Most descriptions of Claude Code and Codex skew too far in one of two directions. They either make the tools sound like toys for non-technical curiosity, or they describe them purely as code generators for engineers. Both framings miss the part I find most interesting.

I use Claude Code and Codex as product tools. That means I use them to pressure-test workflows, prototype interfaces, turn research into working artifacts, and collapse the time between “I think this might be useful” and “now I can actually see how it behaves.”

The leverage is not just speed. It is tighter loops. A product person used to need a larger handoff before seeing anything real. Now the better model is often: think, scope, test, refine, break, repeat. Claude Code and Codex are valuable because they make those loops much cheaper.

The important caveat is that the tools do not remove the need for judgment. In some ways they raise the premium on it. If you cannot tell the difference between an impressive-looking artifact and a useful one, the tools can accelerate confusion. If you know what problem actually matters, they can accelerate clarity.

The part of this that fits me well is that my background has never been narrowly one thing. I have worked across product, GTM, growth, strategy, diligence, and execution. That makes me less interested in whether a tool can write a perfect function in isolation, and more interested in whether it can help a team build a better operating system.

In practice, that might mean using Claude Code or Codex to explore a recruiting workflow, prototype a research tool, stand up an internal dashboard, or turn a fuzzy product idea into something concrete enough for a real conversation. The code matters, but the workflow matters more.

That is why terms like AI product builder, AI workflow design, or product people using Claude Code and Codex feel more honest to me than generic AI boosterism. The actual value is in seeing where the tools belong, where they do not, and how to structure work around them so the result becomes durable.

If you are an AI-native team, an AI lab, or a company trying to turn model capability into something people can reliably use, that is the lane I am trying to stay in.