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 (00:00:00) – Kepler was a high temperature LLM (00:11:44) – How would we know if there’s a new unifying concept within heaps of AI slop? (00:26:10) – The deductive overhang (00:30:31) – Selection bias in reported AI discoveries (00:46:43) – AI makes papers richer and broader, but not deeper (00:53:00) – If AI solves a problem, can humans get understanding out of it? (00:59:20) – We need a semi-formal language for the way that scientists actually talk to each other (01:09:48) – How Terry uses his time (01:17:05) – Human-AI hybrids will dominate math for a lot longer Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

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 realism across domains, and their in-house tooling lets them continuously tweak task difficulty to optimize learning. Reach out at labelbox.com/dwarkesh. * Jane Street sent me another puzzle… this time, they’ve trained backdoors into 3 different language models — they want you to find the triggers. Jane Street isn’t even sure this is possible, but they’ve set aside $50,000 for the best attempts and write-ups. They’re accepting submissions until April 1st at janestreet.com/dwarkesh. * Mercury’s personal accounts make it easy to share finances with a partner, a roommate… or OpenClaw. Last week, I wanted to try OpenClaw for myself, so I used Mercury to spin up a virtual debit card with a small spend limit, and then I let my agent loose. No matter your use case, apply at mercury.com/personal-banking. Timestamps (00:00:00) - What exactly are we scaling? (00:12:36) - Is diffusion cope? (00:29:42) - Is continual learning necessary? (00:46:20) - If AGI is imminent, why not buy more compute? (00:58:49) - How will AI labs actually make profit? (01:31:19) - Will regulations destroy the boons of AGI? (01:47:41) - Why can’t China and America both have a country of geniuses in a datacenter? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

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 puzzle last week: they trained a neural net, shuffled all 96 layers, and asked me to put them back in order. I tried but… I didn’t quite nail it. If you’re curious, or if you think you can do better, you should take a stab at janestreet.com/dwarkesh * Labelbox can get you robotics and RL data at scale. Labelbox starts by helping you define your ideal data distribution, and then their massive Alignerr network collects frontier-grade data that you can use to train your models. Learn more at labelbox.com/dwarkesh Timestamps (00:00:00) - Orbital data centers (00:36:46) - Grok and alignment (00:59:56) - xAI’s business plan (01:17:21) - Optimus and humanoid manufacturing (01:30:22) - Does China win by default? (01:44:16) - Lessons from running SpaceX (02:20:08) - DOGE (02:38:28) - TeraFab Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

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 Pro recently helped me run an experiment to test multi-agent scaling: basically, if you have a fixed budget of compute, what is the optimal way to split it up across agents? Gemini was my colleague throughout the process — honestly, I couldn’t have investigated this question without it. Try Gemini 3 Pro today gemini.google.com * Labelbox helps you train agents to do economically-valuable, real-world tasks. Labelbox’s network of subject-matter experts ensures you get hyper-realistic RL environments, and their custom tooling lets you generate the highest-quality training data possible from those environments. Learn more at labelbox.com/dwarkesh To sponsor a future episode, visit dwarkesh.com/advertise. Timestamps (00:00:00) – The brain’s secret sauce is the reward functions, not the architecture (00:22:20) – Amortized inference and what the genome actually stores (00:42:42) – Model-based vs model-free RL in the brain (00:50:31) – Is biological hardware a limitation or an advantage? (01:03:59) – Why a map of the human brain is important (01:23:28) – What value will automating math have? (01:38:18) – Architecture of the brain Further reading Intro to Brain-Like-AGI Safety - Steven Byrnes’s theory of the learning vs steering subsystem; referenced throughout the episode. A Brief History of Intelligence - Great book by Max Bennett on connections between neuroscience and AI Adam’s blog, and Convergent Research’s blog on essential technologies. A Tutorial on Energy-Based Learning by Yann LeCun What Does It Mean to Understand a Neural Network? - Kording & Lillicrap E11 Bio and their brain connectomics approach Sam Gershman on what dopamine is doing in the brain Gwern’s proposal on training models on the brain’s hidden states Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

