ChatGPT

For chief data and information officers, especially in tightly regulated sectors, data governance has been a major cause preventing enterprise adoption of AI models. The issue of data sovereignty – which concerns where company data is handled and kept – has held many back, forcing them to use complex private cloud solutions. Others have simply given up. OpenAI’s recent announcement to offer *** data residency shows that big AI model providers are changing their setups to match the strict data protection and rules that enterprise and public sector clients require. This directly deals with the top governance concern in the market and should speed up AI adoption. It will move AI past test projects and into important business functions. From public sector tests to full adoption The new *** data residency choice, starting October 24, will apply to OpenAI’s main business products: the API Platform, ChatGPT Enterprise, and ChatGPT Edu. This lets *** clients keep their enterprise data in the ***, assisting with AI governance and meeting local data protection rules. The *** Ministry of Justice (MoJ) is the first major client for this. They’ve signed a deal to give 2,500 civil servants access to ChatGPT Enterprise. This is a full deployment after the MoJ said a recent trial run showed people saved time on routine tasks including writing help, compliance and legal jobs, research, and document work. The deal backs the department’s AI Action Plan and aims to help workers be more productive and better serve the public. This specific use in a government legal section gives a reliable example for others – such as those in finance and healthcare – to measure how much they can gain from using AI on complicated, knowledge-based tasks. Adoption by the MoJ joins other AI tools already in action in Whitehall, like ‘Humphrey,’ an AI assistant that helps with admin, and ‘Consult,’ a tool that looks at public feedback in minutes instead of weeks. What it takes to implement and challenges This announcement points out two paths for OpenAI’s *** setup plans. The new data residency choice looks like the fix for enterprise AI data governance right now. It is separate from Stargate ***, the earlier AI project with NVIDIA and Nscale. That one is about building sovereign AI by delivering models on local computing for specific uses in the long run. For IT leaders, this complicates the AI platform market, which already has many choices. OpenAI’s move to offer data residency goes against what cloud providers offer. Before, companies wanting to use OpenAI models within a specific place were pointed towards platforms like Microsoft’s Azure AI, which mixes model access with data governance. Now, they must consider things more. Businesses can access the models straight from OpenAI – getting new features and *** residency – or keep using platforms like Azure AI, AWS Bedrock, or Google Vertex AI. These may connect better with current data and company applications. This choice will be compared against platforms like IBM watsonx or AI in business software like SAP Joule, which focus on data privacy and fitting into current workflows. Sam Altman, CEO of OpenAI, said: “The number of people using our products in the *** has increased fourfold in the past year. It’s exciting to see them using AI to save time, increase productivity, and get more done. “Civil servants are using ChatGPT to improve public services and established firms are reimagining operations. We’re proud to continue supporting the *** and the Government’s AI plan.” *** Deputy Prime Minister David Lammy added: “Our partnership with OpenAI places Britain firmly in the driving seat of the global tech revolution—leading the world in innovation and using technology to deliver fairness and opportunity for every corner of the United Kingdom.” Key points for enterprise leaders navigating AI data governance OpenAI’s move from a US-focused setup to offering local data options responds to what companies and governments want. For business leaders, this change calls for a review. Look again at governance issues: CISOs and Data Protection Officers should check any risk reviews that blocked OpenAI tools because of data residency. This change might now permit AI projects. Consider government uses: The MoJ’s good test case offers a strong reason to use this change. CIOs and COOs in other fields can point to a government using this tech for work like document analysis, making a business case for investing. Think about total costs: CTOs should now compare the total cost of working directly with OpenAI against using a cloud platform. This should include API prices and the cost of integration, security, and meeting rules. Get ready for sovereign AI: This is part of a ******* trend. The Stargate *** project and the government’s MOU show that sovereign AI is a long-term goal. Business tech planners should get ready for a mix of AI where models and data are handled in different places, with some running locally for rules, speed, or security. Enterprise leaders can now more easily use the AI platform directly, since a big data governance issue has been solved. The question is no longer whether we can use AI tools securely, but how to include, manage, and grow them to get real business results. See also: How AI adoption is moving IT operations from reactive to proactive Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post OpenAI data residency advances enterprise AI governance appeared first on AI News. View the full article

Reports are circulating this week that Meta is cutting approximately 600 positions from its AI division, a move that seems paradoxical given the company’s aggressive recruitment campaign over recent months. The contradiction raises important questions about Meta’s AI strategy and what it signals for the broader tech industry. For those following Meta AI job cuts, the timing is striking. Just months after the company went on a highly publicised hiring spree – offering compensation packages reportedly reaching up to hundreds of millions of dollars to lure top researchers from OpenAI, Google, and other competitors – Meta is now scaling back parts of its AI workforce. The numbers behind Meta AI job cuts The cuts will affect Meta’sFAIR AI research, product-related AI, and AI infrastructure units in the company’s Superintelligence Labs, which employs several thousand people, according to a report byAxios. However, the newly-formed TBD Lab unit will be spared from cuts. Following the layoffs, Meta’s Superintelligence Labs’ workforce now sits at just under 3,000, CNBC stated. The company has offered affected employees 16 weeks of severance plus two weeks for every completed year of service, and is encouraging them to apply for other positions in Meta. Why is Meta making these cuts? According to Axios, an internal memo from Meta Chief AI Officer Alexandr Wang indicated that the restructuring aims to address what the company concluded was an overly bureaucratic structure. “By reducing the size of our team, fewer conversations will be required to make a decision, and each person will be more load-bearing and have more scope and impact,” Wang wrote. The backstory reveals deeper concerns. CEO Mark Zuckerberg grew concerned several months ago that the company’s existing AI efforts weren’t leading to needed breakthroughs or improved performance. The dissatisfaction reportedly stemmed from the lukewarm response to Meta’s Llama 4 models released in April. The expensive hiring spree To understand the current Meta AI job cuts, we need to examine what came before. In June 2025, Meta made a US$14.3 billion investment in Scale AI and brought on the startup’s CEO, Alexandr Wang, as Meta’s first-ever Chief AI Officer. The company then embarked on an aggressive talent acquisition campaign. Meta hired multiple researchers from OpenAI, including Shengjia Zhao, Jiahui Yu, Shuchao ***, and Hongyu Ren. Meta reportedly also poached more than 50 researchers from competitors, with OpenAI CEO Sam Altman claiming Meta was offering “US$100 million signing bonuses.” Zuckerberg stated he was focused on “building the most elite and talent-dense team in the industry” for Meta’s new Superintelligence Labs. The company also hired prominent executives, including former GitHub CEO Nat Friedman and Safe Superintelligence co-founder Daniel Gross. Then, unexpectedly, Meta paused hiring for its AI division in August 2025, just weeks after the massive recruitment push. The new guard vs. the old guard What makes these Meta AI job cuts particularly revealing is who’s being affected – and who isn’t. The cuts did not impact employees in TBD Labs, which includes many of the top-tier AI hires brought into Meta this summer, CNBC stated. In Meta, the AI unit was considered bloated, with teams like FAIR and product-oriented groups often vying for computing resources. The restructuring appears to be a calculated bet on the new talent over legacy teams. Timing that raises eyebrows The timing of these Meta AI job cuts is particularly noteworthy. Just a day before the layoffs were announced, Meta secured a US$27 billion financing deal with Blue Owl Capital to fund the Hyperion data centre in Louisiana. The juxtaposition is stark: Meta is simultaneously cutting AI personnel while investing tens of billions in AI infrastructure. This suggests the company isn’t pulling back from AI – it’s redirecting resources toward specific initiatives it deems more promising. What this means for the AI industry The Meta AI job cuts may signal a broader shift in the tech industry’s approach to AI talent. This raises questions in Silicon Valley about whether AI layoffs are beginning to surface just as the hype cycle peaks. After months of frenzied hiring and astronomical compensation packages, Meta’s restructuring suggests that simply accumulating AI talent isn’t enough. The company appears to be learning that organisational structure, decision-making speed, and team coherence matter as much as individual brilliance. Tech analyst Dan Ives of Wedbush Securities described Meta as being in “digestion mode” after a spending spree, while Daniel Newman, CEO of Futurum Group, called the hiring freeze “a natural resting point for Meta,” as per CNBC‘s report. The ******* picture Despite the layoffs, Meta insists its commitment to AI remains unwavering. The company continues to actively recruit and hire for its TBD Lab unit, and Zuckerberg has said Meta’s AI initiatives will result in a 2026 year-over-year expense growth rate above 2025’s growth. What we’re witnessing isn’t a retreat from AI but a strategic realignment. Meta is consolidating its AI efforts around a smaller, more agile core team led by Wang and populated by the expensive talent acquisitions from earlier this year. The company is betting that this leaner structure will deliver the breakthroughs that eluded its larger, more established teams. The bottom line The contradiction of Meta AI job cuts alongside continued hiring isn’t contradictory at all – it’s a deliberate strategy. Meta is cutting the old to make room for the new, streamlining bureaucracy, and betting that its expensive new hires will succeed where legacy teams struggled. Whether this gamble pays off remains to be seen. The company is creating a startup-in-a-giant, protecting its prized recruits yet trimming the organisational ****. As Wang noted in his memo, “This is a talented group of individuals, and we need their skills in other parts of the company.” Whether Meta can successfully redeploy this talent internally – or whether they’ll join competitors – will be another chapter in the ongoing AI talent wars. Meta’s approach reflects a broader truth about the AI industry: throwing money and people at the problem isn’t enough. Success requires the right structure, the right strategy, and increasingly, the courage to make difficult decisions about what – and who – to prioritise. See also: Zuckerberg outlines Meta’s AI vision for ‘personal superintelligence’ 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 Meta hires and fires AI workers: Behind the contradiction appeared first on AI News. View the full article

Security experts at JFrog have found a ‘prompt **********’ threat that exploits weak spots in how AI systems talk to each other using MCP (Model Context Protocol). Business leaders want to make AI more helpful by directly using company data and tools. But, hooking AI up like this also opens up new security risks, not in the AI itself, but in how it’s all connected. This means CIOs and CISOs need to think about a new problem: keeping the data stream that feeds AI safe, just like they protect the AI itself. Why AI attacks targeting protocols like MCP are so dangerous AI models – no matter if they’re on Google, Amazon, or running on local devices – have a basic problem: they don’t know what’s happening right now. They only know what they were trained on. They don’t know what code a programmer is working on or what’s in a file on a computer. The boffins at Anthropic created the MCP to fix this. MCP is a way for AI to connect to the real world, letting it safely use local data and online services. It’s what lets an assistant like Claude understand what this means when you point to a piece of code and ask it to rework this. However, JFrog’s research shows that a certain way of using MCP has a prompt ********** weakness that can turn this dream AI tool into a nightmare security problem. Imagine that a programmer asks an AI assistant to recommend a standard Python tool for working with images. The AI should suggest Pillow, which is a good and popular choice. But, because of a flaw (CVE-2025-6515) in the oatpp-mcp system, someone could sneak into the user’s session. They could send their own fake request and the server would treat it like it came from the real user. So, the programmer gets a bad suggestion from the AI assistant recommending a fake tool called theBestImageProcessingPackage. This is a serious attack on the software supply chain. Someone could use this prompt ********** to inject bad code, steal data, or run commands, all while looking like a helpful part of the programmer’s toolkit. How this MCP prompt ********** attack works This prompt ********** attack messes with the way the system communicates using MCP, rather than the security of the AI itself. The specific weakness was found in the Oat++ C++ system’s MCP setup, which connects programs to the MCP standard. The issue is in how the system handles connections using Server-Sent Events (SSE). When a real user connects, the server gives them a session ID. However, the flawed function uses the computer’s memory address of the session as the session ID. This goes against the protocol’s rule that session IDs should be unique and cryptographically secure. This is a bad design because computers often reuse memory addresses to save resources. An attacker can take advantage of this by quickly creating and closing lots of sessions to record these predictable session IDs. Later, when a real user connects, they might get one of these recycled IDs that the attacker already has. Once the attacker has a valid session ID, they can send their own requests to the server. The server can’t tell the difference between the attacker and the real user, so it sends the malicious responses back to the real user’s connection. Even if some programs only accept certain responses, attackers can often get around this by sending lots of messages with common event numbers until one is accepted. This lets the attacker mess up the model’s behaviour without changing the AI model itself. Any company using oatpp-mcp with HTTP SSE enabled on a network that an attacker can access is at risk. What should AI security leaders do? The discovery of this MCP prompt ********** attack is a serious warning for all tech leaders, especially CISOs and CTOs, who are building or using AI assistants. As AI becomes more and more a part of our workflows through protocols like MCP, it also gains new risks. Keeping the area around the AI safe is now a top priority. Even though this specific CVE affects one system, the idea of prompt ********** is a general one. To protect against this and similar attacks, leaders need to set new rules for their AI systems. First, make sure all AI services use secure session management. Development teams need to make sure servers create session IDs using strong, random generators. This should be a must-have on any security checklist for AI programs. Using predictable identifiers like memory addresses is not okay. Second, strengthen the defenses on the user side. Client programs should be designed to reject any event that doesn’t match the expected IDs and types. Simple, incrementing event IDs are at risk of spraying attacks and need to be replaced with unpredictable identifiers that don’t collide. Finally, use zero-trust principles for AI protocols. Security teams need to check the entire AI setup, from the basic model to the protocols and middleware that connect it to data. These channels need strong session separation and expiration, like the session management used in web applications. This MCP prompt ********** attack is a perfect example of how a known web application problem, session **********, is showing up in a new and dangerous way in AI. Securing these new AI tools means applying these strong security basics to stop attacks at the protocol level. See also: How AI adoption is moving IT operations from reactive to proactive 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 MCP prompt **********: Examining the major AI security threat appeared first on AI News. View the full article

