Dwarkesh Podcast

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?


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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


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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?


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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


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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


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