I got into coding over a decade before it was my career because of the exploration, learning, and puzzle/challenge aspect.

Every time I have tried to be extrinsically driven (career or OSS wise) it's never worked out anyway. I could have done more to make it successful but I never cared about getting validation or getting users for my stuff (and the stress that brings).

I've been lucky that up until this point, the intrinsic rewards I have gotten from my job have aligned with company goals.

LLMs take all the intrinsic wins and leaves only the extrinsic ones. That makes me sad, but it is what it is I guess.

I have been thinking about a tool for months but didn't have the time. I finally gave in and built it at work in a week with LLM tokens. It worked fantastically. But I felt no accomplishment. It felt just the same as if I downloaded the tool from someone else's repo (and who had an overly eager maintainer that would implement my GitHub issue requests).

The hard part for me is ignoring LLMs in my free time to try and keep some of the intrinsic rewards to myself, without being annoyed that I could do it faster if I just "gave in".

I can strongly relate to what you‘re writing, because I share that same sentiment often in my daily (non-AI) work. In fact, coming from that background, the switch from coding to working with agents feels eerily similar to moving into management. You encounter the same challenges minus the „human people and emotions“ part: having to explain properly, the agents doing something different than what you intended, feeling detached from the actual work, only focusing on the bigger picture and so on

To me it feels very natural, it is what I do every day. But then again, I made that choice and it wasn‘t forced on me. So I understand frustration.

100% agree, neither do I, but I see this as an opportunity to think "how can we gain trust in the outputs AI produced for us?"

Is it about tests, reviews, some methodology? Better observability? Formal specification? It's really interesting to think how you can relieve this pain. I think the answer to this question will show the path ahead for agentic coding.

The main output of my work is gaining a better mental model of systems I work with. That's what lets me grow and that's what makes people want to pay me rather than someone else to work on these things. Anything else, including the produced code itself, is secondary to that. In general I find it pretty hard, although not impossible, to use LLMs in a way that doesn't diminish my output, especially with this tooling that seems explicitly designed to make it hard. After all, reviewing things is so much harder than writing them yourself, and you can't feel accomplished by something you haven't done.