Dwarkesh Podcast

Dwarkesh Patel

Deeply researched interviews www.dwarkesh.com

  1. 1 day ago

    Noam Brown – Agent swarms, alignment, & recursive self-improvement

    New episode with Noam Brown. We talk about multi-agent, Navier-Stokes, and what the current explosion of maths progress tells us about what happens once you automate AI research. And we also discuss how we will know if the models are actually aligned before we kick off RSI. Watch on YouTube; read the transcript. Sponsors * Jane Street has been interested in AI for a lot longer than you’d think, and not just for trading. In 2011, a full year before AlexNet and over a decade before ChatGPT launched, they hosted the first FOOM Debate between Eliezer Yudkowsky and Robin Hanson on whether AI would lead to an intelligence explosion. Now Jane Street is revisiting the question with a new panel: Daniel Kokotajlo, Ege Erdil, Ryan Greenblatt, and Jaime Sevilla, hosted by Ron Minsky in San Francisco this October. I expect it to be a truly excellent conversation. Register at janestreet.com/dwarkesh * Grok Bot has made handing off work super easy. It runs on its own cloud computer, where it installs the tools it needs to handle tasks end-to-end. For the podcast, we use Grok Bot to help produce our videos. You may have noticed that our ads feature animations of real websites. Getting these pixel-perfect used to mean running a convoluted, multi-step workflow ourselves. Now we just let Grok Bot handle it. Best of all, Grok Bot has learned all of our specs and preferences, so we don’t have to redescribe the task each time! Try Grok Bot for yourself at x.ai/bot * Antithesis gives you the confidence of a giant test suite without actually having to write one. Say you’re doing a major backend refactor: building enough tests to trust it could take weeks. Antithesis solves this by running your software through countless simulated worlds, injecting faults and hunting for failures. On any PR, you can turn a dial to decide exactly how much testing you want. And because every run is fully deterministic, agents can branch off the moment a bug appears, rewind it, inspect memory, and replay it, all while the original test keeps running. Learn more at antithesis.com/dwarkesh Timestamps (00:00:00) – Multi-agent and Navier-Stokes (00:15:28) – How will AI firms work? (00:22:02) – What math progress tells us about recursive self improvement (00:40:22) – Hugging Face and alignment (01:01:18) – The internal/external model gap (01:08:34) – Chain of thought is degrading (01:14:12) – How will we know when alignment is solved? 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

    Noam Brown – Agent swarms, alignment, & recursive self-improvement

  2. 31 Aug

    The rise and fall of agent civilizations

    This is a video recording of a post I wrote last week. You can read the original here. 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

    The rise and fall of agent civilizations

  3. 7 Aug

    8 Predictions for the Era of Continual Learning

    Read the essay here. 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

    8 Predictions for the Era of Continual Learning

  4. 3 Aug

    Why smarter AI models could drive up compute prices 10x

    This is a video recording of a post I wrote last week. If you want to read the original you can check it out here. Thanks to Mercury for sponsoring this video. Mercury’s built-in AI, Command, helps me close my books and saves me a bunch of time. At the end of each month, Command categorizes my transactions and provides its rationale for every choice: I just review, fix anything that’s off, and approve... and then Mercury syncs everything to QuickBooks. Get started at mercury.com/command 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

    Why smarter AI models could drive up compute prices 10x

  5. 10 Jul

    Adam Brown – A deep but accessible introduction to general relativity

    Adam Brown is back! General relativity is said to be the most beautiful idea the human mind has ever produced. Most of us will never get to fully appreciate its elegance by taking the 20-lecture graduate course Adam taught on it at Stanford. But in this episode, Adam distills the key idea at its heart so clearly and compellingly that even I could keep up lol. At the core of general relativity, Einstein is trying to figure out the principle behind a particular coincidence: that the mass that resists acceleration and the mass that gravity pulls on just happen to be exactly the same. Adam then leads us through the path of insight which Einstein called his “happiest thought.” Then Adam lectures on black holes. First, by showing how even under special relativity you could create a perpetual motion machine if black holes weren’t truly black. And then, by explaining why the observations of an infalling observer and a distant bystander to the black hole would be so radically different Adam leads Blueshift, the team at Google DeepMind cracking science and reasoning, which gave us the opportunity to discuss at the very end how close we are to AIs that could rediscover general relativity from scratch. Stay till the close for some philosophy of science. Watch on YouTube; read the transcript. Sponsors * Jane Street has traders from all sorts of different backgrounds. For example, I recently got to speak with Jed Thompson, a trader who started his career in particle physics. Jed told me how the habits he built as a physicist (like never running a calculation without first having a good guess at the answer) helped him build good trading intuition. So no matter what field you’re working in right now, your experience may be more applicable than you think. Check out open positions at janestreet.com/dwarkesh * Crusoe gave me early access to their new serverless fine-tuning product, so I decided to try fine-tuning a Dwarkesh-style question generator. Crusoe made this really easy: I just turned my interview transcripts into training data and then kicked off a run – I never had to touch infra or tweak hyperparameters. After training was done, I ran a blind eval with my team: they preferred the fine-tuned model’s proposed questions over my own suggestions about 30% of the time. Serverless fine-tuning goes live next week. Learn more at crusoe.ai/dwarkesh * Cursor’s iOS app lets me kick off real work no matter where I am. For example, recently I was at dinner with friends when I had an idea about how to investigate the past few years of progress in sample efficiency. I pulled out the Cursor app, dumped my thoughts into a voice note, and 15 minutes later, Cursor had cloned the relevant repo, done the necessary analysis, and written up its findings. And now I’m expanding that work into a full write-up. Without the Cursor app, the idea would’ve floated away. Check out the app now at cursor.com/dwarkesh Timestamps (00:00:00) – The coincidence that led Einstein to general relativity (00:16:42) – Gravity is a consequence of curved spacetime, not a force (00:31:46) – Why black holes prevent unlimited energy extraction (00:47:12) – Black holes are the ultimate power plants (01:13:50) – What falling into a black hole would actually feel like (01:18:51) – The three ways we know black holes are real (01:24:21) – The first time we saw gravity bend light (01:29:33) – How far can AI get without experimental evidence? 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

