Ep. 36: Joshua Gans on the Economics of AI

Bright Future

When Joshua Gans and his co-authors released their book Prediction Machines in 2018, they were writing about a topic that seemed quite niche. At this time, machine learning was just starting out. In the last year, the speed at which artificial intelligence has advanced has surprised almost everyone.

In this conversation, we hear how the analytical framework that he and his colleagues developed helps to sort through the hype. He argues artificial intelligence is best thought of as a prediction machine. You’ll hear why he’s optimistic that artificial intelligence will be able to help people remove some of the drudgery from some jobs, but at this time, doesn’t seem likely to take over full jobs. He’ll share how understanding artificial intelligence as an advance in predictive statistics will help leaders assess how artificial intelligence may or may not be useful.

About our guest:

Joshua Gans is a Professor of Strategic Management and holder of the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at the Rotman School of Management, the University of Toronto (with a cross-appointment in the Department of Economics). Joshua is also Chief Economist of the University of Toronto's Creative Destruction Lab. Prior to 2011, he was the foundation Professor of Management (Information Economics) at the Melbourne Business School, University of Melbourne and before that, he was at the School of Economics, University of New South Wales.

At Rotman, he teaches MBA students entrepreneurial strategy. He has also co-authored (with Stephen King and Robin Stonecash) the Australasian edition of Greg Mankiw's Principles of Economics (published by Cengage), Core Economics for Managers (Cengage), Finishing the Job (MUP), Parentonomics (New South/MIT Press) and Information Wants to be Shared (Harvard Business Review Press) and The Disruption Dilemma (MIT Press, 2016);