Dr. Maxwell Ramstead grills Guillaume Verdon (AKA “Beff Jezos”) who&#39;s the founder of Thermodynamic computing startup Extropic.***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***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&#39;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: <a href="https://cqi.inf.usi.ch/qic/wheeler.pdf">https://cqi.inf.usi.ch/qic/wheeler.pdf</a> 2. AdS/CFT Correspondence (Holographic Principle)00:05:15 - Theoretical physics duality connecting quantum gravity in Anti-de Sitter space with conformal field theory<a href="https://en.wikipedia.org/wiki/Holographic_principle">https://en.wikipedia.org/wiki/Holographic_principle</a> 3. Renormalization Group Theory00:06:15 - Mathematical framework for analyzing physical systems across different length scales <a href="https://www.damtp.cam.ac.uk/user/dbs26/AQFT/Wilsonchap.pdf">https://www.damtp.cam.ac.uk/user/dbs26/AQFT/Wilsonchap.pdf</a> 4. Maxwell&#39;s Demon and Information Theory00:21:15 - Thought experiment linking information processing to thermodynamics and entropy<a href="https://plato.stanford.edu/entries/information-entropy/">https://plato.stanford.edu/entries/information-entropy/</a> 5. Landauer&#39;s Principle00:29:45 - Fundamental limit establishing minimum energy required for information erasure <a href="https://en.wikipedia.org/wiki/Landauer%27s_principle">https://en.wikipedia.org/wiki/Landauer%27s_principle</a> 6. Free Energy Principle and Active Inference01:03:00 - Mathematical framework for understanding self-organizing systems and perception-action loops<a href="https://www.nature.com/articles/nrn2787">https://www.nature.com/articles/nrn2787</a> 7. Max Tegmark - Information Bottleneck Principle01:07:00 - Connections between information theory and renormalization in machine learning<a href="https://arxiv.org/abs/1907.07331">https://arxiv.org/abs/1907.07331</a> 8. Fisher&#39;s Fundamental Theorem of Natural Selection01:11:45 - Mathematical relationship between genetic variance and evolutionary fitness<a href="https://en.wikipedia.org/wiki/Fisher%27s_fundamental_theorem_of_natural_selection">https://en.wikipedia.org/wiki/Fisher%27s_fundamental_theorem_of_natural_selection</a> 9. Tensor Networks in Quantum Systems00:06:45 - Computational framework for simulating many-body quantum systems <a href="https://arxiv.org/abs/1912.10049">https://arxiv.org/abs/1912.10049</a> 10. Quantum Neural Networks00:09:30 - Hybrid quantum-classical models for machine learning applications<a href="https://en.wikipedia.org/wiki/Quantum_neural_network">https://en.wikipedia.org/wiki/Quantum_neural_network</a> 11. Energy-Based Models (EBMs)00:40:00 - Probabilistic framework for unsupervised learning based on energy functions<a href="https://www.researchgate.net/publication/200744586_A_tutorial_on_energy-based_learning">https://www.researchgate.net/publication/200744586_A_tutorial_on_energy-based_learning</a> 12. Markov Chain Monte Carlo (MCMC)00:20:00 - Sampling algorithm fundamental to modern AI and statistical physics <a href="https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo">https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo</a> 13. Metropolis-Hastings Algorithm00:23:00 - Core sampling method for probability distributions<a href="https://arxiv.org/abs/1504.01896">https://arxiv.org/abs/1504.01896</a>

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