One of the important things about location-based services is that they enable a host of other applications, delivering value greater than the sum of their parts. According to Kenneth Hyers, ABI Research's principal analyst of global wireless operator research, "It's not just a technology in your mobile phone that says 'you are here.' It can also pass that information to many other applications." Do you want a route home? A friend's location, relative to where you are? A location-stamp on that picture you took with your camera phone? LBS enable all these functions. A real-estate appraiser might photograph a house with a GPS-equipped phone, then combine the picture, its subject's GPS coordinates and, if linked to a navigation program, its actual address, and send that information to the office's billing system to identify the party to be invoiced for the service. "Mobile operators don't want to sell 'location technologies' -- that's too esoteric," concludes Hyers. "But they believe that if you tell an end-user, 'call us to check movie times and we'll route you to the theater', consumers will be willing to pay for that service." Is this fantasy or reality? That depends, ironically, on where you live and who your mobile operator is. Nextel has pioneered LBS in the US and maintains the most advanced portfolio. This year Sprint has shown a real interest. Hyers believes that "the combination of Sprint and Nextel could lead to some really interesting synergies where lessons learned on the corporate side at Nextel can be translated to Sprint's good consumer base." Outside the US, KDDI in Japan is successfully rolling out LBS. In South Korea, SK Telecom is pushing LBS strongly, while in Europe, Hutchison is attracting attention with its 3G LBS which Hyers terms "the best in the region."
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...