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: royalsocietypublishing.org/r… What do you think is the most important missing ingredient in today’s AI systems?

Virtual fruit fly brains are the latest fad in AI 🧠

Introducing Fugu Max and Fugu Ultra v2: Orchestrating the Pareto Frontier The AI industry has spent a decade optimizing along a single axis: build bigger, more expensive models. But intelligence has never been a monolith. It is a collective, distributed system. Humanity itself is a collective intelligence. We built Sakana Fugu on this conviction: the most powerful AI systems will not be isolated giants, but collaborative ecosystems that learn to coordinate. Evolution innovates under constraints, and the future belongs to systems that know not just how to solve a problem, but which machinery to deploy for the lowest possible cost. Today we are releasing Fugu Max and Fugu Ultra v2. Fugu Max expands the Pareto frontier outward, orchestrating our largest pool of open and specialized models to deliver frontier-grade results at a fraction of the token spend. Fugu Ultra v2 pushes that frontier upward, achieving peak performance on complex multi-step tasks without depending on the very frontier models it competes against. Together, they show that orchestration does not force a choice between cost and performance. It can push both at the same time, advancing the Pareto frontier. Relying on a single company’s model for critical infrastructure is a massive risk. As recent export controls have shown, access can disappear overnight. Collective intelligence is the practical hedge against this concentration of power. An orchestration system simply routes around vendor restrictions by relying on an entirely swappable agent pool. I am incredibly proud of our team for shipping this. By orchestrating the world’s models, we are building the resilient infrastructure required for AI sovereignty.