Dwarkesh Podcast

Economics of AGI episode w Alex Imas and Phil Trammell.There’s a bunch of important questions about how we deal with AI that only economics can answer.What is the optimal way to tax and redistribute the wealth that will be generated? How should countries not in the AI supply chain index into the gains? Is there any world where inequality doesn’t explode?It might seem like these questions have obvious answers, but the first thing economics teaches you is that your intuitions can often be entirely wrong.It was very helpful to chat through these things with Alex and Phil.Watch on YouTube; read the transcript.SponsorsJane Street invests heavily in turning smart people into exceptional researchers and engineers. In addition to their apprenticeship model, Jane Street runs lectures and bootcamps in their in-office classrooms -- managers clear their teams’ schedules to encourage attendance. If you’d like to work at a place that takes learning this seriously, Jane Street is hiring. Check out their open roles at janestreet.com/dwarkeshGoogle’s Gemini Omni has incredible video editing capabilities -- you can upload a video and have Omni change the background, adjust lighting, or add specific elements. But Omni is also a preview of how future frontier models will be trained -- fully multimodal on both input and output. You can try it yourself in the Gemini app at gemini.google or in Flow at flow.googleCursor used targeted RL with textual feedback to help train their Composer 2.5 model. One of their researchers, Sasha Rush, gave me an impromptu blackboard lecture to explain how this form of on-policy self-distillation works -- I posted the full thing on X. If you want to try Composer 2.5, go to cursor.com/dwarkeshTimestamps(00:00:00) – Will capital share increase?(00:19:36) – Messy Middle scenario(00:25:57) – How to tax and redistribute AI wealth(00:30:02) – Why demand collapse is unlikely(00:39:26) – Human employees would be hard to integrate into the machine economy(00:43:08) – What if some humans (or AIs) value wealth accumulation intrinsically?(01:01:28) – What should developing countries do? 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/dwarkeshTo 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

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.SponsorsMercury 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.comLabelbox 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/dwarkeshTo 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

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/employersTo 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

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/dwarkeshTimestamps(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

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/dwarkeshTo sponsor a future episode, visit dwarkesh.com/advertise.Timestamps00:00:00 – How WW1 shaped WW200:15:10 – Hitler and Churchill’s battle to command the Atlantic00:30:10 – Peripheral theaters leading up to Normandy00:37:13 – The Eastern front00:48:04 – Russia’s & China’s geographic prisons01:00:28 – Hitler’s blunders & America’s industrial might01:15:03 – Bismarck’s limited wars vs Hitler’s total war Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe