Every developer I know uses AI for coding now. The productivity gains are real, but there are costs that don't show up on any dashboard. Imagine a spectrum from humans typing code to full AGI autonomy. Somewhere between those extremes is where you use AI today, and that threshold keeps moving right as models improve.

Recently I stumbled upon this awesome daxfohl comment on Hacker News asking whether using AI too much or too little is higher risk. It made me think about LLMs for coding differently, especially after reading what other devs share about AI adoption in different workplaces. You can be wrong in both directions, but is the desired amount of AI usage at work changing as the models improve?

In 2026, a randomized study by Shen and Tamkin tested this directly: 52 professional developers learning a new async library were split into AI-assisted and unassisted groups. The AI group scored 17% lower on conceptual understanding, debugging, and code reading. The largest gap was in debugging, the exact skill you need to catch what AI gets wrong. The insidious part is that you don't notice the decline because the tool compensates for it.