CPO, Teqblaze Image source: Unsplash The post Local AI models: How to keep control of the bidstream without losing your data appeared first on AI News. View the full article

In its pursuit of a direct-to-consumer (DTC) first business model, Levi Strauss is weaving AI and cloud platforms into its core operations. The nearly 175-year-old apparel company is leveraging Microsoft technologies to modernise its consumer experiences and improve internal productivity. Levi Straus’ approach provides a case study for other enterprises in using a unified technology stack to address a specific commercial objective. AI ‘superagent’: A unified front-end for operations at Levi Strauss A central component of this initiative is the development of agentic AI solutions. Levi Strauss is deploying an Azure-native “orchestrator agent” embedded within Microsoft Teams, which functions as a “superagent”. This agent serves as a single conversational portal for employees across corporate, retail, and warehouse environments. Operationally, it fields employee questions and routes them to specialist behind-the-scenes subagents, some of which are already deployed. This points toward a consolidation of employee-facing tools; instead of training staff on multiple applications, the agent provides a single interface to streamline workflows. This AI-centric business model is part of a wider goal outlined by Jason Gowans, Chief Digital and Technology Officer at Levi Strauss & Co. “We’re rewiring Levi Strauss & Co to be a DTC-first, fan-obsessed retailer making every interaction faster, smarter, and more personal,” said Gowans. “AI sits at the center of that pivot—fueling innovation, elevating employee creativity, unlocking productivity, and helping us deliver the connected, memorable experiences that keep our fans returning again and again.” This focus on productivity extends to the developer side. Teams are using GitHub Copilot for key projects involving quality engineering and release management. At the same time, other employees are being equipped with Microsoft Surface Copilot+ PCs. Employee feedback indicates these devices have led to improvements in speed and data handling, with features like the Copilot key reducing time spent searching for information. The foundational cloud and security posture Levi Strauss’ AI implementation rests on prerequisite infrastructure work. For business leaders, this highlights that advanced AI models require a consolidated cloud environment. As part of its broader digital efforts, Levi Strauss is relying on Microsoft Azure, having moved application workloads from its on-premises data centres. The company used Azure Migrate and GitHub Copilot to plan and execute the consolidation of its private data centre environment. This cloud foundation is also central to the company’s security posture. Levi Strauss is leveraging Azure AI Foundry and Semantic Kernel to build intelligent automation capabilities. Security is being integrated into the AI framework itself and the tools are being used to power security agents and policy orchestration; a method that allows Levi Strauss to maintain a zero-trust security model while continuing to scale its AI-driven initiatives across global operations. To manage the new hardware endpoints, the company is also using Microsoft Intune for zero-touch device onboarding and application deployment. Leveraging the full AI ecosystem to pivot business model Levi Strauss’ initiative demonstrates an ecosystem-based approach to adopting AI. Rather than a piecemeal rollout of individual tools, the company is integrating AI agents, developer tools, and new hardware on top of a common cloud platform. Keith Mercier, VP of Worldwide Retail and Consumer Goods Industry at Microsoft, noted that Levi Strauss “exemplifies how iconic brands can reinvent themselves with cloud and AI technologies.” For other enterprises, the Levi Strauss case study serves as a blueprint for linking AI and foundational cloud migration directly to high-value outcomes like the pivot to a DTC business model. See also: IBM: Data silos are holding back enterprise AI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is 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 Levi Strauss is using AI for its DTC-first business model appeared first on AI News. View the full article

New insight from the CQF Institute, a worldwide network for quantitative finance professionals (quants), reveals that fewer than one in ten specialists believe new graduates possess the AI and machine learning skills necessary to succeed in the industry. This highlights a growing issue in quantitative finance: a lack of human understanding and fluency in the language of machines. The CQF survey underscores a serious shortage of skills among those working in or entering the quantitative finance sector. As AI becomes increasingly important for success, it’s a worrying trend. Experts say the industry must close this skills gap through improved education, training, and upskilling initiatives. AI adoption is increasing. Despite the limited understanding of AI and machine learning, the survey found that 83% of respondents use or develop AI tools, with 31% using machine learning and AI. Popular tools include ChatGPT (31%), Microsoft/GitHub Copilot (17%), and Gemini/Bard (15%), while 18% use deep learning. A significant 54% of quants use these tools daily. Thirty percent of quants use generative AI for coding and debugging, 21% for market sentiment analysis and research, and 20% for generating reports. AI and machine learning have become influential in key quantitative finance areas. For example, 26% harness AI for research/alpha generation, 19% for algorithmic trading, and 17% for risk management. Forty-four percent of respondents reported substantial productivity improvements thanks to AI, while 25% said they save over ten hours weekly with AI-assisted processes. Challenges remain, however. According to the report, 16% of respondents have regulatory concerns, 17% worry about computer costs, and model explainability – understanding how AI reaches conclusions – is the number one barrier, with 41% reporting it as a key concern. Formal AI training is also a challenge, as just 14% of firms offer such programmes and workforce development. Consequently, only 9% of new graduates are considered “AI-ready.” Dr.Randeep Gug, Managing Director of the CQF Institute, emphasises the importance of equipping graduates with the skills to use AI effectively. “Our future professionals must hit the ground running and know when an AI tool truly adds value.” Nevertheless, momentum exists despite these obstacles. Twenty-five percent of firms have established formal AI strategies, 24% are developing plans, and 23% anticipate increases to budgets to support company infrastructure over the next year. The future of quantitative finance will likely depend more on human collaboration with technology than on traditional mathematical expertise. While the industry faces challenges, the key to overcoming them is for humans to be prepared and skilled enough to implement these tools effectively. Dr.Gug concluded, “Embracing ongoing education and innovative technologies are important to shape the future of quantitative finance.” (Image source: “In Quantity” by MTSOfan is licensed under CC BY-NC-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 Quantitative finance experts believe graduates ill-equipped for AI future appeared first on AI News. View the full article

Alibaba is updating its artificial intelligence chatbot Qwen as it pushes to catch up with tools like OpenAI’s ChatGPT. The revised app replaces the older Tongyi version and became available on both major app stores on Friday, last week. In its app-store descriptions, Alibaba calls Qwen the “most powerful official AI assistant for its models” and the main way for users to try its newest Qwen model. The company also plans to add agent-style features that can help shoppers on platforms such as Taobao, according to Bloomberg. Alibaba did not respond to a request for comment. Alibaba has spent the past two years trying to expand the use of its Qwen models during the global rush toward AI tools. Along with newer ******** players like DeepSeek and Moonshot AI, Alibaba has become one of the country’s ******* AI developers. It has also supported an open-source approach by making its models available for others to use and adapt. Alibaba has been trying to turn these models into steady revenue, and the push appears to be paying off. In the June quarter, sales from its AI products grew at triple-digit rates for the eighth quarter in a row. At the same time, Alibaba cut the cost of using Qwen3-Max, its largest model, by almost half, according to the South China Morning Post. The trillion-parameter system launched in September with some of the highest prices on Alibaba Cloud. The company has now reduced its lowest API rates from US$0.861 to US$0.459 per million input tokens and from US$3.441 to US$1.836 per million output tokens. Users who run batch tasks during off-peak hours get another 50 per cent discount. The model recently placed first in a cryptocurrency investment contest that compared top models from both the US and China. Its price drop comes amid sharper competition in China’s model market. Several start-ups — including Moonshot AI, Zhipu AI, and MiniMax — have released new systems in recent months and have promoted their performance and low costs. China has already seen several rounds of price cuts across the AI sector. There have been earlier battles between major model developers, followed by new competition in areas such as coding tools. This week, Volcano Engine, the cloud unit of ByteDance, introduced a new coding agent for 9.90 yuan (US$1.30) for the first month. Companies have also tried new ways to attract customers. Last week on Tuesday, Moonshot AI — which Alibaba backs — rolled out an offer that lets new users try its Kimi K2 Thinking model for as little as 0.99 yuan. People were encouraged to negotiate their own discount with the Kimi chatbot, which led some users to share “prompt injection” tips online, including messages that tricked the system into thinking they worked for Moonshot. Within hours, the company said the chatbot had started “hallucinating,” and engineers were called to fix the issues. Alibaba’s fast progress in AI has also been noticed in the United States. “Silicon Valley doesn’t want to admit it, but the symptoms are obvious: we’re witnessing a full-blown Qwen panic,” marketing specialist Tulsi Soni wrote on social media on Saturday. At the same time, Alibaba had to defend itself against claims reported by the Financial Times. The report said a White House memo alleged Alibaba had provided China’s People’s Liberation Army with certain technical support, including access to some customer data such as IP addresses, Wi-Fi details, payment information, and AI services. The memo also claimed that Alibaba staff had passed along information about “zero-day” security flaws. The FT said it could not confirm the memo on its own but said the claims pointed to rising concern in Washington about the risks tied to ******** cloud and AI providers. Alibaba rejected the allegations, saying they were “completely false” and questioning the motives behind the leak. A spokesperson described the report as a “malicious PR operation” meant to weaken a recent trade deal between the US and China. A spokesperson for the ******** Embassy in the US criticised the FT’s reporting as well, calling the accusations “groundless” and a distortion of facts. (Photo by zhang hui) See also: Alibaba’s new Qwen model to supercharge AI transcription tools Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is 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 Alibaba rolls out revamped Qwen chatbot as model pricing drops appeared first on AI News. View the full article

