Aggregate predictions about AI and jobs — 'X million jobs displaced, Y million created' — obscure vastly different outcomes across sectors, skill levels, geographies, and time horizons. A contextual analysis reveals where displacement is already happening, where it's overstated, and where the actual risks concentrate.

Labor market disruption from AI is not evenly distributed and is not primarily determined by task automation capability — it's determined by the intersection of automation capability with labor market structure, employer adoption rates, regulatory constraints, and worker bargaining power. In knowledge work, the pattern emerging from 2023–2025 deployments is task displacement rather than job displacement: AI handles specific subtasks (first drafts, code scaffolding, data summarization) while workers shift time to higher-judgment tasks. Net employment effects in knowledge work sectors have been neutral to mildly positive so far, though productivity gains have not uniformly translated to wage gains. In call center and customer service roles, automation adoption has been rapid and displacement has been direct, with documented headcount reductions of 20–40% in early-adopter firms. In creative industries — copywriting, illustration, music production — the impact is highly stratified: top-tier talent is using AI to increase output volume while mid-tier commoditized work faces significant price compression. Geographic distribution of AI impact correlates with broadband access, educational attainment, and industry concentration in ways that deepen existing regional inequalities. The policy implications are specific: retraining programs need to target the specific task transitions workers face, not generic 'digital skills' training; social safety nets need update speed calibrated to disruption pace; and regulatory frameworks need sector-specific analysis rather than one-size-fits-all approaches.

AI and Labor Market Disruption: Contextual Analysis by Sector