For decades, software engineering had a fairly predictable constraint. Writing software was expensive. Every feature required engineers, every system required implementation, and every new capability required time, effort, and people. That constraint shaped how we built software. Today, it is disappearing. AI can generate code faster than most teams can review it, and that changes something fundamental about the job. The Old Bottleneck For most of software history, engineering organizations optimized for output....

Walk into any AI infrastructure discussion and you’ll hear the same question: What’s your GPU utilization? It’s become the infrastructure equivalent of asking a web service for its CPU utilization. The assumption is simple: higher utilization is better. After all, GPUs are expensive, and a cluster running at 90% utilization sounds far more impressive than one running at 50%. For a long time, I believed that too. Then I spent more time working on GPU scheduling and multi-tenant AI workloads....

I used to think DNS was boring. You ask for a name, you get an IP, the application connects. That was my entire mental model, until a bug dismantled it completely. The service worked when addressed by IP but failed with a hostname. It worked from one machine and failed from another. Then, oddly, it started working the moment I added a trailing dot to the domain name. That day I learned that DNS is never simple, not because it is poorly designed, but because decades of accumulated behaviour are hiding behind what looks like a plain string....