Dwarkesh Podcast

The Robert Caro interview.Watch on YouTube; read the transcript.Sponsors* Antithesis lets you run thorough simulation tests on every PR your coding agents ship. That’s because Antithesis is FAST. On a recent project, it packed fifteen hours of simulation into each fifteen-minute run: every change could be tested in thousands of hostile worlds before being merged. Every minute your software spends in Antithesis is one of the unluckiest hours it will ever experience. Learn more at antithesis.com/dwarkesh* WorkOS’s Airlock is a judgment layer that sits between your agents and your most important tools. When an agent makes a request, Airlock evaluates it against your company’s policies and decides whether to allow it, block it, or escalate it to a human. If it allows it, Airlock takes action on behalf of the agent, which means your agents never see your API keys or other credentials. Airlock is in early access. If you’re interested, go to workos.com/airlock* Jane Street’s mystery ASIC puzzle is over, and they’ve published a blog post that reveals the chip’s purpose. You can read it here: blog.janestreet.com/asic-puzzle-results/. More importantly, there’s another challenge live now! For this one, you have to design a protocol-emulator ASIC that can be used to test chips outside of a live system. Jane Street will pay to fabricate their favorite submissions. The competition is open until January 18th. Get started at janestreet.com/dwarkeshTimestamps(00:00:00) – How Caro gets people to talk(00:05:03) – Does America need a little more Moses today?(00:12:42) – Moses the young idealist(00:17:57) – Watching Moses lose power(00:24:01) – Did Moses realize he was changing?(00:27:53) – Building during the Great Depression(00:32:51) – The threats and promises of Lyndon Johnson(00:36:08) – Could Kennedy have passed Johnson’s laws?(00:41:00) – Getting a book down to one sentence(00:46:21) – All the material that never made it into the books(00:52:59) – Loneliness 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

I recently got to interview Robert Caro, who is my favorite author of all time. This gave me the excuse to reread his biographies of Lyndon Johnson and Robert Moses, and to compile my prep notes into this video essay.These guys pulled off some truly incredible schemes.Thanks to Max Farrens, who paired with me in writing the script for this video essay.If you just want to read the essay: https://www.dwarkesh.com/p/lessons-in-power 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

I spent two days touring Intel’s most advanced fab and talking to its technical leadership to understand what the chips of the future will look like.A lot of what's coming next – gate-all-around transistors, backside power, CFET, hybrid bonding – is hard to follow without actually seeing it, so we made all the awesome animated explainers that I wish I had while I was prepping.I also asked Intel’s leadership how the semiconductor industry will cope with the enormous shock in demand from AI.AI compute has grown 3x a year for the last decade, but according to Intel's CTO, Intel could only double leading-edge wafer production with today’s global stock of chipmaking tools.What kinds of tricks can foundries pull to extend this crossover point where demand surges way ahead of supply?This was a totally new format for us - hope you enjoy!I also made flashcards for the concepts I had to learn to prep for this episode here.Watch on YouTube; read the transcript.SponsorsWorkOS helps you do business with enterprises. It gives you access to all the features most commonly required by enterprises: SSO, SCIM, RBAC, audit logs, and more. It’s easy to plug these features in — it only takes a few lines of code — and WorkOS is releasing new functionality all the time, including things like auth for agents. If you want to join the 3,000+ companies that rely on WorkOS, go to workos.comJane Street built a powerful tool called magic-trace to help analyze and debug their trading systems. It only exists because one engineer noticed a feature on Intel chips called Processor Trace and thought it’d be valuable to build on top of. Now, traders and developers across Jane Street use magic-trace every single day! If you want to be trusted to pursue the ideas you think matter most, Jane Street is hiring. Go to janestreet.com/dwarkeshAntithesis’ testing platform lets you perfectly reproduce every single bug you find. That’s because it runs on a fully deterministic computer, which strips out all the unpredictability that can make certain bugs flaky and hard to pin down. To build it, Antithesis found every source of non-determinism in Intel’s x86 architecture and either sealed it off or replaced it with a controlled simulation. Learn more at antithesis.com/dwarkeshTimestamps(00:00:00) – Intro(00:00:47) – How Intel brings up a new process node(00:03:22) – What Intel learned from 10nm(00:06:28) – From planar transistors to gate-all-around(00:13:13) – Why High-NA EUV pays off for Intel but not TSMC(00:19:20) – Could chips have evolved completely differently?(00:23:33) – Inside Intel’s “Hot Topics” meeting(00:29:30) – Replacing copper wires with ruthenium(00:32:41) – Why stacked transistors take a decade to ship(00:39:45) – Hybrid bonding and the chip-to-chip bottleneck(00:48:00) – There aren’t enough tools to 10x chip production(00:53:14) – Can Intel catch up to TSMC?(01:01:02) – Terafab, and rethinking the fab from scratch 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