Telegram’s Cocoon AI network is getting ready for prime time with a massive deployment of graphics processing units courtesy of one of the Ton blockchain ecosystem’s leading investors, AlphaTON Capital. AlphaTON is working with SingularityNET, CUDO Compute and Vertical Data to deploy a huge fleet of high-performance GPUs on the network, housed in a sustainably-powered data centre in Sweden that runs on hydroelectric power. The intention is to provide the massive amounts of computing power needed by Cocoon to support Telegram’s vision of a decentralised AI ecosystem. Cocoon, which stands for “Confidential Compute Open Network”, is a decentralised AI infrastructure network built on the Ton blockchain that allows anyone to contribute computing resources. Those who make their GPUs and other AI accelerators available to the network can earn $TON cryptocurrency for renting their hardware out to the network’s customers. With Cocoon AI, users benefit from being able to maintain control of any data they provide to AI systems, in-line with the privacy-preserving ethos of the Ton blockchain. The project is the brainchild of Telegram founder and CEO Pavel Durov, and represents an effort to make AI more open and beneficial to the masses. Instead of paying to access AI services like ChatGPT in both money and data, users can access them for free, and potentially earn $TON by monetising their personal data or contributing to the network. Cocoon AI targets developers looking for access to low-cost, private infrastructure to host their applications. It’s extremely ambitious, and though some might think it has little chance of overthrowing heavyweights like OpenAI and Google, it has an ace up its sleeve in the shape of Telegram itself. The messenger app, which boasts more than 1 billion users globally, plans to use Cocoon AI to power its own AI services, and will also encourage developers in its Mini App ecosystem to do the same. This explains the interest of AlphaTON. By supporting the network, it’s helping to increase adoption of AI and grow the broader Ton blockchain ecosystem, which it is already heavily invested in. It can also earn a ton of $TON in revenue from the GPUs it deploys. An ethical & sustainable foundation for AI AlphaTON said its GPU fleet is being funded through Vertical Data’s GPUFinancing subsidiary, which offers structured financing for large-scale deployments of decentralised AI hardware. SingularityNET, creator of a decentralised platform for building and monetising AI services, and CUDO Compute, are supporting the deployment with their specialised expertise in AI infrastructure. The deployment represents a key milestone in the convergence of ethical AI, data privacy and environmental responsibility, said Janet Adams of the SingularityNET Foundation. By hosting them in a hydroelectric-powered data centre, they’ll be able to minimise the carbon footprint of AI training and inference workloads. “Decentralised AI requires decentralised infrastructure,” she said, adding that privacy and sustainability represent a significant “competitive advantage” in the AI industry. AlphaTON CEO Brittany Kaiser said there’s a growing recognition about the need for AI infrastructure that prioritises user sovereignty, environmental responsibility and decentralised governance. She said there are valid concerns about AI services continually slurping user’s data, the out-of-control energy consumption of AI server frams, and the level of centralised control in the industry. “The partnership represents the future of AI infrastructure, where privacy, sustainability and decentralisation aren’t competing priorities, but foundational principles,” Kaiser said. “The AI revolution demands massive computational resources, but it doesn’t have to come at the expense of our planet or our privacy.” AlphaTON and its partners are planning to get started on their fleet of GPUs right away, and plan to continue scaling it throughout 2026 and very possibly beyond that, should Cocoon AI help decentralised AI to take off. Image source: Unsplash The post Ton Ecosystem partners to help Telegram’s decentralised AI network unseat OpenAI appeared first on AI News. View the full article

Security leaders face a new class of autonomous threat as Anthropic details the first cyber espionage campaign orchestrated by AI. In a report released this week, the company’s Threat Intelligence team outlined its disruption of a sophisticated operation by a ******** state-sponsored group – an assessment made with high confidence – dubbed GTG-1002 and detected in mid-September 2025. The operation targeted approximately 30 entities, including large tech companies, financial institutions, chemical manufacturing companies, and government agencies. Rather than AI assisting human operators, the attackers successfully manipulated Anthropic’s Claude Code model to function as an autonomous agent to execute the vast majority of tactical operations independently. This marks a worrying development for CISOs, moving cyber attacks from human-directed efforts to a model where AI agents perform 80-90 percent of the offensive work with humans acting only as high-level supervisors. Anthropic believes this is the first documented case of a large-scale cyberattack executed without substantial human intervention. AI agents: A new operational model for cyberattacks The group used an orchestration system that tasked instances of Claude Code to function as autonomous penetration testing agents. These AI agents were directed as part of the espionage campaign to perform reconnaissance, discover vulnerabilities, develop exploits, harvest credentials, move laterally across networks, and exfiltrate data. This enabled the AI to perform reconnaissance in a fraction of the time it would have taken a team of human hackers. Human involvement was limited to 10-20 percent of the total effort, primarily focused on campaign initiation and providing authorisation at a few key escalation points. For example, human operators would approve the transition from reconnaissance to active exploitation or authorise the final scope of data exfiltration. The attackers bypassed the AI model’s built-in safeguards, which are trained to avoid harmful behaviours. They did this by jailbreaking the model, tricking it by breaking down attacks into seemingly innocent tasks and by adopting a “role-play” persona. Operators told Claude that it was an employee of a legitimate cybersecurity firm and was being used in defensive testing. This allowed the operation to proceed long enough to gain access to a handful of validated targets. The technical sophistication of the attack lay not in novel malware, but in orchestration. The report notes the framework relied “overwhelmingly on open-source penetration testing tools”. The attackers used Model Context Protocol (MCP) servers as an interface between the AI and these commodity tools, enabling the AI to execute commands, analyse results, and maintain operational state across multiple targets and sessions. The AI was even directed to research and write its own exploit code for the espionage campaign. AI hallucinations become a good thing While the campaign successfully breached high-value targets, Anthropic’s investigation uncovered a noteworthy limitation: the AI hallucinated during offensive operations. The report states that Claude “frequently overstated findings and occasionally fabricated data”. This manifested as the AI claiming to have obtained credentials that did not work or identifying discoveries that “proved to be publicly available information.” This tendency required the human operators to carefully validate all results, presenting challenges for the attackers’ operational effectiveness. According to Anthropic, this “remains an obstacle to fully autonomous cyberattacks”. For security leaders, this highlights a potential weakness in AI-driven attacks: they may generate a high volume of noise and false positives that can be identified with robust monitoring. A defensive AI arms race against new cyber espionage threats The primary implication for business and technology leaders is that the barriers to performing sophisticated cyberattacks have dropped considerably. Groups with fewer resources may now be able to execute campaigns that previously required entire teams of experienced hackers. This attack demonstrates a capability beyond “vibe hacking,” where humans remained firmly in control of operations. The GTG-1002 campaign proves that AI can be used to autonomously discover and exploit vulnerabilities in live operations. Anthropic, which banned the accounts and notified authorities over a ten-day investigation, argues that this development shows the urgent need for AI-powered defence. The company states that “the very abilities that allow Claude to be used in these attacks also make it essential for cyber defense”. The company’s own Threat Intelligence team “used Claude extensively to analyse “the enormous amounts of data generated” during this investigation. Security teams should operate under the assumption that a major change has occurred in cybersecurity. The report urges defenders to “experiment with applying AI for defense in areas like SOC automation, threat detection, vulnerability assessment, and incident response.” The contest between AI-driven attacks and AI-powered defence has begun, and proactive adaptation to counter new espionage threats is the only viable path forward. See also: Wiz: Security lapses emerge amid the global AI race 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 Anthropic details cyber espionage campaign orchestrated by AI appeared first on AI News. View the full article

