20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

This podcast episode critically examines the current state and future of AI, highlighting key challenges such as the saturation of data availability, the limitations of scaling models, and the urgent need to capture tacit knowledge for effective deployment. Narayanan provides a thoughtful analysis of the transition from striving for artificial general intelligence to developing practical, user-focused applications, while also cautioning against overhyped predictions from AI leaders. With a strong emphasis on ethical considerations and the evolving role of AI in society, the conversation underscores the importance of balancing innovation with responsible governance to address both immediate and long-term impacts of AI technology.