Dwarkesh Podcast

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

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

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

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

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

Had Ada Palmer back on – this time to talk about Machiavelli, perhaps the most misunderstood thinker of all time. Machiavelli cut his teeth as a high-level diplomat for Florence, a position from which he got to closely observe the most important rulers in Europe at the time, including the ones who were on the path to destroying his dearly beloved Florence. In 1513 the Medici retook control of Florence and, wrongly suspecting Machiavelli of participating in a coup attempt, fired, tortured, and exiled him. Machiavelli could have left exile and worked for any number of different principalities that would have been eager to make use of his talents. Instead, he decided to rot in the countryside and compile his career’s lessons about power, politics, and human nature into a book he dedicated to the very man whose new regime had tortured and exiled him, Lorenzo di Piero de’ Medici. But at least the Medici were in a position to use his insights to defend Florence. Machiavelli the patriot did not want any other hands to touch these books, because those hands, armed further with these lessons, might pose an existential danger to Florence. The closest modern analogy, at least as Machiavelli would have seen it, would be Szilard’s letter warning FDR about the possibility of a nuclear fission bomb. What were those insights? And how were they inspired by Machiavelli’s dangerous diplomatic missions all across Europe, and his extensive reading of antiquity? Watch this episode with Ada Palmer to find out! By the way, Ada is launching a new podcast which I’m very excited about. The first season will be about Machiavelli - a perfect way to dive deeper into the topics we discussed in this episode. Subscribe at Beforecast’s website to be notified of the first episode, subscribe on YouTube , follow her on Patreon , and if you want even more Ada, check out her FixTheNews Podcast episode, and check out her books and more . Watch on YouTube ; read the transcript . Sponsors * Cursor recently saved one of my podcast recordings. When a video file from a shoot came out corrupted, I pointed Cursor at it: it recovered the footage on its own, tracking down the right reference file from the file’s metadata and realigning the out-of-sync audio. My whole team now uses Cursor for everyday tasks, not just coding. Get started at cursor.com/dwarkesh * Jane Street ’s hiring process has been going viral on Twitter lately. The memes are pretty funny, but I wanted to see what their interviews were actually like. So I had Ricson, one of Jane Street’s ML researchers, walk me through a retired puzzle: he gave me an image dataset where 50% of the files had been corrupted – I had to figure out how to recover them. If you’re interested in these sorts of puzzles, you can find Jane Street’s open roles at janestreet.com/dwarkesh * Crusoe is turning the AI datacenter buildout into an industrial process. At their massive Colorado factory, they assemble Spark units, modular datacenters with power, cooling, and fire suppression built in. They also manufacture specific components in-house to skip the longest lead times. Crusoe has experience running these Spark units on a range of energy sources, including solar and used EV batteries, ensuring they don’t get bottlenecked by grid availability. Learn more at crusoe.ai/dwarkesh Timestamps (00:00:00) – How Florence bargained with Cesare Borgia for survival (00:15:08) – Machiavelli’s analytical innovations (00:23:58) – Why popes became warlords (00:36:13) – Why the common people demanded nepotism (00:47:57) – Cesare Borgia brought terror to rulers and justice to the people (00:57:55) – Art as a proxy for war (01:06:41) – Florence, a city famous in hell (01:15:57) – The Prince was a job application to Machiavelli’s torturers (01:41:39) – During the Renaissance, original ideas had to be couched in antiquity (01:50:44) – Why copyright began with the Inquisition (02:02:12) – Machiavelli wasn’t Machiavellian Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

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 . Sponsors Jane 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/dwarkesh Google’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.google Cursor 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/dwarkesh Timestamps (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

