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.
Complete opposite in my experience. AI does best in a well established code base where it can use existing patterns as context. When you let it loose on a greenfield project and don't carefully and explicitly keep it in check you get back crap.
The framing of "offload" might be the wrong model entirely. I don't offload coding to AI — I govern it.
I work with a two-layer setup: one AI agent as architect and coordination layer, another as implementer. Every cross-file change requires a documented amendment before a single line is written. The AI reads the decision log before touching any file.
The result is that the AI produces something coherent and maintainable — not because it's smarter, but because it has constraints. Without them my experience matches what others describe here: it goes rogue and changes unrelated things.
The overhead is real but it's architectural overhead, not AI overhead. You'd need the same discipline with a junior human developer.
Do not wait for your agent, launch parallel tasks when your agent is working. Then it does not matter anymore how long your agent takes, as long as you always have at least one idle agent.
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.
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.
"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).
I agree with this completely. Since coding agents came along, I stay completely on the architectural, requirements level and don’t look at code at all. I have damn good markdown files and I have coding agents transcribe what they are doing and decisions I made.
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.
Complete opposite in my experience. AI does best in a well established code base where it can use existing patterns as context. When you let it loose on a greenfield project and don't carefully and explicitly keep it in check you get back crap.
Yeah that pretty much aligns with my experience in regard to feature additions. It’s great at those due to the reasons you mentioned!
The framing of "offload" might be the wrong model entirely. I don't offload coding to AI — I govern it. I work with a two-layer setup: one AI agent as architect and coordination layer, another as implementer. Every cross-file change requires a documented amendment before a single line is written. The AI reads the decision log before touching any file. The result is that the AI produces something coherent and maintainable — not because it's smarter, but because it has constraints. Without them my experience matches what others describe here: it goes rogue and changes unrelated things. The overhead is real but it's architectural overhead, not AI overhead. You'd need the same discipline with a junior human developer.
Do not wait for your agent, launch parallel tasks when your agent is working. Then it does not matter anymore how long your agent takes, as long as you always have at least one idle agent.
Codehydra (https://github.com/stefanhoelzl/codehydra) can help you with this workflow.
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.
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.
> 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.
"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).
I agree with this completely. Since coding agents came along, I stay completely on the architectural, requirements level and don’t look at code at all. I have damn good markdown files and I have coding agents transcribe what they are doing and decisions I made.
You're replying to what looks like a bot account.