When Visa unveiled its Intelligent Commerce platform for Asia Pacific on November 12, it wasn’t just launching another payment feature—it was building AI commerce infrastructure to solve a crisis most merchants haven’t noticed yet: their websites are being flooded by AI agents, and there’s no reliable way to tell which ones are legitimate shoppers and which are malicious bots. With AI-driven traffic to retail sites exploding by 4,700% in just one year, Visa’s early 2026 regional pilots give businesses 14 months to prepare their payment systems for a world where artificial intelligence handles shopping and transactions on behalf of consumers. Why Asia Pacific, why now Visa’s strategic decision to pilot its agentic commerce capabilities in Asia Pacific by early 2026 reflects more than a geographic preference—it acknowledges the region’s leadership in mobile payments adoption and digital-first consumer behaviour. Deploying the AI commerce infrastructure represents a fundamental architectural shift: payment systems designed from the ground up to accommodate machine-initiated transactions at speeds and volumes beyond what human shoppers can handle. “Agentic commerce is transforming the very fabric of online payment transactions, requiring a unified ecosystem to unlock its full potential,” said T.R. Ramachandran, head of products and solutions for Asia Pacific at Visa. “With Visa Intelligent Commerce and its cornerstone, Trusted Agent Protocol, Visa is connecting consumers, AI agents and merchants through secure, scalable solutions.” The numbers underscore why this infrastructure matters now. According to Adobe Data Insights cited in Visa’s announcement, 85% of consumers who’ve used AI for shopping report improved experiences. But this enthusiasm masks a brewing crisis: merchants can’t reliably distinguish between legitimate AI agents making purchases and sophisticated bots attempting fraud or data scraping. The technical architecture behind Agentic Commerce Visa Intelligent Commerce comprises integrated APIs spanning tokenisation, authentication, payment instructions, and transaction signals—creating what amounts to a new protocol layer for AI commerce infrastructure. At its core sits the Trusted Agent Protocol, which uses agent-specific cryptographic signatures to verify that AI assistants possess genuine commerce intent and valid consumer authorisation. This verification layer solves a problem that traditional payment security wasn’t designed to address. Fraud detection systems identify suspicious patterns in human behaviour—unusual purchase locations, strange timing, or atypical product combinations. AI agents naturally exhibit behaviour that would trigger these alerts: simultaneous transactions across multiple merchants, machine-speed checkouts, and purchasing patterns optimised by algorithms rather than human impulse. The infrastructure Visa is building maintains consumer visibility even as AI intermediates transactions. When an AI agent books a hotel or orders groceries, merchants can still identify the actual consumer, preserving customer relationship data that businesses depend on for marketing, loyalty programs, and service personalisation. Critically, Visa designed its AI commerce infrastructure as an open, low-code framework. This architectural choice lowers integration barriers for merchants while enabling interoperability across the ecosystem of AI platforms, payment processors, and commerce applications emerging across the Asia Pacific. The ecosystem emerging around AI payments Visa’s partnerships with Ant International, LG Uplus, Microsoft, Perplexity, Stripe, and Tencent reveal the collaborative nature of building AI commerce infrastructure at scale. These aren’t traditional payment processing relationships—they represent nodes in a network where AI agents will need to authenticate across platforms, access payment credentials securely, and execute transactions that span multiple services ina single consumer intent. Consider a scenario where a consumer tells Microsoft’s AI assistant to “plan a weekend in Kuala Lumpur.” The agent might use Perplexity to research options, Stripe to process payment for flights, and transact on Visa’s network—all while maintaining secure authentication and consumer authorisation throughout the journey. This requires infrastructure that enables seamless handoffs between platforms while maintaining security and transparency. The early 2026 pilot timeline suggests that Visa is moving in parallel with regulatory frameworks still taking shape across the Asia Pacific markets. Different countries will approach AI agent authorisation, consumer protection in automated transactions, and cross-border AI commerce differently—creating complexity that will inform global standards as the technology scales. What this means for digital commerce The shift toward AI-mediated transactions changes fundamental assumptions about online retail. Consumer journeys that traditionally involved browsing, comparing, and clicking “buy” will increasingly happen through conversational instructions to AI assistants. Merchants optimising for human attention spans and click-through rates will need to rethink strategies for an environment where AI agents evaluate options through algorithmic comparison rather than emotional appeal. Visa’s AI commerce infrastructure also introduces new competitive dynamics. Businesses that integrate early gain experience with agent-driven sales flows, develop strategies for maintaining customer relationships through AI intermediation, and refine fraud detection for machine-initiated transactions. Those who wait risk operational gaps when consumer adoption reaches critical mass. The payment giant showcased Intelligent Commerce at Singapore Fintech Festival from November 12-14, offering businesses concrete visibility into integration requirements and implementation challenges. With Visa’s 4.8 billion credentials potentially accessible to AI agents across millions of merchant locations worldwide, the infrastructure being piloted in the Asia Pacific will likely define how agentic commerce operates globally. The road to 2026 Fourteen months until regional pilots launch may sound distant, but the technical, operational, and strategic preparations required make it a tight timeline. Businesses need to audit payment infrastructure for AI compatibility, evaluate customer experience design for agent-mediated interactions, and recalibrate security systems to distinguish legitimate AI commerce from threats. The AI commerce infrastructure Visa is deploying doesn’t just enable a new payment method—it establishes the foundation for a different model of digital transactions. As the Asia Pacific becomes the proving ground for this transformation, the lessons learned will shape how commerce operates in an AI-driven world. (Photo by: Yoco Photography) See also: How Huawei is building agentic AI systems that make decisions independently 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 Visa builds AI commerce infrastructure for the Asia Pacific’s 2026 Pilot appeared first on AI News. View the full article

According to IBM, the primary barrier holding back enterprise AI isn’t the technology itself but the persistent issue of data silos. Ed Lovely, VP and Chief Data Officer at IBM, describes data silos as the “Achilles’ heel” of modern data strategy. Lovely made the comments following the release of a new study from the IBM Institute for Business Value that found AI is ready to scale, but enterprise data is not. The report, which surveyed 1,700 senior data leaders, found that functional data remains stubbornly isolated. Finance, HR, marketing, and supply chain data all operate in isolation, with no common taxonomy or shared standards. This fragmentation is having a direct, negative impact on AI projects. “When data lives in disconnected silos, every AI initiative becomes a drawn-out, six-to-twelve-month data cleansing project,” said Ed Lovely, VP and Chief Data Officer at IBM. “Teams spend more time hunting for and aligning data than generating meaningful insights”. This is a direct threat to competitive advantage. For CIOs and CDOs, the mission is no longer just to collect and protect data, but to deploy it effectively to power these new AI systems. From data janitor to value driver The consensus from the study is that data leaders must be relentlessly focused on business outcomes, with 92 percent of CDOs agreeing their success depends on this focus. Herein lies the central tension: while 92 percent are aiming for business value, only 29 percent are confident they have “clear measures to determine the business value of data-driven outcomes.” This gap between ambition and reality is where AI agents that can learn and act autonomously to achieve goals are expected to help. Leaders are showing a growing confidence in these tools, with 83 percent of CDOs in IBM’s research stating the potential benefits of deploying AI agents outweigh the risks. At global medical technology company Medtronic, teams were bogged down matching invoices, purchase orders, and proofs of delivery. By deploying an AI solution, the company automated this workflow. The result was a drop in document matching time from 20 minutes per invoice to just eight seconds, with an accuracy rate exceeding 99 percent. This allowed staff to be redeployed from low-value data entry to higher-value work. Similarly, renewable energy company Matrix Renewables implemented a centralised data platform to monitor its assets. This led to a 75 percent reduction in reporting time and a 10 percent reduction in costly downtime. IBM finds the AI hurdles: Architecture, governance, and the talent gap Achieving these results requires a new approach to data architecture while avoiding silos. The old model of costly, slow data relocation into a central lake is being replaced. IBM’s study finds 81 percent of CDOs now practice bringing AI to the data, rather than moving data to AI. This approach relies on modern architectural patterns like data mesh and data fabric, which provide a virtualised layer to access data where it lives. It also champions the concept of “data products” (packaged, reusable data assets designed for a specific business purpose, such as a “customer 360” view or a financial forecast dataset.) However, making data more accessible introduces governance challenges. The CDO-CISO alliance is now essential to balance speed with security. Data sovereignty is a particular concern, with 82 percent of CDOs viewing it as a core part of their risk management strategy. The biggest hurdle, however, may be people. The report reveals a widening talent gap that threatens to stall progress. In 2025, 77 percent of CDOs report difficulty attracting or retaining top data talent, a sharp increase from 62 percent in 2024. This scarcity is exacerbated by the fact that the required skills are a moving target. IBM found that 82 percent of CDOs are “hiring for data roles that didn’t exist last year related to generative AI”. This cultural and skills challenge is often the hardest part. Hiroshi Okuyama, Chief Digital Officer at Yanmar Holdings, explained: “Changing culture is hard, but people are becoming more aware that their decisions must be based on data and facts, and that they need to collect evidence when making decisions.” Opening the data silos to launch enterprise AI On the technical front, enterprise leaders must champion the move away from siloed data estates. This means investing in modern, federated data architectures and pushing teams to develop and use “data products” that can be securely shared and reused across the organisation. Second, on the cultural front, data literacy must become a business-wide priority, not just an IT concern. The 80 percent of CDOs who say data democratisation helps their organisation move faster are correct. This means fostering a data-driven culture and investing in intuitive tools that make it simpler for non-technical employees to interact with data. The goal is to elevate the organisation from running isolated AI experiments to scaling intelligent automation across core business processes. The companies that succeed will be those that treat their data not as an application byproduct, but as their most valuable asset. Ed Lovely, VP and Chief Data Officer at IBM, said: “Enterprise AI at scale is within reach, but success depends on organisations powering it with the right data. For CDOs, this means establishing a seamlessly integrated enterprise data architecture that fuels innovation and unlocks business value. “Organisations that get this right won’t just improve their AI, they’ll transform how they operate, make faster decisions, adapt to change more quickly, and gain a competitive edge.” See also: New data centre projects mark Anthropic’s biggest US expansion yet Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post IBM: Data silos are holding back enterprise AI appeared first on AI News. View the full article

