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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.
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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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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
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
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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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Iran War, Oil Shock, Off Ramps, AI's Revenue Explosion and PR Nightmare
(0:00) The Besties welcome Brad Gerstner!
(3:48) Economic fallout of the Iran War, escalation scenarios, impact on midterms
(19:18) Off ramp strategies, Gulf state involvement, the China angle
(27:05) Anthropic and OpenAI scaling revenue faster than any company ever
(46:11) AI's PR disaster, open source's future
(1:07:51) Washington passes "Millionaire Tax," Howard Schultz bails for Miami
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Intro Music Credit:
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Referenced in the show:
https://www.google.com/finance/quote/BZW00:NYMEX
https://www.cnbc.com/2026/03/11/cargo-ship-struck-strait-of-hormuz-uk-iran-war.html
https://polymarket.com/event/us-forces-enter-iran-by
https://x.com/altcap/status/2029223717356879931
https://www.wsj.com/opinion/iran-war-oil-operation-epic-fury-mojtaba-khamenei-0d2edb9c
https://www.cnn.com/world/live-news/iran-war-us-israel-trump-03-12-26
https://x.com/sentdefender/status/2031827082934665293
https://polymarket.com/event/balance-of-power-2026-midterms
https://polymarket.com/event/march-inflation-us-annual
https://www.ft.com/content/7cab4ec7-4712-4137-b602-119a44f771de
https://www.axios.com/2025/05/28/ai-jobs-white-collar-unemployment-anthropic
https://www.axios.com/2026/03/06/pentagon-anthropic-amodei-apology
https://www.ft.com/content/97bda2ef-fc06-40b3-a867-f61a711b148b
https://x.com/WallStreetMav/status/2032115119879045512
https://x.com/TheChiefNerd/status/2032012809433723158
https://hai.stanford.edu/news/most-read-the-stanford-hai-stories-that-defined-ai-in-2025
https://x.com/DrTechlash/status/2030734402339365220
https://www.semafor.com/article/12/07/2025/ai-critics-funded-ai-coverage-at-top-newsrooms
https://www.politico.com/story/2019/02/13/howard-schultz-2020-taxes-1167363
https://x.com/chamath/status/2032135944284094910
https://www.visualcapitalist.com/mapped-which-u-s-states-gained-the-most-residents-in-2025
#2468 - Luke Grimes
Dylan Patel — Deep dive on the 3 big bottlenecks to scaling AI compute
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
(00:56:06) – Can’t we just use TSMC’s older fabs?
(01:05:56) – When will China outscale the West in semis?
(01:16:20) – The enormous incoming memory crunch
(01:42:53) – Scaling power in the US will not be a problem
(01:55:03) – Space GPUs aren’t happening this decade
(02:14:26) – Why aren’t more hedge funds making the AGI trade?
(02:18:49) – Will TSMC kick Apple out from N2?
(02:24:35) – Robots and Taiwan risk
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