New blackboard lecture with Reiner Pope: how do chips actually work - starting with basic logic gates, and working up to why GPUs, TPUs, FPGAs, and the human brain each look the way they do. Reiner is CEO of MatX , a new chip startup (full disclosure - I’m an angel investor). He was previously at Google, where he worked on software efficiency , compilers, and TPU architecture. Watch this one on YouTube so you can see the chalkboard. Read the transcript . Sponsors * Crusoe was one of only five GPU clouds that made the gold tier in SemiAnalysis' most recent ClusterMAX report. Gold-tier providers like Crusoe delivered 5-15% lower TCO than silver-tier clouds, even with identical GPU pricing. This is because optimizations like early fault detection and rapid node replacement don't necessarily show up in the sticker price, but still matter a ton in the real world. Learn more at crusoe.ai/dwarkesh * Cursor is where I do most of my work—from reading research papers to visualizing technical concepts to coding up internal tools for the podcast. Most recently, I used it to build two different review interfaces for my essay contest, one that anonymizes submissions for scoring and another that lets me see applicants' essays next to their resumes and websites. Whatever you're working on, you should try doing it in Cursor. Get started at cursor.com/dwarkesh * Jane Street let me ask Ron Minsky and Dan Pontecorvo, two senior Jane Streeters, a bunch of questions about how they use AI. We discussed everything from the types of models they're training to how they think about the future of trading to why they're more bullish than ever on hiring technical talent. You can watch the full conversation and learn more about their open positions at janestreet.com/dwarkesh Timestamps 00:00:00 – Building a multiply-accumulate from logic gates 00:16:31 – Muxes and the cost of data movement 00:26:10 – How systolic arrays work 00:39:11 – Clock cycles and pipeline registers 00:51:51 – FPGAs vs ASICs 01:03:25 – Cache vs scratchpad 01:07:27 – Why CPU cores are much bigger than GPU cores 01:12:00 – Brains vs chips 01:15:33 – A GPU is just a bunch of tiny TPUs Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Eric Jang walks through how to build AlphaGo from scratch, but with modern AI tools. Sometimes you understand the future better by stepping backward. AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning from experience, and self-play. You have to go back to 2017 to get insight into how the more general AIs of the future might learn. Once he explained how AlphaGo works, it gave us the context to have a discussion about how RL works in LLMs and how it could work better – naive policy gradient RL has to figure out which of the 100k+ tokens in your trajectory actually got you the right answer, while AlphaGo’s MCTS suggests a strictly better action every single move, giving you a training target that sidesteps the credit assignment problem. The way humans learn is surely closer to the second. Eric also kickstarted an Autoresearch loop on his project. And it was very interesting to discuss which parts of AI research LLMs can already automate pretty well (implementing and running experiments, optimizing hyperparameters) and which they still struggle with (choosing the right question to investigate next, escaping research dead ends). Informative to all the recent discussion about when we should expect an intelligence explosion, and what it would look like from the inside. Watch on YouTube . Read the transcript . And check out the flashcards I wrote to retain the insights. Sponsors * Cursor ‘s agent SDK let me build a pipeline to generate flashcards for this episode. For each card, I had an agent read the transcript, ingest blackboard screenshots, generate an SVG visual, and run everything through a critic. A durable agent is much better at this kind of work than a chain of LLM calls, and Cursor’s SDK made it easy. Check out the cards at flashcards.dwarkesh.com and get started with the SDK at cursor.com/dwarkesh * Jane Street gave me a real deep-dive tour of one of their datacenters. I got to ask a bunch of questions to Ron Minsky, who co-leads Jane Street’s tech group, and Dan Pontecorvo, who runs Jane Street’s physical engineering team. They were willing to literally pull up the floorboards and take out racks to explain how everything works. Check out the full tour at janestreet.com/dwarkesh Timestamps (00:00:00) – Basics of Go (00:08:17) – Monte Carlo Tree Search (00:32:04) – What the neural network does (01:00:33) – Self-play (01:25:38) – Alternative RL approaches (01:45:47) – Why doesn't MCTS work for LLMs (02:01:09) – Off-policy training (02:12:02) – RL is even more information inefficient than you thought (02:22:16) – Automated AI researchers Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

