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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

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

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/

* Labelbox has a massive network of domain experts (called Alignerrs) who help train AI models in a way that ensures they understand the world deeply, not superficially. These Alignerrs are true experts — one even tutored me in chemistry as I prepped for this episode. Learn more at labelbox.com/dwarkesh

* Lighthouse helps frontier technology companies like Cursor and Physical Intelligence navigate the U.S. immigration system and hire top talent from around the world. Lighthouse handles everything, maximizing the probability of visa approval while minimizing the work you have to do. Learn more at lighthousehq.com/employers

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

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

Adam Marblestone is CEO of Convergent Research. He’s had a very interesting past life: he was a research scientist at Google Deepmind on their neuroscience team and has worked on everything from brain-computer interfaces to quantum computing to nanotech and even formal mathematics.

In this episode, we discuss how the brain learns so much from so little, what the AI field can learn from neuroscience, and the answer to Ilya’s question: how does the genome encode abstract reward functions? Turns out, they’re all the same question.

Watch on YouTube; read the transcript.

Sponsors

* Gemini 3 Pro recently helped me run an experiment to test multi-agent scaling: basically, if you have a fixed budget of compute, what is the optimal way to split it up across agents? Gemini was my colleague throughout the process — honestly, I couldn’t have investigated this question without it. Try Gemini 3 Pro today gemini.google.com

* Labelbox helps you train agents to do economically-valuable, real-world tasks. Labelbox’s network of subject-matter experts ensures you get hyper-realistic RL environments, and their custom tooling lets you generate the highest-quality training data possible from those environments. Learn more at labelbox.com/dwarkesh

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

Timestamps

(00:00:00) – The brain’s secret sauce is the reward functions, not the architecture

(00:22:20) – Amortized inference and what the genome actually stores

(00:42:42) – Model-based vs model-free RL in the brain

(00:50:31) – Is biological hardware a limitation or an advantage?

(01:03:59) – Why a map of the human brain is important

(01:23:28) – What value will automating math have?

(01:38:18) – Architecture of the brain

Further reading

Intro to Brain-Like-AGI Safety - Steven Byrnes’s theory of the learning vs steering subsystem; referenced throughout the episode.

A Brief History of Intelligence - Great book by Max Bennett on connections between neuroscience and AI

Adam’s blog, and Convergent Research’s blog on essential technologies.

A Tutorial on Energy-Based Learning by Yann LeCun

What Does It Mean to Understand a Neural Network? - Kording & Lillicrap

E11 Bio and their brain connectomics approach

Sam Gershman on what dopamine is doing in the brain

Gwern’s proposal on training models on the brain’s hidden states

It turns out that lasers are even cooler than they look. And as far as acronyms go, they’re pretty solid in that respect too. There’s way too much cool stuff about lasers to tease here so listen to this old school SYSK episode and let lasers blow away.

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