Ironically, according to a study by the Information Technology Association of America (ITAA) entitled Race to the Horizon -- "U.S. leadership in high technology faces challenges domestically and abroad. This pressure comes from global competitors who share America�s appreciation for the economic, political and social benefits of high tech; and from domestic legislators who would subject parts of the IT industry to burdensome regulation or discriminatory taxation. Foreign nations are willing and able to do what it takes to race ahead of today�s market incumbents and our own lawmakers seem to always rationalize the need for further control � despite the negative impacts this control would have on the industry. Over the next four years the next Administration, the Congress and the states will play a critical role in determining whether the U.S. retains its lead or, through lack of clear vision and halting public policy resolve, allows itself to slip into also ran status."
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...