you're a lot more skilled than I am if you're able to know the abstractions and patterns you want while only have a weak grasp on the actual code. For me the abstractions emerge as I understand what I'm actually working with

IMO "get the model to do all the planning work upfront, review and iterate the plan" is backwards. It works better if you do an initial plan yourself and then have the AI review it (and iterate as necessary). Makes you think about the design for a bit so you can have a semblance of a mental model.

This just doesn't match my experience writing idiomatic C code for embedded real-time systems with ChatGPT-5.5 and -5.6. Are Opus and Fable really that different? I general I find that a fully correct implementation for some function may take an hour and be nearly one-shot, but going from there to code I'd be willing to actually commit is at least 4x the time commitment with regular back-and-forth -- so four hours of deep engagement for every one hour of one-shot. ChatGPT-5.6 Sol seems to be better that -5.5 at the one hour do-it portion, but worse at the four hour do-it-well portion.

Sure. But try it on a project on non-trivial size and then tell us the cost of it. LLMS are billed per token.

Depends on how long that planning/review cycle takes.

Agreed, all of the staff/principal engineers I work with are ~99%+ AI generated code, and increasing business value delivered as a result. This is on planet scale infra not CRUD apps. (And yes, you do need to carefully review the output and give steers/corrections. It’s still faster.)

At this point if you can’t get the agent to write good code then either I) you are in a very specific niche (like Karpathy trying to write NanoGPT) that is extremely out-of-distribution, or II) skill issue, you need to learn how to prompt better.

It’s fine to have a skill gap! Just don’t delude yourself that the tools are bad and everyone claiming they are good is wrong.