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Dwarkesh Podcast: Daily Summaries Delivered
by Dwarkesh Patel
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- 1 weeks agointerview1h 16m7-min read
Alex Imas and Phil Trammell – What remains scarce after AGI?
The conversation explores the economic implications of advanced AI and automation, focusing on what will remain scarce in a future of abundance, how labor and capital shares might evolve, and the challenges of redistribution. The guests argue that predictions are inherently uncertain, emphasizing the need for better data and models rather than relying on intuition.
- 3 weeks agointerview1h 20m6-min read
Reiner Pope – Chip design from the bottom up
AI chips are optimized to maximize compute efficiency by minimizing data movement, with systolic arrays enabling massive matrix multiplication efficiency through spatial computation and local storage of weights. The clock cycle synchronizes all chip operations, and design trade-offs center on balancing compute, communication, and area.
- May 15, 2026interview2h 37m8-min read
Eric Jang – Building AlphaGo from scratch
AlphaGo's breakthrough lies in using neural networks to make intractable search problems computationally feasible, demonstrating that deep learning can effectively amortize complex reasoning. This insight has profound implications for AI's ability to solve problems previously thought to be beyond reach.
- May 8, 2026interview2h 13m7-min read
David Reich – Why the Bronze Age was an inflection point in human evolution
Ancient DNA research has revealed that natural selection in humans over the last 10,000 years has been far more active than previously believed, particularly during the Bronze Age. Contrary to the long-held view that human evolution slowed after the Paleolithic, new data shows strong selection pressures on immune and metabolic traits, with surprising evidence of selection on traits linked to modern measures of intelligence and education.
- Apr 29, 2026narrative2h 13m7-min read
Reiner Pope – The math behind how LLMs are trained and served
This podcast episode is a technical deep-dive lecture on the infrastructure and economics of AI inference, focusing on how model architecture, hardware constraints, and batching strategies determine latency, cost, and scalability. It lands by revealing the hidden engineering trade-offs behind real-world AI pricing and performance.
- Apr 15, 20261h 43m
Jensen Huang – TPU competition, why we should sell chips to China, & Nvidia’s supply chain moat
I asked Jensen about TPU competition, Nvidia’s lock on the ever more bottlenecked supply chain needed to make advanced chips, whether we should be selling AI chips to China, why Nvidia doesn’t just become a hyperscaler, how it makes its investments, and much more. Enjoy!Watch on YouTube; read the transcript.Sponsors* Crusoe’s cloud runs on state-of-the-art Blackwell GPUs, with Vera Rubin deployment scheduled for later this year. But hardware is only part of the story—for inference, Crusoe’s MemoryAlloy tech implements a cluster-wide KV cache, delivering up to 10x faster TTFT and 5x better throughput than vLLM. Learn more at crusoe.ai/dwarkesh* Cursor helped me build an AI co-researcher over the course of a weekend. Now I have an AI agent that I can collaborate with in Google Docs via inline comment threads! And while other agentic coding tools feel like a total black-box, Cursor let me stay on top of the full implementation. You can try my co-researcher out at github.com/dwarkeshsp/ai_coworker, or get started on your own Cursor project today at cursor.com/dwarkesh* Jane Street spent ~20,000 GPU hours training backdoors into 3 different language models, then challenged my audience to find the triggers. They received some clever solutions—like comparing the base and fine-tuned versions and extrapolating any differences to reveal the hidden backdoor—but no one was able to solve all 3. So if open problems like this excite you, Jane Street is hiring. Learn more at janestreet.com/dwarkeshTimestamps – Is Nvidia’s biggest moat its grip on scarce supply chains? – Will TPUs break Nvidia’s hold on AI compute? – Why doesn’t Nvidia become a hyperscaler? – Should we be selling AI chips to China? – Why doesn’t Nvidia make multiple different chip architectures? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
- Apr 7, 2026interview2h 3m7-min read
Michael Nielsen – How science actually progresses
Scientific progress is not a straightforward, rule-based process but a complex, often messy endeavor shaped by human judgment, historical context, and aesthetic intuition. Despite the myth of clean falsification and logical induction, real scientific breakthroughs often emerge from ambiguous data, competing interpretations, and long verification loops—highlighting that science advances not just through data, but through taste, bias, and the willingness to explore multiple research programs simultaneously.
- Mar 20, 20261h 23m
Terence Tao – Kepler, Newton, and the true nature of mathematical discovery
We begin the episode with the absolutely ingenious and surprising way in which Kepler discovered the laws of planetary motion. People sometimes say that AI will make especially fast progress at scientific discovery because of tight verification loops. But the story of how we discovered the shape of our solar system shows how the verification loop for correct ideas can be decades (or even millennia) long. During this time, what we know today as the better theory can actually make worse predictions. And the reasons it survives this epistemic hell is some mixture of judgment and heuristics that we don’t even understand well enough to actually articulate, much less codify into an RL loop. Hope you enjoy! Watch on YouTube; read the transcript. Sponsors - Jane Street loves challenging my audience with different creative puzzles. One of my listeners, Shawn, solved Jane Street’s ResNet challenge and posted a great walk-through on X. If you want to try one of these puzzles yourself, there’s one live now at janestreet.com/dwarkesh. - Labelbox can get you rubric-based evals, no matter your domain. These rubrics allow you to give your model feedback on all the dimensions you care about, so you can train how it thinks, not just what it thinks. Whatever you’re focused on—math, physics, finance, psychology or something else—Labelbox can help. Learn more at labelbox.com/dwarkesh. - Mercury just released a new feature called Insights. Insights summarizes your money in and out, showing you your biggest transactions and calling out anything worth paying attention to. It’s a super low-friction way to stay on top of your business. Learn more at mercury.com/insights. Timestamps – Kepler was a high temperature LLM – How would we know if there’s a new unifying concept within heaps of AI slop? – The deductive overhang – Selection bias in reported AI discoveries – AI makes papers richer and broader, but not deeper – If AI solves a problem, can humans get understanding out of it? – We need a semi-formal language for the way that scientists actually talk to each other – How Terry uses his time – Human-AI hybrids will dominate math for a lot longer Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
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