What Comes After ChatGPT? The Mother of ImageNet Predicts The Future

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Dec 5, 2025

Guest

Justin Johnson

Guest

Fei-Fei Li

Fei-Fei Li, a Stanford professor and co-founder of World Labs, and Justin Johnson, her former PhD student and co-founder of World Labs, explore the future of spatial intelligence. They discuss the differences between spatial and linguistic intelligence, highlighting the importance of physics in world models. The duo introduces Marble, a groundbreaking generative 3D world model designed for creative applications. They delve into how Marble can enhance synthetic training data for robotics and redefine our understanding of visual data structures.

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INSIGHT

Scaling Is Powering The Next Frontier

Deep learning's progress largely tracked the exponential scaling of compute and data availability.
Spatial models will soak up massive compute as we move beyond language to richer visual and 3D data.

ADVICE

Sustain Open Science And Fund Academia

Preserve open science while recognizing industry plays different roles; support diverse ecosystem funding.
Resource academia and public infrastructure to enable experimentation beyond mere scale chasing.

ADVICE

Bet On Hardware-Aware Research

Explore new hardware-aware architectures rather than assuming GPU matrix-multiplication primitives scale forever.
Start long-term academic projects to invent primitives for future distributed hardware.

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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.

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