Comment by keeda

> My personal theory is that getting a significant productivity boost from LLM assistance and AI tools has a much steeper learning curve than most people expect.

Yes, and I'll add that there is likely no single "golden workflow" that works for everybody, and everybody needs to figure it out for themselves. It took me months to figure out how to be effective with these tools, and I doubt my approach will transfer over to others' situations.

For instance, I'm working solo on smallish, research-y projects and I had the freedom to structure my code and workflows in a way that works best for me and the AI. Briefly: I follow an ad-hoc, pair-programming paradigm, fluidly switching between manual coding and AI-codegen depending on an instinctive evaluation of whether a prompt would be faster. This rapid manual-vs-prompt assessment is second nature to me now, but it took me a while to build that muscle.

I've not worked with coding agents, but I doubt this approach will transfer over well to them.

I've said it before, but this is technology that behaves like people, and so you have to approach it like working with a colleague, with all their quirks and fallibilities and potentially-unbound capabilities, rather than a deterministic, single-purpose tool.

I'd love to see a follow-up of the study where they let the same developers get more familiar with AI-assisted coding for a few months and repeat the experiment.