The Impact of Generative AI on Employment: Hype, Reality, and the Future of Work w/ Aaron Benanav

Aaron Benanav (Cornell University) provides a critical analysis of the potential impact of generative AI technologies, such as ChatGPT, on the job market. While researchers and tech leaders have predicted that AI will eliminate a significant portion of human jobs, Benanav argues that similar automation forecasts in the past, such as those made about robots in the early 2010s, largely failed to materialize in the predicted manner. Part of the reason for that failure was that robot technologies were hyped up far beyond their actual capabilities. But another reason is that these technologies tend to transform job tasks rather than eliminate professions. The discussion will delve into the limitations of AI in replicating human cognitive tasks, the use of AI to control and monitor work, and the role of technology in reshaping rather than eradicating employment. Emphasizing the interplay between technological, economic, social, and political factors, this conversation aims to provide a different perspective on how AI might change the nature of work and the potential paths forward, including worker organization and alternative frameworks for technological development.

Aaron Benanav is an Assistant Professor in the Department of Global Development at Cornell University (Ithaca, New York).
As a historian, sociologist, and economic and social theorist, Benanav’s research spans a variety of topics, including automation and the future of work, unemployment and underemployment, histories of social and economic development, critical theory, and alternative economic systems.

In addition to academic journals, Benanav's writing has appeared in publications such as The Nation, The Guardian, New Statesman, Boston Review, New Left Review, and Dissent. An overview of his work can be found here: https://www.aaronbenanav.com/

Moderation: Dr. Dennis Büscher-Ulbrich
Assistant Professor of American Cultural and Media Studies, English Department | University of Kiel