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 Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Ilya & I discuss SSI’s strategy, the problems with pre-training, how to improve the generalization of AI models, and how to ensure AGI goes well. Watch on YouTube; read the transcript. Sponsors * Gemini 3 is the first model I’ve used that can find connections I haven’t anticipated. I recently wrote a blog post on RL’s information efficiency, and Gemini 3 helped me think it all through. It also generated the relevant charts and ran toy ML experiments for me with zero bugs. Try Gemini 3 today at gemini.google * Labelbox helped me create a tool to transcribe our episodes! I’ve struggled with transcription in the past because I don’t just want verbatim transcripts, I want transcripts reworded to read like essays. Labelbox helped me generate the exact data I needed for this. If you want to learn how Labelbox can help you (or if you want to try out the transcriber tool yourself), go to labelbox.com/dwarkesh * Sardine is an AI risk management platform that brings together thousands of device, behavior, and identity signals to help you assess a user’s risk of fraud & abuse. Sardine also offers a suite of agents to automate investigations so that as fraudsters use AI to scale their attacks, you can use AI to scale your defenses. Learn more at sardine.ai/dwarkesh To sponsor a future episode, visit dwarkesh.com/advertise. Timestamps (00:00:00) – Explaining model jaggedness (00:09:39) - Emotions and value functions (00:18:49) – What are we scaling? (00:25:13) – Why humans generalize better than models (00:35:45) – SSI’s plan to straight-shot superintelligence (00:46:47) – SSI’s model will learn from deployment (00:55:07) – How to think about powerful AGIs (01:18:13) – “We are squarely an age of research company” (01:20:23) – Self-play and multi-agent (01:32:42) – Research taste Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

As part of this interview, Satya Nadella gave Dylan Patel (founder of SemiAnalysis) and me an exclusive first-look at their brand-new Fairwater 2 datacenter. Microsoft is building multiple Fairwaters, each of which has hundreds of thousands of GB200s & GB300s. Between all these interconnected buildings, they’ll have over 2 GW of total capacity. Just to give a frame of reference, even a single one of these Fairwater buildings is more powerful than any other AI datacenter that currently exists. Satya then answered a bunch of questions about how Microsoft is preparing for AGI across all layers of the stack. Watch on YouTube; read the transcript. Sponsors * Labelbox produces high-quality data at massive scale, powering any capability you want your model to have. Whether you’re building a voice agent, a coding assistant, or a robotics model, Labelbox gets you the exact data you need, fast. Reach out at labelbox.com/dwarkesh * CodeRabbit automatically reviews and summarizes PRs so you can understand changes and catch bugs in half the time. This is helpful whether you’re coding solo, collaborating with agents, or leading a full team. To learn how CodeRabbit integrates directly into your workflow, go to coderabbit.ai To sponsor a future episode, visit dwarkesh.com/advertise. Timestamps (00:00:00) - Fairwater 2 (00:03:20) - Business models for AGI (00:12:48) - Copilot (00:20:02) - Whose margins will expand most? (00:36:17) - MAI (00:47:47) - The hyperscale business (01:02:44) - In-house chip & OpenAI partnership (01:09:35) - The CAPEX explosion (01:15:07) - Will the world trust US companies to lead AI? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

The Andrej Karpathy episode. During this interview, Andrej explains why reinforcement learning is terrible (but everything else is much worse), why AGI will just blend into the previous ~2.5 centuries of 2% GDP growth, why self driving took so long to crack, and what he sees as the future of education. It was a pleasure chatting with him. Watch on YouTube; read the transcript. Sponsors * Labelbox helps you get data that is more detailed, more accurate, and higher signal than you could get by default, no matter your domain or training paradigm. Reach out today at labelbox.com/dwarkesh * Mercury helps you run your business better. It’s the banking platform we use for the podcast — we love that we can see our accounts, cash flows, AR, and AP all in one place. Apply online in minutes at mercury.com * Google’s Veo 3.1 update is a notable improvement to an already great model. Veo 3.1’s generations are more coherent and the audio is even higher-quality. If you have a Google AI Pro or Ultra plan, you can try it in Gemini today by visiting https://gemini.google Timestamps (00:00:00) – AGI is still a decade away (00:29:45) – LLM cognitive deficits (00:40:05) – RL is terrible (00:49:38) – How do humans learn? (01:06:25) – AGI will blend into 2% GDP growth (01:17:36) – ASI (01:32:50) – Evolution of intelligence & culture (01:42:55) - Why self driving took so long (01:56:20) - Future of education Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Nick Lane has some pretty wild ideas about the evolution of life. He thinks early life was continuous with the spontaneous chemistry of undersea hydrothermal vents. Nick’s story may be wrong, but I find it remarkable that with just that starting point, you can explain so much about why life is the way that it is — the things you’re supposed to just take as givens in biology class: * Why are there two sexes? Why sex at all? * Why are bacteria so simple despite being around for 4 billion years? Why is there so much shared structure between all eukaryotic cells despite the enormous morphological variety between animals, plants, fungi, and protists? * Why did the endosymbiosis event that led to eukaryotes happen only once, and in the particular way that it did? * Why is all life powered by proton gradients? Why does all life on Earth share not only the Krebs Cycle, but even the intermediate molecules like Acetyl-CoA? His theory implies that early life is almost chemically inevitable (potentially blooming on hundreds of millions of planets in the Milky Way alone), and that the real bottleneck is the complex eukaryotic cell. Watch on YouTube; listen on Apple Podcasts or Spotify. Sponsors * Gemini in Sheets lets you turn messy text into structured data. We used it to classify all our episodes by type and topic, no manual tagging required. If you’re a Google Workspace user, you can get started today at docs.google.com/spreadsheets/ * Labelbox has a massive network of domain experts (called Alignerrs) who help train AI models in a way that ensures they understand the world deeply, not superficially. These Alignerrs are true experts — one even tutored me in chemistry as I prepped for this episode. Learn more at labelbox.com/dwarkesh * Lighthouse helps frontier technology companies like Cursor and Physical Intelligence navigate the U.S. immigration system and hire top talent from around the world. Lighthouse handles everything, maximizing the probability of visa approval while minimizing the work you have to do. Learn more at lighthousehq.com/employers To sponsor a future episode, visit dwarkesh.com/advertise. Timestamps (00:00:00) – The singularity that unlocked complex life (00:08:26) – Early life continuous with Earth's geochemistry (00:23:36) – Eukaryotes are the great filter for intelligent life (00:42:16) – Mitochondria are the reason we have sex (01:08:12) – Are bioelectric fields linked to consciousness? Ref: 868329 Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

I have a much better understanding of Sutton’s perspective now. I wanted to reflect on it a bit. (00:00:00) - The steelman (00:02:42) - TLDR of my current thoughts (00:03:22) - Imitation learning is continuous with and complementary to RL (00:08:26) - Continual learning (00:10:31) - Concluding thoughts Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Richard Sutton is the father of reinforcement learning, winner of the 2024 Turing Award, and author of The Bitter Lesson. And he thinks LLMs are a dead end. After interviewing him, my steel man of Richard’s position is this: LLMs aren’t capable of learning on-the-job, so no matter how much we scale, we’ll need some new architecture to enable continual learning. And once we have it, we won’t need a special training phase — the agent will just learn on-the-fly, like all humans, and indeed, like all animals. This new paradigm will render our current approach with LLMs obsolete. In our interview, I did my best to represent the view that LLMs might function as the foundation on which experiential learning can happen… Some sparks flew. A big thanks to the Alberta Machine Intelligence Institute for inviting me up to Edmonton and for letting me use their studio and equipment. Enjoy! Watch on YouTube; listen on Apple Podcasts or Spotify. Sponsors * Labelbox makes it possible to train AI agents in hyperrealistic RL environments. With an experienced team of applied researchers and a massive network of subject-matter experts, Labelbox ensures your training reflects important, real-world nuance. Turn your demo projects into working systems at labelbox.com/dwarkesh * Gemini Deep Research is designed for thorough exploration of hard topics. For this episode, it helped me trace reinforcement learning from early policy gradients up to current-day methods, combining clear explanations with curated examples. Try it out yourself at gemini.google.com * Hudson River Trading doesn’t silo their teams. Instead, HRT researchers openly trade ideas and share strategy code in a mono-repo. This means you’re able to learn at incredible speed and your contributions have impact across the entire firm. Find open roles at hudsonrivertrading.com/dwarkesh Timestamps (00:00:00) – Are LLMs a dead end? (00:13:04) – Do humans do imitation learning? (00:23:10) – The Era of Experience (00:33:39) – Current architectures generalize poorly out of distribution (00:41:29) – Surprises in the AI field (00:46:41) – Will The Bitter Lesson still apply post AGI? (00:53:48) – Succession to AIs Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Sergey Levine, one of the world’s top robotics researchers and co-founder of Physical Intelligence, thinks we’re on the cusp of a “self-improvement flywheel” for general-purpose robots. His median estimate for when robots will be able to run households entirely autonomously? 2030. If Sergey’s right, the world 5 years from now will be an insanely different place than it is today. This conversation focuses on understanding how we get there: we dive into foundation models for robotics, and how we scale both the data and the hardware necessary to enable a full-blown robotics explosion. Watch on YouTube; listen on Apple Podcasts or Spotify. Sponsors * Labelbox provides high-quality robotics training data across a wide range of platforms and tasks. From simple object handling to complex workflows, Labelbox can get you the data you need to scale your robotics research. Learn more at labelbox.com/dwarkesh * Hudson River Trading uses cutting-edge ML and terabytes of historical market data to predict future prices. I got to try my hand at this fascinating prediction problem with help from one of HRT’s senior researchers. If you’re curious about how it all works, go to hudson-trading.com/dwarkesh * Gemini 2.5 Flash Image (aka nano banana) isn’t just for generating fun images — it’s also a powerful tool for restoring old photos and digitizing documents. Test it yourself in the Gemini App or in Google’s AI Studio: ai.studio/banana To sponsor a future episode, visit dwarkesh.com/advertise. Timestamps (00:00:00) – Timeline to widely deployed autonomous robots (00:17:25) – Why robotics will scale faster than self-driving cars (00:27:28) – How vision-language-action models work (00:45:37) – Changes needed for brainlike efficiency in robots (00:57:59) – Learning from simulation (01:09:18) – How much will robots speed up AI buildouts? (01:18:01) – If hardware’s the bottleneck, does China win by default? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

