AI Data Center

In my recent trilogy analyzing AI market concentration — “Is There An AI Concentration Crisis: When 42 Stocks Become the Entire Market,” “Why Enterprise AI Strategy Must Diverge From Hyperscaler …

This is not a cloud strategy debate. It is a financial calculation with specific inputs — utilization rate, workload profile, commitment horizon, and hidden operational costs — and most enterprises …

Language models run in discrete request-response cycles. Physical AI systems — robots, autonomous vehicles, surgical assistants — never stop. The GPU demand they generate is structurally different, and it is …

Lead times now stretch past a year. Hyperscalers have committed $630 billion in AI capex. CoWoS packaging capacity remains structurally oversubscribed. This is not a procurement inconvenience — it is …

In my recent trilogy analyzing AI market concentration—”Is There An AI Concentration Crisis: When 42 Stocks Become the Entire Market,” “Why Enterprise AI Strategy Must Diverge From Hyperscaler Playbooks,” and …

Let me start with a number that should make every CIO, CFO, and technology strategist stop what they’re doing: 42. In Douglas Adams’ cult classic The Hitchhiker’s Guide to the …

Building an AI Factory requires more than just infrastructure—it demands three critical capabilities that span all operational tiers, ensuring the system operates reliably, securely, and efficiently. These cross-cutting concerns form …

In the rapidly evolving landscape of enterprise AI adoption, most organizations struggle with a fundamental challenge: bridging the gap between AI capability and workforce readiness. SoftBank Corp.’s (https://www.softbank.jp/en/sbnews/entry/20251217_01) audacious experiment …

Project Rainier is an AI supercomputing cluster distributed across multiple U.S. data centers, designed to provide infrastructure for training large AI models. The project is a collaboration between AWS and …