People looking to relocate or planning a sight-seeing trip to a metro area may be adding another item to their checklists: Does the city offer wireless access? -- Increasingly, the answer will be yes. This month, San Francisco and New Haven, CT became the two latest major U.S. urban areas to take another step toward providing Wi-Fi connections. They've submitted requests for proposals from technology companies and hired consultants. And Philadelphia -- the first major urban area to initiate a city government-led wireless program -- just announced that it has narrowed its search for an Internet provider to two finalists: Earthlink and Hewlett-Packard. The service will be rolled out in "mid-October," according to Dianah Neff, CIO of the city of Philadelphia. Indeed, approximately 300 U.S. cities and municipalities are now in various stages of wireless rollouts -- up from barely any just a year and a half ago. Greg Richardson, founder of Civitium, an Atlanta-based organization that assists cities in their Wi-Fi efforts, sometimes uses the word "crazy" to describe this phenomenon.
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...