Stanford Analysis Shows AI Disruption Half-Life Tracking Typical 10-Year Median Since 2022

BLS and Federal Reserve series document 20 million jobs lost in disrupted sectors over two decades alongside 25.7 million net payroll gains. AI-exposed roles such as customer-service and IT support display employment trajectories consistent with the historical median half-life of ten years rather than the one-to-five-year fast collapses seen in photo processing. Offshoring and post-COVID adjustments remain larger near-term factors than LLM deployment.

The structural risk lies in the erosion of entry-level experience pipelines in software engineering, where aggregate unemployment sits at 4.2 percent with no statistically detectable AI effect according to Yale Budget Lab analysis. This breaks the traditional apprenticeship mechanism without yet registering in headline employment counts.

States retain incentives to accelerate domestic AI adoption for productivity and strategic advantage while managing transition costs through immigration and training policy. Primary records from BLS occupational statistics and central bank labor-flow data show the pattern of simultaneous destruction and creation continuing without acceleration beyond prior technological shifts.

Continued monitoring of occupation-level payrolls through 2027 will determine whether the current trajectory remains absorbable or shifts toward faster displacement.