Both satellite companies grew their subscriber base by around 13 percent over the last year, but there are signs that the rate of growth may be slowing. EchoStar, the second-largest satellite broadcaster in the United States, added 225,000 subscribers to its Dish Network in the last quarter, bringing the total number of subscribers to 11.46 million. Rival DirecTV added the same number of new customers in the United States over the same period, taking its total subscriber base to 14.67 million. The company signed 964,000 subscribers in the quarter, but the number of net additions was down on the same period the previous year, due to slightly higher churn. EchoStar reported total revenue of $2.1 billion for the three months to the end of June, up 18 percent on the corresponding period the previous year, with a net income of $856 million, ten times more than for the same period the preceding year. DirecTV revenues for the same period were $3.18 billion, with a net income of $162 million, the first profit in two years, compared to a loss of $13.3 million a year ago.
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...