What Comes After ChatGPT? The Mother of ImageNet Predicts The Future
Fei-Fei Li is a Stanford professor, co-director of Stanford Institute for Human-Centered Artificial Intelligence, and co-founder of World Labs. She created ImageNet, the dataset that sparked the deep learning revolution.
Justin Johnson is her former PhD student, ex-professor at Michigan, ex-Meta researcher, and now co-founder of World Labs.
Together, they just launched Marble—the first model that generates explorable 3D worlds from text or images.
In this episode Fei-Fei and Justin explore why spatial intelligence is fundamentally different from language, what's missing from current world models (hint: physics), and the architectural insight that transformers are actually set models, not sequence models.
Resources:
Follow Fei-Fei on X: https://x.com/drfeifei
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After LLMs: Spatial Intelligence and World Models — Fei-Fei Li & Justin Johnson, World Labs
Fei-Fei Li and Justin Johnson are cofounders of World Labs, who have recently launched Marble (https://marble.worldlabs.ai/), a new kind of generative “world model” that can create editable 3D environments from text, images, and other spatial inputs. Marble lets creators generate persistent 3D worlds, precisely control cameras, and interactively edit scenes, making it a powerful tool for games, film, VR, robotics simulation, and more. In this episode, Fei-Fei and Justin share how their journey from ImageNet and Stanford research led to World Labs, why spatial intelligence is the next frontier after LLMs, and how world models could change how machines see, understand, and build in 3D.
We discuss:
* The massive compute scaling from AlexNet to today and why world models and spatial data are the most compelling way to “soak up” modern GPU clusters compared to language alone.
* What Marble actually is: a generative model of 3D worlds that turns text and images into editable scenes using Gaussian splats, supports precise camera control and recording, and runs interactively on phones, laptops, and VR headsets.
* Fei-fei’s essay:
on spatial intelligence as a distinct form of intelligence from language: from picking up a mug to inferring the 3D structure of DNA, and why language is a lossy, low-bandwidth channel for describing the rich 3D/4D world we live in.
* Whether current models “understand” physics or just fit patterns: the gap between predicting orbits and discovering F=ma, and how attaching physical properties to splats and distilling physics engines into neural networks could lead to genuine causal reasoning.
* The changing role of academia in AI, why Fei-Fei worries more about under-resourced universities than “open vs closed,” and how initiatives like national AI compute clouds and open benchmarks can rebalance the ecosystem.
* Why transformers are fundamentally set models, not sequence models, and how that perspective opens up new architectures for world models, especially as hardware shifts from single GPUs to massive distributed clusters.
* Real use cases for Marble today: previsualization and VFX, game environments, virtual production, interior and architectural design (including kitchen remodels), and generating synthetic simulation worlds for training embodied agents and robots.
* How spatial intelligence and language intelligence will work together in multimodal systems, and why the goal isn’t to throw away LLMs but to complement them with rich, embodied models of the world.
* Fei-Fei and Justin’s long-term vision for spatial intelligence: from creative tools for artists and game devs to broader applications in science, medicine, and real-world decision-making.
—
Fei-Fei Li
* X: https://x.com/drfeifei
* LinkedIn: https://www.linkedin.com/in/fei-fei-li-4541247
Justin Johnson
* X: https://x.com/jcjohnss
* LinkedIn: https://www.linkedin.com/in/justin-johnson-41b43664
Where to find Latent Space
* X: https://x.com/latentspacepod
Full Video Episode
Timestamps
00:00:00 Introduction and the Fei-Fei Li & Justin Johnson Partnership00:02:00 From ImageNet to World Models: The Evolution of Computer Vision00:12:42 Dense Captioning and Early Vision-Language Work00:19:57 Spatial Intelligence: Beyond Language Models00:28:46 Introducing Marble: World Labs' First Spatial Intelligence Model00:33:21 Gaussian Splats and the Technical Architecture of Marble00:22:10 Physics, Dynamics, and the Future of World Models00:41:09 Multimodality and the Interplay of Language and Space00:37:37 Use Cases: From Creative Industries to Robotics and Embodied AI00:56:58 Hiring, Research Directions, and the Future of World Labs
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe
The Frontier of Spatial Intelligence with Fei-Fei Li
Fei-Fei Li and Justin Johnson are pioneers in AI. While the world has only recently witnessed a surge in consumer AI, they have long been laying the groundwork for the innovations transforming industries today.
With the recent launch of Marble, the first product from their company World Labs, we are revisiting this conversation to explore the ideas that started it all. World Labs is focused on spatial intelligence, building Large World Models that can perceive, generate, and interact with the 3D world. Marble brings that vision to life, allowing anyone, from individual creators to major platforms, to generate 3D scenes directly from text or image prompts and turn complex 3D creation into a simple, creative process.
In this episode, a16z general partner Martin Casado talks with Fei-Fei and Justin about the journey from early AI winters to the rise of deep learning and multimodal AI. From foundational breakthroughs like ImageNet to the cutting-edge realm of spatial intelligence, they discuss the evolution of the field and what is next for innovation at World Labs.
Timecode:
0:00 – The Next Decade of AI
2:45 – Origins: Backgrounds of the Founders
6:50 – The Rise of Deep Learning & ImageNet
8:00 – Algorithmic Unlocks: Compute, Data, and Supervised Learning
12:00 – From Predictive to Generative AI
16:20 – The Journey to Spatial Intelligence
18:35 – Defining Spatial Intelligence
21:15 – 3D Data, Computer Vision, and Breakthroughs
23:15 – Reconstruction vs. Generation in Computer Vision
24:45 – Spatial Intelligence vs. Language Models
29:00 – Applications: Virtual, Augmented, and Physical Worlds
39:55 – Building World Labs: Team and Vision
41:55 – The North Star: Measuring Success in Spatial Intelligence
Resources:
Learn more about World Labs: https://www.worldlabs.ai
Learn more about Marble: https://Marble.WorldLabs.ai
Find Fei-Fei on Twitter: https://x.com/drfeifei
Find Justin on Twitter: https://x.com/jcjohnss
Find Martin on Twitter: https://x.com/martin_casado
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The Frontier of Spatial Intelligence with Fei-Fei Li
Fei-Fei Li and Justin Johnson are pioneers in AI. While the world has only recently witnessed a surge in consumer AI, our guests have long been laying the groundwork for innovations that are transforming industries today.
In this episode, a16z General Partner Martin Casado joins Fei-Fei and Justin to explore the journey from early AI winters to the rise of deep learning and the rapid expansion of multimodal AI. From foundational advancements like ImageNet to the cutting-edge realm of spatial intelligence, Fei-Fei and Justin share the breakthroughs that have shaped the AI landscape and reveal what's next for innovation at World Labs.
If you're curious about how AI is evolving beyond language models and into a new realm of 3D, generative worlds, this episode is a must-listen.
Resources:
Learn more about World Labs: https://www.worldlabs.ai
Find Fei-Fei on Twitter: https://x.com/drfeifei
Find Justin on Twitter: https://x.com/jcjohnss
Find Martin on Twitter: https://x.com/martin_casado
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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 a16z.com/disclosures.
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