New episode with military historian Si Sheppard.The conquests of the Aztec and Inca empires might be the most shocking events in human history.Hernan Cortés and some hundreds of Conquistadors landed on a new continent they knew basically nothing about, and within 2 and a half years, conquered the 6 million strong Aztec empire.Then, a decade later, Francisco Pizarro did the same thing to an Incan Empire of some 10 million inhabitants.In both cases, the Conquistadors consistently beat native armies while being literally 100x (or even in some cases 1000x) outnumbered - and in many cases, did so without taking a single casualty.And they did this with 99% of their military forces being made up of the very natives they were trying to subjugate.We often forget how wild human history is. People talk about AI takeover as if it would be some crazy, unprecedented sci-fi outcome. But this would not be the first time an alien force whose motives the defenders barely understand use more advanced technology and diplomatic cunning to outmaneuver and then take over a much larger and long established existing order.Watch on YouTube; read the transcript.Sponsors* Jane Street’s intern projects are even more ambitious than I realized. One intern spent his summer researching LLM memorization, figuring out how to quantify and reduce it. This matters because, when Jane Street tests their models on historical data, they need to know that performance reflects predictive power, not just memorized outcomes. Another intern spent his time developing a new activation checkpointing scheme: his custom version ended up outperforming PyTorch’s native one. If you want to read about more 2026 intern projects or apply for Jane Street’s 2027 internships, go to janestreet.com/dwarkesh* Grok Bot can tackle the tasks you’ve been meaning to handle but just haven’t gotten around to. For example, SpaceXAI recently gave Grok Bot access to their procurement contracts, vendor spend, and usage data: it ended up identifying over $100,000 in potential savings for them. I decided to try this on my own spend, and Grok Bot found over $5,000 of savings for me, even though I run a pretty small team. Try it out yourself at x.ai/bot* Antithesis solves one of the biggest problems with traditional software testing: to test for a bug, you first have to be able to imagine it. Antithesis runs your system through thousands of parallel histories and steers your tests for you, injecting faults and generally trying to make everything as adversarial as possible. That way, your software gets tested against scenarios you’d never imagine but that could still arise in the real world. Learn more at antithesis.com/dwarkeshTimestamps(00:00:00) – Cortés’s conquest of the Aztecs(00:22:39) – Pizarro’s conquest of the Inca(00:26:59) – Explaining Conquistador military superiority(00:34:39) – Could the natives have beat the Conquistadors?(00:48:56) – The Aztecs and Incas did not get to learn from each other(00:56:11) – Why Spain still lost in the long run(01:07:47) – Why didn’t the Aztecs/Incas adapt better to European warfare?(01:14:16) – Conquistador motivation(01:26:22) – Why Mughal India also couldn’t play the Europeans off each other(01:33:42) – Diplomacy was just as important as technology 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

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/dwarkeshTimestamps(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

