Robotics Startup Generalist Reaches $3 Billion Valuation, According to Reports

Generalist, a startup in the robotics industry, has reached a valuation of $3 billion after a recent funding round led by 8VC, according to sources close to the transaction.

The funding round raised nearly $200 million, based on a regulatory filing. This follows a $400 million Series B funding round initiated by Radical Ventures in June, where the company’s valuation was estimated at $2 billion, according to the same sources. With this latest capital influx, the total raised in this round now totals $600 million.

Neither Generalist nor 8VC have responded to inquiries for comments.

Established in 2024 by former Google DeepMind researchers Pete Florence and Andy Zeng, along with ex-Boston Dynamics engineer Andrew Barry, Generalist initially received backing from 8VC and Radical Ventures, as well as notable investors including Nvidia, Union Square Ventures, Bezos Expeditions, and AI expert Fei-Fei Li.

Until recently, Generalist has been operating mostly under the radar with minimal media coverage.

The startup is focused on creating an AI foundation model aimed at improving robot interactions. Generalist claims that its newly released Gen 1.5 model enables robots to learn new skills through short video demonstrations that last between 3 and 12 seconds.

One source mentioned that Generalist is working with a select group of clients to tailor the model for specific use cases based on their feedback.

Generalist faces competition in the pursuit of a versatile neural framework applicable to diverse robots. Rivals such as Physical Intelligence, valued at approximately $11 billion, SoftBank-backed Skild AI, estimated at around $14 billion, and Genesis AI, reportedly seeking funding last month at a $3 billion valuation, are also prominent players in this competitive landscape.

The surge in investment suggests that some investors anticipate the robotics sector approaching its own “ChatGPT moment,” indicating a future where robots can execute multiple tasks without needing specific training for each task. However, due to the complexities of training robots with the vast amounts of internet data utilized by large language models, some venture capitalists warn that achieving a truly universal robotics model could still take several years.