According to Strategy Analytics, the strong evolution of cellular-only markets will dramatically slow the global adoption of fixed-mobile convergence (FMC). By 2010 there will be 500 million cellular-only users generating three times the voice revenues of their FMC counterparts. With major carriers such as BT, Korea Telecom and BellSouth deploying FMC services, convergence is back on the agenda and the specter of WiFi or Bluetooth destabilizing in-building cellular revenue flows again looms large. However, handset prices and availability will represent the biggest barrier to FMC adoption. "The 'voice goes mobile' mantra of the cellular industry is now a reality rather than a mission statement." comments David Kerr, Vice President, Strategy Analytics' Global Wireless Practice. "Cellular already accounts for 60 percent of the world's access lines, one third of all voice minutes and one half of all voice revenues, with 10 percent of homes relying on only a mobile phone for voice. FMC providers will have to work hard to turn this tide as players like Vodafone and O2 fine tune their wireless-only solutions."
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...