Link strategies are important in improving search engine optimisation (SEO) and online visibility. Artificial intelligence is changing these strategies, making them more accurate and efficient. This article examines AI’s impact on link strategies, highlighting current tools and future trends. Businesses are always looking for new ways to improve their online presence. Link strategies are the key to boosting SEO rankings, ensuring websites get the visibility they need. With AI integration, businesses can achieve better results by using data-driven insights and automation. This guide explores Bazoom’s backlink building service, showing how it can change traditional methods and help companies prepare for the future. How AI is reshaping link strategies Traditional link strategies often relied on manual processes, like contacting potential partners and tracking backlinks. These methods, while useful, were labour-intensive and prone to error. AI addresses these issues by automating many aspects of link building. Machine learning algorithms allow AI to quickly analyse large data sets, identifying the most relevant linking opportunities for your business. By using AI technologies, businesses can improve the accuracy of their backlink profiles. AI tools can predict which links will most impact search rankings based on historical data and current trends. The predictive ability helps businesses allocate resources more effectively and focus on high-value opportunities. Besides accuracy, AI saves time by automating repetitive tasks, allowing professionals to focus on strategy development. AI-enhanced link strategies offer benefits beyond efficiency and accuracy. The technologies provide insights into competitive landscapes, helping businesses understand their position relative to competitors. By gaining a comprehensive market view, companies can make informed decisions about their link building efforts and adapt quickly to changes in search engine algorithms. AI tools that enhance link strategies Today, various AI tools significantly improve link building processes. Tools use machine learning to identify data patterns that may not be immediately apparent to humans. Features like automated outreach, relationship management, and real-time analytics streamline the process from start to finish. AI tools offer more than automation. They provide actionable insights that help optimise link strategies for maximum impact. By analysing competitor links and industry trends, the tools offer strategic recommendations tailored to specific business needs. Integrating such technology enables tracking and reporting, providing visibility into each campaign’s effectiveness. For businesses aiming to stay competitive in SEO, implementing advanced tools is essential. They simplify complex processes and ensure every decision is backed by solid data analysis. The ability to forecast potential outcomes allows companies to adjust their strategies proactively rather than reactively. Practical uses of AI in link strategies AI’s practical applications in link strategies are diverse. For example, businesses can automate their backlink initiatives by using tools that facilitate efficient outreach and engagement with potential partners. The automation ensures consistent and targeted communication without overwhelming resources. AI’s impact on data analysis is significant; it transforms raw data into valuable insights that drive decision-making in SEO campaigns. By identifying patterns and predicting search engine trends, AI helps businesses optimise their link building efforts effectively. The capability ensures companies remain competitive in a constantly changing digital landscape. Moreover, AI enables businesses to predict future industry trends accurately. By forecasting shifts in consumer behaviour or search engine algorithm changes, companies can adjust their strategies accordingly and maintain a competitive edge. The proactive approach reduces the risk associated with sudden changes in SEO dynamics. Future trends in AI and link building practices Several emerging trends suggest further integration between AI technologies and digital marketing efforts. One notable tendency is the increased use of natural language processing (NLP) in SEO tools, allowing for better understanding of contextually relevant content creation. The potential for AI integration extends beyond improving existing systems; it offers opportunities for creating new marketing paradigms through collaborative approaches in various platforms like social media channels or content management systems. As these technologies continue to advance, expect more innovation in the future. While predicting what’s around the corner can be challenging, one thing is clear: embracing technology advances will be crucial for success. Adopting cutting-edge solutions now ensures readiness for whatever comes next, and provides a foundation for sustainable growth in the long term. 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 AI is changing how we build links for SEO appeared first on AI News. View the full article

CIOs want to fix IT problems faster without expanding headcount, and many see AI adoption as the solution for their operations. For ages, they’ve used things like automation and self-help portals to handle this, so their teams can solve issues quickly. Now, AI is getting involved, and lots of companies are trying to use it for their IT support. There’s been a ton of buzz, but leaders want proof that it actually works. SolarWinds looked into how these new tools are doing. The company analysed a bunch of info from over 2,000 IT systems and 60,000 pieces of data collected over a year, from August 2024 to July 2025. For their study, SolarWinds checked out AI stuff that’s supposed to make things easier, like automatically suggesting answers to tickets, finding helpful articles, and making summaries of problems. The results give a good idea of how much more efficient companies can get. How much more efficient does AI make IT operations? The main thing the report found is that it takes way less time to fix an IT issue after a company starts using AI. Before AI, it took about 27.42 hours to solve a problem. After, it dropped to 22.55 hours. That’s about 17.8 percent faster, saving about 4.87 hours per problem. This lets IT teams spend more time on tricky stuff instead of getting bogged down with everyday issues. This can save companies a bunch of money. The report talks about a medium-sized IT team handling 5,000 problems a year. By saving about 4.87 hours on each one, they’d get back 24,350 hours of work each year. If you figure a help desk person costs $28 an hour, that’s like saving over $680,000. But it’s not just about saving money. The report says IT can use that time to work on important projects and fix problems before they happen. This helps IT go from just fixing things to actually helping the company do better. The report also shows that there’s a big difference between companies that use AI and those that don’t. Companies using AI fix tickets in about 22.55 hours, while others take about 32.46 hours. That’s about a 30.5 percent difference which means almost 10 hours saved per problem for those using AI. However, it’s not just about the AI The report makes it clear that AI isn’t a magic fix for IT operations. It only works if you have good processes and are ready to make broader, company-wide changes. The best example is a group called the ‘Top 10 AI Adopters’. These ten companies stood out because they cut their resolution times the most. They went from about 51 hours to 23 hours, which is more than half the time saved. Their secret wasn’t special software, but how they used it. These companies all had one thing in common: they didn’t just try out AI as a side project. They made it a part of their daily work to help fix problems. The report basically says that AI works best when you also make changes to your processes and are willing to improve things. The report also says that it helps to have a culture that’s already into things like self-service portals and automation. These teams are already trying to make their support desks strong and ready for anything. AI just works better when you have these things in place. What to do if you’re in charge The SolarWinds report shows that AI for IT support operations isn’t just a possibility, it can really work. Cutting resolution times by almost 18 percent is a big deal for leaders. Here’s what they should do: See where you’re at: Before you spend money, figure out how long it takes to fix things now. The report says the average for companies not using AI is about 32.46 hours. Knowing your own number helps you decide if it’s worth it. Make it part of the job: The top users show that it’s better to use AI every day than to just try it out on the side. This means making changes to how you work and focusing on making things better. AI is a tool, not a miracle: AI can really help speed things up, especially if you already have good IT practices. Check your knowledge base and automation rules. AI works best when you have clear processes in place. Figure out how much you can save: The report gives a simple way to show your team how much time and money AI can save. Just multiply the number of incidents you have each year by the average saving of 4.87 hours. This gives you a clear idea of how much more efficient you can be. The difference between companies using AI and those who aren’t is growing. Leaders need to set up their operations so they can use AI to help IT become a real partner in the company’s success. See also: China’s generative AI user base doubles to 515 million in six months 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 AI adoption is moving IT operations from reactive to proactive appeared first on AI News. View the full article

A few years ago, the business technology world’s favourite buzzword was ‘Big Data’ – a reference to organisations’ mass collection of information that could be used to suggest previously unexplored ways of operating, and float ideas about what strategies they may best pursue. What’s becoming increasingly apparent is that the problems companies faced in using Big Data to their advantage still remain, and it’s a new technology – AI – that’s making those problems rise once again to the surface. Without tackling the problems that beset Big Data, AI implementations will continue to fail. So what are the issues stopping AI deliver on its promises? The vast majority of problems stem from the data resources themselves. To understand the issue, consider the following sources of information used in a very average working day. In a small-to-medium sized business: Spreadsheets, stored on users’ laptops, in Google Sheets, Office 365 cloud. The customer relationship manager (CRM) platform. Email exchanges between colleagues, customers, suppliers. Word documents, PDFs, web forms. Messaging apps. In an enterprise business: All of the above, plus, Enterprise resource planning (ERP) systems. Real-time data feeds. Data lakes. Disparate databases behind multiple point-products. It’s worth noting that the simple list above isn’t comprehensive, and nor is it intended to be. What it demonstrates is that in just five lines, there are around a dozen places where information can be found. What Big Data needed (perhaps still needs) and what AI projects also rest on, is somehow bringing all those elements together in such a way that a computer algorithm can make sense of it. Marketing behemoth Gartner’s hype cycle for artificial intelligence, 2024, placed AI-Ready Data on the upward curve of the hype cycle, estimating it would be 2-5 years before it reached the ‘plateau of productivity’. Given that AI systems mine and extract data, most organisations – save those of the very largest size – don’t have the foundations on which to build, and may not have AI assistance in the endeavour for another 1-4 years. The underlying problem for AI implementation is the same as dogged Big Data innovations as they, in the past, made their way through the hype cycle – from innovation trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, to plateau of productivity – data comes in many forms; it can be inconsistent; perhaps it adheres to different standards; it may be inaccurate or biased; it could be highly sensitive information, or old and therefore irrelevant. Transforming data so it’s AI-ready remains a process that’s as relevant today (perhaps more so) than it’s ever been. Those companies wanting to get a jump start could experiment with the many data treatment platforms currently available, and as is becoming the common advice, might begin with discrete projects as test-beds to assess the effectiveness of emerging technologies. The advantage of the latest data preparation and assembly systems is that they are designed to prepare an organisation’s information resources in ways that are designed for the data to be used by AI value-creation platforms. They can offer, for example, carefully-coded guardrails that will help ensure data compliance, and protect users from accessing biased or commercially-sensitive information. But the challenge of producing coherent, safe, and well-formulated data resources remains an ongoing issue. As organisations gain more data in their everyday operations, compiling up-to-date data resources on which to draw is a constant process. Where big data could be considered a static asset, data for AI ingestion has to be prepared and treated in as close to real-time as possible. The situation therefore remains a three-way balance between opportunity, risk, and cost. Never before has the choice of vendor or platform been so crucial to the modern business. (Source: “Inside the business school” by Darien and Neil is licensed under CC BY-NC 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 Businesses still face the AI data challenge appeared first on AI News. View the full article

For CFOs and CIOs under pressure to modernise finance operations, automation, as seen in several generations of RPA (robotic process automation), isn’t enough. It’s apparent that transparency and explainability matter just as much. Accounting firms and finance functions inside organisations are now turning to AI systems that reason, not just compute. One of the most ambitious examples is Basis, a US-based start-up founded just two years ago that builds AI agents designed to automate structured accounting work, and keep human oversight firmly in the loop. Such systems signal a shift in enterprise automation. Instead of replacing people, AI agents extend human expertise and combine the precision of AI models with the oversight finance professionals need for compliance and client trust. Business impact: efficiency with accountability Basis develops AI agents that handle routine finance tasks such as reconciliations, journal entries, and financial summaries. The platform is built on OpenAI’s GPT-4.1 and GPT-5 models, models that give the facility to operators to examine each decision step taken autonomously. Accounting firms using Basis report up to 30% time savings and an ensuing higher capacity for advisory work. That’s the kind of value creation traditional automation cannot deliver as quickly or at similar cost to the business. Unlike many automation tools that operate as ****** boxes, Basis emphasises review-able reasoning. Every recommendation includes an account of the data used and the logic behind it. Visibility means accountants can validate each outcome and remain responsible for results, a feature that’s always important in financial operations, and especially in highly-regulated industries. Implementation and challenges: building systems that learn Agentic AI can treat accounting as a network of workflows, not isolated tasks. A supervising AI agent, powered by GPT-5 in the case of Basis’s platform, manages the entirety of processes. It can delegate specific tasks sub-agents running on different models, with the choice of AI model depending on the job’s complexity and the type of data that’s to be processed. For example, for quick queries or clarifications, Basis uses GPT-4.1 for its speed, while for complex classifications or month-end close, GPT-5 provides better reasoning and context handling. Company benchmarks each of its models against real-world accounting workflows to decide when it’s safe to let agents handle more responsibility. Finance professionals can always see what the system has done, why it made specific choices, and how confident it is in its recommendations. This malleable architecture lets firms scale AI and help ensure accuracy as levels of automation increase. The process mirrors the hybrid human–AI collaboration now emerging as the norm in sectors like legal services and risk management. Lessons for other sectors What makes Basis and financial multi-agentic AI relevant beyond accounting is the model-orchestration approach, routing tasks to the most appropriate AI model based on its performance and latency. The format could inform similar deployments in procurement, HR, or compliance operations; anywhere, in fact, where large volumes of structured decisions need transparency and – to use a terrible pun – accountability. Basis’s collaboration with OpenAI shows how AI reasoning engines in secure data environments can be effective. The goal isn’t pure speed, but automation that increases trust in the operator, and in the models themselves. These are systems that evolve without humans losing control of the outcomes. Conclusion AI in accounting isn’t limited to automating entries, it’s turning more towards building systems that think like accountants, not machines. For enterprise leaders, Basis’s model shows a way toward automation that improves over time. Each improvement in model makes teams faster and smarter without surrendering control to the automation process. (Image source: “Accounting charts” by World Bank Photo Collection 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 How accounting firms are using AI agents to reclaim time and trust appeared first on AI News. View the full article

