{"generated_at": "2026-07-14T03:18:30.960323Z", "slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "name": "Dwarkesh Podcast", "episode_count": 11, "avg_signal": 82.1, "median_signal": 82.9, "top_signal": 92.1, "latest_episode_at": "2026-07-10T16:23:56Z", "earliest_episode_at": "2026-04-15T15:45:23Z", "category_mode": "ai", "cover_image_url": "https://substackcdn.com/image/fetch/$s_!QEPJ!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F90fa9666-5b8b-4685-a8fb-4b64cb7e0333_1080x1080.png", "rank_score": 196.029, "episodes": [{"episode_id": "ep_dwarkesh_podcast_79b4d205b9fb", "episode_title": "Adam Brown \u2013 A deep but accessible introduction to general relativity", "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "category": "ai", "publish_date": "2026-07-10T16:23:56Z", "overall_score": 82.9, "score_breakdown": {"clarity": 43.6, "originality": 50.7, "hype_penalty": 0.0, "actionability": 100.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://substackcdn.com/image/fetch/$s_!QEPJ!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F90fa9666-5b8b-4685-a8fb-4b64cb7e0333_1080x1080.png", "source_link": "https://www.dwarkesh.com/p/adam-brown-gr", "audio_url": "https://api.substack.com/feed/podcast/206445844/faa55a6705208b2496c98fb2dffb24d0.mp3", "listen_url": "https://www.dwarkesh.com/p/adam-brown-gr", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of black, hole, it's, including: You currently need Blue Shift at Google Deep Mind, which is cracking science and reasoning.", "summary": "I'm back with Adam Brown."}, {"episode_id": "ep_dwarkesh_podcast_d60a6b93369e", "episode_title": "Grant Sanderson \u2013 AI and the future of math", "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "category": "ai", "publish_date": "2026-06-30T15:53:19Z", "overall_score": 75.3, "score_breakdown": {"clarity": 61.3, "originality": 55.0, "hype_penalty": 12.0, "actionability": 100.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://substackcdn.com/image/fetch/$s_!QEPJ!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F90fa9666-5b8b-4685-a8fb-4b64cb7e0333_1080x1080.png", "source_link": "https://www.dwarkesh.com/p/grant-sanderson-2", "audio_url": "https://api.substack.com/feed/podcast/204275916/96bb07d6147c4ef4193fd586a2c304af.mp3", "listen_url": "https://www.dwarkesh.com/p/grant-sanderson-2", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of like, it's, think, including: And I wanted to talk to you about this, because AI has been making the fastest progress in mathematics as of any other field.", "summary": "Today I'm chatting with Grant Sanderson, who runs Stoblew and Brown, and is now working on a new project documenting the progress AI is making in math."}, {"episode_id": "ep_dwarkesh_podcast_2f7e1f4deaa8", "episode_title": "The next big breakthrough will be AIs learning on the job", "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "category": "ai", "publish_date": "2026-06-26T15:51:34Z", "overall_score": 92.1, "score_breakdown": {"clarity": 80.6, "originality": 60.1, "hype_penalty": 0.0, "actionability": 100.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://substackcdn.com/image/fetch/$s_!QEPJ!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F90fa9666-5b8b-4685-a8fb-4b64cb7e0333_1080x1080.png", "source_link": "https://www.dwarkesh.com/p/the-next-paradigm", "audio_url": "https://api.substack.com/feed/podcast/203704568/53d7d1bee7d0a5057222b2a37ebf9e67.mp3", "listen_url": "https://www.dwarkesh.com/p/the-next-paradigm", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of model, learning, training, including: They think that if we train AIs to accomplish millions of verifiable tasks across thousands of diverse RL environments, then we will have basically built AGI.", "summary": "So here's the big research bet that all the labs are making. Because this kind of training will have created a kind of problem-solving agent, the kind of thing that can make progress on open-ended tasks for weeks on end in the face of errors and mistakes and ambiguity."}, {"episode_id": "ep_dwarkesh_podcast_da5baa8125da", "episode_title": "The data black hole at the center of AI", "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "category": "ai", "publish_date": "2026-06-19T16:45:03Z", "overall_score": 79.4, "score_breakdown": {"clarity": 65.2, "originality": 52.7, "hype_penalty": 0.0, "actionability": 59.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://substackcdn.com/image/fetch/$s_!QEPJ!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F90fa9666-5b8b-4685-a8fb-4b64cb7e0333_1080x1080.png", "source_link": "https://www.dwarkesh.com/p/the-sample-efficiency-black-hole", "audio_url": "https://api.substack.com/feed/podcast/202734201/8586aab0c95c1b9d772549aae349b176.mp3", "listen_url": "https://www.dwarkesh.com/p/the-sample-efficiency-black-hole", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of these, data, models, including: That is to say, how much data do you need in a given domain to operate fluently and competently?.", "summary": "So one definition of intelligence is sample efficiency. That is to say, how much data do you need in a given domain to operate fluently and competently?."