In this episode of Interpreting India, host Nidhi Singh is joined by Sayash Kapoor, co-author of AI Snake Oil, to unpack the myths, misconceptions, and exaggerated expectations around artificial intelligence. Kapoor challenges the dominant narratives of both utopian and dystopian AI futures and advocates instead for a more grounded perspective, viewing AI as a “normal technology,” akin to electricity or the internet, whose impact will unfold gradually over decades.
In this episode of Interpreting India, host Nidhi Singh is joined by Sayash Kapoor, co-author of AI Snake Oil, to unpack the myths, misconceptions, and exaggerated expectations around artificial intelligence. Kapoor challenges the dominant narratives of both utopian and dystopian AI futures and advocates instead for a more grounded perspective, viewing AI as a “normal technology,” akin to electricity or the internet, whose impact will unfold gradually over decades. Through a wide-ranging conversation, the episode examines the limitations of benchmark-based evaluation, the dangers of speculative AI policy, and the need for domain experts in shaping meaningful governance frameworks.
The episode begins with Kapoor explaining the origins of AI Snake Oil, tracing it back to his PhD research at Princeton on AI's limited predictive capabilities in social science domains. He shares how he and co-author Arvind Narayanan uncovered major methodological flaws in civil war prediction models, which later extended to other fields misapplying machine learning.
The conversation then turns to the disconnect between academic findings and media narratives. Kapoor critiques the hype cycle around AI, emphasizing how its real-world adoption is slower, more fragmented, and often augmentative rather than fully automating human labor. He cites the enduring demand for radiologists as a case in point.
Kapoor introduces the concept of “AI as normal technology,” which rejects both the notion of imminent superintelligence and the dismissal of AI as a passing fad. He argues that, like other general-purpose technologies (electricity, the internet), AI will gradually reshape industries, mediated by social, economic, and organizational factors—not just technical capabilities.
The episode also examines the speculative worldviews put forth by documents like AI 2027, which warn of AGI-induced catastrophe. Kapoor outlines two key disagreements: current AI systems are not technically on track to achieve general intelligence, and even capable systems require human and institutional choices to wield real-world power.
On policy, Kapoor emphasizes the importance of investing in AI complements—such as education, workforce training, and regulatory frameworks—to enable meaningful and equitable AI integration. He advocates for resilience-focused policies, including cybersecurity preparedness, unemployment protection, and broader access to AI tools.
The episode concludes with a discussion on recalibrating expectations. Kapoor urges policymakers to move beyond benchmark scores and collaborate with domain experts to measure AI’s real impact. In a rapid-fire segment, he names the myth of AI predicting the future as the most misleading and humorously imagines a superintelligent AI fixing global cybersecurity first if it ever emerged.
Carnegie India does not take institutional positions on public policy issues; the views represented herein are those of the author(s) and do not necessarily reflect the views of Carnegie, its staff, or its trustees.
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Konark Bhandari, Edward Fishman
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Adarsh Ranjan, Jaspreet Bindra