New episode with John Schulman, Beren Millidge and Charlie O’Neill. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next.Watch on YouTube; read the transcript.Sponsors* Antithesis helps you trust your code. As agents generate more and more of your software, the bottleneck shifts from your engineers actually writing code to verifying it. Antithesis does that testing for you. Ron Minsky, who co-leads Jane Street’s tech group, told me that Antithesis was able to help his team shake out bugs in software that had already undergone heavy review. If you want to see how it fits into your development process, go to antithesis.com/dwarkesh* Grok Bot has been a great way to hand off tasks. My team uses it as a producer: whenever my editor posts a rough cut of an interview in Slack, Grok Bot opens the transcript on its own computer, matches my notes to the exact moments they refer to, and uses a file of my preferences to suggest edits. Then it sends me its top clip candidates so I can review everything from my phone, which saves my editors from sorting through hours of footage. Try Grok Bot for yourself at x.ai/bot* Jane Street just launched its most ambitious competition yet: design a protocol-emulator ASIC. Basically, if you have a chip you want to test outside of a live system, you should be able to connect it to your design and have it simulate realistic traffic. Jane Street wants general-purpose, reprogrammable designs that can work across multiple protocols and remain useful as new ones emerge. The most novel submissions will actually get taped out, and the winners will receive a physical copy! The competition is open until January 18, 2027, and teams are encouraged. To get started download the template code at janestreet.com/dwarkeshTimestamps(00:00:00) – Steelmanning the case against RSI(00:18:39) – What’s driving the Chinese labs’ progress(00:28:06) – How will automated AI researchers be trained(00:33:51) – Will long-horizon RL elicit AGI?(00:45:24) – The sim-to-real gap(01:00:33) – How much progress is explained by data?(01:18:03) – Why is RL working so well?(01:24:54) – Move 37 and entropy collapse(01:28:32) – Rapid-fire timelines 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

Ajeya Cotra is a researcher at METR, where she works on threat modeling for loss-of-control risks from advanced AI. Before that, she led the technical AI safety program at what is now Coefficient Giving.She is one the three authors of METR and Redwood Research’s “Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident”.We go through not only what she and her coauthors discovered during this investigation, but what it means for how we should train future, smarter AIs which might be involved in the process of recursive self-improvement.Watch on YouTube; read the transcript.Sponsors* Jane Street’s ML engineering internships start with an intense four-day bootcamp: PyTorch, autograd, writing kernels, profiling workloads… all the things that Jane Street engineers need to know for their daily work. After that, interns tackle real projects, things the firm actually wants in its codebase. If you want to apply, or if you want to watch my recent conversation with Axel, one of Jane Street’s ML engineers, go to janestreet.com/dwarkesh* Cursor, which is now part of SpaceX, noticed that their MoE layers were eating more than half of total training time. So they wrote and open-sourced Mixture-of-Kittens, which is a custom megakernel for training MoE models on NVL72s. This kernel sped up an end-to-end run across 512 GPUs by 1.4x, from about 760 to over 1000 tokens per second per GPU. If you want to read more about the ML research that Cursor and SpaceX are doing, go to cursor.com/dwarkesh* Antithesis hands you (or your agents) a bug’s root cause so you can avoid days of manual debugging. If your test run crashes, Antithesis rewinds, branches off hundreds of slightly varied rollouts, and checks in how many of them the crash still appears. Then it rewinds further and does this all again. As Antithesis rewinds, it eventually finds the spot where the frequency of the crash plummets: that’s where the root cause lives! If you want to see it in action, go to antithesis.com/dwarkeshTimestamps(00:00:00) - Agents get kicked off(00:06:45) - Self-sacrificing behavior(00:13:43) - Potemkin villages(00:23:27) - The Hugging Face attack(00:35:23) - The slopvestigation(00:52:02) - Understanding the AI's motives(01:05:31) - The actual dangers of anthropomorphizing(01:14:30) - What smarter models might do(01:30:29) - The implications for recursive self-improvement(01:38:10) - Is this the case for open source?(01:53:04) - How do we prevent this in the future?(02:15:58) - The clearest warning shot we might ever get 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

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

Had a lot of fun chatting again with my twin brother Dylan Patel.We went through lab economics over the next few years - the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone).And then we discuss whether the >$10T of total AI capex we’ll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities.One question we weren’t able to resolve is whether there’s anything that can counter all the forces barrelling towards centralization in this industry - the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI.Watch on YouTube; read the transcript.Sponsors* Grok Bot has been quite helpful with my search for a new editor. I created a recruiter bot and described the type of editor I was looking for. That bot then spun up a handful of subagents that combed through my emails and X DMs, read the end credits of various documentaries I like, and figured out who edits for some of my favorite YouTubers. It took all of those results, and then delivered me a shortlist of candidates that matched my criteria. Try Grok Bot for yourself at x.ai/bot* Antithesis lets you add time travel to your software testing toolkit. Since the Antithesis platform is fully deterministic, everything that happens inside of it is perfectly reproducible. So if your software crashes, you can rewind to the exact right moment, freeze time, and investigate. Or you can test different hypotheses by perturbing the system: kill a node or disable a feature, see what happens, then reset the trajectory and try something else. Learn more at antithesis.com/dwarkesh* Jane Street is hiring for two separate ML internships right now, one focused primarily on research and one focused on engineering. In both cases, interns are expected to contribute to real work, not contrived exercises: one common project is adapting a frontier LLM paper to financial markets, which tend to come with a ton of different gnarly challenges. Importantly, you don’t need any finance background to apply. 2027 applications are open now at janestreet.com/dwarkeshTimestamps(00:00:00) – Two labs will soon control most of the world’s compute(00:07:01) – $6 billion in fab capex enables $1t+ of end revenue(00:13:08) – Compute prices will rise if the labs outbid everyone(00:18:22) – Which layer will capture most of the surplus?(00:25:40) – What could slow down progress?(00:29:43) – Labs are shifting compute from inference to R&D(00:33:27) – China gets less than 10% of new compute, but its labs need less(00:48:48) – Will AI cause a sovereign debt crisis?(01:07:52) – Will the world’s future workforce belong to a few companies? 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

