谁将任务委托给AI？来自53000个智能体配置的证据
Who Delegates to AI? Evidence from Agent Configurations in Github
中文总结 AI 辅助
本文提出智能体采用指数（AAI）衡量职业对AI的委托暴露，基于53000个智能体技能规范等数据得出委托集中职业与此前风险职业不同等三项发现，为研究AI采用提供新维度。
中文摘要 AI 辅助
现有越来越多的文献在衡量职业对AI的暴露程度，但这些衡量指标仅反映AI可能执行任务的领域，而非工人是否已采用AI。本文提出一种新的暴露维度——委托暴露，用于记录工人是否通过将任务整合进工作流而将任务交由AI执行，我们将其操作化为智能体采用指数（AAI），该指数衡量某职业的任务与从业者已构建并共享的智能体例程的匹配程度。我们从Manus技能市场嵌入约53000个智能体技能规范，计算其与约18000个O*NET任务陈述的语义相似度，并聚合至职业层面，由此得出三项发现：其一，委托集中的职业与此前AI框架认定的最具风险职业存在显著差异；其二，AAI更能反映AI可能执行的任务，而非工人当前实际使用AI的情况；其三，AAI在工资分布的中下部、学士学位水平处达到峰值，在两端均呈下降趋势。技术可用性可解释大部分此类差异，但无法解释最高学历职业中的委托不足现象，因此仅可行性不足以说明谁会采用AI。这种委托不足可能源于难以提前规范的工作，或对编码 pace 的专业裁量权，区分这两种情况并跟踪这些指标随时间的差异需要重复测量。
英文摘要
A growing body of literature measures the extent to which occupations are exposed to AI, yet existing measures capture where AI could perform tasks rather than whether workers have actually adopted it. We introduce a distinct tier of exposure, delegated exposure, which records whether a worker has committed a task to AI by embedding it into a structured workflow. We operationalize this concept through the Agentic Adoption Index (AAI), measuring how closely an occupation's tasks align with the agentic routines that practitioners have built and shared. Using semantic embeddings of roughly 888,000 agent skill specifications from public GitHub repositories, we compute their similarity to nearly 18,000 O*NET task statements and aggregate these scores to the occupational level. We present three main findings. First, the occupations where task delegation concentrates differ sharply from those identified as most vulnerable by pre-AI automation frameworks. Second, the AAI aligns more closely with measures of technical capability than with measures of current conversational LLM use. Third, for occupations requiring a bachelor's degree or less, the AAI increases alongside average wage levels; however, this relationship reverses for occupations requiring a master's degree or higher, where adoption declines among higher earners. These patterns replicate on an independently collected corpus of agent skills from the Manus Skills Marketplace. This lower adoption among highly educated, high-earning workers may reflect tasks that inherently resist advance specification or professional discretion over the pacing of workflow codification. Distinguishing these mechanisms will require longitudinal measurement.
