I don't buy the math here because it seems to only model half of what AI coding agents do. The entire argument treats AI as a code-generation accelerant -- more output, therefore more maintenance burden, therefore compounding debt. But in my experience (solo dev, ~30k LOC apps), Claude Code has decimated my maintenance costs. I throw broken tests at it. I use it to diagnose bugs, trace data flows, reason through unfamiliar code, and refactor when things get unwieldy. AI isn't just a faster typist -- it's a faster debugger, reader, refactorer. Modeling AI's impact on codebase growth without modeling its impact on maintenance speed seems like a very selective way to model the future. The maintenance cost curves cited here come from pre-AI dev data; using them to predict post-AI outcomes assumes the answer to the most important question (does AI reduce per-line maintenance cost?) rather than investigating it directly. Nobody has nine years of data on this because halfway-decent coding agents have existed for < 6 months. I like the cautionary advice -- watch out for how much maintenance burden you're incurring with all that delicious AI code slop, folks -- but I don't think his confident quantitative predictions ("gains erased after 5 months") are justified. Am I missing something obvious here?