Spiralling consumer demand for network services will lead to wireless home bandwidth requirements jumping by a whopping 1,800 per cent to 57Mbps by 2009. According to a newly released study from JupiterResearch, wireless bandwidth requirements for the typical broadband home with a wireless network will grow from less than 3Mbps in 2004 to a likely 57Mbps in 2009. JupiterResearch estimates that tech-savvy households of three individuals will require wireless bandwidth of up to 84Mbps, driven primarily by changes in the home use of consumer electronics and changing consumption patterns for digital media at home. Overall, in 2004 some 7.5 million US households indicated that they have a home network that is at least partly wireless. JupiterResearch forecasts that the number of wireless home network households in the US will rise to 34.3 million by 2009. "Consumers are beginning to shift their paradigms for internet access, home networking and digital content management." "The number of consumer electronics devices using a wireless network in the home could explode over the next five years, driving bandwidth requirements beyond today's offerings." "To exploit this trend, consumer electronics manufacturers will increasingly need to conceive of their products as always-on nodes in a wireless network."
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...