A founder's guide to AI-native engineers, starting with what the term doesn't mean: it isn't a list of tools on a CV.
Why this matters
Every CV now says AI. Prompting is a commodity skill anyone acquires in a weekend, so hiring by tool list gets you the buzzwords without the value, and the difference only surfaces months later, in production, where it's expensive to learn.
The common mistake
AI-native isn't knowing the tools; the tools change monthly. It's the reflex that never changes: draft until reviewed, no matter how polished the draft looks.
How we approach it
What AI-native actually looks like is a reflex, three markers you can test for: they treat AI output as a draft until reviewed, no matter how confident it reads. They test generated code like a stranger wrote it, because functionally, one did. And they know the failure patterns, the confident bug, the plausible-but-wrong pattern, the subtle behavior change across files, before those patterns bite. The volume comes from the machine; the judgment is theirs, and the judgment is what you're hiring.
A checklist you can use
AI-native isn't a tool list — every CV says AI now
Prompting is a weekend skill; judgment is the hire
Marker 1: treats AI output as a draft until reviewed
Marker 2: tests generated code like a stranger wrote it
The trial that works: plant a confident bug, say nothing
When to bring in help
In your next trial task, plant one confident bug inside an AI-generated diff and say nothing. The candidates who catch it are the hire; the ones who ship it just showed you their production behavior. If the honest answer is that nobody on the team owns this end to end, that's the moment to borrow the depth rather than improvise it.
Takeaway
What to do next: skip the tool-list screen entirely. Trial candidates on your real code with AI switched on, and watch one thing, what they do with the output before they trust it.
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