Adam Marblestone — AI is missing something fundamental about the brain
December 30, 2025
AI Summary
5 min read🎙️ The Voices & The Context
- The Format: This deep-dive interview unpacks neuroscience's secrets to supercharge AI, blending cutting-edge speculation with rigorous science in a probing, optimistic exchange between host Dwarkesh Patel and guest Adam Marblestone.
- The Key Players:
- Guest - Adam Marblestone: Neuroscientist, AI researcher, and founder of focused research organizations like E11 Bio and Convergent Research; famous for bridging brain science with machine learning paradigms and pushing "connectomics" to map brains at scale.
🗝️ Key Themes & Topics
The episode dives into why human brains vastly outperform LLMs despite less data, proposing neuroscience as the key to AI's future breakthroughs like omnidirectional prediction and robust reward functions.
- Topic 1: Brain's "Secret Sauce" – Architecture, Loss Functions, and Omnidirectional Inference: Guests explore how evolution crafted complex, curriculum-like loss functions for the brain—far beyond LLMs' simple next-token prediction—enabling the cortex to predict any input subset from any other, like vision from sound or reflexes from abstract thoughts, echoing Yann LeCun's energy-based models.
- Topic 2: Steve Burns' Learning vs. Steering Subsystems: A core theory: Cortex as a universal learning algorithm builds world models, while subcortical "steering" areas (amygdala,
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What you'll learn
- 1 (00:00) **🎙️ Introduction: Adam Marblestone**
- 2 (00:07) **Brain vs. LLMs: Key Components of Intelligence**
- 3 (02:39) **Cortex as Omnidirectional Prediction Engine**
- 4 (06:27) **Encoding High-Level Desires: Steve Byrne's Steering Subsystem**
- 5 (11:10) **Generalization and Predictor Training**
- 6 (15:07) **Achieving Omnidirectional Inference in AI**
- 7 (19:14) **Amortized Inference and Test-Time Compute**
+ Full timestamped outline available in the app
Show Notes
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.
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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 More from this podcast