New US data centre projects in Texas and New York will receive $50 billion in new funding, part of a plan to grow US computing capacity for advanced AI work. The facilities, built with Fluidstack, are designed for Anthropic’s systems and will focus on power and efficiency needs that come with training and running large models across these data centre sites. Fluidstack provides large GPU clusters to companies such as Meta, Midjourney, and Mistral. The partnership reflects a wider push across the tech industry this year, as many firms increase spending on US infrastructure while the Trump administration urges companies to build and invest inside the country. These moves show how much demand there is for US data centre capacity as AI workloads grow. In January, President Donald Trump instructed his administration to craft an AI Action Plan aimed at making “America the world capital in artificial intelligence.” Several firms later outlined major AI and energy spending plans during Trump’s tech and AI summit in July, many of which involved expanding US data centre operations or securing more compute across the country. The new sites are expected to bring about 800 full-time jobs and 2,400 construction jobs. They are scheduled to come online in phases through 2026 and are meant to support the goals in the AI Action Plan by strengthening domestic compute resources. Company leaders say they want these projects to create stable jobs and improve America’s position in AI research by adding more US data centre capacity. The investment also comes at a time when lawmakers are paying closer attention to where high-end compute capacity sits and how much of it stays in the US. Anthropic’s growing US data centre footprint places the company among the largest builders of physical AI infrastructure in the country, reinforcing the push to keep more AI development rooted in the US rather than overseas. “We’re getting closer to AI that can accelerate scientific discovery and help solve complex problems in ways that weren’t possible before. Realising that potential requires infrastructure that can support continued development at the frontier,” said Dario Amodei, CEO and co-founder of Anthropic. “These sites will help us build more capable AI systems that can drive those breakthroughs, while creating American jobs.” Anthropic’s move comes as OpenAI builds out its own network. The ChatGPT maker has secured more than $1.4 trillion in long-term commitments through partners such as Nvidia, Broadcom, Oracle, and major cloud platforms like Microsoft, Google, and Amazon. The scale of these plans has raised questions about whether the US power grid and related industries can support such rapid expansion, especially as more firms compete for space, energy, and equipment tied to US data centre growth. Anthropic says its growth has been driven by its technical staff, its focus on safety work, and its research on alignment and interpretability. Claude is now used by more than 300,000 business customers, and the number of large accounts—those producing more than $100,000 in yearly revenue—has grown almost seven times over the past year. Internal projections reported by The Wall Street Journal suggest the company expects to break even by 2028. OpenAI, by comparison, is said to be projecting $74 billion in operating losses that same year. To keep up with rising demand, Anthropic chose Fluidstack to build facilities tailored to its hardware needs, pointing to the company’s speed and its ability to deliver large-scale power capacity on tight timelines. “We selected Fluidstack as our partner for its ability to move with exceptional agility, enabling rapid delivery of gigawatts of power,” an Anthropic leader said. Gary Wu, co-founder and CEO of Fluidstack, added: “Fluidstack was built for this moment. We’re proud to partner with frontier AI leaders like Anthropic to accelerate and deploy the infrastructure necessary to realise their vision.” Anthropic says this level of spending is needed to support fast-rising usage while keeping its research momentum. The company also plans to focus on cost-efficient ways to scale. Earlier this fall, the firm was valued at $183 billion. It is backed by Alphabet and Amazon, and a separate 1,200-acre data centre campus built for Anthropic by Amazon in Indiana is already in operation. That $11 billion site is running today, while many others in the sector are still in planning stages. Anthropic has also expanded its compute arrangement with Google by tens of billions of dollars. These developments come as the federal government’s role in AI infrastructure funding becomes more contested. Last week, OpenAI asked the Trump administration to broaden a CHIPS Act tax credit so it would cover AI data centres and grid equipment such as transformers, according to a letter reported by Bloomberg. The request followed criticism over earlier comments from CFO Sarah Friar, who mentioned the possibility of a government “backstop” for the company’s compute deals. OpenAI has since stepped away from the idea, but the episode highlighted ongoing uncertainty over how America’s AI infrastructure will be financed — and who will pay for it. (Photo by Scott Blake) See also: Google reveals its own version of Apple’s AI cloud 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 New data centre projects mark Anthropic’s biggest US expansion yet appeared first on AI News. View the full article

Baidu’s latest ERNIE model, a super-efficient multimodal AI, is beating GPT and Gemini on key benchmarks and targets enterprise data often ignored by text-focused models. For many businesses, valuable insights are locked in engineering schematics, factory-floor video feeds, medical scans, and logistics dashboards. Baidu’s new model, ERNIE-4.5-VL-28B-A3B-Thinking, is designed to fill this gap. What’s interesting to enterprise architects is not just its multimodal capability, but its architecture. It’s described as a “lightweight” model, activating only three billion parameters during operation. This approach targets the high inference costs that often stall AI-scaling projects. Baidu is betting on efficiency as a path to adoption, training the system as a foundation for “multimodal agents” that can reason and act, not just perceive. Complex visual data analysis capabilities supported by AI benchmarks Baidu’s multimodal ERNIE AI model excels at handling dense, non-text data. For example, it can interpret a “Peak Time Reminder” chart to find optimal visiting hours, a task that reflects the resource-scheduling challenges in logistics or retail. ERNIE 4.5 also shows capability in technical domains, like solving a bridge circuit diagram by applying Ohm’s and Kirchhoff’s laws. For R&D and engineering arms, a future assistant could validate designs or explain complex schematics to new hires. This capability is supported by Baidu’s benchmarks, which show ERNIE-4.5-VL-28B-A3B-Thinking outperforming competitors like GPT-5-High and Gemini 2.5 Pro on some key tests: MathVista: ERNIE (82.5) vs Gemini (82.3) and GPT (81.3) ChartQA: ERNIE (87.1) vs Gemini (76.3) and GPT (78.2) VLMs Are Blind: ERNIE (77.3) vs Gemini (76.5) and GPT (69.6) It’s worth noting, of course, that AI benchmarks provide a guide but can be flawed. Always perform internal tests for your needs before deploying any AI model for mission-critical applications. Baidu shifts from perception to automation with its latest ERNIE AI model The primary hurdle for enterprise AI is moving from perception (“what is this?”) to automation (“what now?”). ERNIE 4.5 claims to address this by integrating visual grounding with tool use. Asking the multimodal AI to find all people wearing suits in an image and return their coordinates in JSON format works. The model generates the structured data, a function easily transferable to a production line for visual inspection or to a system auditing site images for safety compliance. The model also manages external tools and can autonomously zoom in on a photograph to read small text. If it faces an unknown object, it can trigger an image search to identify it. This represents a less passive form of AI that could power an agent to not only flag a data centre error, but also zoom in on the code, search the internal knowledge base, and suggest the fix. Unlocking business intelligence with multimodal AI Baidu’s latest ERNIE AI model also targets corporate video archives from training sessions and meetings to security footage. It can extract all on-screen subtitles and map them to their precise timestamps. It also demonstrates temporal awareness, finding specific scenes (like those “filmed on a bridge”) by analysing visual cues. The clear end-goal is making vast video libraries searchable, allowing an employee to find the exact moment a specific topic was discussed in a two-hour webinar they may have dozed off a couple of times during. Baidu provides deployment guidance for several paths, including transformers, vLLM, and FastDeploy. However, the hardware requirements are a major barrier. A single-card deployment needs 80GB of GPU memory. This is not a tool for casual experimentation, but for organisations with existing and high-performance AI infrastructure. For those with the hardware, Baidu’s ERNIEKit toolkit allows fine-tuning on proprietary data; a necessity for most high-value use cases. Baidu is providing its latest ERNIE AI model with an Apache 2.0 licence that permits commercial use, which is essential for adoption. The market is finally moving toward multimodal AI that can see, read, and act within a specific business context, and the benchmarks suggest it’s doing so with impressive capability. The immediate task is to identify high-value visual reasoning jobs within your own operation and weigh them against the substantial hardware and governance costs. See also: Wiz: Security lapses emerge amid the global AI race 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 Baidu ERNIE multimodal AI beats GPT and Gemini in benchmarks appeared first on AI News. View the full article

