Ilya & I discuss SSI’s strategy, the problems with pre-training, how to improve the generalization of AI models, and how to ensure AGI goes well.

Watch on YouTube; read the transcript.

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Timestamps

(00:00:00) – Explaining model jaggedness

(00:09:39) - Emotions and value functions

(00:18:49) – What are we scaling?

(00:25:13) – Why humans generalize better than models

(00:35:45) – SSI’s plan to straight-shot superintelligence

(00:46:47) – SSI’s model will learn from deployment

(00:55:07) – How to think about powerful AGIs

(01:18:13) – “We are squarely an age of research company”

(01:30:26) – Self-play and multi-agent

(01:32:42) – Research taste

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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.

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* 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

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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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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.

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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

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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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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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* 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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Nick Lane has some pretty wild ideas about the evolution of life.

He thinks early life was continuous with the spontaneous chemistry of undersea hydrothermal vents.

Nick’s story may be wrong, but I find it remarkable that with just that starting point, you can explain so much about why life is the way that it is — the things you’re supposed to just take as givens in biology class:

* Why are there two sexes? Why sex at all?

* Why are bacteria so simple despite being around for 4 billion years? Why is there so much shared structure between all eukaryotic cells despite the enormous morphological variety between animals, plants, fungi, and protists?

* Why did the endosymbiosis event that led to eukaryotes happen only once, and in the particular way that it did?

* Why is all life powered by proton gradients? Why does all life on Earth share not only the Krebs Cycle, but even the intermediate molecules like Acetyl-CoA?

His theory implies that early life is almost chemically inevitable (potentially blooming on hundreds of millions of planets in the Milky Way alone), and that the real bottleneck is the complex eukaryotic cell.

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* Gemini in Sheets lets you turn messy text into structured data. We used it to classify all our episodes by type and topic, no manual tagging required. If you’re a Google Workspace user, you can get started today at docs.google.com/spreadsheets/

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Timestamps

(00:00:00) – The singularity that unlocked complex life

(00:08:26) – Early life continuous with Earth's geochemistry

(00:23:36) – Eukaryotes are the great filter for intelligent life

(00:42:16) – Mitochondria are the reason we have sex

(01:08:12) – Are bioelectric fields linked to consciousness?

Ref: 868329

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I have a much better understanding of Sutton’s perspective now. I wanted to reflect on it a bit.

(00:00:00) - The steelman

(00:02:42) - TLDR of my current thoughts

(00:03:22) - Imitation learning is continuous with and complementary to RL

(00:08:26) - Continual learning

(00:10:31) - Concluding thoughts

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Richard Sutton is the father of reinforcement learning, winner of the 2024 Turing Award, and author of The Bitter Lesson. And he thinks LLMs are a dead end.

After interviewing him, my steel man of Richard’s position is this: LLMs aren’t capable of learning on-the-job, so no matter how much we scale, we’ll need some new architecture to enable continual learning.

And once we have it, we won’t need a special training phase — the agent will just learn on-the-fly, like all humans, and indeed, like all animals.

This new paradigm will render our current approach with LLMs obsolete.

In our interview, I did my best to represent the view that LLMs might function as the foundation on which experiential learning can happen… Some sparks flew.

A big thanks to the Alberta Machine Intelligence Institute for inviting me up to Edmonton and for letting me use their studio and equipment.

Enjoy!

Watch on YouTube; listen on Apple Podcasts or Spotify.

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* Labelbox makes it possible to train AI agents in hyperrealistic RL environments. With an experienced team of applied researchers and a massive network of subject-matter experts, Labelbox ensures your training reflects important, real-world nuance. Turn your demo projects into working systems at labelbox.com/dwarkesh

* Gemini Deep Research is designed for thorough exploration of hard topics. For this episode, it helped me trace reinforcement learning from early policy gradients up to current-day methods, combining clear explanations with curated examples. Try it out yourself at gemini.google.com

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Timestamps

(00:00:00) – Are LLMs a dead end?

(00:13:04) – Do humans do imitation learning?

(00:23:10) – The Era of Experience

(00:33:39) – Current architectures generalize poorly out of distribution

(00:41:29) – Surprises in the AI field

(00:46:41) – Will The Bitter Lesson still apply post AGI?

(00:53:48) – Succession to AIs

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Sergey Levine, one of the world’s top robotics researchers and co-founder of Physical Intelligence, thinks we’re on the cusp of a “self-improvement flywheel” for general-purpose robots. His median estimate for when robots will be able to run households entirely autonomously? 2030.

If Sergey’s right, the world 5 years from now will be an insanely different place than it is today. This conversation focuses on understanding how we get there: we dive into foundation models for robotics, and how we scale both the data and the hardware necessary to enable a full-blown robotics explosion.

Watch on YouTube; listen on Apple Podcasts or Spotify.

Sponsors

* Labelbox provides high-quality robotics training data across a wide range of platforms and tasks. From simple object handling to complex workflows, Labelbox can get you the data you need to scale your robotics research. Learn more at labelbox.com/dwarkesh

* Hudson River Trading uses cutting-edge ML and terabytes of historical market data to predict future prices. I got to try my hand at this fascinating prediction problem with help from one of HRT’s senior researchers. If you’re curious about how it all works, go to hudson-trading.com/dwarkesh

* Gemini 2.5 Flash Image (aka nano banana) isn’t just for generating fun images — it’s also a powerful tool for restoring old photos and digitizing documents. Test it yourself in the Gemini App or in Google’s AI Studio: ai.studio/banana

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Timestamps

(00:00:00) – Timeline to widely deployed autonomous robots

(00:17:25) – Why robotics will scale faster than self-driving cars

(00:27:28) – How vision-language-action models work

(00:45:37) – Changes needed for brainlike efficiency in robots

(00:57:59) – Learning from simulation

(01:09:18) – How much will robots speed up AI buildouts?

(01:18:01) – If hardware’s the bottleneck, does China win by default?

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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

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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

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Jacob Kimmel thinks he can find the transcription factors to reverse aging. We do a deep dive on why this might be plausible and why evolution hasn’t optimized for longevity. We also talk about why drug discovery has been getting exponentially harder, and what a new platform for biological understanding to speed up progress would look like. As a bonus, we get into the nitty gritty of gene delivery and Jacob’s controversial takes on CAR-T cells. For full disclosure, I am an angel investor in NewLimit. This did not impact my decision to interview Jacob, nor the questions I asked him.

Watch on YouTube; listen on Apple Podcasts or Spotify.

SPONSORS

* Hudson River Trading uses deep learning to tackle one of the world's most complex systems: global capital allocation. They have a massive in-house GPU cluster, and they’re constantly adding new racks of B200s to ensure their researchers are never constrained by compute. Explore opportunities at hudsonrivertrading.com/dwarkesh\

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TIMESTAMPS

(00:00:00) – Three reasons evolution didn’t optimize for longevity

(00:12:07) – Why didn't humans evolve their own antibiotics?

(00:25:26) – De-aging cells via epigenetic reprogramming

(00:44:43) – Viral vectors and other delivery mechanisms

(01:06:22) – Synthetic transcription factors

(01:09:31) – Can virtual cells break Eroom’s Law?

(01:31:32) – Economic models for pharma

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