The Future Beyond LLMs: Safe and Reliable AI through World Models

Session Overview

To realize AI's potential as humanity's most significant leap since the computer, we must move beyond the limitations of current systems. The industry's reliance on scaling large language models—a “Great AI Scaling Fallacy”—creates opaque, probabilistic systems unfit for mission-critical applications. This approach cannot deliver the trust and reliability required for the future of global infrastructure.

This presentation introduces world models as the transformative alternative. Unlike LLMs, which master language through pattern-matching, world models develop an intuitive understanding of reality by building internal, physics-based simulations. They learn the causal relationships that govern the real world, enabling a new class of deterministic and safe AI.

For business and government, this unlocks critical capabilities where precision and reliability are paramount. Imagine autonomous shipping navigating securely without GPS, resilient energy grids that predict and preempt failures, or aerospace systems that optimize trajectories in real time with guaranteed safety. These applications, vital for national security and economic stability, demand the engineering-grade accuracy that only physics-driven world models can provide. This is how we will transform our ability to conceive, test, and perfect the world's most complex systems, from global logistics to interstellar travel.