Claim◆Video · 1:11 — 1:50

Spatial intelligence—the ability to generate, understand, reason with, and interact with physical or virtual spaces—is the next frontier of AI, and building large world models is the means to achieve it.

Fei-Fei Li introduces World Labs as a frontier model lab building spatial intelligence through large world models. ✦ AI generated

Fei-Fei Li · a16z Podcast · 2026-07-28 · original ↗

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“So, Fei-Fei, for the listeners that may not have the background, maybe you can give an overview of what World Labs does.”

World Lab has is a two-year-old startup. I think we should just recognize it's a frontier model lab. We are building the next frontier of AI which is what we call spatial intelligence. And spatial intelligence is about creating AI that has the ability to generate, understand, reason with, and interact with spaces whether it's physical or virtual. And of course a means to an end towards spatial intelligence is building large world models.

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1:14you can give an overview of what World Labs does. >> Yeah. Well, World Lab has is a two-year-old startup. I I I think we should just recognize it's a frontier model lab. It's uh we we are building the next frontier of AI which is what we call spatial intelligence. And uh spatial intelligence is about um creating AI that has the ability to generate uh understand reason with and interact with spaces

1:50whether it's physical or virtual. And of course uh a means to an end towards spatial intelligence is building large world models. And that's what uh World Labs is mostly focused on. >> Yeah. So you've been saying this since the very beginning which is um you know the machine's ability to perceive and reason about spaces and act on spaces but I always had the assumption that the acting on spaces was some like longist

2:17future thing but now you're acquiring a robotics company and so maybe talk a little bit about the timeliness of this and the intentions. >> Yeah. So first of all, it doesn't just take robotics to act within spaces or to interact, right? I mean, look at the creative field, whether it's VFX or gaming and or design. Many use cases, you can create and act within virtual spaces. World Warlab's thesis has always

▦ World Labs ✳ spatial intelligence ◉ Fei-Fei Li

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The next frontier of AI is spatial intelligence — creating AI that can generate, understand, reason with, and interact with physical or virtual spaces.Fei-Fei Li · a16z Podcast · conf 90%

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supports → Spatial intelligence is the next frontier of AI, and it requires building large world models—AI that can generate, understand, reason with, and interact with physical and virtual spaces.Fei-Fei Li · a16z Podcast supports → The next frontier of AI is spatial intelligence — creating AI that can generate, understand, reason with, and interact with physical or virtual spaces.Fei-Fei Li · a16z Podcast explains mechanism → The key role simulation plays that real-world data cannot is counterfactual reasoning—playing out events that haven't happened or cannot happen—which is essential because robotics cannot possibly collect enough real-world data for all scenarios.Fei-Fei Li · a16z Podcast explains mechanism → Simulation provides two distinct benefits for robotics: reliability through systematic randomization covering the full state space, and efficiency through controllable speed-up of robot behaviors that is impossible in the physical world.Yunzhu Li · a16z Podcast explains mechanism → A foundation model for robotics will be an omni-model that takes multimodal input—including actions as input (a forward simulator predicting environment changes) or actions as output (a policy model)—and serves as a backbone fine-tuned for specific robotic applications.Fei-Fei Li and Yunzhu Li · a16z Podcast explains mechanism → The unique bottleneck in general-purpose robotics is the lack of data for training and evaluation, which can be solved by a 'real-to-sim-to-real' pipeline that aligns digital worlds with physical environments so that simulation data replaces real-world data at scale.Yunzhu Li · a16z Podcast provides context → Human-level power efficiency in robotics will take a very long time to achieve because a working robot is always a system problem integrating hardware, software, and countless details, and we should take a measured, realistic approach rather than over-promising.Yunzhu Li and Fei-Fei Li · a16z Podcast