Artificial Intelligence and the Transformation of White-Collar Work: Productivity, Job Displacement, and Skill Reconfiguration

Abstract

The rapid diffusion of generative artificial intelligence has renewed debates concerning technological unemployment, productivity growth, and the changing value of human expertise. Unlike earlier waves of automation that concentrated heavily on routine manual and clerical tasks, contemporary large language models can perform activities traditionally associated with educated white-collar workers, including drafting, coding, research support, document analysis, translation, customer service, and decision assistance. This paper examines whether generative AI is primarily replacing knowledge workers or transforming the tasks that constitute their occupations. A structured review of experimental, field, occupational-exposure, and labour-market research indicates substantial productivity gains in bounded knowledge-work tasks, but also considerable variation across occupations, workers, and task environments. Less-experienced workers frequently receive the largest gains, suggesting that AI can diffuse elements of expert knowledge. At the same time, experienced professionals may receive smaller benefits, and in complex settings may even lose productivity when verification and correction costs exceed the value of machine assistance. Current labour-market evidence does not support an immediate economy-wide collapse in white-collar employment, although emerging evidence suggests that entry-level opportunities in highly exposed occupations may weaken. The paper argues that the task, rather than the occupation, is the appropriate unit of analysis. Generative AI automates some activities, complements others, and creates new responsibilities in verification, orchestration, accountability, and judgment. The likely medium-term outcome is therefore occupational restructuring rather than uniform occupational elimination.