On doing "deeper work" with AI

For long stretches of 2025 and 2026, most of my work involved some combination of prototyping, very well-known implementation patters, and working in codebases I already knew well. I now have more experience doing (i) production-stage work (ii) in AI-developed parts of codebases (iii) on tricky, details-matter, think-about-them-in-the-shower types of problems. Here are some impressionistic reflections about this: Yes, it feels strange. Yes, there is plenty of tech debt to pay down, much of which would not exist had I hand-coded it. But there's also tech debt that doesn't exist that would exist if I had hand-coded it. So, for example, sometimes AI will use a slightly less familiar, but still obviously correct, data structure or algorithm, and this will make a refactor trivial. I very much default to simple algorithms, but my sense of what is simple is bounded in part by my knowledge and experience. The AI knows more simple implementations. Paying down AI tech debt for hours on end is a…