Masses have been crying for a Beren episode for so long bro


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Nomination created on December 4, 2025

Understanding Intelligence.

Dive into the cutting-edge worlds of technology and science with Dwarkesh Patel's meticulously researched interviews that bring together brilliant minds to unpack revolutionary ideas in AI, biotech, and beyond. Each episode delivers profound, thought-provoking conversations that challenge conventional wisdom and illuminate the paths shaping humanity's future.
Beren Millidge is shaping the current AI debate right now, arguing that much of what people call algorithmic progress is really data progress and kicking off 2026 with sharp essays on alignment and geopolitics. On December 31, 2025 he also dropped a fresh arXiv note linking Personalized PageRank and Successor Representations, a timely result for how we think about memory, navigation, and agentic planning in AI.
Dwarkesh has already been engaging with Millidge’s claims, quoting his August 2025 post in a December 23, 2025 episode about what labs are really scaling, so a full interview today could directly influence how builders and policymakers approach 2026 training strategies. He has not been on the Dwarkesh Podcast before, which makes this the right moment to get his unvarnished take as labeling budgets, mid‑training, and evals become this year’s flashpoints.
If you want clarity on what is actually driving frontier model gains, support this nomination today.
Masses have been crying for a Beren episode for so long bro
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Suggested
Active inference reframes intelligence as variational inference, not reward maximization
Active inference suggests that agents minimize free energy by updating their generative models, making reinforcement learning a special case of a deeper inferential principle rather than the foundation of intelligence.
What empirical test would distinguish the free energy principle from parsimonious alternatives?
Since the FEP claims to apply to all self-organizing systems, what concrete experimental design — in neuroscience or biology — could actually falsify it, as opposed to merely re-describing observations in its vocabulary?
What does a principled curiosity drive look like under active inference?
Can we design AI agents whose intrinsic motivation comes from managing epistemic surprise, rather than hand-coded exploration bonuses? Does the free energy principle give us a rigorous foundation for curiosity, and how should we implement it in robots?