In this lecture, military historian Sarah Paine explains how Britain used sea control, peripheral campaigns, and alliances to defeat Nazi Germany during WWII. She then applies this framework to today, arguing that Russia and China are similarly constrained by their geography, making them vulnerable in any conflict with maritime powers (like the U.S. and its allies). Watch on YouTube; listen on Apple Podcasts or Spotify. Sponsors * Labelbox partners with researchers to scope, generate, and deliver the exact data frontier models need, no matter the domain. Whether that’s multi-turn audio, SOTA robotics data, advanced STEM problem sets, or even novel RL environments, Labelbox delivers high-quality data, fast. Learn more at labelbox.com/dwarkesh * Warp is the best interface I’ve found for coding with agents. It makes building custom tools easy: Warp’s UI helps you understand agent behavior and its in-line text editor is great for making tweaks. You can try Warp for free, or, for a limited time, use code DWARKESH to get Warp’s Pro Plan for only $5. Go to warp.dev/dwarkesh To sponsor a future episode, visit dwarkesh.com/advertise. Timestamps 00:00:00 – How WW1 shaped WW2 00:15:10 – Hitler and Churchill’s battle to command the Atlantic 00:30:10 – Peripheral theaters leading up to Normandy 00:37:13 – The Eastern front 00:48:04 – Russia’s & China’s geographic prisons 01:00:28 – Hitler’s blunders & America’s industrial might 01:15:03 – Bismarck’s limited wars vs Hitler’s total war Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Jacob Kimmel thinks he can find the transcription factors to reverse aging. We do a deep dive on why this might be plausible and why evolution hasn’t optimized for longevity. We also talk about why drug discovery has been getting exponentially harder, and what a new platform for biological understanding to speed up progress would look like. As a bonus, we get into the nitty gritty of gene delivery and Jacob’s controversial takes on CAR-T cells. For full disclosure, I am an angel investor in NewLimit. This did not impact my decision to interview Jacob, nor the questions I asked him. Watch on YouTube; listen on Apple Podcasts or Spotify. SPONSORS * Hudson River Trading uses deep learning to tackle one of the world's most complex systems: global capital allocation. They have a massive in-house GPU cluster, and they’re constantly adding new racks of B200s to ensure their researchers are never constrained by compute. Explore opportunities at hudsonrivertrading.com/dwarkesh\ * Google’s Gemini CLI turns ideas into working applications FAST, no coding required. It built a complete podcast post-production tool in 10 minutes, including fully functional backend logic, and the entire build used less than 10% of Gemini’s session context. Check it out on Github now! * To sponsor a future episode, visit dwarkesh.com/advertise. TIMESTAMPS (00:00:00) – Three reasons evolution didn’t optimize for longevity (00:12:07) – Why didn't humans evolve their own antibiotics? (00:25:26) – De-aging cells via epigenetic reprogramming (00:44:43) – Viral vectors and other delivery mechanisms (01:06:22) – Synthetic transcription factors (01:09:31) – Can virtual cells break Eroom’s Law? (01:31:32) – Economic models for pharma Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

How will we feed the 100s of GWs of extra energy demand that AI will create over the coming decade? On this episode, Casey Handmer (Caltech PhD, former NASA JPL, founder & CEO of Terraform Industries) walks me through how we can pull it off, and why he thinks a major part of this energy singularity will be powered by solar. His views are contrarian, but he came armed to defend them. Watch on YouTube; listen on Apple Podcasts or Spotify. SPONSORS - Lighthouse helps frontier technology companies like Cursor and Physical Intelligence navigate the U.S. immigration system and hire top talent from around the world. Lighthouse handles everything for you, maximizing the probability of visa approval while minimizing the work you have to do. Learn more at lighthousehq.com/employers - To sponsor a future episode, visit dwarkesh.com/advertise. TIMESTAMPS (00:00:00) – Why doesn’t China win by default? (00:08:28) – Why hyperscalers choose natural gas over solar (00:18:01) – Solar's astonishing learning rates (00:27:02) – How to build 50,000 acre solar-powered data centers (00:40:24) – Environmental regulations blocking clean energy (00:44:04) – Batteries replacing the grid (00:49:14) – GDP is broken, AGI's true value must be measured in total energy use (00:58:45) – Silicon wafers in space with one mind each Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe