Economics & Capacity

I see people everywhere anxious about whether AI will disrupt their jobs, their industries, their lives. I’ve always approached this with calm. Not indifference—calm. The future rarely sends advance notice, but it is always arriving. This isn’t news. It’s the human condition. A few years ago, I attended a keynote by Michio Kaku where he framed—perfectly, for me—the relationship between humanity and technological change. What follows is my version. I can’t claim novelty, and I’m not a domain expert in sociology or economics. I’m an infrastructure builder observing the same pattern from the inside. ...

A few years ago, I was having dinner with the Americas VP of a European energy supermajor — one of those companies that extracts oil from war zones, negotiates with regimes that don’t appear on polite lists, and operates in places where “political risk” means your assets might get nationalized or your personnel kidnapped. Seventy-plus countries. Active operations in Libya, Nigeria, Angola, Myanmar, Yemen. The kinds of places where security briefings come before breakfast. ...

In 2015, I did what seemed like the mature thing to do. I created a Production Engineering department. My college foundation was production engineering. I was a true believer: if we formalized standards and assigned a dedicated group to own operational rigor, the organization would naturally converge toward consistency. The mandate: Create SOPs. Define standards. Reduce variance. Improve reliability. On paper, it was textbook. In practice, it was a slow-motion collision with reality. ...

I’m currently working on the design of a framework for GPU fleet management. We’re living in a crowded data center reality where everybody wants “hero” compute — dense GPUs, fast networking, and delivery that’s closer to the edge. We’re in a land-grab phase where every business wants to be everywhere, but most teams are discovering the same thing: buying GPUs is the easy part. Operating them as a coherent fleet is the hard part. ...

Atlas is a single pane of glass for multi-cloud cost visibility. This post documents the pipeline: ingestion, streaming, storage, query, forecasting, and visualization.

Traditional internet architecture solved latency with caching. Static content, images, JavaScript bundles—all pushed to edge nodes milliseconds from users. CDNs achieve 95-99% cache hit rates. The compute stays centralized; the content moves to the edge. AI breaks this model completely. Every inference requires real GPU cycles. You can’t cache a conversation. You can’t pre-compute a response to a question that hasn’t been asked. The token that completes a sentence depends on every token before it. ...