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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

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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

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?

In this lecture, military historian Sarah Paine explains how Britain used sea control, peripheral campaigns, and alliances to defeat Nazi Germany during WWII. She then applies this framework to today, arguing that Russia and China are similarly constrained by their geography, making them vulnerable in any conflict with maritime powers (like the U.S. and its allies).

Watch on YouTube; listen on Apple Podcasts or Spotify.

Sponsors

* Labelbox partners with researchers to scope, generate, and deliver the exact data frontier models need, no matter the domain. Whether that’s multi-turn audio, SOTA robotics data, advanced STEM problem sets, or even novel RL environments, Labelbox delivers high-quality data, fast. Learn more at labelbox.com/dwarkesh

* Warp is the best interface I’ve found for coding with agents. It makes building custom tools easy: Warp’s UI helps you understand agent behavior and its in-line text editor is great for making tweaks. You can try Warp for free, or, for a limited time, use code DWARKESH to get Warp’s Pro Plan for only $5. Go to warp.dev/dwarkesh

To sponsor a future episode, visit dwarkesh.com/advertise.

Timestamps

00:00:00 – How WW1 shaped WW2

00:15:10 – Hitler and Churchill’s battle to command the Atlantic

00:30:10 – Peripheral theaters leading up to Normandy

00:37:13 – The Eastern front

00:48:04 – Russia’s & China’s geographic prisons

01:00:28 – Hitler’s blunders & America’s industrial might

01:15:03 – Bismarck’s limited wars vs Hitler’s total war

By all rights there should be three sets of molars in your mouth. But it turns out that our skulls aren’t really set up to accommodate that many anymore. Exactly why depends on who you ask.

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