Google has rolled out Private AI Compute, a new cloud-based processing system designed to bring the privacy of on-device AI to the cloud. The platform aims to give users faster, more capable AI experiences without compromising data security. It combines Google’s most advanced Gemini models with strict privacy safeguards, reflecting the company’s ongoing effort to make AI both powerful and responsible. The feature closely resembles Apple’s Private Cloud Compute, signalling how major tech firms are rethinking privacy in the age of large-scale AI. Both companies are trying to balance two competing needs — the huge computing power required to run advanced AI models and users’ expectations for data privacy. Why Google built Private AI Compute As AI systems get smarter, they’re also becoming more personal. What started as tools that completed simple tasks or answered direct questions are now systems that can anticipate user needs, suggest actions, and handle complex processes in real time. That kind of intelligence demands a level of reasoning and computation that often exceeds what’s possible on a single device. Private AI Compute bridges that gap. It lets Gemini models in the cloud process data faster and more efficiently while ensuring that sensitive information remains private and inaccessible to anyone else — not even Google engineers. Google describes it as combining the power of cloud AI with the security users expect from local processing. In practical terms, this means you could get quicker responses, smarter suggestions, and more personalised results without your personal data ever leaving your control. How Private AI Compute keeps data secure Google claims the new platform is based on the same principles that underpin its broader AI and privacy strategy: giving users control, maintaining security, and earning trust. The system acts as a protected computing environment, isolating data so it can be processed safely and privately. It uses a multi-layered design centred on three key components: Unified Google tech stack: Private AI Compute runs entirely on Google’s own infrastructure, powered by custom Tensor Processing Units (TPUs). It’s secured through Titanium Intelligence Enclaves (TIE), which create an additional layer of protection for data processed in the cloud. Encrypted connections: Before data is sent for processing, remote attestation and encryption verify that it’s connecting to a trusted, hardware-secured environment. Once inside this sealed cloud space, information stays private to the user. Zero access assurance: Google says the system is designed so that no one — not even the company itself — can access the data processed within Private AI Compute. This design builds on Google’s Secure AI Framework (SAIF), AI Principles, and Privacy Principles, which outline how the company develops and deploys AI responsibly. What users can expect Private AI Compute also improves the performance of AI features that are already running on devices. Magic Cue on the Pixel 10 can now offer more relevant and timely suggestions by leveraging cloud-level processing power. Similarly, the Recorder app can use the system to summarise transcriptions across a wider range of languages — something that would be difficult to do entirely on-device. These examples hint at what’s ahead. With Private AI Compute, Google can deliver AI experiences that combine the privacy of local models with the intelligence of cloud-based ones. It’s an approach that could eventually apply to everything from personal assistants and photo organisation to productivity and accessibility tools. Google calls this launch “just the beginning.” The company says Private AI Compute opens the door to a new generation of AI tools that are both more capable and more private. As AI becomes increasingly woven into everyday tasks, users are demanding greater transparency and control over how their data is used — and Google appears to be positioning this technology as part of that answer. For those interested in the technical details, Google has published a technical brief explaining how Private AI Compute works and how it fits into the company’s larger vision for responsible AI development. (Photo by Solen Feyissa) See also: Apple plans big Siri update with help from Google AI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is 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 reveals its own version of Apple’s AI cloud appeared first on AI News. View the full article

According to Wiz, the race among AI companies is causing many to overlook basic security hygiene practices. 65 percent of the 50 leading AI firms the cybersecurity firm analysed had leaked verified secrets on GitHub. The exposures include API keys, tokens, and sensitive credentials, often buried in code repositories that standard security tools do not check. Glyn Morgan, Country Manager for ***&I at Salt Security, described this trend as a preventable and basic error. “When AI firms accidentally expose their API keys they lay bare a glaring avoidable security failure,” he said. “It’s the textbook example of governance paired with a security configuration, two of the risk categories that OWASP flags. By pushing credentials into code repositories they hand attackers a golden ticket to systems, data, and models, effectively sidestepping the usual defensive layers.” Wiz’s report highlights the increasingly complex supply chain security risk. The problem extends beyond internal development teams; as enterprises increasingly partner with AI startups, they may inherit their security posture. The researchers warn that some of the leaks they found “could have exposed organisational structures, training data, or even private models.” The financial stakes are considerable. The companies analysed with verified leaks have a combined valuation of over $400 billion. The report, which focused on companies listed in the Forbes AI 50, provides examples of the risks: LangChain was found to have exposed multiple Langsmith API keys, some with permissions to manage the organisation and list its members. This type of information is highly valued by attackers for reconnaissance. An enterprise-tier API key for ElevenLabs was discovered sitting in a plaintext file. An unnamed AI 50 company had a HuggingFace token exposed in a deleted code fork. This single token “allow[ed] access to about 1K private models”. The same company also leaked WeightsAndBiases keys, exposing the “training data for many private models.” The Wiz report suggests this problem is so prevalent because traditional security scanning methods are no longer sufficient. Relying on basic scans of a company’s main GitHub repositories is a “commoditised approach” that misses the most severe risks . The researchers describe the situation as an “iceberg” (i.e. the most obvious risks are visible, but the greater danger lies “below the surface”.) To find these hidden risks, the researchers adopted a three-dimensional scanning methodology they call “Depth, Perimeter, and Coverage”: Depth: Their deep scan analysed the “full commit history, commit history on forks, deleted forks, workflow logs and gists”—areas most scanners “never touch”. Perimeter: The scan was expanded beyond the core company organisation to include organisation members and contributors. These individuals might “inadvertently check company-related secrets into their own public repositories”. The team identified these adjacent accounts by tracking code contributors, organisation followers, and even “correlations in related networks like HuggingFace and npm.” Coverage: The researchers specifically looked for new AI-related secret types that traditional scanners often miss, such as keys for platforms like WeightsAndBiases, Groq, and Perplexity. This expanded attack surface is particularly worrying given the apparent lack of security maturity at many fast-moving companies. The report notes that when researchers tried to disclose the leaks, almost half of disclosures either failed to reach the target or received no response. Many firms lacked an official disclosure channel or simply failed to resolve the issue when notified. Wiz’s findings serve as a warning for enterprise technology executives, highlighting three immediate action items for managing both internal and third-party security risk. Security leaders must treat their employees as part of their company’s attack surface. The report recommends creating a Version Control System (VCS) member policy to be applied during employee onboarding. This policy should mandate practices such as using multi-factor authentication for personal accounts and maintaining a strict separation between personal and professional activity on platforms like GitHub. Internal secret scanning must evolve beyond basic repository checks. The report urges companies to mandate public VCS secret scanning as a “non-negotiable defense”. This scanning must adopt the aforementioned “Depth, Perimeter, and Coverage” mindset to find threats lurking below the surface. This level of scrutiny must be extended to the entire AI supply chain. When evaluating or integrating tools from AI vendors, CISOs should probe their secrets management and vulnerability disclosure practices. The report notes that many AI service providers are leaking their own API keys and should “prioritise detection for their own secret types.” The central message for enterprises is that the tools and platforms defining the next generation of technology are being built at a pace that often outstrips security governance. As Wiz concludes, “For AI innovators, the message is clear: speed cannot compromise security”. For the enterprises that depend on that innovation, the same warning applies. See also: Exclusive: Dubai’s Digital Government chief says speed trumps spending in AI efficiency race 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 Wiz: Security lapses emerge amid the global AI race appeared first on AI News. View the full article

