华人 AI 招聘 2 年 ARR 超 1000 万美金，Mercor 年化收入已 5 亿美金

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]