}, {"episode_id": "ep_dwarkesh_podcast_a6b7652b98d1", "episode_title": "The data black hole at the center of AI", "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "category": "ai", "publish_date": "2026-06-19T16:45:03Z", "overall_score": 78.5, "score_breakdown": {"clarity": 65.2, "originality": 48.5, "hype_penalty": 0.0, "actionability": 59.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://substackcdn.com/image/fetch/$s_!QEPJ!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F90fa9666-5b8b-4685-a8fb-4b64cb7e0333_1080x1080.png", "source_link": "https://www.dwarkesh.com/p/the-sample-efficiency-black-hole-2", "audio_url": "https://api.substack.com/feed/podcast/202734201/8586aab0c95c1b9d772549aae349b176.mp3", "listen_url": "https://www.dwarkesh.com/p/the-sample-efficiency-black-hole-2", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of these, data, models, including: That is to say, how much data do you need in a given domain to operate fluently and competently?.", "summary": "So one definition of intelligence is sample efficiency. That is to say, how much data do you need in a given domain to operate fluently and competently?."}, {"episode_id": "ep_dwarkesh_podcast_1798824e819c", "episode_title": "Ada Palmer \u2013 Machiavelli is the most misunderstood thinker of all time", "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "category": "ai", "publish_date": "2026-06-16T17:27:51Z", "overall_score": 67.9, "score_breakdown": {"clarity": 64.4, "originality": 45.0, "hype_penalty": 18.0, "actionability": 100.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://substackcdn.com/image/fetch/$s_!QEPJ!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F90fa9666-5b8b-4685-a8fb-4b64cb7e0333_1080x1080.png", "source_link": "https://www.dwarkesh.com/p/ada-palmer-2", "audio_url": "https://api.substack.com/feed/podcast/202271948/86d8188a61055b682e315bbbf910e784.mp3", "listen_url": "https://www.dwarkesh.com/p/ada-palmer-2", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of like, because, machiavelli, including: Ada, this time I want to talk to you about Machiavelli.", "summary": "Okay, I'm back with Ada Palmer, who is a science fiction author, composer, historian at the University of Chicago. Ada, this time I want to talk to you about Machiavelli."}, {"episode_id": "ep_dwarkesh_podcast_7683378523b8", "episode_title": "Alex Imas and Phil Trammell \u2013 What remains scarce after AGI?", "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "category": "ai", "publish_date": "2026-06-04T16:14:49Z", "overall_score": 87.8, "score_breakdown": {"clarity": 72.1, "originality": 47.0, "hype_penalty": 0.0, "actionability": 100.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://substackcdn.com/image/fetch/$s_!QEPJ!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F90fa9666-5b8b-4685-a8fb-4b64cb7e0333_1080x1080.png", "source_link": "https://www.dwarkesh.com/p/alex-imas-phil-trammell", "audio_url": "https://api.substack.com/feed/podcast/200613353/cc9231d96754b37a8935be6734872dcd.mp3", "listen_url": "https://www.dwarkesh.com/p/alex-imas-phil-trammell", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of like, right, it's, including: In general, in this interview, what I want to understand is what economics tells us about what we can expect in a world with more and more automation, more and more advanced AI, wh.", "summary": "Today, I'm chatting with Alex Emas, who is director of AGI economics at Google DeepMind and professor of economics at University of Chicago, and Phil Trammell, who is head of economics at EPOC and research scholar at Stanford."}, {"episode_id": "ep_dwarkesh_podcast_9201844e19e0", "episode_title": "Reiner Pope \u2013 Chip design from the bottom up", "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "category": "ai", "publish_date": "2026-05-22T15:38:34Z", "overall_score": 91.2, "score_breakdown": {"clarity": 92.0, "originality": 94.0, "hype_penalty": 1.0, "actionability": 78.0, "technical_depth": 92.0, "information_density": 85.0}, "podcast_cover_url": "https://substackcdn.com/image/fetch/$s_!QEPJ!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F90fa9666-5b8b-4685-a8fb-4b64cb7e0333_1080x1080.png", "source_link": "https://www.dwarkesh.com/p/reiner-pope-2", "audio_url": "https://api.substack.com/feed/podcast/198847047/2743eb4e51ed68a1e96ee4dd1d97ebdf.mp3", "listen_url": "https://www.dwarkesh.com/p/reiner-pope-2", "verdict": "must_listen", "why_listen": "Understand how the physics of data movement and logic gate timing dictate the design of modern AI accelerators.", "summary": "Chip design is fundamentally constrained by communication costs, not compute, with matrix multiplication efficiency hinging on minimizing data movement between register files and logic units. Systolic arrays and custom AI chips optimize this by structuring computation to bound bandwidth usage to X rather than XY growth. The physical realities of gate propagation, clock synchronization, and energy use further shape architectural trade-offs in AI hardware."