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What AI-related postings ask for that others do not

The question. Within one discipline, what changes about a job posting when it mentions AI?

What it found

Evaluation language, interpretation and verification all rise. Governance barely moves, and cross-functional briefing does not move at all. The comparison is made inside a single discipline, so both arms pass through the same classifier and a misclassification lands a posting in the wrong discipline while keeping it on the correct side of the AI split.

The numbers

Term groupAIEM elementAINon-AILift
AI oversightDiscernment4.2%0.2%12.99
handing work to a systemDelegation8.4%0.9%7.95
evaluation languageDiscernment42.4%23.2%1.62
ethics, bias, responsible AIDiligence6.5%3.1%1.56
interpretationDiscernment30.7%16.4%1.45
verificationDiscernment36.9%24.4%1.37
experimentation, pilotsTinkering Disposition5.4%4.0%1.29
governanceDiligence9.3%7.7%1.07
cross-functional briefingDescription46.9%46.1%1.00
ownershipInternal Locus of Evaluation15.6%15.1%0.93
ambiguityTolerance for Ambiguity8.3%9.3%0.78

Greenhouse only. Lift is length-adjusted and is therefore smaller than the two percentages divided.

How it was computed

Greenhouse boards only, so the compared groups share a platform, a collection window and an employer pool. Lift is a Mantel-Haenszel estimate pooled over description-length quartiles, because term presence rises with posting length and AI-related postings are longer. lift_unstratified is the plain ratio published before 2026-09-11, kept for provenance only.

How to read it

Lift is adjusted for length, so it is deliberately NOT the ratio of the two percentages beside it. A group appears only where the discipline has 50+ non-AI postings and 5+ postings carry the term in each arm. Only mappings ratified against the AI Engagement Model are shown.

Source view: competency_lift · published 2026-09-11