AI-native hiring criteria

Bakes AI-native fluency into every JD and every interview rubric as a hard requirement — not a nice-to-have. Concrete artifacts: an explicit "AI workflow fluency — daily user in the last year (deal-breaker)" must-have on every JD; an Apply checklist that asks "how do you use AI in work (concrete example, not 'I follow trends')"; interview questions probing real tool usage with screen-shares. Targets a deliberate cohort of AI-native hires (10–15 per year at mid-size scale) — each one accelerates 5–10 surrounding people.

AI-native hires accelerate the team around them. A large fintech sized the impact at "each AI-native hire accelerates 5–10 surrounding people" — meaning a 10-hire cohort lifts ~50–100 incumbents. At 30–100-person scale this compounds even faster because there are fewer cultural defenders of the old way of working.

1. Add "AI workflow fluency — daily user last year (deal-breaker)" to every JD's Must-have section.
2. Add to Apply checklist: "Send a concrete example of how you used AI in work this month. Screenshots welcome."
3. Add to founder call: "Walk me through your AI workflow for one of your real tasks last week — share screen."
4. Score in the role-competency matrix: AI fluency as 1–5 with evidence status (Demonstrated / Self-Reported / Missing).
5. Reject candidates with "Missing" — no exceptions for senior titles.
6. Target hiring plan: 10–15 AI-native hires per year (PM, ML eng, AI-fluent devs, AI-fluent ops).

• Don't accept "I read Twitter on AI" as evidence — the bar is a real workflow shipped in the last 30 days. Senior candidates without it should be coached out, not hired with a "learning plan."