Predicting AI's Impact on Labor Is a Core Machine Learning Problem

Artificial intelligence is increasingly reshaping how work is performed, organized, and valued. Predicting AI's impact on labor is a broader scientific question that examines how evolving AI capabilities interact with adoption, organizational change, and political and economic adjustments to reshape tasks, workflows, employment, productivity, wages, and inequality. We argue that predicting AI's impact on labor should be treated as a core machine learning problem — one that the AI and ML community has a distinctive role in shaping — rather than solely a societal or ethical question. This prediction task sits at the center of modern ML: prediction under non-stationarity, distribution shift, endogenous feedback, and high-stakes uncertainty. We discuss key prediction targets across units of analysis and time horizons, review current approaches in economics, management, and ML, identify technical obstacles that limit existing methods, and propose a research agenda for ML-driven labor prediction.