The AI adoption in China has reached unprecedented levels, with the country’s generative artificial intelligence user base doubling to 515 million in just six months, according to a report released by the China Internet Network Information Centre (CNNIC). This dramatic expansion represents an adoption rate of 36.5% in the first half of 2025, positioning China as a formidable force in the global AI landscape. The CNNIC report, published on Saturday, reveals that China’s AI adoption more than doubled between the end of December 2024 and June 2025. This growth trajectory underscores the rapid integration of AI technologies into ******** society, driven by what the report describes as “advanced infrastructure and state encouragement.” Demographic profile of users The survey of 30,000 respondents across 31 provinces reveals distinct patterns in who is embracing these technologies. Young and middle-aged professionals dominate the user base, with those under 40 years old accounting for 74.6% of all users. Education levels also play a significant role, as individuals with degrees from higher education institutions represent 37.5% of the total user base. This demographic concentration suggests that in China, the AI adoption is currently strongest among digitally native, educated populations who are positioned to leverage these tools in professional and personal contexts. Domestic models dominate market preferences Perhaps most notably, the report found that over 90% of respondents indicated their first choice was a domestic AI model. This preference for homegrown solutions reflects both Beijing’s strategic emphasis on technological self-reliance and the practical reality that leading American models from OpenAI and Google DeepMind are officially blocked on the mainland. ******** AI platforms have filled this void effectively. Models such as DeepSeek, Alibaba Cloud’s Qwen, and ByteDance’s Doubao have “exploded in popularity among ******** users,” according to a South China Morning Post report. Alibaba Cloud serves as the AI and cloud computing unit of Alibaba Group Holding. Global context and comparative analysis Despite restrictions on access to US-based AI models, China’s market has already established itself as the world’s largest. A Microsoft study published in September found that China had an estimated AI user base exceeding 195 million as of June 2024. The company’s research utilised a metric that quantifies China’s AI adoption as the share of the working-age population actively using AI tools. The Microsoft study identified a crucial inflexion point: the launch of DeepSeek’s R1 model in January 2025 led to AI adoption in China more than doubling to 20% in the subsequent six months. By comparison, the United States maintained a relatively steady adoption rate of approximately 25% over the past year. Zhang Xiao, deputy director of the CNNIC, told state news agency Xinhua that further innovations are anticipated in “open source AI, embodied intelligence, AI agents and AI governance.” Innovation and patent leadership Beyond user adoption metrics, China has established significant leadership in AI innovation as measured by patent applications. The CNNIC report noted that China had filed 1.576 million AI-related patent applications as of April 2025, representing 38.58% of the global total—the highest share of any country worldwide. This patent activity suggests that the AI adoption in China extends beyond consumer usage to encompass substantial research and development efforts across the technology stack. Strategic policy framework The rapid expansion in China’s AI adoption aligns with Beijing’s “AI Plus” initiative, which calls for widespread diffusion of AI technologies throughout society and the economy. The government has emphasised the importance of self-reliance across the entire AI ecosystem, from foundational models to computing hardware. This policy framework has created an environment where domestic technology companies are incentivised to develop competitive AI solutions while users are encouraged to integrate these tools into their daily workflows. Implications for the global AI landscape The doubling of China’s generative AI user base to 515 million users in six months represents more than a statistical milestone. It signals the emergence of a parallel AI ecosystem that operates largely independently of Western platforms yet serves a massive user population. As China’s AI adoption continues to accelerate, the global technology landscape may increasingly feature two distinct spheres of influence—one centred on American models and platforms, another on ******** alternatives. This bifurcation could have profound implications for how AI technologies evolve, how innovation diffuses across borders, and how global standards for AI governance take shape. The coming months will reveal whether this growth trajectory can be sustained and how effectively ******** AI platforms can continue to meet user needs in an increasingly competitive and rapidly evolving market. See also: Will the budget China AI chip from Nvidia survive Huawei’s growth? 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 China’s generative AI user base doubles to 515 million in six months appeared first on AI News. 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Arm has announced that it’s providing its most powerful edge AI platform, Armv9, to startups via its Flexible Access programme. The “Flexible Access” model is essentially a ‘try before you buy’ for chip designers. It gives companies upfront, low-cost, or no-cost (for qualifying startups) access to a wide range of Arm technology, tools, and resources. They can experiment and iterate designs freely while only paying license fees for the technology they use in final designs. This approach has already been a “catalyst for innovation,” according to Arm. The model helped to create around 400 successful chip designs (or “tape-outs”) over the last five years. You’ve likely heard of some of the companies already using it, like Raspberry Pi, Hailo, and SiMa.ai. The Armv9 edge AI platform pairs the super-efficient Arm Cortex-A320 processor with the Arm Ethos-U85 NPU, which is the bit that handles the heavy AI lifting. This duo is capable of running AI models with over one billion parameters right on the device itself, no cloud connection needed. This is the tech that will power the next-gen edge AI applications such as smart cameras that don’t just record but understand what they’re seeing, smart home gadgets that learn your habits, and robots you can interact with using vision, voice, and gesture. Paul Williamson, who runs the IoT business at Arm, believes the next wave of AI innovation will happen “at the edge – in the devices, interfaces, and systems that bring intelligence closer to where data is created.” A huge benefit here is privacy and security. By processing everything locally, machines can “perceive and respond like humans, while keeping inference and data processing securely on-device”. Your personal data doesn’t have to be sent off to a server just to figure out what you said. The Armv9 platform also bakes in security features like Pointer Authentication Code (PAC) and Memory Tagging Extension (MTE) to keep that on-device data safe. Research from VDC predicts that, by 2028, AI will be the “dominant technology used across IoT projects”. Arm’s technology is “already at the center of this transformation,” and this move just solidifies its position. For all the developers keen to get started with edge AI, the Arm Cortex-A320 will be available through the programme in November 2025, with the Ethos-U85 AI processor following in early 2026. See also: NVIDIA GPUs to power Oracle’s next-gen enterprise AI services 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 Arm provides edge AI platform to startups via flexible access appeared first on AI News. View the full article

Google has announced DeepSomatic, an AI tool that can identify *******-related mutations in tumour genetic sequences more accurately. ******* starts when the controls governing cell division malfunction. Finding the specific genetic mutations driving a tumour’s growth is essential for creating effective treatment plans. Doctors now regularly sequence tumour cell genomes from biopsies to inform treatments that can target how a particular ******* grows and spreads. Published in Nature Biotechnology, this work presents a tool that uses convolutional neural networks to identify genetic variants in tumour cells with greater accuracy than current methods. Google has made both DeepSomatic and the high-quality training dataset created for it openly available. The challenge of somatic variants ******* genetics is complex. While genome sequencing finds genetic ******* variations, distinguishing real variants from sequencing errors is difficult and where an AI tool would provide welcome assistance. Most cancers are driven by ‘somatic’ variants acquired after birth rather than inherited ‘germline’ variants from parents. Somatic mutations happen when environmental factors like UV light damage DNA, or when random errors occur during DNA replication. When these variants alter normal cell behaviour, they can cause uncontrolled replication, driving ******* development and progression. Identifying somatic variants is harder than finding inherited ones because they can exist at low frequencies within tumour cells, sometimes at rates lower than the sequencing error rate itself. How DeepSomatic works In clinical settings, scientists sequence both tumour cells from a biopsy and normal cells from the patient. DeepSomatic spots the differences, identifying variations in tumour cells that aren’t inherited. These variations reveal what’s fuelling the tumour’s growth. The model converts raw genetic sequencing data from both tumour and normal samples into images representing various data points, including the sequencing data and its alignment along the chromosome. A convolutional neural network analyses these images to differentiate between the standard reference genome, the individual’s normal inherited variants, and *******-causing somatic variants while filtering out sequencing errors. The output is a list of *******-related mutations. DeepSomatic can also work in ‘tumour-only’ mode when normal cell samples are unavailable, which happens frequently with blood cancers like leukaemia. This makes the tool applicable across many research and clinical scenarios. Training a more precise AI ******* research tool Training an accurate AI model requires high-quality data. For its AI tool, Google and its partners at the UC Santa Cruz Genomics Institute and the National ******* Institute created a benchmark dataset called CASTLE. They sequenced tumour and normal cells from four breast ******* samples and two lung ******* samples. These samples were analysed using three leading sequencing platforms to create a single, accurate reference dataset by combining the outputs and removing platform-specific errors. The data shows how even the same ******* type can have vastly different mutational signatures, information that can help predict patient response to specific treatments. DeepSomatic models performed better than other established methods across all three major sequencing platforms. The tool excelled at identifying complex mutations called insertions and deletions, or ‘Indels’. For these variants, DeepSomatic achieved a 90% F1-score on Illumina sequencing data, compared to 80% for the next-best method. The improvement was more dramatic on Pacific Biosciences data, where DeepSomatic scored over 80% while the next-best tool scored less than 50%. The AI performed well when analysing challenging samples. Testing included a breast ******* sample preserved with formalin-fixed-paraffin-embedded (FFPE), a common method that can introduce DNA damage and complicate analysis. It was also tested on data from whole exome sequencing (WES), a more affordable method that sequences only the 1% of the genome coding for proteins. In both scenarios, DeepSomatic outperformed other tools, suggesting its utility for analysing lower-quality or historical samples. An AI tool for all cancers The AI tool has shown it can apply its learning to new ******* types it wasn’t trained on. When used to analyse a glioblastoma sample, an aggressive brain *******, it successfully pinpointed the few variants known to drive the disease. In a partnership with Children’s Mercy in Kansas City, it analysed eight samples of paediatric leukaemia and found the previously known variants while identifying 10 new ones, despite working with tumour-only samples. Google hopes research labs and clinicians will adopt this tool to better understand individual tumours. By detecting known ******* variants, it could help guide choices for existing treatments. By identifying new ones, it could lead to new therapies. The goal is to advance precision medicine and deliver more effective treatments to patients. See also: MHRA fast-tracks next wave of AI tools for patient care 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 Google AI tool pinpoints genetic drivers of ******* appeared first on AI News. View the full article

The South Korean government spent 1.2 trillion won ($850m) on developing AI textbooks for schools, but the national programme has been rolled back after just four months, amid allegations of inaccurate texts, concerns about privacy, and increased workloads on staff and pupils. Writing in Rest Of World, journalist Junhyup Kwon quotes a student as saying, “All our classes were delayed because of technical problems with the textbooks. […] I found it hard to stay focused and keep on track. The textbooks didn’t provide lessons tailored to my level.” Kim Jong-hee, chief digital officer of *****-A Publishing, one of the textbook developers, spoke of the advantages of AI books: “Using digital devices [students] are familiar with keeps them more focused, awake, and more willing to participate. The textbooks provide more personalised support for students struggling with lessons.” The Korean government originally commissioned publishers to produce the AI textbooks, who in turn spent around $567m to develop the online, digital texts. The use of AI textbooks was made mandatory in the country from the beginning of the school year in March, but has since been classed as ‘optional’ after just one semester. The number of schools using the AI textbooks has halved in that time. Speaking in the National Assembly in January this year, legislator Kang Kyung-sook asked the Minister for Education, “Traditional print textbooks take 18 months to develop, nine months for review, and six months for preparation. But the AI textbooks took only 12 [months to develop], three [months for review], and three months [for preparation] […]. Why was it rushed? Since they target children, they require careful verification and careful procedures.” The failure of the AI textbook scheme has also been blamed on the politicisation of the issue, and a change of government as the programme was being rolled out. Technology programmes in schools since the widespread adoption of the internet are relatively common, have cost taxpayers considerably less, and lasted much longer – despite eventual failure or wholesale realignment. In South Africa’s Guateng Province in the early 2000s, the Online Schools Project was designed to equip schools with computer labs and internet connections, but was scrapped in 2013 at a cost of R1-billion rand ($57m), according to some reports. In 2019, Malaysia’s 1BestariNet – a cloud-based VLE (virtual learning environment) – was terminated after eight years amid investigations into alleged inconsistencies between internet speed claims and the reality experienced by many schools. The overall cost of that project was put in the billions of ringgit (one billion ringgit is around $235m). However, the speed of the failure of the South Korean AI textbooks project and its high cost, suggest the educational adoption of AI texts delivered digitally is pitted with difficulty. An academic study conducted by the Massachusetts Institute of Technology published earlier this year hinted that using AI in educational contexts lowers brain activity in the long-term, which suggests the technology may not be suitable for developing minds. (Image source: “Adorable sleeping students in the undergraduate library” by benchilada 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 South Korea scraps AI textbook programme appeared first on AI News. View the full article

