How to keep thinking

Imagine you’re the guest on some kind of frenetic, software-engineering-themed game show. The host is constantly flipping over new cards with questions that you have to answer as fast as possible: Is this adjustment to the database schema right? Do these bits of data look plausible? Do these five paragraphs of text describe an actual series of manual tests that took place? Does this suggested architecture pass the smell test? Is this implementation better than the current code? Or this one? Or this one? Working in 2026 feels a bit like this. When frontier AI models can do most of the tasks in your queue, the most efficient way to work is often spinning off tasks for an AI agent and continually context-switching between the results1. This isn’t quite mindless — in fact, it requires quite a lot of skill to skim the AI response and rapidly decide what to do with it — but it certainly involves less time for slow, careful reflection. Why not slow down? Why does it have to be frenetic? Why…