Videos
Exploring AI’s Role in the Future of Work with Stanford's Dr. Ruyu Chen
Insights
- AI is not just replacing jobs—it’s reshaping them at the task level, altering the value and demand of specific skills across occupations.
- Generative AI’s impact varies by industry, with traditional sectors experiencing slower disruption compared to tech, highlighting the importance of sector-specific analysis.
- To unlock GenAI’s full potential, businesses and policymakers must distinguish between augmentation and automation when planning workforce training and transformation strategies.
How is AI reshaping the future of work?
Recorded at The Business and Economics of AI workshop co-hosted by Stanford University and Infosys on May 14, 2025, this thought-provoking interview features Dr. Ruyu Chen, Postdoctoral Researcher at Stanford’s Digital Economy Lab.
Dr. Chen shares new research on how generative AI is transforming labor markets—deconstructing jobs into thousands of tasks to measure AI’s real-world impact. She explores:
- How GenAI is altering task value and demand across industries
- Why the effects of AI vary between augmentation and replacement
- What these shifts mean for workforce strategy and upskilling initiatives
With data-driven insights and practical implications for business and policy, Dr. Chen offers a fresh perspective on the evolving relationship between AI and employment. A must-watch for leaders navigating the future of talent and technology.
Dr. Ruyu Chen:
I'm Ruyu Chen. I'm a postdoc at the Digital Economy Lab at Stanford.
Can you summarise the key focus of your session?
We focus on AI and future work, basically to understand what has been AI changing on the current labor market. We started from a task-based approach, where we decompose each occupation into thousands of job tasks using the ADP payroll data and job posting data, so that we can track the up-to-date changes in the composition of tasks and skills across occupations. And then we look at what is the impact of AI on each of those tasks, the number of employment, and also use the hedonic regression to recover the value to those tasks. In this way, we'll be able to know with the GenAI, the disruptive change brought by generative AI, which scales have higher value and higher demand versus which skills are shrinking or declining.
What have you discovered thus far?
On the news every day we saw, we thought there's a tremendous layoffs caused by AI. However, maybe that's just a part of the story. If you look at the broader industry, the downstream industries, including not only those tech giants, but also the manufacturing firms, service firms, or more of those traditional firms. So the overall employment of developers, for example, did not change as tremendously as in the tech sector, which means it takes time for AI to replace people. Because there are two effects of AI. One is replacing, one is augmenting. So actually, for many of the occupations, we find both the effects on the worker.
What were you asked today that stood out?
One is how my research, what are the implications of my research. So to predict the value of GenAI adoption at different sectors and also for worker trainings, like which set of skills will be more valuable. This is something I need to think more.