}, {"episode_id": "ep_dwarkesh_podcast_908d713b3ae4", "episode_title": "Eric Jang \u2013 Building AlphaGo from scratch", "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "category": "ai", "publish_date": "2026-05-15T16:04:58Z", "overall_score": 75.0, "score_breakdown": {"clarity": 85.0, "originality": 85.0, "hype_penalty": 2.0, "actionability": 75.0, "technical_depth": 62.0, "information_density": 58.0}, "podcast_cover_url": "https://substackcdn.com/image/fetch/$s_!QEPJ!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F90fa9666-5b8b-4685-a8fb-4b64cb7e0333_1080x1080.png", "source_link": "https://www.dwarkesh.com/p/eric-jang", "audio_url": "https://api.substack.com/feed/podcast/197852876/67d1346b253e5834d07b4510c3fc98b9.mp3", "listen_url": "https://www.dwarkesh.com/p/eric-jang", "verdict": "must_listen", "why_listen": "You\u2019ll gain a clear, implementation-aware understanding of how AlphaGo\u2019s architecture combines deep learning and tree search to master combinatorially complex problems.", "summary": "Eric Jang explains the technical foundations of AlphaGo, emphasizing the integration of Monte Carlo Tree Search (MCTS) with deep neural networks for policy and value functions. He details how MCTS provides a data structure that enables efficient search in high-dimensional action spaces like Go, and how self-play combined with supervised learning on expert data accelerates training. The discussion also touches on the broader implications for AI systems in complex, sequential decision-making domains."}, {"episode_id": "ep_dwarkesh_podcast_e13312e16875", "episode_title": "David Reich \u2013 Why the Bronze Age was an inflection point in human evolution", "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "category": "ai", "publish_date": "2026-05-08T16:38:22Z", "overall_score": 83.4, "score_breakdown": {"clarity": 90.0, "originality": 94.0, "hype_penalty": 2.0, "actionability": 60.0, "technical_depth": 88.0, "information_density": 75.0}, "podcast_cover_url": "https://substackcdn.com/image/fetch/$s_!QEPJ!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F90fa9666-5b8b-4685-a8fb-4b64cb7e0333_1080x1080.png", "source_link": "https://www.dwarkesh.com/p/david-reich-2", "audio_url": "https://api.substack.com/feed/podcast/196892360/c03625a044a70e5f333cd67b0c06c1e7.mp3", "listen_url": "https://www.dwarkesh.com/p/david-reich-2", "verdict": "must_listen", "why_listen": "Access a leading geneticist's synthesis of how ancient DNA is rewriting our understanding of human evolution, with concrete evidence of how societal shifts like urbanization and animal domestication directly shaped our genome.", "summary": "David Reich presents evidence that the Bronze Age was a pivotal period in human evolution due to intensified natural selection driven by higher population densities, closer contact with domesticated animals, and the spread of infectious diseases. Ancient DNA analysis reveals that many genetic adaptations\u2014especially in immune function\u2014emerged during this time, with some traits showing reversals in selective pressure. The data also uncover complex patterns of migration, admixture, and sex-biased gene flow that reshaped human populations across Europe and Asia."}, {"episode_id": "ep_dwarkesh_podcast_3d4ac0d00b54", "episode_title": "Jensen Huang \u2013 TPU competition, why we should sell chips to China, & Nvidia\u2019s supply chain moat", "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh_podcast", "source_id": "src_dwarkesh_podcast", "category": "ai", "publish_date": "2026-04-15T15:45:23Z", "overall_score": 89.4, "score_breakdown": {"clarity": 90.0, "originality": 95.0, "hype_penalty": 2.0, "actionability": 75.0, "technical_depth": 92.0, "information_density": 85.0}, "podcast_cover_url": "https://apple.dwarkesh-podcast.workers.dev/art/v1/c3Vic3RhY2s6cG9zdDoxOTQyODk4ODk.jpg", "source_link": "https://www.dwarkesh.com/p/jensen-huang", "audio_url": "https://api.substack.com/feed/podcast/194289889/4ad9f4582dd59f13b5a5cc452125b1f6.mp3", "listen_url": "https://www.dwarkesh.com/p/jensen-huang", "verdict": "must_listen", "why_listen": "Huang reveals how NVIDIA's strategic control over the entire AI compute stack\u2014from electron to token\u2014creates a defensible, non-commoditized position in the face of rising competition.", "summary": "Jensen Huang frames NVIDIA's core mission as transforming electrons into valuable AI tokens through a vertically integrated ecosystem that spans hardware, software, and supply chain, arguing this process is too complex to be commoditized. He emphasizes that NVIDIA's moat lies not just in chip design but in securing long-term supply chain commitments and aligning global partners through vision and scale. Huang also predicts a surge in AI agent-driven tool usage, which will amplify demand for enterprise software rather than displace it."}], "category_breakdown": [{"category": "ai", "count": 11}]}