Do not index
Do not index
Working with AI is still strange territory. We reach for metaphors: manager, intern, editor, surgeon. Each gets close, but none explain the back-and-forth dance of giving inputs, interpreting outputs, and adjusting course as you work alongside a machine. We want scale, but we also want the output to reflect our judgment, our standards, and our voice. It’s not just about producing more; it’s about making sure what’s produced still feels like us.
The Old Metaphors
Manager: Too detached. You end up supervising outputs instead of shaping them.
Intern: Too one-way. You spend more time teaching and correcting than creating.
Editor: Too reactive. You polish what already exists instead of driving direction.
Surgeon: Too specialized. Great for precision work, but not for broad creative problem-solving.
The Head Chef Frame
The head chef isn’t a distant strategist. They’re in the kitchen, hands-on, guiding the team, tasting, and keeping everything aligned with their creative vision. They delegate, but they also step in when something needs attention.
A head chef also has a certain level of competency with the craft. They understand ingredients, technique, timing, and flavor. They can jump on the line if needed. They know what good looks and feels like.
Putting It Into Practice
Here’s how I apply the Head Chef model in my own stack, using AI agents embedded in my dev environment to handle execution while I guide direction.
- Set up your stations. I work in two Ghostty terminals. The left side is for planning and viewing, the right for synchronous agents running through OpenCode.
- Prep your ingredients. I pick the next task from my list and break it into clear, sequential phases using a custom /create-plan command.
- Open a shared recipe. Once the direction feels right, I open a GitHub issue so every agent can read from the same context.
- Cook in phases. I offload each phase to the execution agents with a /create-instruction command and let them work through it.
- Taste and adjust. As outputs come back, I use a /review command to inspect and refine them before committing changes.
- Serve and reset. When all phases are complete, I close the issue and pull the next most important task.
This loop keeps me in the kitchen. I’m orchestrating, tasting, and directing while AI handles execution without pulling me out of the work.
Conclusion
The metaphors we use for AI are not just storytelling. They shape how we work. When we frame AI as an intern, a coworker, or a copilot, we interact with it in fundamentally different ways. These mental models influence trust, delegation, and how hands-on we stay (source
The Head Chef model reflects what great product work really requires. You need to understand the tools, the timing, and the ingredients. You need competency with the material.
And for builders, when AI enters the kitchen, that competency matters even more. The more fluent you are across design, code, marketing, and storytelling, the better you can shape what AI produces.
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