AI spending in Asia Pacific continues to rise, yet many companies still struggle to get value from their AI projects. Much of this comes down to the infrastructure that supports AI, as most systems are not built to run inference at the speed or scale real applications need. Industry studies show many projects miss their ROI goals even after heavy investment in GenAI tools because of the issue.
Enterprise AI deployment faces a fundamental tension: organisations need sophisticated language models but baulk at the infrastructure costs and energy consumption of frontier systems.
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The convergence of mobile and desktop operating systems is a goal that has remained elusive for big tech firms since the early days of the smartphone. Microsoft’s attempt in the form of Windows Mobile was reaching the end of its road by 2010, and despite Apple’s iOS/iPadOS and macOS moving very slowly towards one another for the last few years, Cupertino has not yet reached the fabled goal of the-one-OS-to-rule-them-all.
At its London Symbiosis 4 event on 22 October, Druid AI introduced what it terms Virtual Authoring Teams – a new generation of AI agents that can design, test, and deploy other AI agents. The announcement marks a move towards what the company calls a ‘factory model’ for AI automation.
In modern software development, speed and security must go hand in hand. Teams are shipping code faster than ever, but such a rapid pace can introduce security vulnerabilities if not managed correctly. Dynamic Application Security Testing (DAST) is an important practice for finding security flaws in running applications. However, manual DAST scans can be slow and cumbersome, creating bottlenecks that undermine the very agility they are meant to support.
Thomson Reuters and Imperial College London have established a frontier AI research lab to overcome historic deployment challenges.
Adopting AI at scale can be difficult. Enterprises around the world are discovering the pace of AI deployment is frustratingly slow as they face implementation, integration, and customisation challenges. Generative AI is undoubtedly powerful, but it can be complex, particularly for businesses starting from scratch.
AI startup company, Counterintuitive, has set out to build “reasoning-native computing,” enabling machines to understand rather than simply mimic. Such a breakthrough has the potential to shift AI from pattern recognition to genuine comprehension, paving the way for systems that can think and make decisions – in other words, to be more “human-like.”
The U.S. Department of Energy (DOE) and AMD are collaborating on two new AI supercomputers at Oak Ridge National Laboratory (ORNL) as part of a larger AI strategy to advance research in science, energy, and national security — and strengthen the nation’s position in high-performance computing.
OpenAI has completed a major reorganisation and, in the same breath, signed a new definitive partnership agreement with Microsoft.
Baidu’s latest ERNIE model, a super-efficient multimodal AI, is beating GPT and Gemini on key benchmarks and targets enterprise data often ignored by text-focused models.
Security experts at JFrog have found a ‘prompt hijacking’ threat that exploits weak spots in how AI systems talk to each other using MCP (Model Context Protocol).
Author: Olga Zharuk, CPO, Teqblaze
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.
AI is the cream of the crop in today’s tech field, with industries relying on generative AI to improve operations and boost productivity. One sector that using AI with measurable results is agriculture, with vegetable seed companies harnessing the technology to identify the best vegetable varieties out of thousands of options. This facility can help growers in diverse markets who work in very different conditions from one another.
For many UK executives, AI investment has become a necessity, not an experiment in innovation. Boards now demand evidence of measurable impact – whether through efficiency gains, revenue growth, or reduced operational risk. Yet, as Pete Smyth, CEO of Leading Resolutions notes, many SMEs treat AI as an exploratory exercise, not a structured business strategy. The result is wasted investment and a lack of demonstrable return.
The Royal Navy is handing the first line of its recruitment operations to a real-time AI avatar called Atlas.
Under China’s push to clean up its energy system, AI is starting to shape how power is produced, moved, and used — not in abstract policy terms, but in day-to-day operations.
By 2027, half of all business decisions will be augmented or automated by AI agents for decision intelligence. This seismic shift is changing how organisations operate, and AI leaders are under pressure to adapt, innovate, and guide their teams through complexity. Gartner Data & Analytics Summit 2026 is designed to help these leaders meet their biggest challenges head-on.
Microsoft believes it has a fix for AI prompts being given, the response missing the mark, and the cycle repeating.
F. Scott Fitzgerald observed that “in a real dark night of the soul, it is always three o’clock in the morning.” Microsoft’s latest Copilot usage analysis suggests this nocturnal tendency toward existential contemplation persists in the AI age – with religion and philosophy conversations rising through the rankings during early morning hours.
AI is reshaping how people plan and experience travel. From curated videos on Instagram Reels to booking engines that build entire itineraries in seconds, AI is becoming a powerful force in how journeys are imagined, booked, and lived. But this shift raises an important question: is AI giving travellers more freedom, or quietly steering their choices?
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