Neither "product engineer" nor "AI engineer" describes what a growing number of people actually do: take an idea from inception to production using AI as their primary collaborator across the entire lifecycle.

That's the AI product engineer.

Someone who does the work — hands on, with AI — across strategy, specs, code, testing, evals, and operations. Pressure-testing a product narrative in the morning, co-developing the implementation in the afternoon, generating adversarial test cases before dinner.

Four things define this role: . -- . The full loop from idea to production behavior — on systems where "works" is statistical, not binary. . - . AI in every stage of building, not just in the product. If you can't use AI to accelerate correctly across the lifecycle, you're doing a slower job with a new title. . . Making reliability measurable on systems that behave differently every run. . . Models shift, tools evolve. The job is to experiment fast, adopt without chaos, and converge under uncertainty. Curiosity isn't a personality trait — it's a production skill with receipts.

This changes how you hire. Past titles tell you what someone did in a world that no longer exists. The question is: can they learn fast and ship under uncertainty with tools that didn't exist six months ago?

Full breakdown with sources in the comments.