Generalist, a robotics foundation-model startup founded in 2024 by former Google DeepMind researcher...
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Generalist, a robotics foundation-model startup founded in 2024 by former Google DeepMind researchers Pete Florence and Andy Zeng and former Boston Dynamics engineer Andrew Barry, raised nearly $200 million in additional capital led by 8VC, bringing its reported valuation to $3 billion. The money is an extension of a $400 million Series B led by Radical Ventures that the company announced in June at a $2 billion valuation; the extension lifts total Series B proceeds to $600 million, according to people familiar with the matter and a regulatory filing. Generalist and 8VC did not respond to requests for comment.
The company is building an AI foundation model designed to work across robot types. Its newly released Gen 1.5 model is said to let robots master new tasks from video demonstrations as short as 3 to 12 seconds. Early backers include 8VC, Radical Ventures, Nvidia, Union Square Ventures, Bezos Expeditions, and AI researcher Fei-Fei Li. Until recently the startup stayed quiet; it is now working with a handful of customers to tailor the model for specific use cases.
It is not alone. Physical Intelligence is reportedly valued around $11 billion, SoftBank-backed Skild AI around $14 billion, and Genesis AI was recently in talks near a $3 billion valuation. The capital surge reflects a bet that robotics may approach a general-task “ChatGPT moment,” even as some VCs warn that robots cannot be trained on the open internet the way LLMs are, so a truly general robotics model may still be years away.
The raise updates Segment 10’s Player Map (10.§2) for a DeepMind/Boston Dynamics–founded embodied-foundation entrant stepping from $2B to $3B within months, and supplies fresh capital-cycle evidence (cross.§D) plus talent provenance (cross.§C) for Open Debate Frame 1—the embodied-foundation bull that a universal robot brain is the horizontal layer attracting concentrated capital (10.§7)—while Frame 2 skeptics still argue task-specific systems dominate real floors.