Subscription Services Will Replace Downloads as the Dominant Online Music Model -- While online music stores like Apple's iTunes have attracted millions of customers by selling downloads of songs or albums, changing market conditions will make subscription-based music services the dominant model for selling online music in the future. That is the conclusion of "Online Music: Will Napster Be the Next iTunes as Subscriptions Replace Downloads?" a new report from Strategy Analytics. According to this report, the shift toward subscription music services will be driven by a combination of changing consumer expectations as well as pressure from broadband service providers and record companies. In addition to changing consumer needs, the report notes that many broadband service providers prefer to offer a subscription service, such as Real Networks' Rhapsody, which generates steady monthly revenues and helps deter broadband churn. Finally, major record companies are dissatisfied with the revenue they receive from low-cost download sales, and will increasingly focus on alternative business models for selling music online.
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...