David Reich is back. He and collaborator Ali Akbari just published a paper that overturns a long-standing consensus about human evolution — that natural selection has been dormant in our species since the agricultural revolution. By scaling ancient DNA sequencing and developing a new statistical method, they found that selection has actually sped up. Selection went especially bonkers during the Bronze Age (around 3,000 years ago). That’s when gene frequencies for everything from immune function to body fat to intelligence were most in flux. Over the last 10,000 years, selection pushed the genetic predictor of cognitive performance up by roughly a full standard deviation — most of it between 4,000 and 2,000 years ago. After we finished recording, David sketched out on a whiteboard his new heretical model about who the Neanderthals really were. Luckily, I took out my iPhone and managed to record it. He thinks the standard story (that Neanderthals are some separate archaic lineage we interbred with a little) just doesn’t fit the evidence. Instead, he proposes that Neanderthals are essentially genetically-swamped modern humans. A small population somewhere around the Caucasus invented Middle Stone Age technology roughly 300,000 years ago and expanded outward. The ones that moved into Europe interbred with local archaic humans, got genetically swamped, and became Neanderthals. The same expansion went into Africa, met much more diverged archaic Africans, and that mixture became us. This means Neanderthals and modern humans share the same cultural ancestry — the only difference is which archaic humans they mixed with afterward. David is a brilliant and rigorous scholar. It was a real delight to learn from him again. Watch on YouTube ; read the transcript . Sponsors * Cursor was super useful as I prepped for this episode. Whenever I had a question, I’d have Cursor kick off a few different models simultaneously and then compare their responses. I found that this led to better results than I could get out of any individual LLM. If you’ve only used Cursor for coding, you should try using it for research. Check it out at cursor.com/dwarkesh * Jane Street uses an internal currency called “hive bucks” to allocate compute through a real-time auction – and anyone can change anyone else’s bids or even kill their jobs! Everyone just trusts each other to act in the firm’s best interest, which is what lets the system work in the first place. If this weird and high-trust culture sounds like your kind of thing, Jane Street’s hiring at janestreet.com/dwarkesh * Crusoe’s ML infra team built fastokens, an open-source tokenizer that delivers a ~9x speedup over Hugging Face and up to 40% faster time-to-first token – on real production workloads! Crusoe achieved these results by parallelizing things and using some clever engineering to handle duplicates without cross-thread coordination. Learn more at crusoe.ai/dwarkesh Timestamps (00:00:00) – Ancient DNA suggests strong selection over last 10,000 years (00:15:45) – Natural selection intensified during the Bronze Age (00:35:02) – Why didn’t evolution max out intelligence? (00:57:21) – Evolution is limited by time, not population size (01:09:02) – Why no farming before the Ice Age? (01:17:13) – The Neanderthal puzzle David can’t stop thinking about (01:54:10) – The methodology behind this breakthrough Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Did a very different format with Reiner Pope - a blackboard lecture where he walks through how frontier LLMs are trained and served. It’s shocking how much you can deduce about what the labs are doing from a handful of equations, public API prices, and some chalk. It’s a bit technical, but I encourage you to hang in there – it’s really worth it. There are less than a handful of people who understand the full stack of AI, from chip design to model architecture, as well as Reiner. It was a real delight to learn from him. Recommend watching this one on YouTube so you can see the chalkboard. Reiner is CEO of MatX , a new chip startup (full disclosure - I’m an angel investor). He was previously at Google, where he worked on software efficiency , compilers, and TPU architecture. Download markdown of transcript here to chat with an LLM. Wrote up some flashcards and practice problems to help myself retain what Reiner taught. Hope it's helpful to you too! Sponsors * Jane Street needs constant access to incredibly low-latency compute. I recently asked one of their engineers, Clark, to talk me through how they meet these demands. Our conversation—which touched on everything from FPGAs to liquid cooling—was extremely helpful as I prepped to interview Reiner. You can watch the full discussion and explore Jane Street’s open roles at janestreet.com/dwarkesh * Google’s Gemma 4 is the first open model that’s let me shut off the internet and create a fully disconnected “focus machine”. This is because Gemma is small enough to run on my laptop, but powerful enough to actually be useful. So, to prep for this interview, I downloaded Reiner’s scaling book, disconnected from wifi, and used Gemma to help me break down the material. Check it out at goo.gle/Gemma4 * Cursor helped me turn some notes I took on how gradients flow during large-scale pretraining into a great animation. At first, I wasn’t sure the best way to visualize the concept, but Cursor’s Composer 2 Fast model let me iterate on different ideas almost instantaneously. You can check out the animation in my recent blog post . And if you have something to visualize yourself, go to cursor.com/dwarkesh Timestamps (00:00:00) – How batch size affects token cost and speed (00:32:09) – How MoE models are laid out across GPU racks (00:47:12) – How pipeline parallelism spreads model layers across racks (01:03:37) – Why Ilya said, “As we now know, pipelining is not wise.” (01:18:59) – Because of RL, models may be 100x over-trained beyond Chinchilla-optimal (01:33:02) – Deducing long context memory costs from API pricing (02:04:02) – Convergent evolution between neural nets and cryptography Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

