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Sarah Paine – How Russia sabotaged China's rise
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
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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
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An audio version of my blog post, Thoughts on AI progress (Dec 2025)
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
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Andrej Karpathy — AGI is still a decade away
The Andrej Karpathy episode.
During this interview, Andrej explains why reinforcement learning is terrible (but everything else is much worse), why AGI will just blend into the previous ~2.5 centuries of 2% GDP growth, why self driving took so long to crack, and what he sees as the future of education.
It was a pleasure chatting with him.
Watch on YouTube; read the transcript.
Sponsors
* Labelbox helps you get data that is more detailed, more accurate, and higher signal than you could get by default, no matter your domain or training paradigm. Reach out today at labelbox.com/dwarkesh
* Mercury helps you run your business better. It’s the banking platform we use for the podcast — we love that we can see our accounts, cash flows, AR, and AP all in one place. Apply online in minutes at mercury.com
* Google’s Veo 3.1 update is a notable improvement to an already great model. Veo 3.1’s generations are more coherent and the audio is even higher-quality. If you have a Google AI Pro or Ultra plan, you can try it in Gemini today by visiting https://gemini.google
Timestamps
(00:00:00) – AGI is still a decade away
(00:29:45) – LLM cognitive deficits
(00:40:05) – RL is terrible
(00:49:38) – How do humans learn?
(01:06:25) – AGI will blend into 2% GDP growth
(01:17:36) – ASI
(01:32:50) – Evolution of intelligence & culture
(01:42:55) - Why self driving took so long
(01:56:20) - Future of education
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Satya Nadella — How Microsoft is preparing for AGI
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?
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#2449 - Raul Bilecky
Elon Musk - "In 36 months, the cheapest place to put AI will be space”
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
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Positive Affable Swans: Thursday, February 5th, 2026
