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AMD buys World Labs for $8.2B in stock, names Fei-Fei Li EVP

AMD buys World Labs for $8.2B in stock, names Fei-Fei Li EVP

October 16, 2026
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AMD buys World Labs for $8.2B in stock, names Fei-Fei Li EVP

On September 28, AMD executed a strategic acquisition of San Francisco-based World Labs in a stock-only deal valued at approximately $8.2 billion (roughly 55.1 billion CNY). The move draws significant attention due to the appointment of World Labs co-founder Li Fei-Fei, widely recognized as a pioneer in AI, who will join AMD as Executive Vice President and Chief Scientist, reporting directly to CEO Dr. Lisa Su.

Founded in 2024, World Labs specializes in developing world models capable of generating or reconstructing interactive 3D environments from text, images, and video inputs. This technology extends beyond visual generation, enabling practical applications in robotics training, industrial simulations, and scientific research by creating manipulable digital worlds rather than static imagery.

Li Fei-Fei’s reputation as a foundational figure in AI stems from her creation of ImageNet, a large-scale annotated image database that demonstrated the critical role of big data in advancing computer vision. With prior experience as Chief Scientist for AI and Machine Learning at Google Cloud and Co-Director of Stanford’s Human-Centered AI Institute, her integration into AMD’s core R&D leadership underscores the strategic importance of this partnership.

Earlier this month, World Labs introduced Atlas, a model capable of predicting scene geometry from multiple camera angles using only a few 2D images. Described as analogous to how large language models predict subsequent words, Atlas predicts the next visual perspective within geometric space, effectively bridging 2D inputs with 3D spatial understanding.

Dr. Lisa Su emphasized that acquiring World Labs allows AMD to align its hardware development with the evolving demands of cutting-edge AI models. By integrating the creators of world models into its executive team, AMD aims to drive the design of next-generation chips and computing systems tailored for the future AI landscape, focusing on both technological capability and strategic direction.

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