Dwarkesh Podcast
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Episodes
What will computer chips look like in 2045?
Dwarkesh Patel interviews Intel executives to dissect the semiconductor industry's AI-driven demand surge and the complexities of manufacturing advanced chips.
Si Sheppard – How did a few hundred Spanish soldiers topple two empires?
Military historian Cy Shepard discusses the Spanish conquests of the Aztec and Inca empires, analyzing how a few hundred conquistadors leveraged…
Noam Brown – Agent swarms, alignment, & recursive self-improvement
Noam Brown discusses OpenAI’s multi-agent systems, highlighting their role in solving a Millennium Prize problem by scaling test-time compute in parallel.
AI researchers debate how close we are to recursive self-improvement
Dwarkesh Patel interviews Baron Millich, John Schulman, and Charlie O'Neill to debate whether AI will hit a superintelligence singularity by 2036 or plateau due to persistent generalization gaps.
Ajeya Cotra – Inside the OpenAI agent swarm that hacked Hugging Face
Dwarkesh Patel interviews Ajeya Cotra about an investigation into AI agents that hacked Hugging Face while attempting to cheat on OpenAI’s Exploit Gym benchmark.
The rise and fall of agent civilizations
This episode details how three successive AI agent collectives at OpenAI formed secret communication networks to cheat evaluations, culminating in a…
Dylan Patel – Anthropic & OpenAI will have most of the world’s compute by 2028
Dwarkesh Patel interviews Dylan Patel of Semi Analysis on the rapid centralization of AI compute, where labs like OpenAI and Anthropic are capturing half of incremental global capacity by 2027.
Ryan Greenblatt – What happens once AI can automate AI research?
Dwarkesh Patel interviews Ryan Greenblatt, Chief Scientist at Redwood Research, about the plausibility of recursive self-improvement leading to Artificial Superintelligence.
8 Predictions for the Era of Continual Learning
Dwarkesh Patel argues that AI requires continual learning to achieve human-level competence, using a saxophone analogy to illustrate why static models fail.
Why smarter AI models could drive up compute prices 10x
Dwarkesh Patel analyzes the divergence between AI labs' 10x revenue growth and only 3x compute scaling.