The next leap in AI won't come from more internet text. Models are hitting a ceiling on recycled data. What moves the needle is Frontier Data — the kind that doesn't exist anywhere yet and can't be scraped: → expert knowledge from domains that never made it online → edge cases that simulators can only approximate → robot manipulation footage from real-world environments → verified onchain address labels That's what Codatta contributors are building, task by task.

The next leap in Physical AI will not come from bigger models alone. It needs better real-world data and the computing power to learn from it. NATIX helps solve the data side with multi-camera video from the physical world. 💪

The conventional wisdom in AI is that the next breakthrough will come from more compute, more data, and larger models. But what if the next leap comes from somewhere else? In this episode, @wellingmax—co-founder and CTO of @cusp_ai and professor at the University of Amsterdam—argues that physics may provide some of the ideas behind the next generation of AI systems. We begin with CuspAI’s work using generative AI to design entirely new materials for semiconductors, batteries, carbon capture, and clean energy. Max explains how foundation models for chemistry, agentic workflows, simulation, and automated experimentation are dramatically accelerating the search for new materials and reshaping scientific discovery. The conversation then broadens into a deeper question. Beyond giving AI new scientific problems to solve, can physics also teach us how to build better AI? Max explores surprising connections between machine learning and thermodynamics, why waves may become a new computational primitive for neural networks, and how concepts like symmetry breaking and statistical physics could inspire AI architectures beyond today’s scaling paradigm. 🗒️ Full show notes: 📖 CHAPTERS =============================== 00:00 - Introduction 02:08 - From Equivariant Networks to AI for Science 04:55 - Founding CuspAI 07:30 - Using AI to Discover New Materials 09:03 - Designing Materials for Carbon Capture 13:20 - Partnerships and the CuspAI Business Model 14:59 - The End-to-End Materials Discovery Process 19:13 - Experiments and Self-Driving Labs 23:04 - Open-Source Molecular Dynamics on GPUs 25:23 - Foundation Models for Chemistry 30:20 - The Future of AI-Driven Materials Science 32:22 - Connecting Generative AI and Thermodynamics 39:29 - How Physics and Machine Learning Can Inform Each Other 44:53 - Waves as a New Primitive for Neural Networks 49:34 - Memory, Stability, and the Edge of Chaos 52:40 - Spontaneous Symmetry Breaking in Neural Networks 56:10 - The Two-Way Exchange Between AI and Physics

Everyone’s racing to build bigger models. Fewer people are asking what those models are learning from. Scraped data → noise Synthetic data → echoes Verified, human-validated data → signal The next leap in AI won’t come from more compute. It’ll come from better data. That’s the layer we’re building.

From horses to this. We’ve come a long way. Full Self-Driving is the next leap our nation is going to appreciate more than most people realize right now. Safer roads. Real independence for families and the people who need it most. More time actually living instead of just driving. The future already showed up.

$SKHY $MU $DRAM If you think the memory trio is sitting back, chilling, and relaxing with the huge cash they are about to make, you are dead wrong. SK hynix is about to go through a big transformation at the top. Chairman Chey Tae-won is personally interviewing ~250 executives, one on one. The first round this large in 14 years, since SK acquired hynix in 2012. Last time he did this, it set the strategic tasks that built today's HBM leader. Now the goal is to find the next leap. And it doesn't stop there: -3 of SK Group's 4 manager vice chairmen are moving to SK hynix. Industry calls it "highly unusual," a symbol of where the group's focus now sits. -Their mandate: U.S. vs China chip supremacy, Washington's investment demands, and big tech partnerships. -New Global Growth TF: not just securing HBM supply, but expanding from memory into the entire AI infrastructure stack. Memory Industry Transformation in Full Throttle.

Do large language models actually understand the world, or are they just very good at pretending? Does it even matter? Our recent special issue in the Royal Society, “World Models in Natural and Artificial Intelligence,” brings together pioneers across AI, biology, and philosophy to argue that the path to true intelligence runs through something deeper: the ability to model not just language, but causality, the self, and the physical world. Featuring contributions from Douglas Hofstadter, Michael Levin, Josh Tenenbaum, Samuel Gershman, Melanie Mitchell, and others, the collection asks a radical question: What if the next leap in AI requires not just more data, but systems that model themselves? Here are 3 ideas that might redefine how we build AI: 1. Capability is not the same as true intelligence. Current foundation models are incredibly capable, but they often lack true emergent intelligence. They learn surface statistics instead of compact, causal abstractions. Simply scaling compute will not fix this fundamental issue. 2. Self-modeling is an engineering primitive, not a philosophical luxury. New research in the issue shows that when networks learn to predict their own internal states, they compress and simplify, becoming more efficient as a form of regularization. For physical AI and future agents, a self-model is what will allow them to adapt their own skills and morphologies in real-time. 3. The hardest problems in AI are continuous with the hardest problems of life. Biological minds do not passively ingest data; they actively explore, driven by empowerment to increase control over their environment. If world modeling is about an agent representing itself in relation to its environment to survive and adapt, then general AI may need to look much more like artificial life. The takeaway is that the next leap in AI won’t come from just scaling up next-token prediction, but rather from systems that are agentic, self-referential, and temporally grounded. Read the introductory essay and the full special issue here: What do you think is the most important missing ingredient in today’s AI systems?

Fact: Tech saves lives and brings prosperity. Example: driving. Your commute is likely the most dangerous thing you did today. In the future, that won't be true. A century ago, driving in America cost 24 lives per 100 million miles. Engineers have fought that number down to about one. Autonomous vehicles are the next leap. They can be more than 10x safer than human drivers. It's also an example of tech working well with regulators. This is what technology does for humanity. The future is bright! Credit to @WSJ for this great chart and props to all the engineers working on Autonomous Vehicles.