标题： 基于理论的人工智能自动化暴露指数：将莫拉维克悖论应用于美国劳动力市场 显示英文标题

标题： A theory-based AI automation exposure index: Applying Moravec's Paradox to the US labor market

摘要： 本文开发了一个基于莫拉维克悖论的理论驱动自动化暴露指数。 在绩效方差、隐性知识、数据丰富性和算法差距方面对19,000个O*NET任务进行评分，结果表明管理、STEM和科学职业的暴露程度最高。 相反，维护、农业和建筑行业的暴露程度最低。 工资与暴露程度之间的正相关关系挑战了如果人工智能替代工人的技能偏向型技术变革观念。 同时，隐性知识与工资呈正相关，这与资历偏向型技术变革一致。 该指数识别的是基本的可自动化性，而非当前能力，并且通过与Eloundou等人(2024)首创的AI标注方法的相关性0.72进行了验证。 与预大语言模型指数的非正相关性表明自动化模式发生了范式转变。 显示英文摘要

摘要： 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.