The surge of multi-billion-dollar investments in AI has sparked growing debate over whether the industry is heading for a bubble similar to the dot-com *****. Investors are watching closely for signs that enthusiasm might be fading or that the heavy spending on infrastructure and chips is failing to deliver expected returns. A recent survey by BofA Global Research found that 54% of fund managers believe AI stocks are already in bubble territory, while 38% disagree. Echoes of the dot-com era Despite the optimism surrounding AI, sceptics remain unconvinced of its real-world impact. Some even call it a bluff or a bubble waiting to burst. Speaking during Cisco’s recent Virtual Media Roundtable — AI Readiness Index 2025: Readiness Leads to Value, Ben Dawson, Senior Vice President and President for Asia Pacific, Japan, and Greater China (APJC), compared the current wave of AI hype to the early days of the internet. He said technological shifts of this scale often follow a familiar pattern — early excitement, heavy investment, and eventual market correction before long-term value takes hold. Dawson noted that while some AI projects or business models may not last, the overall transformation is real and lasting. He added that, much like the internet revolution, AI will permanently reshape business and society, and organisations that ignore it do so at their own risk. The role of governments and global policy Public policy is also shaping how the AI cycle unfolds — and how governments might cushion the risks of a potential AI bubble. As Harvard Business Review pointed out, in the US, government involvement has helped define past technology eras — often through incentives and early investments that encourage private innovation. The same pattern is now visible in AI. Both the Trump and Biden administrations have positioned AI as a matter of economic strength and national security, sending a clear message that speed matters. China has taken a state-led approach, directing capital toward local AI firms to reduce reliance on US technology. In Europe, efforts have focused more on regulation, though fears of overregulation have led to new programs — such as the AI Continent Action Plan and a €1 billion Apply AI fund — to boost adoption and competitiveness. Meanwhile, venture capital and sovereign wealth funds are investing heavily, even before widespread AI demand exists. These early bets assume that adoption will eventually justify the buildout. But if that demand slows, some investors could be left with stranded assets, much like the unused fibre networks that followed the dot-com bubble. For businesses, the challenge is different. Instead of financing the next infrastructure wave, they face the question of how to use AI to strengthen their operations. The companies that survived the dot-com downturn — such as Amazon — succeeded by aligning technology with real business value rather than market hype. Market warnings over a possible AI bubble The Bank of England recently warned that markets could suffer a sharp correction if confidence in AI falters, calling the potential impact on the ***’s financial system “material.” The warning reflects growing caution among policymakers about how quickly AI-related valuations have climbed. This concern is shared by some investors and economists who believe the rapid pace of AI spending may outstrip short-term returns. Others, however, argue that building AI infrastructure now is essential groundwork for future innovation. Building long-term AI infrastructure amid bubble fears When asked whether companies are worried about AI infrastructure costs and energy demand, Simon Miceli, Managing Director of Cloud and AI Infrastructure for APJC at Cisco, said he views the issue from the opposite angle. Rather than fearing overcapacity, he said what’s happening now is a large-scale buildout to support the industrialisation of AI. The question, he said, isn’t whether AI demand exists today, but whether the world is preparing fast enough for what’s coming. Miceli acknowledged that some correction in the AI market is likely, but he believes the long-term need for AI computing power justifies current investment levels. “There’s a race to develop AI and build the capability behind it,” he said, adding that demand will eventually meet supply as applications mature. Different shades of caution Across the industry, opinions vary on whether AI’s momentum represents hype or healthy growth. According to Reuters, at the Milken Institute Asia Summit 2025, Singapore’s GIC Chief Investment Officer Bryan Yeo said valuations in early-stage AI ventures appear inflated, with many startups commanding “huge multiples” despite modest revenues. He suggested that while some firms may justify their valuations, others are unlikely to deliver returns that match investor expectations. Jeff Bezos, Amazon’s founder, said that during periods of excitement like this, investors often struggle to separate good ideas from bad ones — though he also noted that innovation-driven bubbles often leave behind real progress once the market settles. At Goldman Sachs, economist Joseph Briggs argued that the current surge in AI infrastructure spending remains economically sustainable. He said the long-term case for AI investment is strong, but the ultimate winners are still uncertain given how quickly technology changes and how easily companies can switch providers. Meanwhile, ABB CEO Morten Wierod told Reuters that while he doesn’t see an AI bubble, supply chain and construction limits could slow the rollout of new data centres. IMF Chief Economist Pierre-Olivier Gourinchas added that even if there’s a downturn, it’s unlikely to cause a systemic financial crisis since AI investments aren’t debt-driven. OpenAI CEO Sam Altman also acknowledged market overexcitement, predicting that some investors will lose large sums while others will profit heavily — an outcome that mirrors past technology bubbles. Despite growing talk of an AI bubble, many investors remain committed to the sector. UBS equity strategists said that about 90% of investors who think the market is overheated are still holding AI-related assets, suggesting most believe the industry has not yet peaked. A cycle, not a collapse While concerns about an AI bubble are valid, most experts agree that the technology’s long-term impact is undeniable. As Cisco’s Ben Dawson put it, every major technological transition goes through a cycle of hype, correction, and consolidation — but what remains afterward reshapes industries for decades. For now, the question isn’t whether AI will endure, but how well businesses and investors can navigate the growing pains that come with every market bubble. (Photo by Growtika) See also: NVIDIA GPUs to power Oracle’s next-gen enterprise AI services 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 What if AI is the next dot-com bubble? appeared first on AI News. View the full article

Ant Group has entered the trillion-parameter AI model arena with Ling-1T, a newly open-sourced language model that the ******** fintech giant positions as a breakthrough in balancing computational efficiency with advanced reasoning capabilities. The October 9 announcement marks a significant milestone for the Alipay operator, which has been rapidly building out its artificial intelligence infrastructure across multiple model architectures. The trillion-parameter AI model demonstrates competitive performance on complex mathematical reasoning tasks, achieving 70.42% accuracy on the 2025 American Invitational Mathematics Examination (AIME) benchmark—a standard used to evaluate AI systems’ problem-solving abilities. According to Ant Group’s technical specifications, Ling-1T maintains this performance level while consuming an average of over 4,000 output tokens per problem, placing it alongside what the company describes as “best-in-class AI models” in terms of result quality. Dual-pronged approach to AI advancement The trillion-parameter AI model release coincides with Ant Group’s launch of dInfer, a specialised inference framework engineered for diffusion language models. This parallel release strategy reflects the company’s bet on multiple technological approaches rather than a single architectural paradigm. Diffusion language models represent a departure from the autoregressive systems that underpin widely used chatbots like ChatGPT. Unlike sequential text generation, diffusion models produce outputs in parallel—an approach already prevalent in image and video generation tools but less common in language processing. Ant Group’s performance metrics for dInfer suggest substantial efficiency gains. Testing on the company’s LLaDA-MoE diffusion model yielded 1,011 tokens per second on the HumanEval coding benchmark, versus 91 tokens per second for Nvidia’s Fast-dLLM framework and 294 for Alibaba’s Qwen-2.5-3B model running on vLLM infrastructure. “We believe that dInfer provides both a practical toolkit and a standardised platform to accelerate research and development in the rapidly growing field of dLLMs,” researchers at Ant Group noted in accompanying technical documentation. Ecosystem expansion beyond language models The Ling-1T trillion-parameter AI model sits within a broader family of AI systems that Ant Group has assembled over recent months. The company’s portfolio now spans three primary series: the Ling non-thinking models for standard language tasks, Ring thinking models designed for complex reasoning (including the previously released Ring-1T-preview), and Ming multimodal models capable of processing images, text, audio, and video. This diversified approach extends to an experimental model designated LLaDA-MoE, which employs Mixture-of-Experts (MoE) architecture—a technique that activates only relevant portions of a large model for specific tasks, theoretically improving efficiency. He Zhengyu, chief technology officer at Ant Group, articulated the company’s positioning around these releases. “At Ant Group, we believe Artificial General Intelligence (AGI) should be a public good—a shared milestone for humanity’s intelligent future,” He stated, adding that the open-source releases of both the trillion-parameter AI model and Ring-1T-preview represent steps toward “open and collaborative advancement.” Competitive dynamics in a constrained environment The timing and nature of Ant Group’s releases illuminate strategic calculations within China’s AI sector. With access to cutting-edge semiconductor technology limited by export restrictions, ******** technology firms have increasingly emphasised algorithmic innovation and software optimisation as competitive differentiators. ByteDance, parent company of TikTok, similarly introduced a diffusion language model called Seed Diffusion Preview in July, claiming five-fold speed improvements over comparable autoregressive architectures. These parallel efforts suggest industry-wide interest in alternative model paradigms that might offer efficiency advantages. However, the practical adoption trajectory for diffusion language models remains uncertain. Autoregressive systems continue dominating commercial deployments due to proven performance in natural language understanding and generation—the core requirements for customer-facing applications. Open-source strategy as market positioning By making the trillion-parameter AI model publicly available alongside the dInfer framework, Ant Group is pursuing a collaborative development model that contrasts with the closed approaches of some competitors. This strategy potentially accelerates innovation while positioning Ant’s technologies as foundational infrastructure for the broader AI community. The company is simultaneously developing AWorld, a framework intended to support continual learning in autonomous AI agents—systems designed to complete tasks independently on behalf of users. Whether these combined efforts can establish Ant Group as a significant force in global AI development depends partly on real-world validation of the performance claims and partly on adoption rates among developers seeking alternatives to established platforms. The trillion-parameter AI model’s open-source nature may facilitate this validation process while building a community of users invested in the technology’s success. For now, the releases demonstrate that major ******** technology firms view the current AI landscape as fluid enough to accommodate new entrants willing to innovate across multiple dimensions simultaneously. See also: Ant Group uses domestic chips to train AI models and cut 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. 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Healthcare regulator MHRA is fast-tracking new AI tools that promise to dramatically improve patient care. The wait for medical test results can stretch from days, to weeks, or even months. That wait ******* is often filled with worry and it always feels like an eternity. But what if that wait could be cut from weeks down to just a few minutes? In the next phase of its ‘AI Airlock’ programme, the Medicines and Healthcare products Regulatory Agency (MHRA) is evaluating seven new technologies designed to tackle some of healthcare’s most pressing challenges. The innovations being trialled could slash the time for bowel ******* test results to mere minutes and enable earlier detection of skin ******* and genetic eye diseases. The AI Airlock initiative provides a secure, controlled environment for manufacturers to test these sophisticated systems. This “regulatory sandbox” allows for evaluation of the AI’s effectiveness and limitations, helping to forge a clear path towards eventual regulatory approval and deployment within the health service. The insights gained from this real-world testing will directly inform the MHRA’s future regulations for AI as a medical device and feed into the National Commission into the Regulation of AI in Healthcare. This commission brings together a diverse group of patient advocates, clinicians, regulators, and technology companies to advise the agency. The seven selected technologies include AI-powered clinical note-taking to reduce administrative burdens on doctors, advanced ******* diagnostics, sophisticated eye disease detection, and tools that can summarise a patient’s entire hospital stay or interpret complex blood tests. The ultimate goal is to use AI to support clinicians, helping them to make faster and better-informed care decisions for their patients. Health Innovation Minister Zubir Ahmed said: “The AI airlock programme is a great example of how we can test new innovations thoroughly while still moving at pace, as we seek to deliver on our promise to shift healthcare from analogue to digital. “Through our ten year health plan we will drive for the NHS to be the most AI-enabled healthcare system in the world.” Pioneering a path for safe AI healthcare innovation The evolution of AI presents unique challenges for regulators tasked with ensuring patient care safety. Lawrence Tallon, MHRA Chief Executive, commented: “As the first country to create a dedicated regulatory environment, or ‘sandbox’, specifically for AI medical devices, we’re pioneering solutions to the unique challenges of regulating these emerging healthcare technologies. “The first phase of AI Airlock demonstrated the value of close collaboration between innovators and regulators. I look forward to seeing the results of this new cohort and how their technologies will shape the next generation of safe, effective AI tools in healthcare.” This second phase builds upon the success of the initial pilot. The MHRA has published four reports detailing the key findings from the first group of participants, which included innovative companies like Philips and OncoFlow. Working with the initial cohort, the AI Airlock programme identified several areas for regulatory improvement. These included better methods for validating synthetic data used to train AI models, ensuring the decisions made by an AI are “explainable” to a clinician, and developing novel approaches to tackle emerging risks such as AI “hallucinations,” where a model generates incorrect or nonsensical information. Yinnon Dolev, Gen AI Product Owner at Philips Medical Systems, said: “Participating in the AI Airlock sandbox was a very positive experience. The chance for a R&D representative to impact the regulatory strategy with the regulator is almost unheard of. “The interaction with the team and experts was exceptional. They provided invaluable insights and support, making the entire process smooth and productive. Meeting with the MHRA on a weekly basis throughout the pilot was also a catalyst for meaningful progress expediting our development activities.” A clinician’s perspective of using AI tools for patient care For frontline medical staff, the promise of AI is coupled with a healthy dose of caution. Sir Andrew Goddard, Chairman of the AI Airlock Governance Board and a Consultant Gastroenterologist at Royal Derby Hospital, emphasised the programme’s role in building trust. “Many clinicians, like myself, are keen to see AI find its place in the NHS, but are worried by over-promise on results and lack of reassurance with regards to patient safety,” explained Sir Goddard. “This programme goes a long way to embedding safety and rapid development of these new technologies in our health service.” By bringing innovators and regulators together at an early stage, the AI Airlock will help to deliver the next wave of medical technology to improve patient care, but in a manner that is also safe, reliable, and worthy of the trust of both patients and doctors. See also: NVIDIA GPUs to power Oracle’s next-gen enterprise AI services 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 MHRA fast-tracks next wave of AI tools for patient care appeared first on AI News. View the full article

