After LLMs: Spatial Intelligence and World Models — Fei-Fei Li & Justin Johnson, World Labs

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Latent Space: The AI Engineer Podcast 59 min 3 speakers 8 chapters transcribed 27 days ago

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What is the origin story of Fei‑Fei Li and Justin Johnson’s partnership and the founding of World Labs?

Alessio Fanelli 0:06

Hey everyone, welcome to the Laden Space Podcast. This is Salesio, founder of Kernel Labs, and I'm joined by Swix, editor of Laden Space.

Swyx 0:13

And we are so excited to be in the studio with Fey Fei and Justin of uh World Labs. Welcome.

Fei-Fei Li 0:18

We're excited too. I'm

Swyx 0:20

really saying marble. Yeah, thanks for having us. I think there's a lot of interest in world models and you've done a you've done a little bit of publicity around spatial intelligence and all that. Um, I guess maybe one of the part of the story that is a rare opportunity to for you to tell is how you two came together uh to start building world labs.

Fei-Fei Li 0:37

That's very easy because Justin was my former student. Yeah. So Justin came to my I you know, uh in my the other hat I wear is a professor of computer science at Stanford. Justin joined my lab when? Which year uh

Justin Johnson 0:51

twenty twelve. Actually the the semester that I uh the quarter that I joined your lab was the same quarter that that uh Alexnet came out.

Fei-Fei Li 0:57

Yeah, yeah. So Justin is uh my first Were you

Justin Johnson 1:00

involved in the whole announcement uh drama? No, no, not at all. But I was sort of watching all the ImageNet excitement around Alexnet at that that quarter.

Fei-Fei Li 1:08

So he was my one of my very best students and uh and then he went on to have a very successful uh early career as a professor in Michigan, University of Michigan and Arbor in Meta. And then when we um I think around You know, more than two years ago for sure. I think both independently, both of us have been looking at the development of the large models and thinking about what's beyond language models and and this idea of building world models, spatial intelligence. uh really was natural for us. So we started talking and decided that we should just put all the eggs in one basket and focus on the solving this problem and started world apps together.

Justin Johnson 1:56

Yeah, pretty much. I mean, like I I after that seeing that kind of Image Net era during my PhD, um, I had the sense that the next sort of decade of computer vision was going to be about getting getting AI out of the out of the data center and out into the world. Um so a lot of my interests post PhD kind of shifted in uh to into 3D vision, a little bit more into into computer graphics, uh more into generative modeling. Um, and I was uh I thought I was kind of drifting away from my advisor post PhD, but then when when we reunited a couple of years later, it turned out she was thinking of very similar things.

Alessio Fanelli 2:25

So if you think about AlexNet, the core pieces of it were obviously ImageNet. It was the move to GPUs and neural networks. How do you think about the AlexNet equivalent model for world models? In a way, it's an idea that has been out there, right? There's been, you know, Young Lagoon is maybe like the most the biggest proponent, most prominent of it. What have you seen in the last two years that you were like, hey, now's the time to do this? And what are maybe the things fundamentally that you want to build? as far as data and kind of like maybe different types of uh algorithms or approaches to compute uh to make world models really come to life.

Justin Johnson 2:58

Yeah, I I I think one is just there is a lot more data in compute generally available. Um I think the whole history of deep learning is in some sense the the history of scaling up compute. Um and if we think about, you know, Alexnet required this jump from CPUs to GPUs, but even from Alexnet to today, we're getting about a thousand times more performance per card um than we had in AlexNet days. And now it's common to train to train models not just on one GPU, but on hundreds or thousands or tens of thousands or even more. So the amount of compute that we can marshal today on on a single model is is, you know, about a million fold more than we could have even at the start of my PhD. So I think language was one of the really um one really interesting things to started to work quite well the last couple of years.

Justin Johnson 3:37

But as we think about moving towards visual data and spatial data and world data, you just need to process a lot more. And I think that's gonna be um a good way to soak up this uh this new compute that's coming online more and more.

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Chapters

8 chapters

1 What is the origin story of Fei‑Fei Li and Justin Johnson’s partnership and the founding of World Labs? 0:06–8:04 2 How did the evolution from ImageNet and AlexNet lead to the idea of world models and spatial intelligence? 8:04–15:42 3 What is dense captioning and how did early vision‑language research pave the way for 3‑D world modeling? 15:42–21:53 4 Why is spatial intelligence considered the next frontier after large language models? 21:53–30:20 5 What is Marble, and how does it generate editable 3‑D scenes from text, images, or other inputs? 30:20–38:03 6 How do Gaussian splats work in Marble, and why do they enable real‑time rendering on phones and VR headsets? 38:03–45:30 7 Can generative world models learn physics and causal reasoning, and what are the current approaches? 45:30–52:31 8 What are the most compelling use cases for Marble today, from VFX and gaming to robotics and interior design? 52:31–59:22

Speakers

3 identified

Fei-Fei Li 20 min Alessio Fanelli 6 min Bruno Satin

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Donald Trump 1 AI 1 Google 1 Nvidia 1 Meta 1 Stanford 1 LLM 1 University of Michigan 1 Sora 1 Isaac Newton 1 Transformers 1 Francis Crick 1

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Show notes

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 (https://drfeifei.substack.com/p/from-words-to-worlds-spatial-intelligence) 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 Substack: https://www.latent.space/ Chapters 00:00:00 Introduction and the Fei-Fei Li & Justin Johnson Partnership 00:02:00 From ImageNet to World Models: The Evolution of Computer Vision 00:12:33 Dense Captioning and Early Vision-Language Work 00:19:39 Spatial Intelligence: Beyond Language Models 00:28:49 Introducing Marble: World Labs' First Spatial Intelligence Model 00:33:24 Gaussian Splats and the Technical Architecture of Marble 00:35:50 Physics, Dynamics, and the Future of World Models 00:44:00 Multimodality and the Interplay of Language and Space 00:37:37 Use Cases: From Creative Industries to Robotics and Embodied AI 00:57:03 Hiring, Research Directions, and the Future of World Labs

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