新京报记者 丛之翔 实习生 张启扬 编辑 杨海 校对 李立军 记不清这是第几次面对她了。这位有着标准职场形象的女性，在整个秋招季里，几乎与许晴的求职邮件 如影随形——这是她的AI面试官。九月以来，许晴已经参加了近20场AI面试，但每一次她面对的，几 乎都是同样的枯燥场景。 眼前的这位AI面试官，脸上始终挂着平静的微笑，并不会因许晴的表现改变神色，也不会与她有任何 多余的互动。只会在她答完一道题后礼貌地点头，再用机械的女声发出指令，"好的，我们进入下一 题。" 许晴性格开朗、能言善辩、热衷挑战，把面试视为一场与面试官的较量。面对真人时，她并不怯场，高 压的氛围、刁钻的提问，反而能激起她的战斗欲望，帮助她快速转动大脑，生成最恰当的回答。面试官 被震慑住的表情，她百看不厌。 但面对AI时，这份表达欲被吞没了。许晴深知眼前只是一团没有生命的算法，那个妆容精致的面试 官，连嘴角上扬的弧度都不曾变化，这让她觉得没劲。 更何况，在许晴看来，AI面试官也并不专业，它抛出三五个问题，尝试探究面试者的性格、能力、经 历。但对许晴来说，这些问题空洞且缺乏针对性，根本不能展现她的优点。 这一次，她终于忍无可忍，面试中途便退出程序，让 ...

Core Insights - The article discusses the shift in demand from Generalist AI Tutors to Specialist AI Tutors across various fields such as STEM, finance, medicine, and security, as evidenced by the rapid growth of Mercor [2][6] - Mercor's annual revenue run rate has surged from 1 million to 500 million USD in just 17 months, indicating a significant acceleration in growth [2][3] - The emergence of new knowledge-based jobs focused on training AI agents is highlighted, suggesting a transformation in the job market [3][12] Group 1: Industry Trends - The overall demand in the AI industry is evolving towards specialized roles, reflecting a broader trend in the economy becoming a reinforcement learning environment [2][6] - The fear of job loss due to technological advancements is contrasted with the creation of new job categories, particularly in training AI agents [6][12] - Historical context is provided, noting that previous technological revolutions have also led to new job categories despite initial fears of unemployment [6][12] Group 2: Company Performance - Mercor's revenue growth is notable, with a reported annual revenue run rate of over 500 million USD, showcasing a rapid increase in demand for AI recruitment services [2][3] - The company is currently paying over 1 million USD daily to experts across various fields, indicating a robust recruitment model [3][14] - Mercor's positioning as an AI recruitment platform is emphasized, with a focus on providing talent for AI companies, particularly in reinforcement learning [14][15] Group 3: Future of Work - The future of work is expected to center around training AI agents, with a significant market for human labor in creating and validating training environments for AI [11][12] - The article posits that as long as there are tasks that humans can perform but AI cannot, there will be a need for human involvement in training and evaluation [11][12] - The concept of an "experience era" is introduced, where models learn to optimize rewards in real-world scenarios, necessitating human feedback and guidance [13]