A ******** AI startup, Moonshot, has disrupted expectations in artificial intelligence development after its Kimi K2 Thinking model surpassed OpenAI’s GPT-5 and Anthropic’s Claude Sonnet 4.5 across multiple performance benchmarks, sparking renewed debate about whether America’s AI dominance is being challenged by cost-efficient ******** innovation. Beijing-based Moonshot AI, valued at US$3.3 billion and backed by tech giants Alibaba Group Holding and Tencent Holdings, released the open-source Kimi K2 Thinking model on November 6, achieving what industry observers are calling another “DeepSeek moment” – a reference to the Hangzhou-based startup’s earlier disruption of AI cost assumptions. Hello, Kimi K2 Thinking! The Open-Source Thinking Agent Model is here. SOTA on HLE (44.9%) and BrowseComp (60.2%) Executes up to 200 – 300 sequential tool calls without human interference Excels in reasoning, agentic search, and coding 256K context window Built… pic.twitter.com/lZCNBIgbV2 — Kimi.ai (@Kimi_Moonshot) November 6, 2025 Performance metrics challenge US models According to the company’s GitHub blog post, Kimi K2 Thinking scored 44.9% on Humanity’s Last Exam, a large language model benchmark consisting of 2,500 questions across a broad range of subjects, exceeding GPT-5’s 41.7%. The model also achieved 60.2% on the BrowseComp benchmark, which evaluates web browsing proficiency and information-seeking persistence of large language model agents, and scored 56.3% to lead in the Seal-0 benchmark designed to challenge search-augmented models on real-world research queries. VentureBeat reported that the fully open-weight release meeting or exceeding GPT-5’s scores marks a turning point where the gap between closed frontier systems and publicly available models has effectively collapsed for high-end reasoning and coding. Kimi K2 Thinking is the new leading open weights model: it demonstrates particular strength in agentic contexts but is very verbose, generating the most tokens of any model in completing our Intelligence Index evals@Kimi_Moonshot's Kimi K2 Thinking achieves a 67 in the… pic.twitter.com/m6SvpW7iif — Artificial Analysis (@ArtificialAnlys) November 7, 2025 Cost efficiency raises questions The popularity of the model grew after CNBC reported its training cost was merely US$4.6 million, though Moonshot AI did not comment on the cost. According to calculations by the South China Morning Post, the cost of Kimi K2 Thinking’s application programming interface was six to 10 times cheaper than that of OpenAI and Anthropic’s models. The model uses a Mixture-of-Experts architecture with one trillion total parameters, of which 32 billion are activated per inference, and was trained using INT4 quantisation to achieve roughly two times generation speed improvement while maintaining state-of-the-art performance. Thomas Wolf, co-founder of Hugging Face, commented on X that Kimi K2 Thinking was another case of an open-source model passing a closed-source model, asking, “Is this another DeepSeek moment? Should we expect [one] every couple of months now?” Technical capabilities and limitations Moonshot AI researchers said Kimi K2 Thinking set “new records across benchmarks that assess reasoning, coding and agent capabilities”. The model can execute up to 200-300 sequential tool calls without human interference, reasoning coherently across hundreds of steps to solve complex problems. Independent testing by consultancy Artificial Analysis placed Kimi K2 on top of its Tau-2 Bench Telecom agentic benchmark with 93% accuracy, which was described as the highest score it has independently measured. However, Nathan Lambert, a researcher at the Allen Institute for AI, suggested there’s still a time lag of approximately four to six months in raw performance between the best closed and open models, though he acknowledged that ******** labs are closing in and performing very strongly on key benchmarks. Market implications and competitive pressure Zhang Ruiwang, a Beijing-based information technology system architect, said the trend was for ******** companies to keep costs down, explaining, “The overall performance of ******** models still lags behind top US models, so they have to compete in the realms of cost-effectiveness to have a way out”. Zhang Yi, chief analyst at consultancy iiMedia, said the training costs of ******** AI models were seeing a “cliff-like drop” driven by innovation in model architecture and training technique, and input of quality training data, marking a shift away from the heaping of computing resources in the early days. The model was released under a Modified MIT License that grants full commercial and derivative rights, with one restriction: deployers serving over 100 million monthly active users or generating over US$20 million per month in revenue must prominently display “Kimi K2” on the product’s user interface. Industry response and future outlook Deedy Das, a partner at early-stage venture capital firm Menlo Ventures, wrote in a post on X that “Today is a turning point in AI. A ******** open-source model is #1. Seminal moment in AI”. Today is a turning point in AI. A ******** open source model is #1. Kimi K2 Thinking scored 51% in Humanity's Last Exam, higher than GPT-5 and every other model. $0.6/M in, $2.5/M output. The best at writing, and does 15tps on two Mac M3 Ultras! Seminal moment in AI. Try it… pic.twitter.com/fmxlxpCGbE — Deedy (@deedydas) November 7, 2025 Nathan Lambert wrote in a Substack article that the success of ******** open-source AI developers, including Moonshot AI and DeepSeek, showed how they “made the closed labs sweat,” adding “There’s serious pricing pressure and expectations that [the US developers] need to manage”. The release positions Moonshot AI alongside other ******** AI companies like DeepSeek, Qwen, and Baichuan that are increasingly challenging the narrative of American AI supremacy through cost-efficient innovation and open-source development strategies. Whether this represents a sustainable competitive advantage or a temporary convergence in capabilities remains to be seen as both US and ******** companies continue advancing their models. the public nature of the statements, and the market’s reaction, suggest substantive discussions may soon be underway. The AI chip landscape is entering a ******* of flux. Organisations should maintain flexibility in their infrastructure strategy and monitor how partnerships like Tesla-Intel might reshape the competitive dynamics of AI hardware manufacturing. The decisions made today about chip manufacturing partnerships could determine which organisations have access to cost-effective, high-performance AI infrastructure in the coming years. Photo by Moonshot AI) See also: DeepSeek disruption: ******** AI innovation narrows global technology divide 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. This 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 ******** AI startup Moonshot outperforms GPT-5 and Claude Sonnet 4.5: What you need to know appeared first on AI News. View the full article

The potential Tesla-Intel chip partnership could deliver AI chips at just 10% of Nvidia’s cost – a claim that represents a significant development in AI infrastructure that enterprise technology leaders cannot afford to ignore. On November 6, 2025, Tesla CEO Elon Musk stated publicly at the company’s annual shareholder meeting that the electric vehicle manufacturer is considering working with Intel to produce its fifth-generation AI chips, signalling a major strategic shift in how AI computing hardware might be manufactured and distributed. “You know, maybe we’ll, we’ll do something with Intel,” Musk told shareholders, according to a Reuters report. “We haven’t signed any deal, but it’s probably worth having discussions with Intel.” The statement sent Intel shares up 4% in after-hours trading, underscoring how seriously the market views the potential collaboration. The strategic context behind the partnership Tesla’s consideration of Intel as a manufacturing partner comes at a important juncture for both companies. Tesla is designing its AI5 chip to power its autonomous driving systems. Currently on its fourth-generation chip, Tesla has identified a significant supply constraint that traditional partnerships with Taiwan’s TSMC and South Korea’s Samsung cannot address fully. “Even when we extrapolate the best-case scenario for chip production from our suppliers, it’s still not enough,” Musk said during the shareholder meeting. The supply gap has led Tesla to consider building what Musk calls a “terafab” – a massive chip fabrication facility capable of producing at least 100,000 wafer starts per month. For Intel, the potential partnership offers an important opportunity. The US chipmaker has lagged significantly behind Nvidia in the AI chip race and desperately needs external customers for its newest manufacturing technology. The US government recently took a 10% stake in Intel, underscoring the strategic importance of maintaining domestic chip manufacturing capabilities. Cost and performance implications At 10% of Nvidia’s manufacturing cost, the technical specifications Musk outlined during the shareholder meeting could reshape enterprise AI economics. According to Musk, Tesla’s AI5 chip would consume approximately one-third of the power used by Nvidia’s flagship Blackwell chip, and cost just 10% as much to manufacture. “I’m super hardcore on chips right now, as you may be able to tell,” Musk said. “I have chips on the brain.” The cost and efficiency projections, if realised, could alter the economics of AI deployment. Enterprise leaders investing heavily in AI infrastructure should monitor whether these performance targets materialise, as they could influence future technology purchasing decisions in the industry. The chip would be inexpensive, power-efficient, and optimised for Tesla’s own software, Musk said. Production timeline and scale Tesla’s chip production roadmap provides a timeline for enterprise planning. A small number of AI5 units would be produced in 2026, with high-volume production possible in 2027. Musk indicated in a post on social media that AI6 will use the same fabrication facilities but achieve roughly twice the performance, with volume production targeted for mid-2028. The scale of Tesla’s ambitions is substantial. The proposed “terafab” would represent an expansion of domestic chip manufacturing capacity, potentially reducing supply chain vulnerabilities that have plagued the technology industry in recent years. “So I think we may have to do a Tesla terafab. It’s like a giga but way *******. I can’t see any other way to get to the volume of chips that we’re looking for. So I think we’re probably going to have to build a gigantic chip fab. It’s got to be done,” Musk said. What this means for enterprise decision-makers Several strategic considerations emerge from any potential Tesla-Intel chip partnership: Supply chain resilience: The move toward domestic chip manufacturing addresses concerns about supply chain concentration in Asia. Enterprise leaders managing technology risk should consider how shifts in chip manufacturing geography might affect their supply chains and vendor relationships. Cost structure changes: If Tesla achieves its stated cost targets, the competitive landscape for AI chips could shift. Organisations should prepare contingency plans for potential price pressure on current suppliers and evaluate whether alternative chip architectures are viable. Technology sovereignty: The US government’s stake in Intel and support for domestic chip manufacturing reflect broader geopolitical considerations. Enterprise leaders in regulated industries or those handling sensitive data should assess how the trends might affect their technology sources. Innovation pace: Tesla’s aggressive timeline for multiple chip generations suggests an accelerating pace of AI hardware innovation. Technology leaders should factor this into refresh cycles and architecture decisions, avoiding premature commitment to current-generation technology. The broader industry context Musk’s statements occur against the backdrop of US-China technology competition. Export restrictions have impacted Nvidia’s business in China, where its market share has reportedly dropped from 95% to near zero. Intel declined to comment on Musk’s remarks, and no formal agreement has been announced. However, the public nature of the statements, and the market’s reaction, suggest substantive discussions may soon be underway. The AI chip landscape is entering a ******* of flux. Organisations should maintain flexibility in their infrastructure strategy and monitor how partnerships like Tesla-Intel might reshape the competitive dynamics of AI hardware manufacturing. The decisions made today about chip manufacturing partnerships could determine which organisations have access to cost-effective, high-performance AI infrastructure in the coming years. 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. This 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 10% of Nvidia’s cost: Why Tesla-Intel chip partnership demands attention appeared first on AI News. View the full article

