Uploaded July 2024 | Updated September 2026, 1 week ago
Disclaimer: This is the third video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview.
Sara Hooker, VP of Research at Cohere and leader of Cohere for AI, returns to challenge the use of compute thresholds (FLOPS) as a serious AI governance tool. She walks through her recent paper showing why the US executive order and EU AI Act get this wrong — compute alone tells you almost nothing about what a model can actually do.
The conversation then pivots to the AI language gap, where Sara lays out how current models systematically fail non-English speakers. She discusses the limitations of RLHF for multilingual alignment, the long tail problem in data representation, and why building models that work across languages requires fundamentally rethinking how we evaluate and train these systems.
---
TIMESTAMPS:
00:00:00 Intro
00:02:12 FLOPS paper and compute thresholds
00:26:42 The hardware lottery
00:30:22 The AI language gap
00:33:25 Safety across languages
00:38:31 Emergent capabilities
00:41:23 Creativity and language models
00:43:40 The long tail problem
00:44:26 LLMs and society
00:45:36 Model bias and representation
00:48:51 Language and capabilities
00:52:27 Ethical frameworks and RLHF
---
REFERENCES:
person:
[00:00:00] Sara Hooker
sarahooker.me
paper:
[00:02:12] On the Limitations of Compute Thresholds as a Governance Strategy
arxiv.org/pdf/2407.05694v1
[00:30:22] The AI Language Gap
cohere.com/research/papers/the-AI-language-gap.pdf
[00:33:25] The Multilingual Alignment Prism
arxiv.org/pdf/2406.18682
[00:52:27] RLHF Can Speak Many Languages
arxiv.org/pdf/2407.02552
[00:52:27] Back to Basics Revisiting REINFORCE for RLHF
arxiv.org/pdf/2402.14740
policy:
[00:02:12] Executive Order on AI Safety
whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence
[00:02:12] EU AI Act
https://www.europarl.europa.eu/doceo/document/TA-9-2024-0138_EN.pdf
article:
[00:02:12] The Bitter Lesson
incompleteideas.net/IncIdeas/BitterLesson.html
website:
[00:30:22] Cohere Aya
cohere.com/research/aya
[00:38:31] ARC-AGI Challenge
github.com/fchollet/ARC-AGI
---
LINKS:
Full Transcript: app.rescript.info/share/a1ebc4751c1643da186faeeabba94abb
Download PDF transcript: app.rescript.info/api/public/sessions/b88defad1a057e21/pdf
Sara Hooker
sarahooker.me
linkedin.com/in/sararosehooker
scholar.google.com/citations?user=2xy6h3sAAAAJ&hl=en
https://x.com/sarahookr
Chollet's ARC challenge
github.com/fchollet/ARC-AGI
Disclaimer: This is the third video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview.
Sara Hooker, VP of Research at Cohere and leader of Cohere for AI, returns to challenge the use of compute thresholds (FLOPS) as a serious AI governance tool. She walks through her recent paper showing why the US executive order and EU AI Act get this wrong — compute alone tells you almost nothing about what a model can actually do.
The conversation then pivots to the AI language gap, where Sara lays out how current models systematically fail non-English speakers. She discusses the limitations of RLHF for multilingual alignment, the long tail problem in data representation, and why building models that work across languages requires fundamentally rethinking how we evaluate and train these systems.