    Adam Brown – A deep but accessible introduction to general relativity

  6. 30 Jun

    Grant Sanderson – AI and the future of math

    Always so much fun to chat with Grant. AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there’s a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? Watch on YouTube; read the transcript. Sponsors * Gemini 3.5 Live Translate is what I wished I’d had on my last trip to China. It detects more than 70 languages and translates them in near real-time… and it preserves your original pacing and intonation. If you’re building an app that needs live translation, you should check out Gemini 3.5 Live Translate. Get started at ai.studio/live * Cursor’s harness lets me use models for a huge range of tasks at the podcast. For example, Cursor cuts out the ads from each episode I produce so I can post them on Bilibili. It also helps me prep for interviews — I have a repo full of books and papers that Cursor sorts through to find the exact right file for any given question. Try Cursor yourself at cursor.com/dwarkesh * Jane Street sponsors 3Blue1Brown, so Grant has gotten to spend a lot of time with various Jane Streeters. He actually just recorded an interview with a few of them, so when we sat down for this episode, he told me about some of the things he learned, like how Jane Street keeps their role definitions fuzzy to make sure their people keep learning and growing. Go check out Grant’s full interview at 3b1b.co/janestreet Timestamps (00:00:00) – AI is discovering new proofs. Is that AGI? (00:11:32) – The verification loop on conceptual breakthroughs can be a century long (00:26:12) – Will we understand an AI proof of the Riemann hypothesis? (00:38:08) – Can AI find the hidden bridges between fields? (00:53:48) – Why real-world tasks don’t fit into RL environments (01:07:07) – Good writing requires theory of mind that AI still lacks (01:16:02) – Why learning will still depend on human curation 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

    Grant Sanderson – AI and the future of math

  7. 26 Jun

    The next big breakthrough will be AIs learning on the job

    Read it here. Thanks to Mercury for sponsoring this essay. Mercury has automated basically my entire bill pay process for my business. I just give contractors a dedicated email address, and when they send an invoice, Mercury automatically creates a draft payment for me to review. I no longer have to hunt through my inbox for invoices or deal with messy spreadsheets to track my bills. Mercury handles it all. Learn more at mercury.com Timestamps: (00:00:00) – The big research bet the labs are making (00:02:12) – Grindability is just as important as verifiability (00:06:10) – Will RLVR alone generalize? (00:08:41) – Getting the learning back to the weights (00:15:22) – Dreaming (00:17:23) – What 2027 looks like 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

    The next big breakthrough will be AIs learning on the job

  8. 19 Jun

    The data black hole at the center of AI

    Read the transcript here. Thanks to Mercury for sponsoring this essay! Mercury just released a new feature called Command, which gives me AI right in my banking platform. And since I use Mercury to run basically my entire business, Command has access to all the info it needs to get real work done. I can ask it to send invoices, or categorize expenses, or even transfer money… and Command just handles it. Learn more at mercury.com/command Timestamps: (00:00:00) – What is really driving AI progress? (00:03:11) – Comparing human vs AI sample efficiency (00:08:46) – Does sample efficiency matter? 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

    The data black hole at the center of AI
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Deeply researched interviews www.dwarkesh.com

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  • Creator
    Dwarkesh Patel
  • Years Active
    2020 - 2026
  • Episodes
    141
  • Rating
    Clean
  • Copyright
    © Dwarkesh Patel
  • Show Website
    Dwarkesh Podcast