Ryan Greenblatt is the Chief Scientist at Redwood Research, where he works on technical AI safety research. He's also lead author on the "Alignment faking in Large Language Models", and is currently working on a third party investigation into the OpenAI/HuggingFace incident. In my opinion, he's one of the most interesting thinkers on the future of AI.Had him on to discuss/debate recursive self-improvement. This might be the most important question in the world right now – whether within a year or so of achieving human-level intelligence, you slingshot towards having 10s of billions of superintelligences, each of which is dramatically more competent than human experts across all fields.I’ve historically been skeptical of this possibility. My intuition has been that we will end up significantly bottlenecked by not only compute scaling but human expert data, which I think underlies most of the AI progress today.If, because of RSI, we got a jump as big as GPT-3 to a Mythos (i.e. 6 years of AI progress) within a single year of achieving AGI, then the thing we get there at the end of that year is definitively and wildly superhuman.We hashed it out, and I think Ryan made a pretty good case that this kind of speedup is plausible. FWIW, Ryan’s median for when we automate AI R&D is 2031.We then discussed the alignment implications of this scenario. Who should these superintelligences be aligned to? In the future, our capacity to steward our votes and our capital, and to make sense of what’s happening in the world, will all be titrated by superintelligences. And I worry that specs like the Claude Constitution are not shaping these ASIs to truly be my personal advocates and guardian angels.And can we get them aligned to anything in the first place? Ryan and I had a long debate about whether the kind of reward hacking we saw with the OAI/Hugging Face hack extrapolates to superintelligences that would team up to literally take over the world.The first piece of advice you get when you’re learning to drive is that it will go much smoother if you look at the horizon instead of directly in front of your tires. And so it is with the trajectory of AI. Hope you enjoy!Watch on YouTube; read the transcript.Sponsors* Antithesis is a software testing platform that finds the failures no human or AI could ever anticipate. It runs thousands of copies of your code inside a fully deterministic computer, injecting faults and steering each trajectory toward the most insidious bugs. This lets you find critical issues in minutes rather than waiting months for your users to uncover them. Learn more at antithesis.com/dwarkesh* Jane Street’s back with a new puzzle. They designed an ASIC and sent me the final masks… but they didn’t tell me what the chip actually does. So that’s the challenge: reverse engineer the circuit and figure out the chip’s purpose. Jane Street has a bunch of swag ready to send to the most creative solutions, and they’re also planning to feature the top write-ups in a blog post. Download the files and get started at janestreet.com/dwarkesh* Cursor and SpaceX recently released Grok 4.5, and I’ve been surprised by just how good the model is. For example, when I tested it against Fable and Sol on a bunch of AI governance questions, all three models gave substantially the same answers, but Grok was faster, more concise, and cheaper. Grok 4.6 is coming soon, but in the meantime, you can try 4.5 at cursor.com/dwarkeshTimestamps(00:00:00) – Is AI R&D verifiable enough to unlock recursive self-improvement?(00:16:52) – Is AI progress bottlenecked by human expert data?(00:34:02) – Flat token prices suggest scaling has been slow(00:39:47) – Skills AI can’t train on: does it even need them?(00:48:07) – Aligned to whom?(01:09:18) – Recent incidents of AIs colluding and deceiving humans(01:19:38) – What could possibly go wrong? A concrete scenario(01:48:02) – From reward hacking to takeover 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