---
TIMESTAMPS:
00:00:00 Intro
00:02:12 FLOPS paper and compute thresholds
00:26:42 The hardware lottery
00:30:22 The AI language gap
00:33:25 Safety across languages
00:38:31 Emergent capabilities
00:41:23 Creativity and language models
00:43:40 The long tail problem
00:44:26 LLMs and society
00:45:36 Model bias and representation
00:48:51 Language and capabilities
00:52:27 Ethical frameworks and RLHF
---
REFERENCES:
person:
[00:00:00] Sara Hooker
sarahooker.me
paper:
[00:02:12] On the Limitations of Compute Thresholds as a Governance Strategy
arxiv.org/pdf/2407.05694v1
[00:30:22] The AI Language Gap
cohere.com/research/papers/the-AI-language-gap.pdf
[00:33:25] The Multilingual Alignment Prism
arxiv.org/pdf/2406.18682
[00:52:27] RLHF Can Speak Many Languages
arxiv.org/pdf/2407.02552
[00:52:27] Back to Basics Revisiting REINFORCE for RLHF
arxiv.org/pdf/2402.14740
policy:
[00:02:12] Executive Order on AI Safety
whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence
[00:02:12] EU AI Act
https://www.europarl.europa.eu/doceo/document/TA-9-2024-0138_EN.pdf
article:
[00:02:12] The Bitter Lesson
incompleteideas.net/IncIdeas/BitterLesson.html
website:
[00:30:22] Cohere Aya
cohere.com/research/aya
[00:38:31] ARC-AGI Challenge
github.com/fchollet/ARC-AGI
---
LINKS:
Full Transcript: app.rescript.info/share/a1ebc4751c1643da186faeeabba94abb
Download PDF transcript: app.rescript.info/api/public/sessions/b88defad1a057e21/pdf
Sara Hooker
sarahooker.me
linkedin.com/in/sararosehooker
scholar.google.com/citations?user=2xy6h3sAAAAJ&hl=en
https://x.com/sarahookr
Chollet's ARC challenge
github.com/fchollet/ARC-AGI
![The Dangerous Illusion of AI Coding? - Jeremy Howard
Dive into the realities of AI-assisted coding, the origins of modern fine-tuning, and the cognitive science behind machine learning with fast.ai founder Jeremy Howard. In this episode, we unpack why AI might be turning software engineering into a slot machine and how to maintain true technical intuition in the age of large language models.
GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg)
Jeremy Howard is a renowned data scientist, researcher, entrepreneur, and educator. As the co-founder of fast.ai, former President of Kaggle, and the creator of ULMFiT, Jeremy has spent decades democratizing deep learning. His pioneering work laid the foundation for modern transfer learning and the pre-training and fine-tuning paradigm that powers todays language models.
Key Topics and Main Insights Discussed:
- The Origins of ULMFiT and Fine-Tuning
- The Vibe Coding Illusion and Software Engineering
- Cognitive Science, Friction, and Learning
- The Future of Developers
RESCRIPT: https://app.rescript.info/public/share/BhX5zP3b0m63srLOQDKBTFTooSzEMh_ARwmDG_h_izk
https://app.rescript.info/api/public/sessions/62d06c0336c567d6/pdf
Jeremy Howard:
https://x.com/jeremyphoward
https://www.answer.ai/
TIMESTAMPS (fixed):
00:00:00 Introduction & GTC Sponsor
00:04:30 ULMFiT & The Birth of Fine-Tuning
00:12:00 Intuition & The Mechanics of Learning
00:18:30 Abstraction Hierarchies & AI Creativity
00:23:00 Claude Code & The Interpolation Illusion
00:27:30 Coding vs. Software Engineering
00:30:00 Cosplaying Intelligence: Dennett vs. Searle
00:36:30 Automation, Radiology & Desirable Difficulty
00:42:30 Organizational Knowledge & The Slope
00:48:00 Vibe Coding as a Slot Machine
00:54:00 The Erosion of Control in Software
01:01:00 Interactive Programming & REPL Environments
01:05:00 The Notebook Debate & Exploratory Science
01:17:30 AI Existential Risk & Power Centralization
01:24:20 Current Risks, Privacy & Enfeeblement
REFERENCES:
Blog Post:
[00:03:00] fast.ai Blog: Self-Supervised Learning
https://www.fast.ai/posts/2020-01-13-self_supervised.html
[00:13:30] DeepMind Blog: Gemini Deep Think
https://deepmind.google/blog/accelerating-mathematical-and-scientific-discovery-with-gemini-deep-think/
[00:19:30] Modular Blog: Claude C Compiler analysis
