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Nomination created on December 4, 2025

@noumenal_labs @UCLIoN @McGillMedPsych free energy principle, Bayesian mechanics, thermodynamics, AI, e/acc

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.
Maxwell Ramstead is at the center of the thermodynamic computing moment that is breaking into the mainstream right now, and as cofounder and chief science officer of Noumenal Labs he laid out a 2025 blueprint for grounded world models and physical AI that directly addresses today’s compute and energy bottlenecks. With the first thermodynamic chips taping out and industry press treating this as the next post-GPU paradigm, Ramstead can explain how active inference and free energy principles translate into practical, energy-efficient AI systems that matter for 2026 deployment decisions.
He has been a visible organizer and speaker at the International Workshop on Active Inference 2025 in Montreal, and he continues to shape the field’s agenda as part of the Active Inference Institute’s leadership. Dwarkesh’s audience is making real bets on architectures and agents today, and Ramstead is one of the few people who can connect the theory to the new hardware and world-modeling stack that is arriving this year.
As of January 20, 2026, there is no record of Ramstead previously appearing on the Dwarkesh Podcast based on the show’s public archives and feeds. Bringing him on now would give listeners a first, timely briefing on how thermodynamic computing and active inference could rewire AI progress in the next 12 months.
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Discussing the implications of Maxwell J.D. Ramstead's recent work on non-equilibrium statistical physics.
Can you elaborate on the practical applications of your work in non-equilibrium statistical physics?
Analyzing the impact of generative models on understanding consciousness and phenomenology.
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