Future of Work with AI Agents
Free while signed in. Answers cite the passages they came from.
Proposes a large-scale framework for understanding where AI agents should automate or augment human labor. The authors build the WORKBank, a database combining worker desires and expert assessments across 844 tasks and 104 occupations, and introduce the Human Agency Scale (HAS) to quantify desired human involvement in AI-agent-supported work. Key findings:
Workers support automation for low-value tasks: 46.1% of tasks received positive worker attitudes toward automation, mainly to free up time for higher-value work. Attitudes vary by sector; workers in creative or interpersonal fields (e.g., media, design) resist automation despite technical feasibility.
Desire-capability gaps reveal 4 AI deployment zones: By cross-referencing worker desire and AI expert capability, tasks were sorted into:
Human Agency Scale shows strong preference for collaboration: 45.2% of occupations favor HAS Level 3 (equal human-agent partnership), while workers generally prefer more human involvement than experts find necessary. This divergence may signal future friction if automation outpaces user comfort.
Interpersonal skills are becoming more valuable: While high-wage skills today emphasize information analysis, the tasks requiring the highest human agency increasingly emphasize interpersonal communication, coordination, and emotional intelligence. This suggests a long-term shift in valued workplace competencies.
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack