时间：2026年11月13日 上午10:00 – 11:30

地点：北京大学国家发展研究院(承泽园) 217

题目：Common AI Information and Capital Allocation

主讲人：孙浩宁  清华大学经济管理学院经济系博士研究生

摘要： This paper studies how AI progress affects capital allocation through financial information aggregation. Using institutional portfolio holdings, we document that manager pairs with greater common alignment with AI-extractable information subsequently hold more similar portfolios. Motivated by this evidence, we develop a model in which investors choose the quantity and composition of private and AI information, and firms learn from equilibrium asset prices before investing. A shared AI model error survives aggregation and enters prices, and investors may underestimate the overlap between AI information and prices. AI progress therefore changes both investors' information precision and prices' exposure to common errors. Cheaper access initially raises aggregate productivity when high costs keep AI use negligible, but further cost reductions can reverse the gain as AI research displaces private research. Improvements from very low model accuracy can instead lower productivity when increased reliance raises common-error exposure more than informed trading reduces noise-trader contamination. Numerical equilibria trace a J-shaped relationship between model accuracy and TFP. These results show how AI progress can expand investors' information acquisition while reducing the information prices convey to firms, worsening capital allocation even when firms use prices optimally.

主讲人简介：孙浩宁，清华大学经济管理学院经济系博士研究生，导师为董丰教授，本科毕业于清华大学经济管理学院，主要研究方向为宏观金融、数字经济与人工智能的宏观影响等，主持国家自然科学基金青年学生基础研究项目（博士研究生），研究成果发表在International Economic Review、Review of Economic Dynamics、《管理世界》、《管理科学学报》、《经济学季刊》等国内外期刊。