I've experienced the same issues with Claude Code. I think it's very important that you sufficiently specify what you want to accomplish. Both overall for the project, but also for the immediate task you're trying to complete (e.g., new feature, bug fix). There are some good frameworks for this: - https://openspec.dev/ - https://github.github.com/spec-kit/

For most applications, it is certainly possible to never write code and still produce something substantial. But in my experience, you have to be really diligent about the specifications and unit/contract/e2e testing. Where I've run into trouble requiring me to dig into the code is when creating software that uses new algorithms that haven't been trained on.

"Offloading all coding" is perhaps a misleading expression. Those who say they no longer write code are often describing a change in what kind of work they do, not that they've stopped writing code entirely. They spend more time on technical specification, architectural decisions, considering differences, and figuring out when the model misinterprets intent—and less time on actual code typing.

Your brownfield instinct is right though. The productivity gap between "fixing it yourself" and "require → plan → evaluate → deploy → evaluate" only narrows when the task is large enough to justify the cost incurred, or when you're running parallel agents. For a bug requiring only two lines of code, the cost of context switching alone can negate the return on investment (ROI).

With brownfield projects, I can’t speak to. All of my (consulting) projects start with an empty AWS account and empty git repo.

My Claude/Codex session have temporary AWS Credentials in environment variables. The AWS SDK and CLI use those variables automatically.

My AWS account is bootstrapped with infrastructure as code.

They both do pretty well with troubleshooting since they have the code and my markdown summaries from the contract, diagrams, call transcripts, project plan etc.

They can both look at live CloudWatch logs to find errors.

The brownfield vs greenfield split is the real answer to "how do people offload all coding." They mostly don't, on legacy codebases. The "never write code" crowd is almost always working on new projects where the AI has full context from the start.

The economics flip on brownfield for exactly the reason you describe: the diagnosis and fix are tightly coupled. You already did the expensive cognitive work to understand the bug. The AI's planning overhead exceeds the writing overhead at that point.

Where the calculus does shift on brownfield is larger feature additions that can be spec'd in isolation. "Add this new endpoint that follows the same pattern as these three existing ones" works well because the AI can pattern-match against existing code. "Fix this subtle race condition in the session handler" almost never does, because the AI's diagnosis phase is unreliable enough that you'd rather just trace it yourself.

> leads me to a point where I can see that the issue is a simple fix with a couple lines of code.

If you can see the problem, know how to fix it, and still ask spicy autocomplete to do it for you, that isn't "using a tool", it's cargo culting.

I disagree that offloading all coding to AI makes sense. We started using Claude heavily for our services work about a year ago and what we found is you still need to understand the codebase deeply to know when the AI is hallucinating or changing wrong things. The prompt-wait-evaluate cycle you mentioned is real and kills productivity on brownfield projects. What actually worked for us was using AI for scaffolding new features or writing tests, not for debugging existing code. On debugging I still do it myself because I know our system and can fix it in 2 minutes vs 20 minutes prompting Claude. The people saying they never write code anymore probably either work on greenfield projects or they're not measuring their actual time cost accurately.

When people say they don't write the code, they mean they don't type it, but if there are not vibe coding garbage they are still watching what the agent outputs, and redirects it when it goes wrong. Instead of fixing the code manually they prompt the LLM.

I don't look at much code in my own work anymore. Sometimes, I'm just lazy. But mostly, I trust but verify. I spent a lot of months looking at every line, hand-editing, etc. I built a fair bit of trust. I learned the best way to assign tasks and structure the work. Beyond that, I definitely keep the architecture for myself. And I do believe that prior manual fluency is a requirement for being this hands off.