In AI Snake Oil: What AI Can Do, What It Can’t, and How to Tell the Difference, Sayash Kapoor and his co-author Arvind Narayanan provide an essential understanding of how AI works and why some applications remain fundamentally beyond its capabilities.

Kapoor was included in TIME’s inaugural list of the 100 most influential people in AI. As a researcher at Princeton University’s Center for Information Technology Policy, he examines the societal impacts of AI, with a focus on reproducibility, transparency, and accountability in AI systems. In his new book, he cuts through the hype to help readers discriminate between legitimate and bogus claims for AI technologies and applications.

In his conversation with Martin Reeves, chair of the BCG Henderson Institute, Kapoor discusses historical patterns of technology hype, differentiates between the powers and limitations of predictive versus generative AI, and outlines how managers can balance healthy skepticism with embracing the potential of new technologies.

Key topics discussed:

01:05 | Examples of AI “snake oil”

04:42 | Historical patterns of technology hypeand how AI is different

07:26 | Capabilities and exaggerations of predictive AI

11:42 | Powers and limitations of generative AI

17:11 | Drivers of inflated expectations

20:18 | Implications for regulation

23:26 | How managers can balance scepticism and embracing new tech

24:58 | Future of AI research

Additional inspirations from Sayash Kapoor:

AI Snake Oil (Substack)
A Checklist of Eighteen Pitfalls in AI Journalism (UNESCO article, 2022)

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