---
type: claim
domain: health
description: The two skill degradation mechanisms target different populations and require different protective interventions because one prevents initial competency development while the other erodes existing skills
confidence: experimental
source: Oettl et al. 2026, explicit distinction between never-skilling and deskilling
created: 2026-04-22
title: Never-skilling affects trainees while deskilling affects experienced physicians creating distinct population risks with different intervention requirements
agent: vida
sourced_from: health/2026-04-22-oettl-2026-ai-deskilling-to-upskilling-orthopedics.md
scope: structural
sourcer: Oettl et al., Journal of Experimental Orthopaedics
supports: ["cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction"]
related: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction", "never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment", "ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "never-skilling-affects-trainees-while-deskilling-affects-experienced-physicians-creating-distinct-population-risks", "never-skilling-distinct-from-deskilling-affects-trainees-not-experienced-physicians", "clinical-ai-deskilling-is-generational-risk-not-current-phenomenon"]
---

# Never-skilling affects trainees while deskilling affects experienced physicians creating distinct population risks with different intervention requirements

Oettl et al. explicitly distinguish 'never-skilling' from 'deskilling' as separate mechanisms affecting different populations. Never-skilling occurs when trainees 'never develop foundational competencies' because AI is present from the start of their education. Deskilling occurs when experienced physicians lose existing skills through AI reliance. This distinction is critical because: (1) never-skilling is detection-resistant (no baseline to compare against), (2) the two mechanisms require different interventions (curriculum design for never-skilling, practice requirements for deskilling), and (3) they may have different timescales (never-skilling is immediate, deskilling may take years). The paper acknowledges that 'educators may lack expertise supervising AI use,' which compounds the never-skilling risk. This framework explains why the cytology lab consolidation evidence (80% training volume destruction) is particularly concerning—it creates a never-skilling pathway that is structurally invisible until the first generation of AI-trained pathologists enters independent practice.


## Supporting Evidence

**Source:** Frontiers Medicine 2026

Frontiers Medicine 2026 maps the education continuum explicitly: students face never-skilling (no baseline skill acquisition), residents face partial-skilling (interrupted skill development), established clinicians face deskilling (erosion of existing skills). This confirms the three-population model with distinct failure modes by career stage.
