AI Coding

In the previous posts, I wrote about speaking at Global Azure ANZ 2026 and the strange speed at which universities seem to be reversing themselves on AI. But the conversation that lingered longest in my head afterwards was about coding itself. Or maybe not coding exactly. More about the feeling of coding. One developer told me they just were not enjoying the new AI coding tools very much. The outputs were fine. Useful even. But something felt missing. The satisfaction had thinned out somehow. ...

For most of modern software history, documentation existed for one core reason: human written code was expensive to create and difficult to replace. Developers carried complex mental models in their heads. Code was the physical imprint of those models. Documentation was the act of extracting that fragile, human knowledge and making it reusable. Documentation existed to protect human intellectual property. That logic is breaking. With systems like OpenAI Codex and other AI coding agents producing usable code in minutes, the implementation itself is no longer the scarce asset. Code can be regenerated, rewritten, or discarded with very little cost. What remains hard, slow, and deeply human is deciding why a system exists, what it must guarantee, and where its limits lie. ...