Longitudinal Monitoring of AI Consequences

Longitudinal monitoring is the systematic observation of AI's consequences over timescales matched to how those consequences actually unfold — not snapshots but sustained tracking across months, years, and developmental periods. Most AI impact research operates on timescales of weeks or months, adequate for detecting immediate behavioral changes (task seepage, productivity gains) but inadequate for detecting slow-accumulation consequences that matter most: the atrophy of embodied expertise, the erosion of professional identity, the transformation of children's relationship to intellectual effort, the shift in what it feels like to know something when knowledge can be borrowed rather than earned. Longitudinal monitoring requires multi-year commitments, epistemically plural methods (combining ethnography, surveys, cognitive assessment, and developmental psychology), and institutional independence from the companies whose products are being studied. It is the essential infrastructure for responsive governance and is nearly absent from the current AI governance landscape.