Oracle and NVIDIA have expanded their partnership to make enterprise AI services more available, powerful, and practical. The announcements, made during Oracle AI World, cover everything from monstrously powerful new hardware to deeply integrated software that aims to put AI at the very core of a company’s data. Ian Buck, VP of Hyperscale and High-Performance Computing at NVIDIA, said: “Through this latest collaboration, Oracle and NVIDIA are marking new frontiers in cutting-edge accelerated computing—streamlining database AI pipelines, speeding data processing, powering enterprise use cases and making inference easier to deploy and scale on OCI.” The headline announcement is the new OCI Zettascale10 computing cluster. This platform is accelerated by NVIDIA GPUs and engineered for the kind of AI training and inference workloads that would make a normal server weep. OCI Zettascale10 promises a mighty 16 zettaflops of peak AI compute performance and is knitted together with NVIDIA’s Spectrum-X Ethernet, a networking fabric designed specifically to stop GPUs from sitting around waiting for data, allowing organisations to scale up to millions of processors efficiently. But raw power is only half the story. The real substance of this partnership lies in the software integrations that aim to weave AI into every layer of a business’s operations. Mahesh Thiagarajan, Executive VP of Oracle Cloud Infrastructure, commented: “OCI Zettascale10 delivers multi‑gigawatt capacity for the most challenging AI workloads with NVIDIA’s next-generation GPU platform. “In addition, the native availability of NVIDIA AI Enterprise on OCI gives our joint customers a leading AI toolset close at hand to OCI’s 200+ cloud services, supporting a long tail of customer innovation.” Giving your Oracle database a brain with AI The foundation of this new strategy is the Oracle AI Database 26ai. For years, the conventional wisdom was to move your data to where the AI models are. Oracle is flipping that on its head, arguing that it’s far more secure and efficient to bring the AI to your data. This latest database release is the embodiment of that “AI for Data” vision. Juan Loaiza, Executive VP of Oracle Database Technologies at Oracle, said: “By architecting AI and data together, Oracle AI Database makes ‘AI for Data’ simple to learn and simple to use. We enable our customers to easily deliver trusted AI insights, innovations, and productivity for all their data, everywhere, including both operational systems and analytic data lakes.” One of the standout features is the ability to run agentic AI workflows inside your database. The AI agents can tackle complex questions by combining your enterprise’s private, sensitive data with public information, all without ever having to move that private data outside your secure environment. This is made possible by features like a Unified Hybrid Vector Search, which lets the AI look for context across all your data types, whether it’s in a relational table, a JSON file, or a spatial map. Oracle is also clearly thinking about the long game with security. The new database implements NIST-approved quantum-resistant algorithms for data both in-flight and at-rest. It’s a defence against “harvest now, decrypt later” attacks, where hackers steal encrypted data today with the hope of breaking it with future quantum computers. Holger Mueller, VP and Principal Analyst at Constellation Research, commented: “Great AI needs great data. With Oracle AI Database 26ai, customers get both. It’s the single place where their business data lives—current, consistent, and secure. And it’s the best place to use AI on that data without moving it. “To help simplify and accelerate AI adoption, AI Database 26ai includes impressive new AI features that go beyond AI Vector Search. A highlight is Oracle’s architecting agentic AI into the database, enabling customers to build, deploy, and manage their own in-database AI agents using a no-code visual platform that includes pre-built agents.” The new database is designed to work with NVIDIA’s toolset. Its programming interfaces can now plug directly into NVIDIA NeMo Retriever, a collection of microservices that handle the complicated plumbing of modern AI for an enterprise. This makes it far easier for developers to implement things like retrieval-augmented generation, or RAG. In simple terms, RAG allows a language model to look up relevant facts in your company documents before it answers a question, making its responses far more accurate and useful. The Oracle Private AI Services Container will also get a GPU-powered boost. This container lets businesses run AI models in their own secure environment. Soon, it will be able to offload the heavy lifting of creating vector embeddings – a core task for AI search – to powerful NVIDIA GPUs using the cuVS library. This promises to slash the time it takes to prepare data for AI applications. Democratising enterprise AI Beyond the database, the partnership aims to simplify the entire AI pipeline. The new Oracle AI Data Platform now includes a built-in NVIDIA GPU option and the NVIDIA RAPIDS Accelerator for Apache Spark. For data scientists and engineers, this is a big deal. It means they can speed up their data processing and machine learning workflows using GPUs, often without having to change a single line of their existing code. All of these tools and capabilities are being consolidated within the Oracle AI Hub. The idea is to give organisations a single place to build, deploy, and manage their AI solutions. From the hub, users can deploy NVIDIA’s NIM microservices – which are like pre-packaged AI skills – through a simple, no-code interface. To lower the barrier to entry even further, the full NVIDIA AI Enterprise software suite is now natively available within the OCI Console. This means that a developer can spin up a GPU instance and enable all the necessary NVIDIA tools with a few clicks, rather than going through a separate procurement process. It’s a small change that makes a big difference in how quickly teams can get started. It’s clear that this collaboration is aimed at solving the real-world challenges businesses face when trying to adopt AI. By bringing the hardware, the data, and the software tools into one cohesive ecosystem, Oracle and NVIDIA are making a case that the era of practical, secure, and scalable enterprise AI has well and truly arrived. See also: Cisco: Only 13% have a solid AI strategy and they’re lapping rivals 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 NVIDIA GPUs to power Oracle’s next-gen enterprise AI services appeared first on AI News. View the full article

In a cement plant operated by Conch Group, an agentic AI system built on Huawei infrastructure now predicts the strength of clinker with over 90% accuracy and autonomously adjusts calcination parameters to cut coal consumption by 1%—decisions that previously required human expertise accumulated over decades This exemplifies how Huawei is developing agentic AI systems that move beyond simple command-response interactions toward platforms capable of independent planning, decision-making, and execution. Huawei’s approach to building these agentic AI systems centres on a comprehensive strategy spanning AI infrastructure, foundation models, specialised tools, and agent platforms. Zhang Yuxin, CTO of Huawei Cloud, outlined this framework at the recent Huawei Cloud AI Summit in Shanghai, where over 1,000 leaders from politics, business, and technology examined practical implementations across finance, shipping ports, chemical manufacturing, healthcare, and autonomous driving. The distinction matters because traditional AI applications respond to user commands within fixed processes, while agentic AI systems operate with autonomy that fundamentally changes their role in enterprise operations. Zhang characterised this as “a major shift in applications and compute,” noting that these systems make decisions independently and adapt dynamically, reshaping how computing systems interact and allocate resources. The question for enterprises becomes: how do you build infrastructure and platforms capable of supporting this level of autonomous operation? What do tomatoes and cement have in common? Watch a behind-the-scenes taster of how Huawei & Conch Group use AI to reshape the construction industry! Next up on the intelligent transformation menu: a mouthwatering new era of architecture—smarter, faster, cheaper, greener! pic.twitter.com/hEVIQ0xtUZ — Huawei (@Huawei) August 28, 2025 Infrastructure challenges drive new computing architectures The computational demands of agentic AI systems have exposed limitations in traditional cloud architectures, particularly as foundation model training and inference requirements surge. Huawei Cloud’s response involves CloudMatrix384 supernodes connected through a high-speed MatrixLink network, creating what the company describes as a flexible hybrid compute system combining general-purpose and intelligent compute capabilities. The architecture specifically addresses bottlenecks in Mixture of Experts (MoE) models through expert parallelism inference, which reduces NPU idle time during data transfers. According to the company’s technical specifications, this approach boosts single-PU inference speed 4-5 times compared to other popular models. The system also incorporates memory-centric AI-Native Storage designed for typical AI tasks, aimed at enhancing both training and inference efficiency. ModelBest, a company specialising in general-purpose AI and device intelligence, demonstrated practical applications of this infrastructure. Li Dahai, co-founder and CEO of ModelBest, detailed how their MiniCPM series—spanning foundation models, multi-modal capabilities, and full-modality integration—integrates with Huawei Cloud AI Compute Service to achieve 20% improvements in training energy efficiency and 10% performance gains over industry standards. The MiniCPM models have found applications in automotive systems, smartphones, embodied AI, and AI-enabled personal computers. From foundation models to industry-specific applications The challenge of adapting foundation models for specific industry needs has driven the development of more sophisticated training methodologies. Huawei Cloud’s approach encompasses three key components: a complete data pipeline handling collection through management, a ready-to-use incremental training workflow, and a smart evaluation platform with preset evaluation sets. The incremental training workflow reportedly boosts model performance by 20-30% through automatic adjustment of data and training settings based on core model features and industry-specific objectives. The evaluation platform enables quick setup of systems aligned with industry or company benchmarks, addressing both accuracy and speed requirements. Real-world implementations illustrate the practical application of these methodologies. Shaanxi Cultural Industry Investment Group partnered with Huawei to integrate AI with cultural tourism operations. Huang Yong, Chairman of Shaanxi Cultural Industry Investment Group, explained that using Huawei Cloud’s data-AI convergence platform, the organisation combined diverse cultural tourism data to create comprehensive datasets spanning history, film, and intangible heritage. The partnership established what they term a “trusted national data space for cultural tourism” on Huawei Cloud, enabling applications including asset verification, copyright transaction, enterprise credit enhancement, and creative development. The collaboration produced the Boguan cultural tourism model, which powers AI-driven tools, including a cultural tourism intelligent brain, smart management assistant, intelligent travel assistant, and an AI short video platform. International implementations demonstrate similar patterns. Dubai Municipality worked with Huawei Cloud to integrate foundation models, virtual humans, digital twins, and geographical information systems into urban systems. Mariam Almheiri, CEO of the Building Regulation and Permits Agency at Dubai Municipality, shared how this integration has improved city planning, facility management, and emergency responses. Enterprise-grade agent platforms emerge The distinction between consumer-focused AI agents and enterprise-grade agentic AI systems centres on integration requirements and operational complexity. Enterprise systems must seamlessly integrate into broader workflows, handle complex situations, and meet higher operational standards than consumer applications designed for quick interactions. Huawei Cloud’s Versatile platform addresses this gap by providing infrastructure for businesses to create agents tailored to production needs. The platform combines AI compute, models, data platforms, tools, and ecosystem capabilities to streamline agent development through deployment, release, usage, and management phases. Conch Group’s implementation in cement manufacturing offers specific performance metrics. The company partnered with Huawei to create what they describe as the cement industry’s first AI-powered cement and building materials model. The resulting cement agents predict clinker strength at 3 and 28 days with predictions deviating less than 1 MPa from actual results, representing over 90% accuracy. For cement calcination optimisation, the model suggests key process parameters and operational solutions that cut standard coal usage by 1% compared to class A energy efficiency standards. Xu Yue, Assistant to Conch Cement’s General Manager, noted that the model’s success with quality control, production optimisation, equipment management, and safety establishes groundwork for end-to-end collaboration and decision-making through cement agents, moving the industry “from relying on traditional expertise to being fully driven by data across all processes.” In corporate travel management, Smartcom developed a travel agent using Huawei Cloud Versatile that provides end-to-end smart services across departure, transfers, and flights. Kong Xianghong, CTO of Shenzhen Smartcom and Director of Smartcom Solutions, reported that the system combines travel industry data, company policies, and individual trip histories to generate recommendations. Employees adopt over half of these suggestions and complete bookings in under two minutes. The agent resolves 80% of issues in an average of three interactions through predictive question matching. What’s next for autonomous AI? The implementations discussed at the summit reflect a broader industry trend toward agentic AI systems that operate with increasing autonomy within defined parameters. The technology’s progression from reactive tools to systems capable of planning and executing complex tasks independently represents a fundamental architectural shift in enterprise computing. However, the transition requires substantial infrastructure investments, sophisticated data engineering, and careful integration with existing business processes. The performance metrics from early implementations—whether in manufacturing efficiency gains, urban management improvements, or travel booking optimisation—provide benchmarks for organisations evaluating similar deployments. As agentic AI systems continue to mature, the focus appears to be shifting from technological capability demonstrationsto operational integration challenges, governance frameworks, and measurable business outcomes. The examples from cement manufacturing, cultural tourism, and corporate travel management suggest that practical value emerges when these systems address specific operational pain points rather than serving as general-purpose automation tools. (Photo by AI News ) See also: Huawei details open-source AI development roadmap at Huawei Connect 2025 Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How Huawei is building agentic AI systems that make decisions independently appeared first on AI News. View the full article