Microsoft is forming a new team to research superintelligence and other advanced forms of artificial intelligence. Mustafa Suleyman, who leads Microsoft’s AI division overseeing Bing and Copilot, announced the creation of the MAI Superintelligence Team in a blog post. He said he will head the group and that Microsoft plans to put “a lot of money” behind the effort. “We are doing this to solve real, concrete problems and do it in such a way that it remains grounded and controllable,” Suleyman wrote. “We are not building an ill-defined and ethereal superintelligence; we are building a practical technology explicitly designed only to serve humanity.” Building a ‘humanist’ approach to superintelligence The move comes as big tech companies race to attract top AI researchers. Meta, Facebook’s parent company, recently created its own Meta Superintelligence Labs and spent billions recruiting experts, even offering signing bonuses as high as $100 million. Suleyman didn’t comment on whether Microsoft plans to match such offers but said the new team will include both internal talent and new hires, with Karen Simonyan as chief scientist. Before joining Microsoft, Suleyman co-founded DeepMind, which Google bought in 2014. He later led the AI startup Inflection, which Microsoft acquired last year along with several of its employees. The hiring push reflects a broader trend. Since OpenAI released ChatGPT in 2022, companies have raced to bring generative AI into their products. Microsoft uses OpenAI’s models in Bing and Copilot, while OpenAI relies on Microsoft’s Azure cloud to power its tools. Microsoft also holds a $135 billion stake in OpenAI after a recent restructuring. Reducing reliance on OpenAI Despite the partnership, Microsoft has been working to diversify its AI sources as it lays the groundwork for future superintelligence research. Following the Inflection acquisition, the company began experimenting with models from Google and Anthropic, another AI startup founded by former OpenAI executives. The new Microsoft AI research group will aim to build useful AI companions that assist people in education and other areas. Suleyman said the team also plans to focus on projects in medicine and renewable energy. A different path from rivals Unlike some peers, Suleyman said Microsoft isn’t trying to build an “infinitely capable generalist” AI. He doubts such systems could be kept under control and instead wants to develop what he calls “humanist superintelligence” – AI that serves human needs and delivers real-world benefits. “Humanism requires us to always ask the question: does this technology serve human interests?” he said. While the risks of AI are widely debated – from bias to existential threats – Suleyman said his team’s goal is to create specialist systems that achieve “superhuman performance” without posing major risks. He cited examples like AI that could improve battery storage or design new molecules, similar to DeepMind’s AlphaFold project that predicts protein structures. Medical superintelligence on the horizon Suleyman said Microsoft is especially focused on healthcare, predicting that AI capable of expert-level diagnosis could emerge in the next two or three years. He described it as technology that can reason through complex medical problems and detect preventable diseases much earlier. “We’ll have expert-level performance at the full range of diagnostics, alongside highly capable planning and prediction in operational clinical settings,” he wrote. As investors question whether massive AI spending will translate into profits, Suleyman emphasised that Microsoft is setting clear limits. “We are not building a superintelligence at any cost, with no limits,” he said. (Photo by Praswin Prakashan) See also: Microsoft gives free Copilot AI services to US government workers 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 Microsoft’s next big AI bet: building a ‘humanist superintelligence’ appeared first on AI News. View the full article

When Nvidia CEO Jensen Huang initially told the Financial Times that China would “win the AI race” before softening his stance, it crystallised a predicament that’s been years in the making. The world’s most valuable chipmaker now finds itself caught between two superpowers, each wielding the Nvidia AI chip ban as a weapon in a broader technological cold war—and the company’s attempt to please both sides may ultimately satisfy neither. A statement from NVIDIA CEO Jensen Huang. pic.twitter.com/Exwx54OYJV — NVIDIA Newsroom (@nvidianewsroom) November 5, 2025 From dominance to zero: A market collapse The numbers tell a stark story. Speaking at a Citadel Securities event in October, Huang revealed that Nvidia’s share of China’s AI accelerator market has collapsed from roughly 95% to zero, with the company now assuming no revenue from China in its forecasts. This isn’t just a revenue hiccup—China previously represented between 20% and 25% of Nvidia’s data centre revenue, a segment that generated more than US$41 billion in its most recent financial results. The latest blow came this week when sources claimed that the White House informed federal agencies it will not permit Nvidia to sell its latest scaled-down AI chips to China, specifically the B30A chip designed to train large language models. Despite Nvidia providing samples to ******** customers and reportedly working to modify the design, the Trump administration has drawn a hard line. But Washington’s restrictions represent only half of Nvidia’s problem. Beijing has issued guidance requiring new data centre projects receiving state funds to use only domestically-made AI chips, with projects less than 30% complete ordered to remove all installed foreign chips or cancel purchase plans. It’s a pincer movement that leaves Nvidia with virtually no room to manoeuvre. The lobbying game: Too much, too late? Huang has long argued that maintaining China’s dependence on American hardware serves US interests. His logic? Keep ******** AI developers hooked on Nvidia’s ecosystem, and America retains technological leverage. Following meetings with President Trump in July, it appeared Huang’s lobbying had worked, with Washington agreeing to ease some chip curbs under a plan where Nvidia and AMD would pay the US government 15% of their ******** revenues. That optimism proved short-lived. Beijing has since shut Nvidia out of the market through a national security review of its chips, with Huang stating the firm’s market share has been reduced to zero. The irony is palpable: while Huang lobbied Washington to allow more sales to China, Beijing was simultaneously building barriers to keep Nvidia out. When Huang contrasted China’s pro-industry energy subsidies with what he described as excessive Western regulation, it revealed the fundamental tension in Nvidia’s position. The company needs a favourable policy from both capitals, but operates in an environment where pleasing one increasingly means antagonising the other. The cost of technological nationalism This isn’t merely a corporate problem—it’s reshaping the global AI landscape. China’s ban would eliminate foreign chipmakers like Nvidia from a significant portion of the market, even if a deal is agreed to allow the resumption ofadvanced chip sales to China. Meanwhile, ******** companies have over US$100 billion in state funding for AI data centre projects since 2021, creating a massive captive market for domestic alternatives. The policy whiplash has real consequences. Following Trump’s meetings with ******** President Xi Jinping, highly anticipated trade talks yielded no concessions from either side on chip policy, with top US officials rallying against Trump’s initial consideration of Huang’s request to allow sales of new AI chips to China. An Nvidia spokesperson’s response to the latest restrictions was telling Reuters: “zero share in China’s highly competitive market for datacenter compute, and do not include it in our guidance”. It’s a public acknowledgement of defeat wrapped in corporate speak. China’s calculated response Beijing’s moves reveal a strategy that extends beyond mere retaliation. China has discouraged local tech giants from purchasing advanced Nvidia chips over security concerns this year, while showing off a new data centre powered solely by domestic AI chips. The message is clear: foreign dependence is a vulnerability to be eliminated, not managed. The ******** government is carving out market share for domestic chipmakers ranging from Huawei Technologies to smaller players like Shanghai-listed Cambricon and startups including MetaX, Moore Threads, and Enflame. While these companies struggle to match Nvidia’s performance and software ecosystem, they’re getting exactly what they need most: time, money, and a protected market to mature. The impossible balance Nvidia’s predicament exposes a broader truth about technology in an era of great power competition: the middle ground is disappearing. Companies can optimise for American national security priorities or ******** market access, but increasingly not both. Huang expressed concerns that the West was being held back by “cynicism” and excessive regulation, contrasting this with China’s energy subsidies aimed at lowering costs for local developers using domestic chips. But this comparison misses the point. The question isn’t whether China’s industrial policy is more effective—it’s whether Nvidia can operate in an environment where technology has become inseparable from geopolitics. The B30A saga illustrates the futility of technical compromises. Even a chip deliberately neutered to comply with US export controls finds no approval from Washington, while Beijing increasingly views any foreign chip as a strategic vulnerability. Nvidia could design a thousand variants, each weaker than the last, and still find itself shut out by one capital or the other. What comes next? In the short term, Nvidia faces a stark reality: the company now assumes 0% revenue from China in all forecasts, with Huang stating, “If anything happens in China… it will be a bonus”. This conservative guidance protects the stock but signals that management sees no near-term resolution. The real question is whether this represents a temporary freeze or a permanent fracture. While the move helps boost sales of domestically developed chips, it also risks widening the US-China gap in AI computing power, as US tech giants continue spending hundreds of billions on data centres powered by Nvidia’s most advanced chips. For Nvidia, the path forward likely involves doubling down on markets where geopolitics align with business—the US, Europe, and friendly Asian nations. The China dream, at least in its previous form, appears over. Huang’s softening of his “China will win” comments reflects this new reality. America might not win by keeping China dependent on its chips, but Nvidia certainly loses by being caught in the middle. The Nvidia AI chip ban—from both directions—represents more than export controls or industrial policy. It’s evidence that in the AI race, there won’t be neutral suppliers. Technology companies will increasingly be forced to choose sides, and those who hesitate will find the choice made for them. Nvidia’s plunge from 95% to zero market share in China took just months. The question now is whether Washington and Beijing will leave any space for global tech companies to operate at all. (Photo by OpenAI and Nvidia plan $100B chip deal for AI future) See also: 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 Nvidia AI chip ban: Can tech giants navigate a geopolitical zero-sum game? appeared first on AI News. View the full article

