标题： 提升人工智能能力与演变劳动结果 显示英文标题

标题： Advancing AI Capabilities and Evolving Labor Outcomes

摘要： 本研究通过分析职业层面就业状态和工作时间的近实时变化，探讨人工智能对劳动力市场的影响，这些变化与人工智能能力的进步相关。 我们构建了一个基于任务层面评估的动态职业人工智能暴露评分，使用了最先进的AI模型，包括ChatGPT 4o和Anthropic Claude 3.5 Sonnet。 我们引入了一个五阶段框架，评估随着技术从传统机器学习发展到代理人工智能，人工智能在职业中执行任务的能力如何变化。 然后将职业人工智能暴露评分与美国当前人口调查数据相结合，从而实现对就业、失业、工作时间和全职状态的近实时分析。 我们进行了一阶差分分析，比较2022年10月至2023年3月期间与2024年10月至2025年3月期间的情况。 较高的AI暴露水平与就业减少、失业率上升和工作时间缩短有关。 我们还观察到某些人群中兼职工作的增加和全职就业的减少有一定的证据。 这些关联在年长和年轻工人、男性以及受过大学教育的人群中更为明显。 受过大学教育的工人往往经历较小的就业下降，但更可能看到工作强度和工作结构的变化。 此外，依赖复杂推理和问题解决的职业，在AI暴露增加的情况下，全职工作和总体就业的下降幅度更大。 相反，涉及手动体力任务的职业似乎受影响较小。 总体而言，结果表明，由人工智能驱动的劳动力变化正在沿着广度边际（失业）和深度边际（工作时间）发生，并且在职业任务内容和人口统计学方面产生不同的影响。 显示英文摘要

摘要： This study investigates the labor market consequences of AI by analyzing near real-time changes in employment status and work hours across occupations in relation to advances in AI capabilities. We construct a dynamic Occupational AI Exposure Score based on a task-level assessment using state-of-the-art AI models, including ChatGPT 4o and Anthropic Claude 3.5 Sonnet. We introduce a five-stage framework that evaluates how AI's capability to perform tasks in occupations changes as technology advances from traditional machine learning to agentic AI. The Occupational AI Exposure Scores are then linked to the US Current Population Survey, allowing for near real-time analysis of employment, unemployment, work hours, and full-time status. We conduct a first-differenced analysis comparing the period from October 2022 to March 2023 with the period from October 2024 to March 2025. Higher exposure to AI is associated with reduced employment, higher unemployment rates, and shorter work hours. We also observe some evidence of increased secondary job holding and a decrease in full-time employment among certain demographics. These associations are more pronounced among older and younger workers, men, and college-educated individuals. College-educated workers tend to experience smaller declines in employment but are more likely to see changes in work intensity and job structure. In addition, occupations that rely heavily on complex reasoning and problem-solving tend to experience larger declines in full-time work and overall employment in association with rising AI exposure. In contrast, those involving manual physical tasks appear less affected. Overall, the results suggest that AI-driven shifts in labor are occurring along both the extensive margin (unemployment) and the intensive margin (work hours), with varying effects across occupational task content and demographics.