https://www.modular.com/blog/the-claude-c-compiler-what-it-reveals-about-the-future-of-software
[00:19:45] Anthropic Engineering Blog: Building C Compiler
https://www.anthropic.com/engineering/building-c-compiler
[00:48:00] Cursor Blog: Scaling Agents
https://cursor.com/blog/scaling-agents
[01:05:15] fast.ai Blog: NB Dev Merged Driver
https://www.fast.ai/posts/2022-08-25-jupyter-git.html
[01:17:30] Jeremy Howard: Response to AI Risk Letter
https://www.normaltech.ai/p/is-avoiding-extinction-from-ai-really
Book:
[00:08:30] M. Chirimuuta: The Brain Abstracted
https://mitpress.mit.edu/9780262548045/the-brain-abstracted/
[00:30:00] Daniel Dennett: Consciousness Explained
https://www.amazon.com/Consciousness-Explained-Daniel-C-Dennett/dp/0316180661
[00:42:30] Cesar Hidalgo: Infinite Alphabet / Laws of Knowledge
https://www.amazon.com/Infinite-Alphabet-Laws-Knowledge/dp/0241655676
Archive Article:
[00:13:45] MLST Archive: Why Creativity Cannot Be Interpolated
https://archive.mlst.ai/read/why-creativity-cannot-be-interpolated
Research Study:
[00:24:30] METR Study: AI OS Development
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/
Paper:
[00:24:45] Fred Brooks: No Silver Bullet
https://www.cs.unc.edu/techreports/86-020.pdf
[00:30:15] John Searle: Minds, Brains, and Programs
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/minds-brains-and-programs/DC644B47A4299C637C89772FACC2706A
Research Paper:
[00:13:50] Mathilde Caron et al.: Emerging Properties in Self-Supervised Vision Transformers (DINO)
https://arxiv.org/abs/2104.14294
[00:25:00] Oxford VGG: Sculptor Identification Paper
https://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12a/arandjelovic12a.pdf
[00:36:30] Anthropic Paper: AI Skill Formation
https://arxiv.org/pdf/2601.20245
Historical Reference:
[00:36:45] Ebbinghaus: Memory / Spaced Repetition
https://www.loc.gov/item/e11000616/
Technical Note:
[00:42:45] John Ousterhout: Slope vs Intercept
https://gist.github.com/gtallen1187/e83ed02eac6cc8d7e185
Video:
[00:59:00] Bret Victor: Inventing on Principle
https://vimeo.com/906418692
[01:05:00] Joel Grus: I Dont Like Notebooks
https://www.youtube.com/watch?v=7jiPeIFXb6U The Dangerous Illusion of AI Coding? - Jeremy Howard](https://i.ytimg.com/vi/dHBEQ-Ryo24/mqdefault.jpg)
![We Built Calculators Because Were STUPID! [Prof. David Krakauer]
Prof. David Krakauer argues that intelligence isnt about knowing more—its about doing more with less.
His core thesis flips our assumptions on their head: while emergence in complex systems is about more is different, intelligence is fundamentally about less is more.
Why Large Language Models Miss the Point
LLMs are more is more, Krakauer explains. Theyre essentially sophisticated libraries that know everything but understand nothing. When a student looks up answers in a library during an exam, we call them a cheater, not intelligent.
Intelligence Across All Life
Perhaps most surprisingly, Krakauer claims that all living things are intelligent—even bacteria. His reasoning: intelligence is the information accumulated through evolutions selective pressures. Its why we can say an elephant is smarter than a worm, but wed never say its more alive. The difference is accumulated capacity.
Three Dimensions of Intelligence
Krakauer proposes intelligence exists in three distinct flavours:
- Strategic Intelligence - Adaptation and survival where viruses actually beat us/
- Inferential Intelligence - Math and computation (where we built calculators because were so bad at it)
- Representational Intelligence - Finding better ways to encode problems (the most uniquely human)
The Soma Cube Insight
Using a Soma Cube puzzle, Krakauer demonstrates something incredible: a four-year-old can solve a combinatorial problem with 15,000 logical clauses—not by computing, but by using physical representation. The physical world does the computing for us. This is intelligence: making impossibly hard problems tractable through clever representation.