If you’ve ever thought companies talk more than act when it comes to their AI strategy, a new Cisco report backs you up. It turns out that just 13 percent globally are actually prepared for the AI revolution. However, this small group – which Cisco calls the ‘Pacesetters’ – are lapping the competition. The third annual Cisco AI Readiness Index found these top performers are four times more likely to get their AI projects out of the pilot stage and into the real world. More importantly, they are 50 percent more likely to see measurable value from their efforts. What they’ve figured out is that winning with AI is about getting the foundations right with a disciplined approach that weaves together strategy, infrastructure, and security. And it pays off, with 90 percent of these Pacesetters seeing real gains in profit, productivity, and innovation, while most of their peers are hovering around the 60 percent mark. Jeetu Patel, Cisco’s President and Chief Product Officer, said: “This year’s Cisco AI Readiness Index makes one thing clear: AI doesn’t fail, readiness fails. “The most AI-ready organisations – the Pacesetters from our research – prove it. They’re four times more likely to move pilots into production and 50 percent more likely to realise measurable value. So, with more than 80 percent of organisations we surveyed about to deploy AI agents, these new findings confirm readiness, discipline, and action are key to unlocking value.” So, what’s their secret? The research shows a clear pattern. Pacesetters don’t treat AI as a side project; it’s a core part of their business strategy. Almost every single one of them (99%) has a proper AI roadmap, something only 58 percent of other companies can claim. They also put their money where their mouth is. For 79 percent of them, AI is the top investment priority, a commitment shared by only 24 percent of the rest. These leaders are building for the long haul, with 98 percent designing their networks to handle the immense scale and complexity of AI, compared to just 46 percent of their peers. It gives them the confidence that their systems can handle whatever is thrown at them; 71 percent say their networks can scale instantly for any AI project, a feeling shared by a worryingly low 15 percent of other organisations. The report also gives us a glimpse into the near future, and for many, it looks rocky. Two huge challenges are looming: the widespread use of AI agents and a problem Cisco has dubbed ‘AI Infrastructure Debt’. 83 percent of companies are planning to deploy AI agents as part of their strategy, with nearly 40 percent expecting them to be working alongside human employees within a year. But here’s the problem: most of these firms are trying to build on shaky ground. Over half of companies admitted their current networks simply can’t handle the data volumes or complexity that these advanced AI systems demand. The Pacesetters, on the other hand, have already done their homework, with 75 percent feeling fully equipped to secure and control these agents, compared to just 31 percent of others. This leads us to the ticking time bomb of ‘AI Infrastructure Debt’. Think of it as the modern version of the technical debt that plagued companies for years. It’s the result of all the compromises, postponed upgrades, and underfunded plans that quietly pile up, slowly strangling the long-term value of AI. The warning signs are already flashing. Nearly two-thirds of leaders expect their workloads to jump by over 30 percent in the next three years, and a similar number are struggling just to get their data organised in one place. Add to that the fact that only a quarter have enough GPU power, and you see a massive gap between ambition and reality. The lesson from Cisco’s report is clear and simple: value follows readiness. In the race to adopt AI, the Pacesetters have shown that the organisations that take the time to build a strong foundation to support their strategy are the ones that will pull away from the pack. See also: Gemini Enterprise: Google aims to put an AI agent on every desk 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 Cisco: Only 13% have a solid AI strategy and they’re lapping rivals appeared first on AI News. View the full article

Salesforce plans to invest $15 billion in San Francisco over the next five years to help businesses adopt AI. The move underscores the company’s push to stay competitive as AI becomes central to enterprise software. Founded and headquartered in San Francisco since 1999, Salesforce has been adding AI features across its products, including the workplace messaging tool Slack. The company is competing with ServiceNow, Oracle, and Microsoft to attract organisations eager to integrate AI into their operations. Part of the new investment will fund an AI incubator on Salesforce’s San Francisco campus and help companies deploy AI agents — digital assistants that can handle tasks for users. “This $15 billion investment reflects our deep commitment to our hometown — advancing AI innovation, creating jobs and helping companies and our communities thrive,” said CEO Marc Benioff. The announcement comes just before Dreamforce, Salesforce’s annual conference, which runs from October 14 to 16 in San Francisco. The company expects around 50,000 people to attend and estimates the event will bring in about $130 million in local revenue. Salesforce, which employs more than 76,000 people worldwide, also announced last week that it will spend $1 billion in Mexico over the next five years. The company has operated there since 2006. Morningstar analyst Dan Romanoff said the new spending aligns with the company’s long-term goals. “If the company wants to remain a leader in an important emerging technology area, it must have a pipeline of talent to innovate and drive the field forward. We already see shortages of AI talent, so this makes sense,” he said. Salesforce shares rose 2.8% on Monday but remain down about 28% since the start of the year. On the same day, Salesforce also launched Agentforce 360, a new AI platform for businesses. While many companies are still experimenting with AI-driven automation, Salesforce says it has already rolled out multiple versions of its “agentic” technology, used by thousands of customers and within its own operations. The company describes the “Agentic Enterprise” as a workplace model where AI supports people rather than replaces them. In this setup, AI agents help teams respond faster, track leads, provide continuous service, and make better decisions. The goal, Salesforce says, is to boost productivity and customer engagement. Agentforce 360 combines four key parts of this model: Agentforce 360 Platform: A framework for building enterprise AI agents, now featuring a conversational builder, hybrid reasoning for more accurate results, and voice support. Data 360: A unified data layer that gives AI systems the context they need. Features like Intelligent Context and Tableau Semantics help turn raw data into meaningful insights. Customer 360 Apps: The tools that record how a company sells, serves, and operates — now enhanced with AI to better understand customer behaviour and internal processes. Slack: A shared space where people and AI agents can work together, linking information and actions in real time. Salesforce says this setup allows businesses to build AI agents that rely on trusted data, function across departments, and integrate directly with existing workflows. Its open ecosystem also lets partners tailor the technology for different industries. Last month, Salesforce forecast third-quarter revenue that fell short of analyst expectations but expanded its share buyback plan by $20 billion. (Photo by Denys Nevozhai) See also: Salesforce Agentforce 3 brings visibility to AI agents 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 Salesforce commits $15 billion to boost AI growth in San Francisco appeared first on AI News. View the full article

Meta and Oracle are upgrading their AI data centres with NVIDIA’s Spectrum-X Ethernet networking switches — technology built to handle the growing demands of large-scale AI systems. Both companies are adopting Spectrum-X as part of an open networking framework designed to improve AI training efficiency and accelerate deployment across massive compute clusters. Jensen Huang, NVIDIA’s founder and CEO, said trillion-parameter models are transforming data centres into “giga-scale AI factories,” adding that Spectrum-X acts as the “nervous system” connecting millions of GPUs to train the largest models ever built. Oracle plans to use Spectrum-X Ethernet with its Vera Rubin architecture to build large-scale AI factories. Mahesh Thiagarajan, Oracle Cloud Infrastructure’s executive vice president, said the new setup will allow the company to connect millions of GPUs more efficiently, helping customers train and deploy new AI models faster. Meta, meanwhile, is expanding its AI infrastructure by integrating Spectrum-X Ethernet switches into the Facebook Open Switching System (FBOSS), its in-house platform for managing network switches at scale. According to Gaya Nagarajan, Meta’s vice president of networking engineering, the company’s next-generation network must be open and efficient to support ever-larger AI models and deliver services to billions of users. Building flexible AI systems According to Joe DeLaere, who leads NVIDIA’s Accelerated Computing Solution Portfolio for Data Centre, flexibility is key as data centres grow more complex. He explained that NVIDIA’s MGX system offers a modular, building-block design that lets partners combine different CPUs, GPUs, storage, and networking components as needed. The system also promotes interoperability, allowing organisations to use the same design across multiple generations of hardware. “It offers flexibility, faster time to market, and future readiness,” DeLaere said to the media. As AI models become larger, power efficiency has become a central challenge for data centres. DeLaere said NVIDIA is working “from chip to grid” to improve energy use and scalability, collaborating closely with power and cooling vendors to maximise performance per watt. One example is the shift to 800-volt DC power delivery, which reduces heat loss and improves efficiency. The company is also introducing power-smoothing technology to reduce spikes on the electrical grid — an approach that can cut maximum power needs by up to 30 per cent, allowing more compute capacity within the same footprint. Scaling up, out, and across NVIDIA’s MGX system also plays a role in how data centres are scaled. Gilad Shainer, the company’s senior vice president of networking, told the media that MGX racks host both compute and switching components, supporting NVLink for scale-up connectivity and Spectrum-X Ethernet for scale-out growth. He added that MGX can connect multiple AI data centres together as a unified system — what companies like Meta need to support massive distributed AI training operations. Depending on distance, they can link sites through dark fibre or additional MGX-based switches, enabling high-speed connections across regions. Meta’s AI adoption of Spectrum-X reflects the growing importance of open networking. Shainer said the company will use FBOSS as its network operating system but noted that Spectrum-X supports several others, including Cumulus, SONiC, and Cisco’s NOS through partnerships. This flexibility allows hyperscalers and enterprises to standardise their infrastructure using the systems that best fit their environments. Expanding the AI ecosystem NVIDIA sees Spectrum-X as a way to make AI infrastructure more efficient and accessible across different scales. Shainer said the Ethernet platform was designed specifically for AI workloads like training and inference, offering up to 95 percent effective bandwidth and outperforming traditional Ethernet by a wide margin. He added that NVIDIA’s partnerships with companies such as Cisco, xAI, Meta, and Oracle Cloud Infrastructure are helping to bring Spectrum-X to a broader range of environments — from hyperscalers to enterprises. Preparing for Vera Rubin and beyond DeLaere said NVIDIA’s upcoming Vera Rubin architecture is expected to be commercially available in the second half of 2026, with the Rubin CPX product arriving by year’s end. Both will work alongside Spectrum-X networking and MGX systems to support the next generation of AI factories. He also clarified that Spectrum-X and XGS share the same core hardware but use different algorithms for varying distances — Spectrum-X for inside data centres and XGS for inter–data centre communication. This approach minimises latency and allows multiple sites to operate together as a single large AI supercomputer. Collaborating across the power chain To support the 800-volt DC transition, NVIDIA is working with partners from chip level to grid. The company is collaborating with Onsemi and Infineon on power components, with Delta, Flex, and Lite-On at the rack level, and with Schneider Electric and Siemens on data centre designs. A technical white paper detailing this approach will be released at the OCP Summit. DeLaere described this as a “holistic design from silicon to power delivery,” ensuring all systems work seamlessly together in high-density AI environments that companies like Meta and Oracle operate. Performance advantages for hyperscalers Spectrum-X Ethernet was built specifically for distributed computing and AI workloads. Shainer said it offers adaptive routing and telemetry-based congestion control to eliminate network hotspots and deliver stable performance. These features enable higher training and inference speeds while allowing multiple workloads to run simultaneously without interference. He added that Spectrum-X is the only Ethernet technology proven to scale at extreme levels, helping organisations get the best performance and return on their GPU investments. For hyperscalers such as Meta, that scalability helps manage growing AI training demands and keep infrastructure efficient. Hardware and software working together While NVIDIA’s focus is often on hardware, DeLaere said software optimisation is equally important. The company continues to improve performance through co-design — aligning hardware and software development to maximise efficiency for AI systems. NVIDIA is investing in FP4 kernels, frameworks such as Dynamo and TensorRT-LLM, and algorithms like speculative decoding to improve throughput and AI model performance. These updates, he said, ensure that systems like Blackwell continue to deliver better results over time for hyperscalers such as Meta that rely on consistent AI performance. Networking for the trillion-parameter era The Spectrum-X platform — which includes Ethernet switches and SuperNICs — is NVIDIA’s first Ethernet system purpose-built for AI workloads. It’s designed to link millions of GPUs efficiently while maintaining predictable performance across AI data centres. With congestion-control technology achieving up to 95 per cent data throughput, Spectrum-X marks a major leap over standard Ethernet, which typically reaches only about 60 per cent due to flow collisions. Its XGS technology also supports long-distance AI data centre links, connecting facilities across regions into unified “AI super factories.” By tying together NVIDIA’s full stack — GPUs, CPUs, NVLink, and software — Spectrum-X provides the consistent performance needed to support trillion-parameter models and the next wave of generative AI workloads. (Photo by Nvidia) See also: OpenAI and Nvidia plan $100B chip deal for AI future 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 Meta and Oracle choose NVIDIA Spectrum-X for AI data centres appeared first on AI News. View the full article

