After years of rapid progress, scaling Large Language Models on internet text is beginning to show diminishing returns. As datasets saturate and performance curves flatten, a growing part of the AI community is shifting its focus toward a broader paradigm - one rooted not in text, but in the structure of the physical world.

In this session, we explore the accelerating momentum behind World Models and Spatial Intelligence. Visionaries such as Fei-Fei Li and Yann LeCun argue that genuine intelligence cannot emerge from language alone - it requires perception, embodiment, and the ability to model how the world evolves. Instead of predicting the next token, future AI systems will predict the next state of the world - learning through interaction, simulation, and physical reasoning.

Attendees will learn why the next wave of breakthroughs is likely to come not from bigger LLMs, but from models that understand space, dynamics, and cause-and-effect. We will demystify the core ideas behind World Models, examine the current state of Spatial Intelligence, and explore what this shift means for developers, researchers, and the future of intelligent systems.