AMD will acquire Fei-Fei Li’s World Labs for $8.2 billion
The acquisition will see World Labs founder Fei-Fei Li join AMD as executive vice president and chief scientist.
AMD has announced it will acquire World Labs, a prominent developer of deep learning models aimed at comprehending physical reality, in a deal valued at $8.2 billion. World Labs explained the acquisition would enable close collaboration across model research, systems, and compute. Additionally, AMD views understanding frontier workloads, like those generated at World Labs, as crucial for shaping its chip-making roadmap.
Fei-Fei Li, the founder of World Labs, will take on the role of executive vice president and chief scientist at AMD. Notably, Li is a Stanford computer science professor, known for pioneering AI, particularly in computer vision. In 2024, Li established World Labs with the goal of developing deep learning models that better understand the physical world. Li posited that true artificial intelligence necessitates grounding in physics and the ability to reason about data beyond textual inputs.
Li termed the partnership with AMD as a means to scale World Labs’ technical achievements beyond the laboratory environment. "Now that we have tangible proof of the possibilities, we want to do everything we can to accelerate the future," Li expressed in a post announcing the deal. "To do this requires scaling our efforts, widening our reach, and getting closer to the hardware."
World Labs’ inaugural product, Marble, serves as a tool for crafting entertainment experiences and simulating environments for robot training. The acquisition is anticipated to bolster AMD’s competitiveness against Nvidia in the realm of AI-specific chips. While Nvidia has already introduced a range of open-weight world models, such as Cosmos, AMD has thus far provided text- and video-based models to the public.
World models are deemed essential for deploying generative AI models on robotic platforms, including autonomous vehicles, industrial robots, and general-purpose humanoids. The scarcity of practical data for training general-purpose robots renders synthetic data from world models crucial in realizing the ambitions of companies like Tesla and Figure. The acquisition is projected to conclude before year-end, contingent on regulatory clearance.
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