Dimension 5: 1st-Derivative Talent¶
Synchronized from the canonical website on 2026-09-06.
“Not what skills people have — the rate at which they acquire new ones. And at CAIO level: is the AI talent pipeline bleeding the best?”
← Epistemically Different · Velocity
STANDARDS — ENACTED GOVERNANCE¶
Rate of Acquisition, Not Inventory¶
Traditional talent governance measures what people know. 1st-derivative measures how fast they're learning. A team with moderate skills and a steep curve beats a team with strong skills and a flat one. Track the derivative, not the function.
Dynamic Role Architecture¶
Roles evolve as AI capability evolves — not static job descriptions frozen at hire date. When AI takes over 40% of a role's tasks, the role must transform — not just lose headcount.
Talent Pipeline Governance¶
At CAIO level: who is leaving and why? If top AI talent exits because governance is too slow or culture punishes experimentation, governance is eating its own seed corn. Retention of high-velocity learners: a governance metric, not an HR metric.
MEASURES — THREE-LAYER ASSESSMENT¶
Self-Report¶
“How much faster are you learning AI skills this quarter vs. last?” “Has your role changed in the last 6 months because of AI?” Self-assessed learning velocity, calibrated against evidence.
Evidence Layer¶
Capability expansion rate (Five Dials). Upskilling velocity: new capabilities per quarter per team. Top AI performer retention rate. Role evolution: % of roles materially redefined in 12 months. Attrition analysis: are fast learners staying or leaving?
Behavioral Observation¶
Are people experimenting with new AI tools — or using the same ones the same way as 6 months ago? Learning velocity is visible in tool adoption patterns, workflow evolution, and the quality of questions asked. Stagnation is equally visible.