Wensu Li
Postdoctoral Associate
Wensu Li is a postdoctoral associate at the MIT Sloan School’s Initiative on the Digital Economy. She is interested in the economic implications of technical change. Currently, she is working on measuring IT’s contribution to economic productivity and how machine learning technologies reshape the economy and the job market. She is also interested in environmental economics and the environmental implications of AI development.
Previously, Wensu was a visiting assistant professor at Trinity College, Hartford. She received her Ph.D. in Econometrics and Quantitative Economics from the University of Connecticut. Wensu did her master’s degree in Finance and her undergraduate degree in Economics, both at Sun Yat-sen University.
Recent Insights
Featured publications
May 22, 2026
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
- Martin Fleming | Research Scientist, IDE
- Wensu Li | Postdoctoral Associate
Maja S. Svanberg
Brian C. Goehring
This paper’s findings suggest that AI job displacement will be substantial, but also gradual – and therefore there is room for policy and retraining to mitigate unemployment impacts.
April 15, 2026
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
- Martin Fleming | Research Scientist, IDE
- Matthias Mertens | Research Scientist at Future Tech
- Brittany Harris | Data Analyst, CSAIL
- Wensu Li | Postdoctoral Associate
The authors propose that AI automation is a continuum between: crashing waves where AI capabilities surge abruptly over small sets of tasks, and rising tides where the increase in AI capabilities is more continuous and broad-based.
March 15, 2026
- Wensu Li | Postdoctoral Associate
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
- Martin Fleming | Research Scientist, IDE
This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation.