| 主讲人简介： | Yanhui Wu is Professor of Economics and of Management and Strategy at the University of Hong Kong, where he is Head of its Economics Department and Director of the Digital Economy Research Institute and the Future of Work and Wellbeing Lab. Before joining HKU, he taught at the Marshall School of Business, University of Southern California after receiving his Ph.D. in Economics from the London School of Economics. He has extensively studied the impacts of technology, particularly social media and AI, on the organization of production, job design, human capital, and labor markets. His work has been published at leading journals in economics, management, and statistics, including the American Economic Review, Econometrica, Management Science, and the Journal of the American Statistical Association. He is co-author of the acclaimed business bestseller, Messy Jobs: The Work that AI Cannot Reach. |
| 简介： | Fears of a jobs apocalypse rest on the observation that AI can perform an ever-growing range of cognitive tasks. But jobs are not tasks. Real-world work consists of messy bundles of tasks that involve human judgment and relationships. When jobs are messy, removing even a clean, easily automated task from the bundle can raise the costs of coordination and motivation. Building on this idea, we develop a novel framework to analyze how AI affects job design, employment, and aggregate productivity. We show that AI exposure does not necessarily lead to AI adoption or worker displacement. We test the theory with experimental evidence at the micro level and large-scale job posting data at the aggregate level. Finally, we discuss the costs of removing messiness with AI tools in learning, production, and management. |