This talk was recorded at DISI 2025 https://disi.org/
David Krakauer
President and William H. Miller Professor of Complex Systems
https://www.santafe.edu/people/profile/david-krakauer
Note this version is edited down, you can watch the full version on our Patreon - https://www.patreon.com/posts/masterclass-on-142898847
Watch interview we published with David - https://www.youtube.com/watch?v=jXa8dHzgV8U
TRANSCRIPT:
https://app.rescript.info/public/share/Cs8QFwMw_FiOEROkNPiIf6Y-e_ahAxNMl0oFiL7_4QA
TOC:
00:00:00 - Complexity, Life & Intelligence
00:00:45 - Ptolemy vs Newton
00:01:30 - Purpose of Science
00:02:15 - Feynman on Rules vs Strategies
00:03:00 - Entropy as Unifying Rule
00:03:45 - Intelligence in Physics
00:04:15 - Complex Systems & Broken Symmetries
00:05:00 - Historical Definitions of Intelligence
00:06:15 - Herbert: Capacity to Acquire Capacity
00:06:45 - Problems with Turing Test
00:07:30 - Intelligence vs Knowledge
00:08:00 - LLMs as Libraries
00:08:30 - Humanistic Perspectives
00:09:00 - Unified Theory: Life & Intelligence
00:09:30 - Intensive vs Extensive Properties
00:10:30 - Selection Gradient Analogy
00:11:15 - Universal Intelligence (Bacteria to Elephants)
00:12:30 - Three Dimensions of Intelligence
00:13:00 - Strategic Intelligence & Viruses
00:13:30 - Inferential Intelligence & Calculators
00:14:15 - Encoding & Representation
00:14:30 - Different Paths: Life vs AI
00:15:15 - Soma Cube Problem
00:16:00 - Principle of Materiality
00:17:15 - Embodied Representation
00:17:45 - Intelligence Makes Hard Problems Easy
00:18:00 - Stupidity & Conclusion
REFS:
Papers and Academic Works
More is Different by Phil Anderson [https://www.tkm.kit.edu/downloads/TKM1_2011_more_is_different_PWA.pdf ]
Recent paper with Melanie and John Krakauer on emergence [https://arxiv.org/abs/2506.11135 ]
Formal treatment paper on intelligence/evolution duality from David - Darwinian demons, evolutionary complexity, and information maximization [https://wiki.santafe.edu/images/b/b1/CHAOEH213037110_1.pdf ]
The Unreasonable Effectiveness of Mathematics in the Natural Sciences by Eugene Wigner [https://webhomes.maths.ed.ac.uk/~v1ranick/papers/wigner.pdf ] We Built Calculators Because Were STUPID! [Prof. David Krakauer]](https://i.ytimg.com/vi/dY46YsGWMIc/mqdefault.jpg)
![He won a Nobel here for AlphaFold. Then he left. - John Jumper
This episode is sponsored by Notion. Learn more about Notions Developer Platform today at https://notion.com/mlst
Protein folding stalled biology for fifty years. A sequence of amino acids dictates a three-dimensional shape, but reading that shape meant a year and roughly $100,000 of crystallography per structure. Then AlphaFold 2 won CASP14 so decisively the organizers called the problem essentially solved.
In this documentary cut, John Jumper, who shared the 2024 Nobel Prize in Chemistry and has since left DeepMind for Anthropic, walks Tim Scarfe through what the system did and, more interestingly, what it did not. The architecture gets a proper dissection: MSAs, the Evoformer, invariant point attention, the FAPE loss, and Jumpers correction of the equivariance story, which ablations valued at roughly 2.5 of 30 GDT points rather than the whole win. He is blunt about the limits. AlphaFold predicts one experiment extraordinarily well; it is not a model of the cell, it does not capture dynamics, and on a given drug target it is wrong nine times out of ten.
From there: the AlphaFold Database of 200M+ predicted structures, AlphaFold 3 and ligands, Isomorphic Labs, and Jumpers quarrel with the bitter lesson, where finite data and human hypotheses still matter. Emmanuel Nji of BioStruct Africa closes the film on what changes when work that took years now takes months, and on training the next thousand structural biologists across Africa.
