The Autonomy Gradient: a framework for how much to delegate to AI
DrKavner · x · 2026-11-16
Cerebratech's blog proposes the "Autonomy Gradient" framework: AI delegation at work isn't binary — the real question is calibrating how much to delegate per task.
- Key trade-off: research on path dependence under adaptive AI delegation shows repeated assistance boosts short-term performance while eroding the skill needed for future independent work; subjective capability lags objective decline.
- Calibration dimensions: stakes, volume, reversibility; own tasks while building a skill, delegate once it's solid.
- Mis-calibration cuts both ways: too conservative makes you slower, too dependent quietly erodes core capabilities.
- Cites a 2025 study of knowledge workers showing measurable drops in independent performance months later, while workers felt their skills were intact.
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