According to Informa, the global mobile market is forecast to pass 2 billion subs this year and be approaching 3 billion by the end of 2010. Overall market growth was boosted in 2004, with 91 million more new customers during the year than there were in 2003. Although the annual growth rate is forecast to fall into single figures within a couple of years, over one billion new subs are due to be added between now and the end of 2010. Almost half of these new subs will be in Asia Pacific. Two huge Asian markets -- India and China -- will account for over 30 percent (371.6 million) of the region's total. Amongst the regions, it is the Middle East and Africa markets that will show the greatest growth. Their combined markets will more than double in size, with over 200 million new customers signing up for mobile services by 2010. As a result of the continued growth, several countries are already reporting penetration rates of over 100 percent and Western Europe's regional penetration is set to breach 100 percent in a couple of years. By 2010, the global penetration is estimated at 43 percent.
Enterprise AI adoption keeps running into the same wall: the moment sensitive data, model weights, or agent memory leave a tightly controlled environment, security teams lose visibility into what happens to them while the workload is actually running. Confidential computing exists to close that gap, and new research from ABI Research shows the technology has matured enough, across CPUs, GPUs, containers, and now agentic AI, to become a foundational requirement for enterprises building sovereign AI infrastructure: computing environments they control end to end rather than simply rent. Protection is Moving From CPUs to GPUs ABI Research finds that CPU-based confidential computing is closest to mass-market adoption, the product of years spent hardening chip-level isolation for general workloads. GPU-based confidential computing is now generating the strongest momentum, and for good reason. It addresses one of the largest unresolved gaps in AI security: protecting data and models while inf...