TIMESTAMPS:
00:00:00 Cold open: predicting nature with a button press
00:01:03 The protein folding bottleneck and CASP
00:04:39 The Nobel, the database, and the move to Anthropic
00:05:50 Sponsor (Notion) and framing: what AlphaFold does not claim
00:07:39 Proteins as self-assembling nanomachines
00:12:24 From structures to biology: drug discovery and Midnolin
00:17:37 The humility of AlphaFold: a narrow predictor
00:22:18 Inside the architecture: Evoformer, IPA and FAPE
00:30:20 Ruthless empiricism: ablations and 100x in data
00:35:20 Predict, control, understand
00:40:00 Against the bitter lesson; AlphaFold 3 as diffusion
00:45:07 Intelligence, representations and AGI
00:49:23 Epilogue: AlphaFold in Africa
00:52:16 Closing: the case for hybrid science models
REFERENCES:
organization:
[00:01:55] Critical Assessment of Structure Prediction (CASP)
https://predictioncenter.org/
[00:04:39] The Nobel Prize in Chemistry 2024
https://www.nobelprize.org/prizes/chemistry/2024/summary/
[00:05:18] BioStruct Africa
https://www.biostructafrica.org/
[00:18:03] Isomorphic Labs
https://www.isomorphiclabs.com/
paper:
[00:03:09] AlphaFold Protein Structure Database
https://doi.org/10.1093/nar/gkab1061
[00:17:25] Accurate structure prediction of biomolecular interactions with AlphaFold 3
https://www.nature.com/articles/s41586-024-07487-w
[00:22:18] Highly accurate protein structure prediction with AlphaFold
https://www.nature.com/articles/s41586-021-03819-2
[00:23:10] Midnolin promotes degradation of substrates independent of ubiquitination
https://doi.org/10.1126/science.adh5021
[00:27:00] Improved protein structure prediction using potentials from deep learning
https://www.nature.com/articles/s41586-019-1923-7
tool:
[00:03:09] AlphaFold Protein Structure Database (EBI)
https://alphafold.ebi.ac.uk/
[00:45:55] AlphaEvolve: a coding agent for designing advanced algorithms
https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/
other:
[00:39:40] The Bitter Lesson
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
ReScript: https://app.rescript.info/share/d8cde5c221fb71e2c0f5aafe94f90dfa
Disclaimer - not sponsored, editorial with us - we filmed it at GDM, London He won a Nobel here for AlphaFold. Then he left. - John Jumper](https://i.ytimg.com/vi/e3gBwLWAerw/mqdefault.jpg)


![Type a Sentence, Get a Playable 3D World in 3 Seconds - Shlomi Fuchter & Jack Parker-Holder
This episode features Shlomi Fuchter and Jack Parker Holder from Google DeepMind, who are unveiling a new AI called Genie 3. The host, Tim Scarfe, describes it as the most mind-blowing technology he has ever seen. We were invited to their offices to conduct the interview (not sponsored).
Imagine you could create a video game world just by describing it. Thats what Genie 3 does. Its an AI world model that learns how the real world works by watching massive amounts of video. Unlike a normal video game engine (like Unreal or the one for Doom) that needs to be programmed manually, Genie generates a realistic, interactive, 3D world from a simple text prompt.
**SPONSOR MESSAGES***
Prolific: Quality data. From real people. For faster breakthroughs.
https://prolific.com/mlst?utm_campaign=98404559-MLST&utm_source=youtube&utm_medium=podcast&utm_content=script-gen
***
Here’s a breakdown of what makes it so revolutionary:
From Text to a Virtual World: You can type a drone flying by a beautiful lake or a ski slope, and Genie 3 creates that world for you in about three seconds. You can then navigate and interact with it in real-time.
Its Consistent: The worlds it creates have a reliable memory. If you look away from an object and then look back, it will still be there, just as it was. The guests explain that this consistency isnt explicitly programmed in; its a surprising, emergent capability of the powerful AI model.
A Huge Leap Forward: The previous version, Genie 2, was a major step, but it wasnt fast enough for real-time interaction and was much lower resolution.
Genie 3 is 720p, interactive, and photorealistic, running smoothly for several minutes at a time.
The Killer App - Training Robots: Beyond entertainment, the team sees Genie 3 as a game-changer for training AI. Instead of training a self-driving car or a robot in the real world (which is slow and dangerous), you can create infinite simulations. You can even prompt rare events to happen, like a deer running across the road, to teach an AI how to handle unexpected situations safely.
The Future of Entertainment: this could lead to a YouTube version 2 or a new form of VR, where users can create and explore endless, interconnected worlds together, like the experience machine from philosophy.
While the technology is still a research prototype and not yet available to the public, it represents a monumental step towards creating true artificial worlds from the ground up.