I asked Jensen about TPU competition, Nvidia’s lock on the ever more bottlenecked supply chain needed to make advanced chips, whether we should be selling AI chips to China, why Nvidia doesn’t just become a hyperscaler, how it makes its investments, and much more. Enjoy! Watch on YouTube ; read the transcript . Sponsors * Crusoe’s cloud runs on state-of-the-art Blackwell GPUs, with Vera Rubin deployment scheduled for later this year. But hardware is only part of the story—for inference, Crusoe’s MemoryAlloy tech implements a cluster-wide KV cache, delivering up to 10x faster TTFT and 5x better throughput than vLLM. Learn more at crusoe.ai/dwarkesh * Cursor helped me build an AI co-researcher over the course of a weekend. Now I have an AI agent that I can collaborate with in Google Docs via inline comment threads! And while other agentic coding tools feel like a total black-box, Cursor let me stay on top of the full implementation. You can try my co-researcher out at github.com/dwarkeshsp/ai_coworker , or get started on your own Cursor project today at cursor.com/dwarkesh * Jane Street spent ~20,000 GPU hours training backdoors into 3 different language models, then challenged my audience to find the triggers. They received some clever solutions—like comparing the base and fine-tuned versions and extrapolating any differences to reveal the hidden backdoor—but no one was able to solve all 3. So if open problems like this excite you, Jane Street is hiring. Learn more at janestreet.com/dwarkesh Timestamps (00:00:00) – Is Nvidia’s biggest moat its grip on scarce supply chains? (00:16:25) – Will TPUs break Nvidia’s hold on AI compute? (00:41:06) – Why doesn’t Nvidia become a hyperscaler? (00:57:36) – Should we be selling AI chips to China? (01:35:06) – Why doesn’t Nvidia make multiple different chip architectures? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Really enjoyed chatting with Michael Nielsen about how we recognize scientific progress. It's especially relevant for closing the RL verification loop for scientific discovery. But it's also a surprisingly mysterious and elusive question when you look at the history of human science. We approach this question stories like Einstein (who claimed that he hadn't even heard of the famous Michelson-Morley experiment, which is supposed to have motivated special relativity, until after he had come up with the theory), Darwin (why did it take till 1859 to lay out an idea whose essence every farmer since antiquity must have observed?), Prout (how do you recognize that isotopes exist if you cannot chemically separate them?), and many others. The verification loop on scientific ideas is often extremely long and weirdly hostile. Ancient Athenians dismissed Aristarchus's heliocentrism in the 3rd century BC because it would imply that the stars should shift in the sky as the Earth orbits the sun. The first successful measurement of stellar parallax was in 1838. That's a 2,000-year verification loop. But clearly human science is able to make progress faster than raw experimental falsification/verification would imply, and in cases where experiments are very ambiguous. How? Michael has some very deep and provocative hypotheses about the nature of progress. One I found especially thought-provoking is that aliens will likely have a VERY different science + tech stack than us. Which contradicts the common sense picture of a linear tech tree that I was assuming. And has some interesting implications about how future civilizations might trade and cooperate with each other. Watch on Youtube ; read the transcript . Sponsors * Labelbox researchers built a new safety benchmark. Why? Well, current safety benchmarks claim that attacks on top models are successful only a few percent of the time, but the prompts in those benchmarks don’t reflect how real bad actors actually write. You can read Labelbox’s research here . If this could be useful for your work, reach out at labelbox.com/dwarkesh * Mercury has an MCP that lets you give an LLM access to your full transaction history, including things like attached receipts and internal notes. I just used it to categorize my 2025 transactions, and it worked shockingly well. Modern functionality like this is exactly why I use Mercury. Learn more at mercury.com * Jane Street’s ML engineers presented some of their GPU optimization workflows at GTC, showing how they use CUDA graphs, streams, and custom kernels to shave real time off their training runs. You can watch the full talk here . And they open-sourced all the relevant code here . If this kind of stuff excites you, Jane Street is hiring — learn more at janestreet.com/dwarkesh Timestamps (00:00:00) – How scientific progress outpaces its verification loops (00:17:51) – Newton was the last of the magicians (00:23:26) – Why wasn’t natural selection obvious much earlier? (00:29:52) – Could gradient descent have discovered general relativity? (00:50:54) – Why aliens will have a different tech stack than us (01:15:26) – Are there infinitely many deep scientific principles left to discover? (01:26:25) – What drew Michael to quantum computing so early? (01:35:29) – Does science need a new way to assign credit? (01:43:57) – Prolificness versus depth (01:49:17) – What it takes to actually internalize what you learn Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

