Enterprise AI budgets are flowing in two directions at once, and only one of them builds an asset the buyer actually controls. Gartner's latest forecast puts worldwide AI spending at $2.7 trillion in 2026, but the composition of that figure matters far more than the total. Executives who read only the headline will miss the tension between what they are buying and what they actually own. The Number Behind the Number Gartner projects 2026 AI spending is a 49.5 percent increase over 2025, with $3.6 trillion forecast for 2027. AI infrastructure accounts for $1.5 trillion of this year's total, more than half. Gartner's John-David Lovelock calls the data center buildout the largest infrastructure project humanity has undertaken, and describes demand as inelastic even as memory prices climb. Compute Concentration is a Sovereignty Issue Gartner expects hyperscalers and service providers buying AI-optimized servers to remain the largest single area of spending. For an enterprise, t...
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...