When Dubai launched its State of AI Report in April 2025, revealing over 100 high-impact AI use cases, the emirate wasn’t just showcasing technological prowess—it was making a calculated bet that speed, not spending, would determine which cities win the global race for AI-powered governance. In an exclusive interview, Matar Al Hemeiri, Chief Executive of Digital Dubai Government Establishment, revealed how Dubai’s approach to AI government efficiency differs fundamentally from both its regional competitors and established Asian tech hubs—and why the emirate believes its model of rapid deployment paired with binding ethical frameworks offers a blueprint other governments will eventually follow. The DubaiAI advantage: 180 services, one virtual assistant While neighbouring Abu Dhabi announced a $4.8 billion investment to become the world’s first fully AI-powered government by 2027, Dubai has taken a different path. “Abu Dhabi’s investment is focused on building an end-to-end AI-powered government infrastructure,” Al Hemeiri explained. “Dubai’s model is to embed AI ethics, interoperability, and explainability into a scalable governance framework.” The results are already visible. DubaiAI, the citywide AI-powered virtual assistant, now provides information on more than 180 public services—a figure that represents one of the most comprehensive government AI chatbot deployments globally. The system handles 60% of routine government inquiries while cutting operational costs by 35%. But Al Hemeiri pushed back against the narrative that AI automation inevitably means job losses. “Automation frees our workforce from repetitive, informational tasks,” he said. “Employees are being reskilled and redeployed into higher-value roles such as AI oversight, service design, and strategic policy work.” The timing couldn’t be more critical. Dubai’s population growth has created an “immense spike in demand for government services,” according to Al Hemeiri, making AI-driven efficiency not just a competitive advantage but an operational necessity. Speed as strategy: From pilot to deployment in months What sets Dubai apart in AI government efficiency isn’t just what it builds—it’s how quickly it deploys. “In Dubai, once an AI initiative is announced, it is swiftly activated, moving from pilot to deployment within months, far faster than the global norm,” Al Hemeiri emphasised. The numbers back this claim. In 2025, over 96% of government entities had adopted at least one AI solution, and 60% of surveyed users preferred AI-supported services. Dubai benchmarks itself against leading smart cities like Singapore, Berlin, Helsinki, and Tallinn, but argues its integration of AI ethics directly into procurement and deployment provides a decisive edge. “Our competitive edge lies in the speed with which Dubai operationalises its ethics,” Al Hemeiri said, addressing a common criticism that AI governance frameworks are purely theoretical. “The AI Policy is not a theoretical framework; it is a binding set of principles and technical requirements applied to every AI deployment across government.” This approach builds on the Ethical AI Toolkit launched in 2019, making Dubai one of the few cities globally where ethical compliance is embedded from procurement to performance evaluation. Beyond chatbots: Healthcare, energy, and predictive services While DubaiAI captures headlines, Al Hemeiri pointed to less-publicised implementations delivering measurable impact. AI models are now detecting chronic conditions such as diabetes at earlier stages, while predictive algorithms improve auditing systems within the Dubai Health Authority. In energy infrastructure, smart grids powered by real-time AI forecasting tools are optimising consumption and reducing environmental impact. The most ambitious project currently in development is Dubai’s predictive public services platform, which will use integrated data and AI to anticipate citizen needs—from automated license renewals to preventive healthcare notifications. “We have begun efforts on building this project, with full rollout targeted for the early 2030s,” Al Hemeiri revealed. Elements of this vision are already being tested through AI-enabled urban planning tools and citywide digital twins that simulate policy outcomes before implementation. Data sovereignty: A hybrid model between China and GDPR Dubai’s approach to data governance offers a middle path between China’s strict localisation requirements and the EU’s GDPR framework. “Dubai’s model offers a hybrid—anonymised citizen data remains within Dubai’s jurisdiction under robust sovereignty laws, but can be securely shared across entities with the user’s consent for government services, through the nation’s official digital identity platform: UAE PASS,” Al Hemeiri explained. A key differentiator is Dubai’s embrace of synthetic data frameworks. “They allow us to develop and test AI systems at scale while preserving privacy and maintaining compliance with Dubai’s data sovereignty requirements,” he said. This approach enables faster innovation cycles while addressing privacy concerns that have hampered AI development in other jurisdictions. The startup sandbox: Real integration, not just regulatory relief Dubai positions itself as a testing ground for AI startups, but Al Hemeiri argued the emirate offers more than regulatory flexibility. “Dubai’s AI sandboxes combine regulatory flexibility with direct access to government datasets and real-world testing environments,” he said. One healthcare diagnostics startup piloted within Dubai’s sandbox has already integrated its AI triage tool into Dubai Health Authority services. “Because our ecosystem operates as an interconnected digital operating system, startups in our sandboxes can test solutions that seamlessly integrate with other city services, from mobility innovations like the Dubai Loop and eVTOL air taxis to healthcare AI diagnostics,” Al Hemeiri explained. Converting global attention into economic returns Dubai AI Week 2025 attracted participants from 100 countries and partnerships with Meta, Google, Microsoft, and OpenAI. But Al Hemeiri insisted the emirate is focused on converting attention into tangible outcomes. “We have established post-event working groups with each of these partners to identify and accelerate joint projects,” he said, citing AI upskilling programs, R&D collaborations, and pilot deployments in healthcare, mobility, and urban planning. These partnerships feed directly into Dubai’s D33 Economic Agenda, which aims to generate AED 100 billion annually from digital innovation. The State of AI Report projects AI could contribute over AED 235 billion to Dubai’s economy by 2030—a figure that represents nearly 20% of the emirate’s targeted economic expansion. Quiet wins and future risks When pressed about initiatives that deliver value without media fanfare, Al Hemeiri highlighted the UN Citiverse Challenge, co-led by Digital Dubai and global partners, which brings together innovators to design AI-powered solutions for inclusive public services and sustainability. He also pointed to Dubai Future Foundation’s autonomous delivery robot, already being piloted on Dubai streets to improve last-mile delivery efficiency while reducing congestion and emissions. On risks, Al Hemeiri was direct: “The greatest risk is scaling without sufficient oversight.” Dubai mitigates this through continuous system audits and a requirement for explainability in all public sector AI. Al Hemeiri added that ensuring ROI “is crucial for us when deciding to build an AI use case. We calculate this when planning a project, and only move ahead once we are convinced we will be able to attain the expected ROI for the city.” The five-year test Asked what would constitute failure five years from now, Al Hemeiri said that it “would mean fragmented AI adoption without improving citizen trust, efficiency, or quality of life.” Success, conversely, would be “when AI-powered public services are seamless, anticipatory, and inclusive, easing the lives of citizens and residents, and naturally becoming a blueprint replicated by other governments globally.” It’s an ambitious vision—one that positions Dubai not just as a fast follower in AI government efficiency, but as a potential model for how cities can deploy transformative technology at speed without sacrificing ethical oversight or public trust. Whether that model proves replicable beyond Dubai’s unique governance structure and resources remains the central question. But with 96% of government entities already adopting AI solutions and deployment timelines measured in months rather than years, Dubai is testing that hypothesis in real-time—and betting that in the race to build AI-powered governments, velocity matters as much as vision. (Photo by David Rodrigo) See also: UAE to teach its children AI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is 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 Exclusive: Dubai’s Digital Government chief says speed trumps spending in AI efficiency race appeared first on AI News. View the full article

Amid pressure to deploy generative and agentic solutions, a familiar question is surfacing: “Is there an AI bubble, and is it about to burst?” For many organisations, this new wave of generative and agentic AI is still very much in experimental stages. The primary focus, and the low-hanging fruit, has been internal. Most businesses are looking to AI to increase efficiency gains, such as automating workflows or streamlining customer support. The trouble is, those gains are proving elusive. Ben Gilbert, VP of 15gifts, points out that “those benefits often take years to show real returns and are hard to measure beyond time savings.” This is where the cracks begin to show. The rush to deploy feels uncomfortably familiar and, for some, may give some feelings of PTSD. “The trend of companies diving headfirst into AI projects or solutions mirrors patterns we have seen time and time again in previous tech bubbles, such as the dot-com era,” explains Gilbert. This gap between experimental spending and measurable profit is precisely where the bubble is weakest. Gilbert argues that AI projects which “focus on efficiency gains and deliver unclear or delayed ROI” will be the first to fail from any bubble pop. When investments “risk becoming costly experiments rather than profitable tools,” the pullback is inevitable. “We could see budgets tighten, startups close, and large enterprises re-evaluate their AI strategies,” says Gilbert. It’s a warning backed by data. Gartner has already predicted “that over 40% of agentic AI projects will fail by 2027 due to rising costs, governance challenges, and lack of ROI”. So, what separates a viable AI strategy that could survive a burst bubble from a costly experiment? Gilbert suggests it comes down to human nuance; something many projects overlook in the rush to automate. There’s a curious discrepancy, he notes: “Why has AI been embraced so fully in efficiency gains and customer support, but not in sales?”. The answer may be that algorithms are highly valuable for sifting through data to inform decision-making, but consumers want the engagement, intuitiveness, and fluidity of human interaction as well. Success, then, isn’t about replacing people but augmenting them. Gilbert advocates that “AI should be taught by real people, so it can understand the nuances of human language, needs, and emotions”. This requires a transparent process, where “human annotation of AI-driven conversations can help to set clear benchmarks and refine a platform’s performance.” A total AI bubble pop isn’t likely to be imminent. Gilbert explains we’re more likely to see a “market correction rather than a complete collapse” and the underlying potential of AI remains strong. However, the hype will deflate. For enterprise leaders, the path forward requires a return to first principles. “AI projects, whether built on hype or business value, need to address a real human need in order to be successful,” Gilbert says. Whether a bubble or healthy market correction, this cooling-off ******* might even be a good thing, offering a chance for businesses to focus on AI quality over hype and smarter ethics. For the CIOs and CFOs managing the budgets, Gilbert believes the brands that thrive “will be the ones using AI to enhance human capability; not automate it away.” “Without empathy, transparency, and human insight, even the smartest AI is destined to fail.” See also: Keep CALM: New model design could fix high enterprise AI costs 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 Is AI in a bubble? Succeed despite a market correction appeared first on AI News. View the full article