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

Dylan Patel , founder of SemiAnalysis , provides a deep dive into the 3 big bottlenecks to scaling AI compute: logic, memory, and power. And walks through the economics of labs, hyperscalers, foundries, and fab equipment manufacturers. Learned a ton about every single level of the stack. Enjoy! Watch on YouTube ; read the transcript . Sponsors * Mercury has already saved me a bunch of time this tax season. Last year, I used Mercury to request W-9s from all the contractors I worked with. Then, when it came time to issue 1099s this year, I literally just clicked a button and Mercury sent them out. Learn more at mercury.com . * Labelbox noticed that even when voice models appear to take interruptions in stride, their performance degrades. To figure out why, they built a new evaluation pipeline called EchoChain. EchoChain diagnoses voice models’ specific failure modes, letting you understand what your model needs to truly handle interruptions. Check it out at labelbox.com/dwarkesh . * Jane Street is basically a research lab with a trading desk attached – and their infrastructure backs this up. They’ve got tens of thousands of GPUs, hundreds of thousands of CPU cores, and exabytes of storage. This is what it takes to find subtle signals hidden deep within noisy market data. If this sounds interesting, you can explore open positions at janestreet.com/dwarkesh . Timestamps (00:00:00) – Why an H100 is worth more today than 3 years ago (00:24:52) – Nvidia secured TSMC allocation early; Google is getting squeezed (00:34:34) – ASML will be the #1 constraint for AI compute scaling by 2030 (00:55:47) – Can't we just use TSMC's older fabs? (01:05:37) – When will China outscale the West in semis? (01:16:01) – The enormous incoming memory crunch (01:42:34) – Scaling power in the US will not be a problem (01:54:44) – Space GPUs aren't happening this decade (02:14:07) – Why aren't more hedge funds making the AGI trade? (02:18:30) – Will TSMC kick Apple out from N2? (02:24:16) – Robots and Taiwan risk Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Read the full essay here: https://www.dwarkesh.com/p/dow-anthropic Timestamps (00:00:00) - Anthropic vs The Pentagon (00:04:16) - The overhangs of tyranny (00:05:54) - AI structurally favors mass surveillance (00:08:25) - Alignment...to whom? (00:13:55) - Coordination not worth the costs Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Renaissance history is so much wilder and weirder than you would have expected. Very fun chatting with Ada Palmer (historian, novelist, and composer based at the University of Chicago). Some especially fascinating things I learned from the conversation and her excellent book, Inventing the Renaissance : Not only did Gutenberg go bankrupt in the 1450s (after inventing the printing press), but so did the bank that foreclosed on him, and so did his apprentices. This is because paper was still very expensive, and so you had to make this big upfront CAPEX decision to print a batch of 300 copies of a book - say the Bible. But he’s in a small landlocked German town where only priests are allowed to read the Bible - so he sells maybe 7 copies. It’s only when this technology ends up in Venice, where you can hand 10 copies to each of 30 ship captains going to 30 different cities, that it starts taking off. Speaking of which, the printing revolution wasn’t just one single discrete event, just as the computer revolution has been this whole century of going from mainframes -> personal computers -> phones -> social media, each with different and accelerating social impact. Books came first, but they’re slow to print, and made in small batches. The real revolution is pamphlets - much faster, much harder to censor. Pamphlet runners are how you can have Luther’s 95 Theses go from Wittenberg to London in 17 days. So much other wild stuff from this episode. For example, did you know that the largest and best-funded experimental laboratory in 17th century Europe was very likely the Roman one run by inquisitors? Ada jokes that the Inquisition accidentally invented peer review. The focus of the Inquisition is really misunderstood - it was obsessed with catching dangerous new heretics like Lutherans and Calvinists - it only executed one person for doing science. And this leads Ada to make an observation that I think is really wise: the authorities and censors