Generative AI and Task Recomposition in Entry-Level IT Work: A Worker-Side Task-Level Measure

Will generative AI (GenAI) displace entry-level IT workers, or change the work they do? The answer depends on whether AI substitutes for or complements the tasks that make up entry-level jobs, a distinction that demand-side employment data cannot resolve because they do not capture task content. We study the intensive margin of entry-level IT work-i.e., how AI shapes the job content conditional on employment-rather than the extensive margin of hiring or job displacement. Using survey data on entry-level IT workers with migration backgrounds, we introduce a worker-side measure of AI-attributed task change: for each task, workers report how their working time has changed and how much of this change they attribute to AI. The measure therefore isolates AI-attributed reallocations of working time across tasks. Within-worker comparisons point more strongly to task complementarity than substitution: Workers spend more time on tasks for which they report AI-related changes, and their task portfolios shift toward a more complex core. These complementarity patterns are strongly associated with AI use, but not with education background or wages, thereby suggesting that the benefits of GenAI in entry-level IT work may depend less on formal credentials than on active engagement with the technology. Our findings provide implications for firms' decisions on job design, task allocation, and AI-related training in IT jobs.