Machine Learning Street Talk (MLST)

Pushing compute to the limits of physics

21 Jul 2025

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Dr. Maxwell Ramstead grills Guillaume Verdon (AKA “Beff Jezos”) who's the founder of Thermodynamic computing startup Extropic.Guillaume shares his unique path – from dreaming about space travel as a kid to becoming a physicist, then working on quantum computing at Google, to developing a radically new form of computing hardware for machine learning. He explains how he hit roadblocks with traditional physics and computing, leading him to start his company – building "thermodynamic computers." These are based on a new design for super-efficient chips that use the natural chaos of electrons (think noise and heat) to power AI tasks, which promises to speed up AND lower the costs of modern probabilistic techniques like sampling. He is driven by the pursuit of building computers that work more like your brain, which (by the way) runs on a banana and a glass of water! Guillaume talks about his alter ego, Beff Jezos, and the "Effective Accelerationism" (e/acc) movement that he initiated. Its objective is to speed up tech progress in order to “grow civilization” (as measured by energy use and innovation), rather than “slowing down out of fear”. Guillaume argues we need to embrace variance, exploration, and optimism to avoid getting stuck or outpaced by competitors like China. He and Maxwell discuss big ideas like merging humans with AI, decentralizing intelligence, and why boundless growth (with smart constraints) is “key to humanity's future”.REFS:1. John Archibald Wheeler - "It From Bit" Concept00:04:45 - Foundational work proposing that physical reality emerges from information at the quantum levelLearn more: https://cqi.inf.usi.ch/qic/wheeler.pdf 2. AdS/CFT Correspondence (Holographic Principle)00:05:15 - Theoretical physics duality connecting quantum gravity in Anti-de Sitter space with conformal field theoryhttps://en.wikipedia.org/wiki/Holographic_principle 3. Renormalization Group Theory00:06:15 - Mathematical framework for analyzing physical systems across different length scales https://www.damtp.cam.ac.uk/user/dbs26/AQFT/Wilsonchap.pdf 4. Maxwell's Demon and Information Theory00:21:15 - Thought experiment linking information processing to thermodynamics and entropyhttps://plato.stanford.edu/entries/information-entropy/ 5. Landauer's Principle00:29:45 - Fundamental limit establishing minimum energy required for information erasure https://en.wikipedia.org/wiki/Landauer%27s_principle 6. Free Energy Principle and Active Inference01:03:00 - Mathematical framework for understanding self-organizing systems and perception-action loopshttps://www.nature.com/articles/nrn2787 7. Max Tegmark - Information Bottleneck Principle01:07:00 - Connections between information theory and renormalization in machine learninghttps://arxiv.org/abs/1907.07331 8. Fisher's Fundamental Theorem of Natural Selection01:11:45 - Mathematical relationship between genetic variance and evolutionary fitnesshttps://en.wikipedia.org/wiki/Fisher%27s_fundamental_theorem_of_natural_selection 9. Tensor Networks in Quantum Systems00:06:45 - Computational framework for simulating many-body quantum systems https://arxiv.org/abs/1912.10049 10. Quantum Neural Networks00:09:30 - Hybrid quantum-classical models for machine learning applicationshttps://en.wikipedia.org/wiki/Quantum_neural_network 11. Energy-Based Models (EBMs)00:40:00 - Probabilistic framework for unsupervised learning based on energy functionshttps://www.researchgate.net/publication/200744586_A_tutorial_on_energy-based_learning 12. Markov Chain Monte Carlo (MCMC)00:20:00 - Sampling algorithm fundamental to modern AI and statistical physics https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo 13. Metropolis-Hastings Algorithm00:23:00 - Core sampling method for probability distributionshttps://arxiv.org/abs/1504.01896 ***SPONSOR MESSAGE***Google Gemini 2.5 Flash is a state-of-the-art language model in the Gemini app. Sign up at https://gemini.google.com

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Machine Learning Street Talk (MLST)

Pushing compute to the limits of physics

This episode hasn't been transcribed yet

Other episodes from Machine Learning Street Talk (MLST)

Abstraction & Idealization: AI's Plato Problem [Mazviita Chirimuuta]

Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION]

Adversarial Examples and Data Modelling - Andrew Ilyas (MIT)

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