In both business and consumer markets, widespread adoption of Voice over Internet Protocol (VoIP) depends on the availability of wireless VoIP handsets, reports In-Stat. Dual-mode cellular/WiFi handsets will be the key driver to mass consumer adoption of VoIP. By 2009, In-Stat forecasts that over 66 million cellular/WiFi handsets will be in operation. �Wireless high-speed broadband access, unified messaging, video, and dual-network cellular/WiFi services are making the mobile triple play a consumer market reality,� says Keith Nissen, In-Stat analyst. �The key to successfully capturing the market for these next-generation personalized services is control of the end-point device.� Worldwide, consumer VoIP subscribers using wireless IP phones will grow from 2 percent currently to 73 percent in 2009. Based on competition from mobile carriers without wireline operations, Europe will be the largest initial market for dual-mode smartphones. While mass production of dual-mode sets is not scheduled until 2007, an In-Stat market survey found that over 80 percent of businesses have an interest in the technology.
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...