Jack Parker Holder [Research Scientist at Google DeepMind in the Open-Endedness Team]
https://jparkerholder.github.io/
Shlomi Fruchter [Research Director, Google DeepMind]
https://shlomifruchter.github.io/
TOC:
[00:00:00] - Introduction: The Most Mind-Blowing Technology Ive Ever Seen
[00:02:30] - The Evolution from Genie 1 to Genie 2
[00:04:30] - Enter Genie 3: Photorealistic, Interactive Worlds from Text
[00:07:00] - Promptable World Events & Training Self-Driving Cars
[00:14:21] - Guest Introductions: Shlomi Fuchter & Jack Parker Holder
[00:15:08] - Core Concepts: What is a World Model?
[00:19:30] - The Challenge of Consistency in a Generated World
[00:21:15] - Context: The Neural Network Doom Simulation
[00:25:25] - How Do You Measure the Quality of a World Model?
[00:28:09] - The Vision: Using Genie to Train Advanced Robots
[00:32:21] - Open-Endedness: Human Skill and Prompting Creativity
[00:38:15] - The Future: Is This the Next YouTube or VR?
[00:42:18] - The Next Step: Multi-Agent Simulations
[00:52:51] - Limitations: Thinking, Computation, and the Sim-to-Real Gap
[00:58:07] - Conclusion & The Future of Game Engines
REFS:
World Models [David Ha, Jürgen Schmidhuber]
https://arxiv.org/abs/1803.10122
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions [Rui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley]
https://arxiv.org/abs/1901.01753
Questioning Representational Optimism in Deep Learning [Akarsh Kumar, Jeff Clune, Joel Lehman, Kenneth O. Stanley]
The Fractured Entangled Representation Hypothesis
https://arxiv.org/pdf/2505.11581
TRANSCRIPT:
https://app.rescript.info/public/share/Zk5tZXk6mb06yYOFh6nSja7Lg6_qZkgkuXQ-kl5AJqM Type a Sentence, Get a Playable 3D World in 3 Seconds - Shlomi Fuchter & Jack Parker-Holder](https://i.ytimg.com/vi/ekgvWeHidJs/mqdefault.jpg)
![SCHMIDHUBER: HOW WE WILL LIVE WITH AIs
SPONSOR MESSAGES:
***
CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments.
https://centml.ai/pricing/
Juergen Schmidhuber — the man behind LSTMs, the 1991 linear transformer, the first generative adversarial networks, and artificial curiosity — sits down with Tim for a wide-ranging conversation about where AI came from, where it is going, and what it means for humanity.
This is the long-awaited second part of their interview, and Schmidhuber wastes no time. He opens with a provocation: the most influential invention of the twentieth century was not the transistor or the internet, but the Haber-Bosch process for synthesising fertiliser — the thing that made half of humanity possible. The twenty-first centurys equivalent, he argues, will be true artificial intelligence.
From there, the conversation traces the lineage of modern AI through Schmidhubers own work: the 1991 linear transformer (which scales linearly rather than quadratically), fast weight controllers, predictive coding, neural network distillation, and the GPU-powered deep learning revolution that his DanNet system helped ignite. He reflects on the hardware lottery, Nvidias rise, and why all of todays breakthroughs rest on algorithms invented in the previous millennium.
The middle section covers consciousness (modelled as a chunker-automatiser system), the path from AGI-as-tool to AGI-with-goals, the concept of Homo Ludens, and the geopolitics of the AI race between Europe, the US, and China. Schmidhuber then offers his characteristically optimistic take on existential risk: superintelligent AIs will be curious scientists fascinated by life, not terminators. He connects this to the Fermi paradox, speculating that Earth may be the first planet in our light cone to spawn an expanding AI bubble — a thought that carries, as he puts it, a lot of responsibility.
REFERENCES:
paper:
[00:05:45] The 1991 Linear Transformer (Unnormalized Fast Weight Controller)
https://people.idsia.ch/~juergen/FKI-147-91ocr.pdf
[00:11:00] Formal Theory of Creativity, Fun, and Intrinsic Motivation
https://arxiv.org/abs/0812.4360
[00:15:57] The Hardware Lottery
https://arxiv.org/abs/2009.06489
reference:
[00:25:00] NNAISENSE - AI for the Physical World
https://nnaisense.com/
book:
[00:35:00] Homo Ludens
https://en.wikipedia.org/wiki/Homo_Ludens
LINKS:
Full Transcript: https://app.rescript.info/share/ab733cfc4ca23500252b59b180c71f4a
Download PDF transcript: https://app.rescript.info/api/public/sessions/e20de2659d4ca617/pdf SCHMIDHUBER: HOW WE WILL LIVE WITH AIs](https://i.ytimg.com/vi/fZYUqICYCAk/mqdefault.jpg)
![An Astrophysicist Debunks the Singularity — Adam Becker
Astrophysicist Adam Becker, author of What Is Real?, joins Tim Scarfe to take apart the futures Silicon Valley keeps selling: the 2045 singularity, mind uploading, Mars colonies, and the AI apocalypse. His new book *More Everything Forever* argues these ideas are hugely influential, mostly evidence-free, and bankrolled by tech billionaires who need a story in which growth never ends.