Every business, big or small, has a wealth of valuable data that can inform impactful decisions. But to extract insights, there’s usually a good deal of manual work that needs to be done on raw data, either by semitechnical users (such as founders and product leaders), or dedicated – and expensive – data specialists. Either way, to produce real value, information has to be collected, shepherded, altered, and drawn from dozens of spreadsheets and different business platforms: the organisation’s CRM, its martech stack, e-commerce system, and website data, to name a few common examples. Clearly, that’s a time consuming process, and the outcomes can be old news, rather than up-to-the-minute insights. Introducing vibe analytics The ideal business solution would be querying real-time data using natural language (vs writing code in SQL or Python), with smart systems working in the background to correlate and parse different data sources and formats. This is vibe analysis, where users can simply ask questions in plain language and let AI do the heavy lifting. Instead of manual data-wrestling and business users spending hours uncovering insights hidden deep in datasets, they get results fast — in text, graphics, summaries, and, where needed, detailed breakdowns. Fast and accurate data analysis is important to every organisation, but for many, real-time insights are crucial. In the agricultural sector, for example, Lumo uses Fabi.ai’s platform to manage large fleets of IoT devices, collecting telemetry data continuously and adjusting its systems based on collated, normalised, and parsed information. Using vibe analysis, Lumo sees device performance immediately, as well as trends that develop over time. It pulls in weather data, and correlates the device fleet’s performance metrics with environmental factors. The data dashboards Lumo has built are not the result of many months of work writing data integration routines and front-end coding, but are a result of vibe analysis. Getting under the hood Sceptics of AI’s abilities often point to vibe-coding as an example of where things can go wrong, raising concerns about quality control and the “****** box” nature of AI-driven analysis. Many users want visibility into how results are generated, with the option to inspect logic, tweak queries, or adjust API calls to ensure accuracy. When done well, vibe analytics addresses these concerns by combining transparency with rigour. Natural language inputs and modular build methods make it accessible to semitechnical users (such as founders and product leaders), while the underlying systems meet the accuracy and reliability standards expected by technical teams. This means users can trust the output whether they’re working independently or in collaboration with data scientists and developers. Designed specifically for both data experts and semitechnical data users, Fabi is a generative *** platform that brings vibe analysis done right to life. The code it produces can be hidden away entirely, or shown verbatim and edited in place, giving semitechnical users a chance to understand how the analysis works under the hood, while allowing technical teams to verify and fine-tune the system’s output. Data flows from an organisation’s systems (the platform mediates connections) or is uploaded. The resultant actionable insights can be pushed/scheduled to email, slack, google sheets, displayed in graphics, text, or a mixture of both. Fabi: A generative *** platform Co-founder and CEO of Fabi, Marc Dupuis, describes how many organisations start using the analysis platform by testing workflows and queries on sample data before progressing to real-world analysis. As users delve into data troves and test their work, they can check its veracity, often in collaboration with someone more technically astute, thanks to the platform’s open, transparent view of Smartbooks to show what’s happening under the hood. It works the other way, too: semitechnical data users can confirm that the data being processed is relevant and accurate. To address common concerns about quality control and “******-box” AI, Fabi limits vibe analysis to internally controlled, carefully accessed data sources, with built-in guardrails. Code can be shown verbatim and edited in place, giving semitechnical users visibility into how results are produced, while allowing technical teams to audit, verify, and fine-tune outputs. Collaborative sharing of reports, findings, and working code helps teams validate results without working outside their areas of expertise. Typical workflows include real-time KPI dashboards; natural-language Q&A over operational and product data; correlation analyses (for example, device performance against weather conditions); cohort and trend exploration; A/B test readouts and experiment summaries; and scheduled, shareable reports that mix text, graphics, summaries, and detailed breakdowns. These collaborative workflows are designed to be efficient and intuitive, so, whether working collectively or solo, users can unlock insights from even the most complex data arrangements. Fabi landed its first round of backing from Eniac Ventures in 2023, so it’s a company on the move. The team continues to expand its capabilities, with plans to make vibe analysis even more seamless for both semitechnical and technical users. Organisations interested in exploring the platform can start by testing workflows on sample data, then scale up to real-world use cases as they grow more confident in the system’s transparency and accuracy. (Photo by Alina Grubnyak) See also: Generative AI trends 2025: LLMs, data scaling & enterprise adoption 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 Vibe analytics for data insights that are simple to surface appeared first on AI News. View the full article

Google Cloud has launched Gemini Enterprise, a new platform it calls “the new front door for AI in the workplace”. Announced during a virtual press conference, the platform brings together Google’s Gemini models, first and third-party agents, and the core technology of what was formerly known as Google Agentspace to create a singular agentic platform. It aims to democratise the creation and use of AI-powered agents for automating complex workflows and boosting productivity across entire organisations. Thomas Kurian, CEO of Google Cloud, introduced the new offering, explaining that as customers moved beyond simply building applications with AI, the company saw them “advancing to build agents”. Gemini Enterprise is Google’s answer to this evolution, bundling its entire AI stack into a cohesive user experience that allows developers and business users alike to build agents with a no-code workbench. The platform is built on six core components. The “brains” are Google’s powerful Gemini models, including the newly available Gemini 2.5 Flash Image. The “workbench” is the agent creation and orchestration technology pioneered with Agentspace, allowing any user to manage agents and automate processes. Finally, this is complemented by the “taskforce,” a suite of pre-built Google agents for specialised jobs like the new Code Assist Agent and the Deep Research Agent. To make these agents effective, there is deep integration with a company’s data through new connectors for systems like Microsoft Teams, Salesforce, Box, Confluence, and Jira. Kurian explained the system’s intelligence, stating, “We remember who you are and what you do and use it to personalise the context you have when we work with a large language model”. A central “governance” framework allows organisations to monitor, secure, and audit all agents from one place, with protections like Model Armor now built-in. Finally, the platform is built on an open “ecosystem” of over 100,000 partners. Gemini Enterprise: A glimpse into the future of work To demonstrate the platform’s capabilities, Maryam Gholami walked through a practical use case. “The beauty of Gemini Enterprise is that it offers the familiar interface of the Gemini but built for enterprise workflows, including full control to enable or disable any of the sources as needed,” Gholami said. Using a custom ‘campaigns agent’, she used four different agents to handle market research, media generation, team communications, and inventory management. The agent identified a market trend towards sci-fi themes, flagged a 25 percent inventory gap, created a purchase order in ServiceNow, drafted an email to store managers, and generated social media assets. “Gemini Enterprise is more than just a chat interface,” Gholami concluded after the demonstration. “It’s an end-to-end AI system that unifies your data, your tools, and your teams, turning weeks of complex work into a single, streamlined conversation”. Customers drive transformation with AI fleets Proving the platform’s real-world value, Nirmal Saverimuttu, CEO of Virgin Voyages, shared his perspective that “any major disruption like AI requires a cultural transformation to be successful”. Importantly, Saverimuttu stressed that AI’s role is to work alongside, not replace, his team. “Our people are our biggest asset. AI. And never replace our people,” he stated. “To me, AI is about getting the best from our people. It’s about unleashing human potential”. The cruise line has deployed a fleet of over 50 specialised AI agents company-wide. The first, ‘Email Ellie’, has boosted content production speed by 40 percent and contributed to a 28 percent year-over-year increase in July sales. Saverimuttu also noted welcome operational gains, including a “35 percent reduction in agency dependency costs, resulting in creative independence”. Another early adopter is Macquarie Bank. The bank, one of Australia’s largest, has rolled out Gemini Enterprise to every employee and reports that 99 percent of its staff have already completed generative AI training. Google emphasised that Gemini Enterprise is an open platform, with partners like Box, Salesforce, and ServiceNow announcing compatible agents. A new AI agent finder will also help customers discover thousands of validated partner solutions. To support adoption, Google has also launched Google Skills, a new free learning platform with 3,000 courses. As part of this, the company announced the Gemini Enterprise Agent Ready (GEAR) program; an educational sprint designed to enable one million developers to build and deploy agents. Pricing and availability of Gemini Enterprise Gemini Enterprise is available globally in all countries where Google Cloud products are sold. Gemini Business, for small businesses, starts at $21 per seat per month, while Gemini Enterprise Standard and Plus editions for larger organisations start at $30 per seat per month. For Kurian, the launch is about democratising powerful technology. “Gemini Enterprise technology is really about reimagining a super powerful AI technology [for the workplace] but making it super easy to use and putting it in the hands of every company and every user in those companies,” Kurian concludes. See also: AI value remains elusive despite soaring investment 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 Gemini Enterprise: Google aims to put an AI agent on every desk appeared first on AI News. View the full article

Cisco has entered an increasingly competitive race to dominate AI data centre interconnect technology, becoming the latest major player to unveil purpose-built routing hardware for connecting distributed AI workloads across multiple facilities. The networking giant unveiled its 8223 routing system on October 8, introducing what it claims is the industry’s first 51.2 terabit per second fixed router specifically designed to link data centres running AI workloads. At its core sits the new Silicon One P200 chip, representing Cisco’s answer to a challenge that’s increasingly constraining the AI industry: what happens when you run out of room to grow. A three-way battle for scale-across supremacy? For context, Cisco isn’t alone in recognising this opportunity. Broadcom fired the first salvo in mid-August with its “Jericho 4” StrataDNX switch/router chips, which began sampling and also offered 51.2 Tb/sec of aggregate bandwidth backed by HBM memory for deep packet buffering to manage congestion. Two weeks after Broadcom’s announcement, Nvidia unveiled its Spectrum-XGS scale-across network—a notably cheeky name given that Broadcom’s “Trident” and “Tomahawk” switch ASICs belong to the StrataXGS family. Nvidia secured CoreWeave as its anchor customer but provided limited technical details about the Spectrum-XGS ASICs. Now Cisco is rolling out its own components for the scale-across networking market, setting up a three-way competition among networking heavyweights. The problem: AI is too big for one building To understand why multiple vendors are rushing into this space, consider the scale of modern AI infrastructure. Training large language models or running complex AI systems requires thousands of high-powered processors working in concert, generating enormous amounts of heat and consuming massive amounts of electricity. Data centres are hitting hard limits—not just on available space, but on how much power they can supply and cool. “AI compute is outgrowing the capacity of even the largest data centre, driving the need for reliable, secure connection of data centres hundreds of miles apart,” said Martin Lund, Executive Vice President of Cisco’s Common Hardware Group. The industry has traditionally addressed capacity challenges through two approaches: scaling up (adding more capability to individual systems) or scaling out (connecting more systems within the same facility). But both strategies are reaching their limits. Data centres are running out of physical space, power grids can’t supply enough electricity, and cooling systems can’t dissipate the heat fast enough. This forces a third approach: “scale-across,” distributing AI workloads across multiple data centres that might be in different cities or even different states. However, this creates a new problem—the connections between these facilities become critical bottlenecks. Why traditional routers fall short AI workloads behave differently from typical data centre traffic. Training runs generate massive, bursty traffic patterns—periods of intense data movement followed by relative quiet. If the network connecting data centres can’t absorb these surges, everything slows down, wasting expensive computing resources and, critically, time and money. Traditional routing equipment wasn’t designed for this. Most routers prioritise either raw speed or sophisticated traffic management, but struggle to deliver both simultaneously while maintaining reasonable power consumption. For AI data centre interconnect applications, organisations need all three: speed, intelligent buffering, and efficiency. Cisco’s answer: The 8223 system Cisco’s 8223 system represents a departure from general-purpose routing equipment. Housed in a compact three-rack-unit chassis, it delivers 64 ports of 800-gigabit connectivity—currently the highest density available in a fixed routing system. More importantly, it can process over 20 billion packets per second and scale up to three Exabytes per second of interconnect bandwidth. The system’s distinguishing feature is deep buffering capability, enabled by the P200 chip. Think of buffers as temporary holding areas for data—like a reservoir that catches water during heavy rain. When AI training generates traffic surges, the 8223’s buffers absorb the spike, preventing network congestion that would otherwise slow down expensive GPU clusters sitting idle waiting for data. Power efficiency is another critical advantage. As a 3RU system, the 8223 achieves what Cisco describes as “switch-like power efficiency” while maintaining routing capabilities—crucial when data centres are already straining power budgets. The system also supports 800G coherent optics, enabling connections spanning up to 1,000 kilometres between facilities—essential for geographic distribution of AI infrastructure. Industry adoption and real-world applications Major hyperscalers are already deploying the technology. Microsoft, an early Silicon One adopter, has found the architecture valuable across multiple use cases. Dave Maltz, technical fellow and corporate vice president of Azure Networking at Microsoft, noted that “the common ASIC architecture has made it easier for us to expand from our initial use cases to multiple roles in DC, WAN, and AI/ML environments.” Alibaba Cloud plans to use the P200 as a foundation for expanding its eCore architecture. Dennis Cai, vice president and head of network Infrastructure at Alibaba Cloud, stated the chip “will enable us to extend into the Core network, replacing traditional chassis-based routers with a cluster of P200-powered devices.” Lumen is also exploring how the technology fits into its network infrastructure plans. Dave Ward, chief technology officer and product officer at Lumen, said the company is “exploring how the new Cisco 8223 technology may fit into our plans to enhance network performance and roll out superior services to our customers.” Programmability: Future-proofing the investment One often-overlooked aspect of AI data centre interconnect infrastructure is adaptability. AI networking requirements are evolving rapidly, with new protocols and standards emerging regularly. Traditional hardware typically requires replacement or expensive upgrades to support new capabilities. The P200’s programmability addresses this challenge. Organisations can update the silicon to support emerging protocols without replacing hardware—important when individual routing systems represent significant capital investments and AI networking standards remain in flux. Security considerations Connecting data centres hundreds of miles apart introduces security challenges. The 8223 includes line-rate encryption using post-quantum resilient algorithms, addressing concerns about future threats from quantum computing. Integration with Cisco’s observability platforms provides detailed network monitoring to identify and resolve issues quickly. Can Cisco compete? With Broadcom and Nvidia already staking their claims in the scale-across networking market, Cisco faces established competition. However, the company brings advantages: a long-standing presence in enterprise and service provider networks, the mature Silicon One portfolio launched in 2019, and relationships with major hyperscalers already using its technology. The 8223 ships initially with open-source SONiC support, with IOS XR planned for future availability. The P200 will be available across multiple platform types, including modular systems and the Nexus portfolio. This flexibility in deployment options could prove decisive as organisations seek to avoid vendor lock-in while building out distributed AI infrastructure. Whether Cisco’s approach becomes the industry standard for AI data centre interconnect remains to be seen, but the fundamental problem all three vendors are addressing—efficiently connecting distributed AI infrastructure—will only grow more pressing as AI systems continue scaling beyond single-facility limits. The real winner may ultimately be determined not by technical specifications alone, but by which vendor can deliver the most complete ecosystem of software, support, and integration capabilities around their silicon. 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 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 Can Cisco’s new AI data centre router tackle the industry’s biggest infrastructure bottleneck? appeared first on AI News. View the full article

