Dwarkesh Patel
@dwarkeshpatel
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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. 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 * Transcript: https://www.dwarkesh.com/p/adam-brown-gr 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 https://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 https://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 https://cursor.com/dwarkesh To sponsor a future episode, visit https://dwarkesh.com/advertise. 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?
Always so much fun to chat with @3blue1brown 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? 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 * Transcript: https://www.dwarkesh.com/p/grant-sanderson-2 * Apple Podcasts: https://podcasts.apple.com/us/podcast/grant-sanderson-ai-and-the-future-of-math/id1516093381?i=1000774870615 * Spotify: https://open.spotify.com/episode/0X3t4uRlpVT4MXPYDIrNYX?si=HZf_0Ky2Q42tOWYZNvWi6w 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 https://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 https://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 https://3b1b.co/janestreet To sponsor a future episode, visit https://dwarkesh.com/advertise. 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
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 https://mercury.com 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 Read the essay here: https://www.dwarkesh.com/p/the-next-paradigm Sasha Rush lecture: https://youtu.be/wxOZWD6wYVY 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
It is easy to forget how much data these models are trained on, and how much more it is than what we humans see in our lifetimes. We see these AIs as a galaxy glittering with capabilities, but at their center, invisible to the naked eye, holding all the constellations together, is an unimaginably massive black hole of data. Thanks to Mercury for sponsoring this essay. Mercury is my banking platform, and they just released a new AI feature called Command. Since I already 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 https://mercury.com/command Read the transcript here: https://www.dwarkesh.com/p/the-sample-efficiency-black-hole-2 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?
