In this episode,
@jcjohnss, co-founder of
@theworldlabs, joins us to discuss world models and the emerging field of spatial AI. We explore why many researchers see capabilities beyond language as an important frontier for AI, and what it means to build models that can understand, generate, and simulate the environments around them.
Justin explains the different approaches to world modeling, including explicit 3D representations and generative models, and why there is still no established recipe for building these systems. We also discuss World Labs’ Marble system, which can generate navigable 3D worlds from images and other inputs, the challenges of evaluating world models, and the role of simulation, planning, and action. Finally, Justin shares his vision for models that bring these capabilities together, supporting everything from interactive virtual environments to agents and robots that can operate in the physical world.
🗒️ Full show notes:
twimlai.com/go/775.
📖 CHAPTERS
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00:00 - Introduction
03:19 - Defining World Models
08:10 - World Models as Theory Builders
11:37 - POMDPs and Agent–World Interaction
15:40 - Training Agents with Behavior Cloning
18:40 - Ground-Truth State and Learned State
23:34 - Explicit 3D vs. Implicit 3D
28:14 - Reconstruction vs. Generative World Modeling
30:38 - Gaussian Splat Anatomy and File Formats
33:52 - Why Gaussian Splats Work with Neural Networks
37:00 - Consistency by Construction and at Scale
40:31 - How Marble Generates 3D Worlds
44:25 - Training Data and Output Representations
47:48 - World Models as Renderers, Planners, and Simulators
51:30 - When Rendering and Simulation Overlap
56:28 - The Path to Unified World Models
59:30 - Architectures, Loss Functions, and Long Contexts
01:02:47 - Where to Learn More