Becker does the physics the boosters skip. Kurzweils law of accelerating returns rests on cherry-picked data, and every exponential ends. Grant Bezos his perpetual energy growth and humanity boils the oceans within a few centuries, then exhausts the observable universe in under 4,000 years. The stars are too far away, Mars dirt is poison, and the day the dinosaur-killing asteroid hit Earth was still nicer than any day on Mars. On AI, Becker calls LLMs pocket calculators for language: hallucination is the model doing exactly what it always does, and the intelligence explosion assumes intelligence is a single number you can buy with compute.
The sting is that Becker thinks the doomers are sincere. Yudkowsky, Bostrom and the effective altruists are not grifters, he says, just wrong, and their warnings that AI could end the world feed the same growth story the money depends on. He closes with his own prescription: take social problems seriously, regulate the whole tech industry, and tax billionaires out of existence.
TIMESTAMPS:
00:00:00 Cold open and the thesis of More Everything Forever
00:04:24 Kurzweils singularity and the physical limits of exponential growth
00:14:02 High agency and the fantasy of imprinting humanity on the cosmos
00:16:55 Mind uploading, functionalism, and embodied cognition
00:24:24 AI psychosis and anthropomorphizing LLMs
00:26:24 Calculators, hallucination, and the limits of scale
00:32:20 Yudkowsky and the intelligence-explosion argument
00:40:37 True believers, venture capital, and the sci-fi growth narrative
00:47:21 From Extropians to EA: utilitarianism and longtermism
00:53:50 Brain worms and Beckers prescription: take social science seriously
00:56:49 Why the AI-ethics discourse is broken
01:01:42 The eugenics and IQ argument against intelligence
01:06:07 Why space settlement fails: Mars, the moon, and orbital data centers
01:10:42 Billionaire myths and the search for purpose
01:13:38 Tax billionaires, regulate tech: closing prescriptions
REFERENCES:
book:
[00:00:07] More Everything Forever (Adam Becker, 2025)
https://www.hachettebookgroup.com/titles/adam-becker/more-everything-forever/9781541619593/
[00:00:15] What Is Real? (Adam Becker, 2018)
https://en.wikipedia.org/wiki/What_Is_Real%3F
[00:15:46] What We Owe the Future (Will MacAskill, 2022)
https://www.hachettebookgroup.com/titles/william-macaskill/what-we-owe-the-future/9781541618626/
other:
[00:00:27] Dreaming Against the Machine (podcast)
https://www.dreamingagainstthemachine.com
[00:01:04] The Useful Idiots of AI Doomsaying (Adam Becker, The Atlantic, 2025)
https://www.theatlantic.com/books/archive/2025/09/what-ais-doomers-and-utopians-have-in-common/684270/
concept:
[00:14:03] Agency (philosophy)
https://en.wikipedia.org/wiki/Agency_(philosophy)
[00:18:08] Embodied cognition
https://plato.stanford.edu/entries/embodied-cognition/
[00:19:06] Functionalism
https://plato.stanford.edu/entries/functionalism/
[00:20:46] Good regulator theorem
https://en.wikipedia.org/wiki/Good_regulator
[00:33:35] Intelligence explosion
https://en.wikipedia.org/wiki/Intelligence_explosion
[00:34:14] Instrumental convergence
https://en.wikipedia.org/wiki/Instrumental_convergence
[00:36:26] Orthogonality thesis
https://en.wikipedia.org/wiki/Orthogonality_thesis
[00:38:06] Intentional stance
https://en.wikipedia.org/wiki/Intentional_stance
[00:44:23] Effective altruism
https://en.wikipedia.org/wiki/Effective_altruism
[00:47:32] Extropianism
https://en.wikipedia.org/wiki/Extropianism
[00:49:50] Utilitarianism
https://en.wikipedia.org/wiki/Utilitarianism
