> Here's a term for what I think is happening: the human reward function problem. In machine learning, a reward function tells an agent what good looks like. Writing code by hand was never easy, but it was full of small rewards. Solving a problem in your head. Understanding a gnarly bit of logic. Watching the code compile. The feeling of control. LLM-assisted programming has automated much of the work that generated those dopamine hits and replaced it with the cognitive load of review and supervision. The satisfying part shrank. The exhausting part grew. And there are no new rewards to fill the gap.

Say what you will about the Claudisms in this piece, this bit certainly rings true for me. With old school coding, there was always a reward at the end, the harder it was, the more satisfying it felt.

With agentic coding, I really doesn’t feel like that, at least not in the same way. It feels more like continually riding a wave of productivity, where small features or huge features have similar levels of interaction required. And that’s exciting in the beginning but quickly becomes very tiring.

Maybe it's different between professional and personal projects, but I get that feeling more often as features are not only easier to create, but also come out more polished and consistent. I'm able to focus on a single project for a month and have something pretty good by the end. Doing rewrites to clean up and reorganize has never been easier, so I get to see and feel more of the design space in action. The can be pretty damn frustrating at times, half of which is me/context, the other their nature