are always worried about the exact wrong things given 20/20 hindsight. When Inquisition raids an underground bookshop during the French Enlightenment, they don’t mind the Rousseau, Voltaire, and Encyclopédie, but they lose their minds about some Jansenist treatises about the technical nature of the Trinity. More broadly, a lesson for me from this episode is that it’s just really hard to shape history in the specific way that you want to impact things. One of the most famous medieval scholars is this guy Petrarch. He survives the Black Death in the 1340s, watches his friends die to plague and bandits, and says: our leaders are selfish and terrible, we need to raise them on the Roman classics so they’ll act like Cicero. So Europe pours money into finding ancient manuscripts, building libraries, and educating princes on classical virtues. Those princes grow up and fight bigger, nastier wars than ever before with new deadlier technology. And this, combined with greater urbanization and endemic plague, results in European life expectancy decreasing from 35 in the medieval period to 18 during the Renaissance (the period which we in retrospect think of as a golden age but which many people living through it thought of as the continuation of the dark ages that had persisted since the fall of Rome). Anyways, the libraries Petrarch inspires stick around, the printing press makes them accessible to everyone, and 200 years later a generation of medical students is reading Lucretius and asking “what if there are atoms and that’s how diseases work?” which eventually leads to germ theory, vaccines, and a cure for the Black Death (Ada has longer more involved explanation of how cosplaying the Romans results through a series of many steps to the scientific revolution). Petrarch wanted to produce philosopher-kings that shared his values. Instead he created a world that doesn’t share his values at all but can cure the disease that destroyed his. Watch on YouTube ; read the transcript . Sponsors * Jane Street is still waiting on someone to solve their backdoor puzzle… They’re accepting submissions until April 1st and have set aside $50,000 for the best attempts. Separately, applications are live for Jane Street’s summer ML internships in NY, London, and Hong Kong. Go check all of this out at janestreet.com/dwarkesh . * Labelbox can help ensure your agents don’t need to rely on overspecified prompts. They tailor real-world scenarios to whatever domain you’re focused on, and they make sure the data you train on rewards real understanding, not just instruction-following. Learn more at labelbox.com/dwarkesh * Mercury ’s personal accounts let you add users, issue cards, and customize permissions. This is super useful for sharing finances with a partner, a roommate… or even an OpenClaw agent. And, if you’re already a Mercury Business user, your personal account is free! See terms and conditions below, and learn more at mercury.com/personal-banking Eligible Mercury Business users who apply for and maintain a Mercury Personal account may have their Mercury Personal subscription fee waived provided they remain a user on an active Mercury Business account in good standing. Standard Mercury Platform Subscription fees will apply if they no longer meet eligibility requirements, including but not limited to no longer being associated with an eligible Mercury Business account, or if the program is modified or terminated. Mercury may modify or discontinue this offering at any time and will provide notice as required by law. See Subscription Terms for full details. * To sponsor a future episode, visit dwarkesh.com/advertise . Timestamps (00:00:00) - How cosplaying Ancient Rome led to the Renaissance (00:28:49) - How Florence’s weird republic worked (00:38:13) - How the Medicis took over Florence (00:58:12) - Why it was so hard for Gutenberg to make any money off the printing press (01:17:34) - Why the industrial revolution didn’t happen in Italy (01:23:02) - The Library of Alexandria isn’t where most ancient books were lost (01:41:21) - The Inquisition accidentally invented peer review 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

This is the final episode of the Sarah Paine lecture series, and it’s probably my favorite one. 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

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

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