The more I build around AI tools, the less I think the job is mainly about features in the old sense. The hard part is increasingly not whether a model can do something impressive in isolation. The hard part is how to structure the system around it so the result is useful, legible, and durable.

That is part of why tools like Claude Code and Codex feel so important to me. They shorten the loop between thought and artifact so dramatically that they expose a deeper question. If it is now cheap to prototype, scaffold, patch, test, and revise, what exactly becomes the scarce skill?

My answer is orchestration. Not orchestration as a buzzword, but in the literal sense: deciding which tool does what, what stays deterministic, what gets delegated, where review happens, what the operator needs to see, and how the whole workflow stays coherent as it scales beyond one prompt and one response.

That is why I think AI product work is becoming orchestration work. Once you can cheaply create artifacts, the role shifts toward system design. You start asking questions like:

What should this agent actually own?
What belongs in a reusable workflow instead of ad hoc chat?
Where does judgment stay human?
What proof does the system need before anyone should trust it?

I keep seeing this in my own work. A recruiting workspace becomes much more useful when it has a target dossier, a goal pack, proof assets, and a tracker instead of just a smart model call. A discoverability system becomes more useful when the writing, schema, profile JSON, project surfaces, and GitHub corroboration all point to the same story instead of existing as disconnected fragments.

None of that is impressive because an agent generated text. It is useful because the workflow was designed so the outputs compound. That feels much closer to product work than to prompt tinkering.

I think this matters especially for AI labs and AI-native teams. The fastest-moving teams will have no shortage of prototypes. What they will need is more judgment around which prototypes deserve structure, what internal tools should exist, what the operator experience should feel like, and where the system needs safeguards before it touches something important.

That is also why I do not think the right framing is generic PM theater dropped onto AI. The better framing is closer to product builder, product operator, or workflow-minded orchestrator. The job is not just to prioritize tasks. It is to create the conditions where a messy capability becomes a useful system.

In practice that means specs matter. Guardrails matter. Design systems matter. Clear proof surfaces matter. Small agent instructions matter. If you want multiple tools or agents to produce good work, you generally need a better operating environment, not just a smarter model.

I suspect that is one of the biggest shifts in software right now. The leverage is moving toward people who can orchestrate capability across tools, agents, and workflows without losing clarity. That is the layer I want to spend more time in.