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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”

(01:20:23) – Self-play and multi-agent

(01:32:42) – Research taste

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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* 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

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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?

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

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.

Watch on YouTube; listen on Apple Podcasts or Spotify.

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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

Jordan Jonas (@hobojordo) grew up on a farm in Idaho, rode freight trains across the US, spent time in remote Russian villages, fur trapped and travelled for several years with nomads in Siberia, and won Alone Season 6, after being the first contestant to truly thrive in the wilderness and harvest big game. You can learn more about Jordan's axes at JordanJonas.com/Axe.

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