This paper develops a theory-driven automation exposure index based on Moravec's Paradox. Scoring 19,000 O*NET tasks on performance variance, tacit knowledge, data abundance, and algorithmic gaps reveals that management, STEM, and sciences occupations show the highest exposure. In contrast, maintenance, agriculture, and construction show the lowest. The positive relationship between wages and exposure challenges the notion of skill-biased technological change if AI substitutes for workers. At the same time, tacit knowledge exhibits a positive relationship with wages consistent with seniority-biased technological change. This index identifies fundamental automatability rather than current capabilities, while also validating the AI annotation method pioneered by Eloundou et al. (2024) with a correlation of 0.72. The non-positive relationship with pre-LLM indices suggests a paradigm shift in automation patterns.

翻译：本文基于莫拉维克悖论构建了一个理论驱动的自动化暴露指数。通过对19,000项O*NET任务在绩效方差、隐性知识、数据丰度和算法差距四个维度进行评分，发现管理、STEM及科学类职业的暴露程度最高，而维护、农业和建筑类职业的暴露程度最低。工资水平与暴露程度呈正相关，这对“若AI替代人工则技术变革将偏向高技能”的论点提出了挑战。同时，隐性知识与工资的正相关关系符合技术变革偏向资历的假设。该指数揭示了根本层面的可自动化潜力而非当前技术能力，并以0.72的相关系数验证了Eloundou等人（2024）首创的AI标注方法。与大型语言模型出现前的自动化指数呈非正相关，表明自动化模式已发生范式转变。