The rapid advancement of Large Language Models (LLMs) has generated considerable speculation regarding their transformative potential for labor markets. However, existing approaches to measuring AI exposure in the workforce predominantly rely on concurrent market conditions, offering limited predictive capacity for anticipating future disruptions. This paper presents a predictive study examining whether online discussions about LLMs can function as early indicators of labor market shifts. We employ four distinct analytical approaches to identify the domains and timeframes in which public discourse serves as a leading signal for employment changes, thereby demonstrating its predictive validity for labor market dynamics. Drawing on a comprehensive dataset that integrates the REALM corpus of LLM discussions, LinkedIn job postings, Indeed employment indices, and over 4 million LinkedIn user profiles, we analyze the relationship between discussion intensity across news media and Reddit forums and subsequent variations in job posting volumes, occupational net change ratios, job tenure patterns, unemployment duration, and transitions to GenAI-related roles across thirteen occupational categories. Our findings reveal that discussion intensity predicts employment changes 1-7 months in advance across multiple indicators, including job postings, net hiring rates, tenure patterns, and unemployment duration. These findings suggest that monitoring online discourse can provide actionable intelligence for workers making reskilling decisions and organizations anticipating skill requirements, offering a real-time complement to traditional labor statistics in navigating technological disruption.

翻译：大型语言模型（LLMs）的快速发展引发了关于其对劳动力市场变革潜力的广泛推测。然而，现有衡量劳动力中人工智能暴露程度的方法主要依赖同期市场状况，对未来颠覆性变化的预测能力有限。本文通过一项预测性研究，探讨关于LLMs的在线讨论是否可作为劳动力市场变动的早期指标。我们采用四种不同的分析方法，识别出公众讨论作为就业变化先行信号的领域与时间范围，从而证明其对劳动力市场动态的预测效度。基于整合了LLM讨论REALM语料库、LinkedIn职位发布、Indeed就业指数及超过400万份LinkedIn用户档案的综合数据集，我们分析了新闻媒体与Reddit论坛的讨论强度与后续13个职业类别中职位发布量、职业净变化率、任职时长模式、失业持续时间及向生成式AI相关岗位转移之间的关联。研究结果表明，讨论强度可提前1-7个月预测多个指标的就业变化，包括职位发布、净雇佣率、任职模式及失业持续时间。这些发现表明，监测在线讨论可为劳动者制定技能重塑决策及组织预判技能需求提供可操作情报，为应对技术颠覆中的传统劳动力统计数据提供实时补充。