Sora 2's most significant advancement is not its visual quality, but its ability to understand and simulate physics. The model accurately portrays how water splashes or vehicles kick up snow, demonstrating a grasp of cause and effect crucial for true world-building.

Language is just one 'keyhole' into intelligence. True artificial general intelligence (AGI) requires 'world modeling'—a spatial intelligence that understands geometry, physics, and actions. This capability to represent and interact with the state of the world is the next critical phase of AI development beyond current language models.

GI discovered their world model, trained on game footage, could generate a realistic camera shake during an in-game explosion—a physical effect not part of the game's engine. This suggests the models are learning an implicit understanding of real-world physics and can generate plausible phenomena that go beyond their source material.

Large language models are insufficient for tasks requiring real-world interaction and spatial understanding, like robotics or disaster response. World models provide this missing piece by generating interactive, reason-able 3D environments. They represent a foundational shift from language-based AI to a more holistic, spatially intelligent AI.

Training AI agents to execute multi-step business workflows demands a new data paradigm. Companies create reinforcement learning (RL) environments—mini world models of business processes—where agents learn by attempting tasks, a more advanced method than simple prompt-completion training (SFT/RLHF).

Beyond supervised fine-tuning (SFT) and human feedback (RLHF), reinforcement learning (RL) in simulated environments is the next evolution. These "playgrounds" teach models to handle messy, multi-step, real-world tasks where current models often fail catastrophically.

Waive integrates Vision-Language-Action models (VLAs) to create a conversational interface for the car. This allows users to talk to the AI chauffeur ("drive faster") and provides engineers with a powerful introspection tool to ask the system why it made a certain decision, demystifying its reasoning.

Waive's core strategy is generalization. By training a single, large AI on diverse global data, vehicles, and sensor sets, they can adapt to new cars and countries in months, not years. This avoids the AV 1.0 pitfall of building bespoke, infrastructure-heavy solutions for each new market.

As reinforcement learning (RL) techniques mature, the core challenge shifts from the algorithm to the problem definition. The competitive moat for AI companies will be their ability to create high-fidelity environments and benchmarks that accurately represent complex, real-world tasks, effectively teaching the AI what matters.