Will AI Agents Free Us From Meaningless Work? A Human-Centered Analysis
For researchers and practitioners in human-AI interaction, this provides a task-level understanding of worker preferences for AI delegation, grounded in sociological theory.
This paper investigates which workplace tasks workers want to delegate to AI, finding that tasks perceived as 'bullshit' (meaningless) are strongly preferred for AI delegation and also seen as requiring less human oversight, based on ratings from 202 workers across 171 tasks.
Some claim that AI agents will free workers from the boring parts of their jobs, yet little is known about how workers themselves identify which tasks should be automated. Prior research focuses on occupations, overlooking that workers experience varying levels of meaning across tasks within the same role. We address this gap with a task-level analysis grounded in Graeber's theory of bullshit jobs. Using ratings from 202 workers on 171 workplace tasks, we (1) validate a five-item scale of perceived bullshitness, (2) show that perceived bullshitness strongly predicts desire for AI delegation, and (3) find that such tasks are also seen as requiring less human oversight. Together, these findings suggest that tasks perceived as bullshit are natural candidates for AI delegation, aligning worker preferences with perceived feasibility.