Are We Automating the Joy Out of Work? Designing AI to Augment Work, Not Meaning

Prior work has mapped which workplace tasks are exposed to AI, but less is known about whether workers perceive these tasks as meaningful or as busywork. We examined: (1) which dimensions of meaningful work do workers associate with tasks exposed to AI; and (2) how do the traits of existing AI systems compare to the traits workers want. We surveyed workers and developers on a representative sample of 171 tasks and use language models to scale ratings to 10,131 tasks across all U.S. computer-assisted tasks. Worryingly, we find that tasks that workers associate with a sense of agency or happiness may be disproportionately exposed to AI. We also document HCI design gaps: developers report emphasizing politeness, strictness, and imagination in system design; by contrast, workers prefer systems that are straightforward, tolerant, and practical. To address these gaps, we call for AI whose design explicitly centers meaningful work and worker needs, proposing a five-part research agenda.

methodology
Overview of Study Design: Worker and developer perspectives on meaningful work and AI system design in the U.S. labor force. (Step 1) Workplace tasks were restricted to those primarily completed on a computer and performed daily or weekly, then filtered by Prolific availability, AI Impact Index, and worker familiarity. (Step 2) Workers rated tasks across five dimensions: perceived bullsh*t, perceived value, well-being scale, status maintenance, and human flourishing. Tasks more likely exposed to AI scored higher on novelty, creativity, happiness, and freedom, while those less likely emphasized emotional awareness, in-person interaction, relationships, and social connection. (Step 3) Workers and developers rated which psychological traits an AI system should possess when augmenting tasks. When designing AI augmented tasks, developers emphasized polite, strict and imaginative systems whereas workers preferred straightforward, tolerant, and practical systems. (Step 4) LMs were prompted as experts to simulate worker and developer ratings, with moderate to high intra-class correlation with human responses.

Publications

  • Are We Automating the Joy Out of Work? Designing AI to Augment Work, Not Meaning. ACM CHI 2026 PDF

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