The most important question nobody's asking about AI

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1. 

   29 Apr 2026 · 2 hr 14 min

   Reiner Pope – The math behind how LLMs are trained and served

   Did a very different format with Reiner Pope - a blackboard lecture where he walks through how frontier LLMs are trained and served. It’s shocking how much you can deduce about what the labs are doing from a handful of equations, public API prices, and some chalk. It’s a bit technical, but I encourage you to hang in there – it’s really worth it. There are less than a handful of people who understand the full stack of AI, from chip design to model architecture, as well as Reiner. It was a real delight to learn from him. Recommend watching this one on YouTube so you can see the chalkboard.…

2. 

   15 Apr 2026 · 1 hr 43 min

   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…

3. 

   7 Apr 2026 · 2 hr 3 min

   Michael Nielsen – How science actually progresses

   Really enjoyed chatting with Michael Nielsen about how we recognize scientific progress. It's especially relevant for closing the RL verification loop for scientific discovery. But it's also a surprisingly mysterious and elusive question when you look at the history of human science. We approach this question stories like Einstein (who claimed that he hadn't even heard of the famous Michelson-Morley experiment, which is supposed to have motivated special relativity, until after he had come up with the theory), Darwin (why did it take till 1859 to lay out an idea whose essence every farmer…

4. 

   20 Mar 2026 · 1 hr 24 min

   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…

5. 

   13 Mar 2026 · 2 hr 31 min

   Dylan Patel — Deep dive on the 3 big bottlenecks to scaling AI compute

   Dylan Patel , founder of SemiAnalysis , provides a deep dive into the 3 big bottlenecks to scaling AI compute: logic, memory, and power. And walks through the economics of labs, hyperscalers, foundries, and fab equipment manufacturers. Learned a ton about every single level of the stack. Enjoy! Watch on YouTube ; read the transcript . Sponsors * Mercury has already saved me a bunch of time this tax season. Last year, I used Mercury to request W-9s from all the contractors I worked with. Then, when it came time to issue 1099s this year, I literally just clicked a button and Mercury sent them…

6. 

   6 Mar 2026 · 2 hr 2 min

   Why Leonardo was a saboteur, Gutenberg went broke, and Florence was weird – Ada Palmer

   Renaissance history is so much wilder and weirder than you would have expected. Very fun chatting with Ada Palmer (historian, novelist, and composer based at the University of Chicago). Some especially fascinating things I learned from the conversation and her excellent book, Inventing the Renaissance : Not only did Gutenberg go bankrupt in the 1450s (after inventing the printing press), but so did the bank that foreclosed on him, and so did his apprentices. This is because paper was still very expensive, and so you had to make this big upfront CAPEX decision to print a batch of 300 copies…

7. 

   13 Feb 2026 · 2 hr 22 min

   Dario Amodei — "We are near the end of the exponential"

   Dario Amodei thinks we are just a few years away from AGI — or as he puts it, from having “a country of geniuses in a data center”. In this episode, we discuss what to make of the scaling hypothesis in the current RL regime, why task-specific RL might lead to generalization, and how AI will diffuse throughout the economy. We also dive into Anthropic’s revenue projections, compute commitments, path to profitability, and more. Watch on YouTube ; read the transcript . Sponsors * Labelbox can get you the RL tasks and environments you need. Their massive network of subject-matter experts ensures…

8. 

   5 Feb 2026 · 2 hr 50 min

   Elon Musk — "In 36 months, the cheapest place to put AI will be space”

   In this episode, John and I got to do a real deep-dive with Elon. We discuss the economics of orbital data centers, the difficulties of scaling power on Earth, what it would take to manufacture humanoids at high-volume in America, xAI’s business and alignment plans, DOGE, and much more. Watch on YouTube ; read the transcript . Sponsors * Mercury just started offering personal banking! I’m already banking with Mercury for business purposes, so getting to bank with them for my personal life makes everything so much simpler. Apply now at mercury.com/personal-banking * Jane Street sent me a new…

9. 

   30 Dec 2025 · 1 hr 50 min

   Adam Marblestone — AI is missing something fundamental about the brain

   Adam Marblestone is CEO of Convergent Research . He’s had a very interesting past life: he was a research scientist at Google Deepmind on their neuroscience team and has worked on everything from brain-computer interfaces to quantum computing to nanotech and even formal mathematics. In this episode, we discuss how the brain learns so much from so little, what the AI field can learn from neuroscience, and the answer to Ilya’s question: how does the genome encode abstract reward functions? Turns out, they’re all the same question. Watch on YouTube ; read the transcript . Sponsors * Gemini 3…

10. 

    23 Dec 2025 · 12 min

    Thoughts on AI progress (Dec 2025)

    Read the essay here . Timestamps 00:00:00 What are we scaling? 00:03:11 The value of human labor 00:05:04 Economic diffusion lag is cope00:06:34 Goal-post shifting is justified 00:08:23 RL scaling 00:09:18 Broadly deployed intelligence explosion This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkesh.com