AI Data Center

The AI chip conversation is dominated by what happens on the silicon: TFLOPS, memory bandwidth, interconnect speed. The infrastructure conversation that determines whether any of that silicon actually works at …

Every discussion of AI chip performance focuses on TFLOPS — the raw compute throughput number that vendors put on their datasheets. It is the wrong metric for most AI workloads …

Compute has become a geopolitical asset. That sentence would have sounded abstract in 2020. In 2026 it is operationally concrete: which chips you can buy, in which quantities, to deploy …

Something significant has shifted in the AI chip market that most enterprise technology analysis has not fully absorbed: the biggest customers of NVIDIA — Google, AWS, Meta, Microsoft — are …

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 …