Anthropic proved that AI coding tools are secretly making developers worse. They paid 52 senior developers $150 each to learn a new Python library (Trio). Half got an AI assistant. Half didn't. Then they tested what they actually learned. The AI group scored 17% lower on the knowledge quiz. And finished the task in roughly the same time as the no-AI group. Zero productivity gain. Two full grade points of skill, gone. They watched the screen recordings of every single participant. 6 distinct AI usage patterns emerged. 3 destroyed learning. 3 preserved it. The losers (avg quiz score: 24–39%): → AI Delegation: paste the prompt, paste the answer back. Fastest. Dumbest. → Progressive Reliance: start strong, then quietly give up by task 2 → Iterative Debugging: 15+ queries asking AI to "check this for me" The winners (avg quiz score: 65–86%): → Conceptual Inquiry, only asked AI to explain. Never asked it to write. → Generation-Then-Comprehension, generated code, then grilled AI on why it worked → Hybrid Code-Explanation, every code request bundled with "explain it to me" The pattern is brutal once you see it: The people who asked AI to think for them lost the skill. The people who asked AI to think with them kept it (and scored higher than no-AI). Same tool. Same task. Same time budget. Two completely different brains at the end of it. And the killer detail: the AI group encountered 3x fewer errors. That sounds like a win. It isn't. Errors are where the skill lives. The control group debugged their way to mastery. The AI group skipped the gym and wondered why they got weaker. Anthropic's own conclusion (from their own researchers): "AI-enhanced productivity is not a shortcut to competence." The juniors copy-pasting Claude Code outputs all day right now? They're not 10x engineers. They're 0.83x engineers who feel like 10x engineers.