You've probably felt this. The first week you wired an AI assistant into your editor, you shipped twice as much. By month three, you were back to your old pace — except now you were debugging weirder bugs. I've been using AI assistants in my daily workflow for about two years across four projects. The pattern keeps showing up: the productivity gains are real but front-loaded, and they erode unless you change how you work. Most of that erosion comes from one specific, fixable problem. The Problem: Plausible Code That Doesn't Actually Work The bug I see most often isn't an obvious syntax error. It's when generated code calls a function, method, or config option that looks exactly like something the library would have — but doesn't. Last month I was building a CSV import feature and the assistant happily produced this: import pandas as pd # Read CSV with progress reporting — looks reasonable, right? df = pd .…