Why World Models Could Change Robotics, 3D, and Creativity

World Labs co-founders Fei-Fei Li, Justin Johnson, and Ben Mildenhall join a16z General Partner Martin Casado to discuss Atlas, their latest world model, and what it reveals about the pursuit of spatial intelligence. At the center of Atlas is what the team calls “new view prediction”: given images or views of a scene, the model predicts what that environment should look like from a different position in space and time. This brings generation and 3D reconstruction into the same model, and raises a broader question about whether predicting views could become a useful primitive for understanding the physical world. They discuss the technical bets behind the model, what it can and can’t yet capture, and the importance of dynamics, editability, and simulation as world models develop. The conversation also explores applications in creative work, architecture, and robotics, where Fei-Fei argues that one of today’s biggest constraints is access to real-world training data. Timestamps: 00:00 - Intro 00:51 - What Atlas Is & Why It Matters 05:15 - Is This a Scaled-Up Video Model or a New Architecture? 08:15 - Spatial Intelligence & Why New View Prediction Matters 21:27 - Did You Know It Was Going to Work? 24:42 - Use Cases: Creatives, Games & Robotics 35:21 - The Elephant in the Room: Video Models vs World Models 37:55 - Will We Get 4D Video You Can Walk Around In? 42:22 - Why New View Prediction Is the Next Token Prediction Resources: Follow Fei-Fei Li on X: https://x.com/drfeifei Follow Justin Johnson on X: https://x.com/jcjohnss Follow Ben Mildenhall on X: https://x.com/BenMildenhall Follow Martin Casado on X: https://x.com/martin_casado Learn more about Atlas: https://www.worldlabs.ai/blog/atlas Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.

Fei-Fei LiguestJustin JohnsonguestMartin Casadohost

Sep 4, 202643mWatch on YouTube ↗

EPISODE INFO

Released: September 4, 2026
Duration: 43m
Channel: a16z
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

World Labs co-founders Fei-Fei Li, Justin Johnson, and Ben Mildenhall join a16z General Partner Martin Casado to discuss Atlas, their latest world model, and what it reveals about the pursuit of spatial intelligence. At the center of Atlas is what the team calls “new view prediction”: given images or views of a scene, the model predicts what that environment should look like from a different position in space and time. This brings generation and 3D reconstruction into the same model, and raises a broader question about whether predicting views could become a useful primitive for understanding the physical world. They discuss the technical bets behind the model, what it can and can’t yet capture, and the importance of dynamics, editability, and simulation as world models develop. The conversation also explores applications in creative work, architecture, and robotics, where Fei-Fei argues that one of today’s biggest constraints is access to real-world training data. Timestamps: 00:00 - Intro 00:51 - What Atlas Is & Why It Matters 05:15 - Is This a Scaled-Up Video Model or a New Architecture? 08:15 - Spatial Intelligence & Why New View Prediction Matters 21:27 - Did You Know It Was Going to Work? 24:42 - Use Cases: Creatives, Games & Robotics 35:21 - The Elephant in the Room: Video Models vs World Models 37:55 - Will We Get 4D Video You Can Walk Around In? 42:22 - Why New View Prediction Is the Next Token Prediction Resources: Follow Fei-Fei Li on X: https://x.com/drfeifei Follow Justin Johnson on X: https://x.com/jcjohnss Follow Ben Mildenhall on X: https://x.com/BenMildenhall Follow Martin Casado on X: https://x.com/martin_casado Learn more about Atlas: https://www.worldlabs.ai/blog/atlas Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.

SPEAKERS

Fei-Fei Li
guest
Stanford computer vision professor and AI leader focused on “spatial intelligence” and embodied AI.
Justin Johnson
guest
Computer vision researcher describing the technical design and roadmap of the Atlas world model.
Martin Casado
host
General Partner at Andreessen Horowitz (a16z) who moderates and interviews guests on AI and infrastructure.

EPISODE SUMMARY

In this episode of a16z, featuring Fei-Fei Li and Justin Johnson, Why World Models Could Change Robotics, 3D, and Creativity explores atlas reframes world models as controllable, pose-grounded new-view prediction Atlas is presented as a next-generation world model whose central capability is controllable “new view prediction,” letting users render a scene from arbitrary camera trajectories using a spatially grounded context.

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