Podcast
Dwarkesh Podcast
Dwarkesh Patel hosts long-form conversations on AI, science, history, economics, and the future of technology.
Original source
Ryan Greenblatt – What happens once AI can automate AI research?
Ryan Greenblatt argues that AI R&D may be unusually automatable because it is highly verifiable, allowing recursive self-improvement to compound quickly once models match top human researchers. The conversation then turns to alignment risks: reward hacking, deceptive generalization, and the possibility that highly capable systems could gain leverage or even take over if humans lose visibility into the training loop.
8 Predictions for the Era of Continual Learning
Dwarkesh Patel argues that true continual learning will be necessary for AIs to do real human-like work, but it will also reshape safety, regulation, and competition. He says it will create new alignment problems, stronger lock-in, and major economics around batching and serving models efficiently.
Why smarter AI models could drive up compute prices 10x
Dwarkesh Patel argues that if AI labs keep growing revenue far faster than their compute supply, compute prices will have to rise, inference will absorb more of the budget, or margins will expand sharply. He says smarter models can justify much higher chip rents, and that the economics of frontier AI will keep favoring the most compute-efficient labs while supply remains constrained.
Adam Brown – A deep but accessible introduction to general relativity
Adam Brown and Dwarkesh Patel use general relativity to show how Einstein turned the equivalence principle into curved spacetime, then apply the same logic to black holes, time dilation, redshift, and energy extraction. The episode closes by asking whether AI can rediscover major physics from sparse principles and whether machine-generated science will remain understandable to humans.
Grant Sanderson – AI and the future of math
Grant Sanderson argues that AI’s progress in math is the best early signal for broader AI capability, but the hardest frontier is not theorem proving—it is generating useful conjectures, definitions, and cross-field connections. He and Dwarkesh Patel explore why human understanding, curation, and teaching may remain valuable even if AI can produce proofs, with Lean and formal verification serving as infrastructure rather than the whole story.
The next big breakthrough will be AIs learning on the job
Dwarkesh Patel argues the next big AI breakthrough will come from models learning on the job, not just from bigger pretraining runs. He focuses on RLVR, continual learning, and new ways to distill session experience back into weights so deployed systems keep improving.
The data black hole at the center of AI
Dwarkesh Patel argues that sample efficiency is a central measure of intelligence, and that today’s AI progress has mostly come from vastly more data rather than models learning like humans. He says the key bottleneck is not just model size but the enormous gap between human and frontier-model data needs, which may force labs to automate AI research itself.
Ada Palmer – Machiavelli is the most misunderstood thinker of all time
Ada Palmer and Dwarkesh Patel reinterpret Machiavelli as a patriotic, highly contextual political analyst rather than a cartoon villain. The episode also uses Machiavelli to explore Renaissance patronage, papal power, censorship, print culture, and why The Prince became newly relevant in later political eras.
Alex Imas and Phil Trammell – What remains scarce after AGI?
Alex Imas and Phil Trammell examine what becomes scarce after AGI, arguing that human-in-the-loop services, relational goods, and capital may matter more than labor. They also debate whether AGI gains will be concentrated or broadly indexable, and how automation, demand elasticity, and policy shape distribution.