According to In-Stat, while residential WLAN equipment shipment volumes have increased strongly since Apple first launched its AirPort line of 802.11b-compliant consumer WLAN gear in 2000, prices have eroded sharply over the past several years, and few vendors are making a profit. In-Stat forecasts the SOHO/consumer AP market will rise from approximately 17.6 million units in 2004 to roughly 32.6 million units in 2009. A major story in this market is a key transition from the 802.11g air standard to MIMO-based products. "In-Stat believes that there will be a gradually shrinking price premium for MIMO/802.11n throughout the forecast period," says Sam Lucero, In-Stat analyst. "The benefits of dramatically increased range appear to be resonating with consumers, actually more so than the increased throughput offered, and we believe customers are willing to pay the extra amount for whole-home coverage." Eventually, while 802.11g will remain throughout the forecast period in very cost-optimized equipment, MIMO/802.11n will become the new primary air standard.
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...