Dwarkesh Podcast

30m ago

Sarah gives a “tour of the arguments” on what ultimately led to the Soviet Union’s collapse, diving into the role of the US, the Sino-Soviet border conflict, the oil bust, ethnic rebellions and even the Roman Catholic Church. As she points out, this is all particularly interesting as we find ourselves potentially at the beginning of another Cold War. As we wrap up this lecture series, I want to take a moment to thank Sarah for doing this with me. It has been such a pleasure. If you want more of her scholarship, I highly recommend checking out the books she’s written. You can find them here . Watch on YouTube; read the transcript . Sponsors * Labelbox can get you the training data you need, no matter the domain. Their Alignerr network includes the STEM PhDs and coding experts you’d expect, but it also has experienced cinematographers and talented voice actors to help train frontier video and audio models. Learn more at labelbox.com/dwarkesh . * Sardine doesn’t just assess customer risk for banking & retail. Their AI risk management platform is also extremely good at detecting fraudulent job applications, which I’ve found useful for my own hiring process. If you need help with hiring risk—or any other type of fraud prevention—go to sardine.ai/dwarkesh . * Gemini’s Nano Banana Pro helped us make many of the visuals in this episode. For example, we used it to turn dense tables into clear charts so that’d it be easier to quickly understand the trends that Sarah discusses. You can try Nano Banana Pro now in the Gemini app. Go to gemini.google.com . Timestamps (00:00:00) – Did Reagan single-handedly win the Cold War? (00:15:53) – Eastern Bloc uprisings & oil crisis (00:30:37) – Gorbachev’s mistakes (00:37:33) – German unification and NATO expansion (00:48:31) – The Gulf War and the Cold War endgame (00:56:10) – How central planning survived so long (01:14:46) – Sarah’s life in the USSR in 1988 Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Nov 25

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

Nov 12

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

Oct 31

In this lecture, military historian Sarah Paine explains how Russia—and specifically Stalin—completely derailed China’s rise, slowing them down for over a century. This lecture was particularly interesting to me because, in my opinion, the Chinese Civil War is 1 of the top 3 most important events of the 20th century. And to understand why it transpired as it did, you need to understand Stalin’s role in the whole thing. Watch on YouTube ; read the transcript . Sponsors Mercury helps you run your business better. It’s the banking platform we use for the podcast — we love that we can see our cash balance, AR, and AP all in one place. Join us (and over 200,000 other entrepreneurs) at mercury.com Labelbox scrutinizes public benchmarks at the single data-row level to probe what’s really being evaluated. Using this knowledge, they can generate custom training data for hill climbing existing benchmarks, or design new benchmarks from scratch. Learn more at labelbox.com/dwarkesh To sponsor a future episode, visit dwarkesh.com/advertise . Timestamps (00:00:00) – How Russia took advantage of China’s weakness (00:22:58) – After Stalin, China’s rise (00:33:52) – Russian imperialism (00:45:23) – China’s and Russia’s existential problems (01:04:55) – Q&A: Sino-Soviet Split (01:22:44) – Stalin’s lessons from WW2 Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Oct 17

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

Oct 10

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

Oct 4

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

Sep 26

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