For a while I kept trying to answer the question of what I do next with titles I already knew: product manager, operator, strategist, chief of staff, builder. Each one was directionally right. None of them felt fully accurate.

The product manager title describes part of my background, and a lot of the instincts are still central: clarify the problem, understand users, pick tradeoffs, sequence the work, and make sure what gets built is actually useful. But AI changes the surrounding shape of the job enough that the traditional label starts to undersell what the work actually is.

In AI-native environments, especially around applied AI companies and AI labs trying to become products, the role increasingly touches workflow design, prototyping, tooling, systems judgment, and even a layer of hands-on execution that used to sit farther away from product leadership. The job is not just deciding what should be built. It is also helping the team see what kind of system should exist around the model in the first place.

That is part of why “AI product builder” feels more honest to me. It captures that I want to stay close to the making. Tools like Claude Code and Codex only increase that pull, because they shorten the path from thought to artifact. They let a product-minded operator explore the shape of a workflow, stand up a prototype, or test a system without waiting for a much larger handoff.

The distinction matters because the market is still using older role language for newer work. A company may say it wants a PM when what it really needs is someone who can bridge product, workflows, internal tooling, AI capability, and execution. A team may say it wants an AI operator when what it really needs is someone who can translate product ambiguity into a working system.

I am less attached to the exact title than I am to the underlying shape of the job. But if I have to choose language that feels closer to the truth, AI product builder gets there faster than product manager alone.

That is the direction I want the site to signal more clearly too: not just past experience in product, but a forward-looking identity around AI-native product work, builder-heavy roles, and the teams trying to turn model capability into durable products.