arXiv:2606. 12430v1 Announce Type: cross Abstract: 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.
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The paper introduces the Agentic Adoption Index (AAI), a new metric that captures whether workers actually delegate tasks to AI within their workflows, rather than merely measuring potential AI applicability. Using 53,000 agent skill specifications and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those previously deemed most at risk, that AAI aligns more closely with AI’s capabilities than current usage, and that adoption peaks at mid‑wage, bachelor’s‑level occupations while declining at both ends of the wage and education spectrum. The study highlights that technical availability explains much of the variation, but other factors—such as resistance to specification or professional discretion—also influence who adopts AI. whyItMatters":"The findings suggest that actual AI adoption patterns differ from prior risk assessments, indicating that factors beyond technical feasibility shape who delegates to AI, which has implications for workforce planning and policy."
The paper examines how users delegate tasks to the AI agent OpenClaw by analyzing 73,093 Reddit posts. It identifies 21 human values grouped into six categories—such as Autonomous Operation, Dependable Operation, Affordable Operation, Bounded Reach, Reviewability, and Equitable Access—and finds that values are largely satisfied when users describe the agent’s outputs but often unmet when users discuss supervising the agent. The authors term this pattern "value‑sensitive delegation," emphasizing that supporting human values requires attention to both what an agent does and the conditions users set around its use.
arXiv:2606. 09833v1 Announce Type: cross Abstract: AI agents are reshaping the workspace, leading to drastic change of how humans work.
The paper introduces the Agentic Adoption Index (AAI), a new measure of delegated exposure that captures whether workers actually commit tasks to AI within structured workflows. Using semantic embeddings of 888,000 agent skill specifications from GitHub and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those most vulnerable to pre-AI automation, that AAI correlates more with technical capability than with current LLM use, and that for lower‑educated occupations AAI rises with wages while it falls for higher‑educated, high‑earning workers. These patterns also appear in an independent corpus from the Manus Skills Marketplace.
arXiv:2603. 02961v2 Announce Type: replace-cross Abstract: As AI systems enter institutional workflows, workers must decide whether to delegate task execution to AI and how much effort to invest in verifying AI outputs, while institutions evaluate workers using outcome-based standards that may misalign with workers' private costs.
How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixed-methods study with computational text analysis of...