[00:52:15] Longtermism
https://en.wikipedia.org/wiki/Longtermism
[00:56:02] Human biodiversity (HBD)
https://en.wikipedia.org/wiki/Human_biodiversity
[01:05:00] Recursive self-improvement
https://en.wikipedia.org/wiki/Recursive_self-improvement
[01:09:25] Speed of light
https://en.wikipedia.org/wiki/Speed_of_light
person:
[00:04:49] Ray Kurzweil
https://en.wikipedia.org/wiki/Ray_Kurzweil
[00:15:42] Will MacAskill
https://en.wikipedia.org/wiki/William_MacAskill
[00:17:37] Adrian Daub
https://en.wikipedia.org/wiki/Adrian_Daub
[00:26:05] Shannon Vallor — The AI Mirror
https://en.wikipedia.org/wiki/Shannon_Vallor
[00:33:05] Eliezer Yudkowsky
https://en.wikipedia.org/wiki/Eliezer_Yudkowsky
[00:35:21] Nick Bostrom
https://en.wikipedia.org/wiki/Nick_Bostrom
[00:50:22] Peter Singer
https://en.wikipedia.org/wiki/Peter_Singer
[00:57:45] Timnit Gebru
https://en.wikipedia.org/wiki/Timnit_Gebru
[01:04:53] I. J. Good
https://en.wikipedia.org/wiki/I._J._Good An Astrophysicist Debunks the Singularity — Adam Becker](https://i.ytimg.com/vi/fgsmq8f3sWQ/mqdefault.jpg)

![Why Frontier AI Labs Fight to Hide Chain of Thought — Ilia Shumailov & Alexander Panfilov
Tim Scarfe speaks with Ilia Shumailov and Alexander Panfilov about their paper, Stealing Reasoning Traces from Proprietary LLM APIs.
The core bug sounds deceptively simple: providers return encrypted reasoning state so conversations can be resumed or forked. But those blobs can be replayed across users and sibling models. A smaller model can ask the provider to decrypt the trace, then repeat the hidden reasoning in plain text. The discussion covers leaked private data, a broadly reusable jailbreak, poisoned agent traces, chain-of-thought monitoring, responsible disclosure, and possible defenses.
Ilia Shumailov is an AI and security researcher, formerly at Google DeepMind, who completed his Cambridge PhD under Ross Anderson. Alexander Panfilov is a PhD researcher at the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems, working on AI safety, adversarial machine learning, and LLM red-teaming. They close by separating the demonstrated jailbreaking threat from ordinary benign distillation, and by arguing for controlled experiments over sweeping claims.
TIMESTAMPS:
00:00:00 Intro montage
00:01:33 Portable encrypted thought and decoded reasoning
00:24:55 How the attack works and what it means
00:39:04 Doom, defense, and scientific restraint
REFERENCES:
paper:
[00:00:00] Stealing Reasoning Traces from Proprietary LLM APIs
https://arxiv.org/abs/2608.09867
[00:09:22] Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
https://arxiv.org/abs/2507.11473
[00:11:30] Reasoning Models Don’t Always Say What They Think
https://www.anthropic.com/research/reasoning-models-dont-say-think
[00:37:22] PostTrainBench: Can LLM Agents Automate LLM Post-Training?
https://arxiv.org/abs/2603.08640
[00:41:02] Large-scale online deanonymization with LLMs
https://arxiv.org/abs/2602.16800
other:
[00:09:28] OpenAI and Hugging Face partner to address security incident during model evaluation
https://openai.com/index/hugging-face-model-evaluation-security-incident/
[00:10:22] Claude, GPT, and Gemini All Struggle to Evade Monitors
https://metr.org/notes/2025-08-22-claude-gpt-gemini-struggle-evade-monitors/
tool:
[00:42:08] Isabelle proof assistant
https://isabelle.in.tum.de/
RESCRIPT:
https://app.rescript.info/share/07fc38276e0823dc9b8986c32e202c7f Why Frontier AI Labs Fight to Hide Chain of Thought — Ilia Shumailov & Alexander Panfilov](https://i.ytimg.com/vi/gasgivVCl2U/mqdefault.jpg)