A new report from Red Hat finds that 89 percent of businesses are yet to see any customer value from their AI endeavours. However, organisations anticipate a 32 percent increase in AI investment by 2026. The survey finds that AI and security are the joint top IT priorities for *** organisations over the next 18 months, with 62 percent of respondents citing them as necessary. These are followed by hybrid or multi-cloud strategies and virtualisation, showing a clear direction in the technological focus of British businesses. However, the path to AI integration isn’t straightforward. The vast majority of organisations are facing barriers to adoption, with the high costs of implementation and maintenance being the biggest concern for 34 percent of respondents. Data privacy and security issues are a close second, troubling 30 percent, while 28 percent are struggling with the integration of AI into their existing systems. A particularly interesting finding is the prevalence of “shadow AI,” with 83 percent of organisations reporting the unauthorised use of AI tools by employees. This suggests a disconnect between official IT strategy and the day-to-day practices of the workforce, potentially introducing security risks and inefficiencies. In an effort to navigate these challenges, *** organisations are increasingly turning to open source software. The survey reveals that 84 percent of respondents consider enterprise open source important for their AI strategy, with similarly high figures for virtualisation, hybrid and multi-cloud, and security. Joanna Hodgson, *** Country Manager at Red Hat, said: “This year’s *** survey results show the gap between ambition and reality. Organisations are investing substantially in AI but currently only a few are delivering customer value. In the journey from experimentation to sustainable production, enterprise knowledge and integration with enterprise systems must pave the road to achieving value from AI. “Openness is a force for greater collaboration, sharing best practice and enabling flexibility. As is the case with successful hybrid cloud investments, open-source will continue to be the bedrock for making AI more consumable and reusable.” The survey also explored the specific areas of AI that are being prioritised. Agentic AI, which involves systems that can operate with a high degree of autonomy, is the top priority for 68 percent of respondents. This is followed by the desire to enable broad employee adoption and to operationalise AI. The skills gap remains a persistent challenge, with AI being the most urgent area of concern for the second consecutive year. Within the field of AI, the talent shortage is most acute in agentic AI, the ability to efficiently use AI capabilities, and educating the wider business on how to use AI. Despite these domestic challenges, there is a strong sense of optimism about the ***’s position on the global AI stage. 83 percent of respondents believe the *** is either already a global AI powerhouse or has the potential to become one within the next three years. However, this confidence is tempered by a lack of talent pipeline, limited public funding, and insufficient private sector engagement, which are seen as the main factors holding the *** back from extracting value from AI. The report also touches on the complexities of cloud adoption, which is further complicated by the integration of AI workloads. Internal silos, sovereignty concerns, and unclear return on investment continue to be barriers. In response, *** organisations are prioritising operational control and autonomy, securing the software supply chain, and maintaining flexibility in their choice of IT suppliers. Hans Roth, SVP and GM for EMEA at Red Hat, commented: “Organisations want greater operational control and IT resiliency to adapt in a world of constant disruption. The survey results, as well as our daily conversations, show sovereignty prominently on the agenda for enterprise’s ongoing cloud strategies and the budding AI opportunity. “Open-source is central to this shift as it provides businesses with the transparency and flexibility to innovate rapidly without compromise.” The findings from Red Hat’s latest survey show the *** is ready to tap the value potential of AI, but is also struggling with the practicalities of implementation, skills shortages, and the complexities of the technological environment. The strong emphasis on open-source suggests a pragmatic approach, supporting collaboration and flexibility in the pursuit of AI-driven innovation. See also: Samsung’s tiny AI model beats giant reasoning LLMs 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 value remains elusive despite soaring investment appeared first on AI News. View the full article

A new paper from a Samsung AI researcher explains how a small network can beat massive Large Language Models (LLMs) in complex reasoning. In the race for AI supremacy, the industry mantra has often been “******* is better.” Tech giants have poured billions into creating ever-larger models, but according to Alexia Jolicoeur-Martineau of Samsung SAIL Montréal, a radically different and more efficient path forward is possible with the Tiny Recursive Model (TRM). Using a model with just 7 million parameters, less than 0.01% of the size of leading LLMs, TRM achieves new state-of-the-art results on notoriously difficult benchmarks like the ARC-AGI intelligence test. Samsung’s work challenges the prevailing assumption that sheer scale is the only way to advance the capabilities of AI models, offering a more sustainable and parameter-efficient alternative. Overcoming the limits of scale While LLMs have shown incredible prowess in generating human-like text, their ability to perform complex, multi-step reasoning can be brittle. Because they generate answers token-by-token, a single mistake early in the process can derail the entire solution, leading to an invalid final answer. Techniques like Chain-of-Thought, where a model “thinks out loud” to break down a problem, have been developed to mitigate this. However, these methods are computationally expensive, often require vast amounts of high-quality reasoning data that may not be available, and can still produce flawed logic. Even with these augmentations, LLMs struggle with certain puzzles where perfect logical execution is necessary. Samsung’s work builds upon a recent AI model known as the Hierarchical Reasoning Model (HRM). HRM introduced a novel method using two small neural networks that recursively work on a problem at different frequencies to refine an answer. It showed great promise but was complicated, relying on uncertain biological arguments and complex fixed-point theorems that were not guaranteed to apply. Instead of HRM’s two networks, TRM uses a single, tiny network that recursively improves both its internal “reasoning” and its proposed “answer”. The model is given the question, an initial guess at the answer, and a latent reasoning feature. It first cycles through several steps to refine its latent reasoning based on all three inputs. Then, using this improved reasoning, it updates its prediction for the final answer. This entire process can be repeated up to 16 times, allowing the model to progressively correct its own mistakes in a highly parameter-efficient manner. Counterintuitively, the research discovered that a tiny network with only two layers achieved far better generalisation than a four-layer version. This reduction in size appears to prevent the model from overfitting; a common problem when training on smaller, specialised datasets. TRM also dispenses with the complex mathematical justifications used by its predecessor. The original HRM model required the assumption that its functions converged to a fixed point to justify its training method. TRM bypasses this entirely by simply back-propagating through its full recursion process. This change alone provided a massive boost in performance, improving accuracy on the Sudoku-Extreme benchmark from 56.5% to 87.4% in an ablation study. Samsung’s model smashes AI benchmarks with fewer resources The results speak for themselves. On the Sudoku-Extreme dataset, which uses only 1,000 training examples, TRM achieves an 87.4% test accuracy, a huge leap from HRM’s 55%. On Maze-Hard, a task involving finding long paths through 30×30 mazes, TRM scores 85.3% compared to HRM’s 74.5%. Most notably, TRM makes huge strides on the Abstraction and Reasoning Corpus (ARC-AGI), a benchmark designed to measure true fluid intelligence in AI. With just 7M parameters, TRM achieves 44.6% accuracy on ARC-AGI-1 and 7.8% on ARC-AGI-2. This outperforms HRM, which used a 27M parameter model, and even surpasses many of the world’s largest LLMs. For comparison, Gemini 2.5 Pro scores only 4.9% on ARC-AGI-2. The training process for TRM has also been made more efficient. An adaptive mechanism called ACT – which decides when the model has improved an answer enough and can move to a new data sample – was simplified to remove the need for a second, costly forward pass through the network during each training step. This change was made with no major difference in final generalisation. This research from Samsung presents a compelling argument against the current trajectory of ever-expanding AI models. It shows that by designing architectures that can iteratively reason and self-correct, it is possible to solve extremely difficult problems with a tiny fraction of the computational resources. See also: Google’s new AI agent rewrites code to automate vulnerability fixes 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 Samsung’s tiny AI model beats giant reasoning LLMs appeared first on AI News. View the full article

The acquisition underscores Tuned Global’s commitment to shaping the future of the music industry by empowering clients with innovative technology and unmatched execution, while continuing to support existing Figaro.ai customers. Wednesday, 8 October, 2025 — Tuned Global, the leading music and media technology platform, has today announced the acquisition of Figaro.ai (by FeedForward), a London-based audio-AI company known for making music catalogues smarter and more discoverable. The acquisition advances Tuned Global’s strategy to be the cloud platform that clients build on to innovate. By bringing Figaro.ai into its partner-friendly platform, Tuned Global is enhancing its platform with AI innovation to deliver practical outcomes for customers: faster innovation, greater engagement and measurable business impact. “With Figaro.ai, Tuned Global cements its position as the most comprehensive music platform, where innovation across AI, fraud detection, rights management, search and recommendations can be built,” said Tuned Global CEO **** Raso. This move is about using technology to improve client outcomes and deliver value for the wider industry. It follows Tuned Global’s earlier acquisition of Pacemaker, known for pioneering AI-powered mixing technology, and reflects a clear strategy: acquiring companies whose innovation and IP help clients and the industry build the future of music technology and streaming. Integrating Figaro.ai strengthens Tuned Global’s ability to power premium, highly relevant music experiences at scale. Tuned Global remains an open ecosystem, with Figaro.ai integrated as another component in a broader platform. It will operate as an integrated but distinct component within Tuned Global’s broader platform, complementing existing partners and expanding client options. Current Figaro.ai clients will continue to be supported, backed by Tuned Global’s global reach, infrastructure and long-term commitment. The Figaro.ai team, including founders Lydia Gregory and Kevin Webster, will be integrated within Tuned Global, ensuring continuity for existing customers and adding deep AI expertise to accelerate the company’s roadmap. Tuned Global CEO **** Raso said he was thrilled to welcome the deeply skilled Figaro.ai team into the fold. “At Tuned Global, we see ourselves as the hub where innovation in music technology takes shape. We are building the largest open ecosystem of AI music intelligence, giving our clients maximum choice and real impact for the music industry,” he said. “With Figaro.ai joining the platform, we’re not only expanding that ecosystem with cutting-edge technology but also welcoming a highly skilled team whose expertise strengthens our ability to deliver music experiences that are powerful, flexible and future-ready.” Figaro.ai CEO and Co-founder Lydia Gregory said she was excited to join Tuned Global and amplify the impact of her company’s objectives. “Figaro.ai has always been about combining deep technical expertise with a passion for music discovery. I’m incredibly proud of the team that built this technology, and I’m thrilled that they are joining me as part of Tuned Global,” she said. “Being integrated into a platform of this scale means we can continue our mission with greater reach and impact, while ensuring continuity for the clients who already rely on us. Together, we’re ready to help the industry deliver music experiences that are more relevant, premium, and engaging than ever before.” About Tuned Global Tuned Global is the leading data-driven cloud and software platform that empowers businesses to integrate commercial music into their apps or launch complete streaming experiences using advanced APIs, real-time analytics, licensing solutions, and customisable white-label apps. Our turnkey solutions for music, audio, and video — coupled with a broad ecosystem of third-party music tech integrations — make us the most comprehensive platform for powering any digital music project. We streamline complexities in licensing, rights management, and content delivery, enabling rapid innovation and bringing new ideas to life. Since 2011, we’ve supported 40+ companies in 70+ countries — across telecom, fitness, media, aviation, and more — to deliver innovative music experiences faster and more cost-effectively. For more information, visit www.tunedglobal.com. About Figaro Figaro is the audio intelligence platform for music search, tagging and content detection – powering smarter discovery and content management across sync, DSPs, UGC, and distribution. The post Tuned Global strengthens its leadership in music technology with the acquisition of Figaro.ai appeared first on AI News. View the full article