General Intuition, which is developing a foundation artificial intelligence model with potential applications in robotics, is finalizing a funding round that would value the company at $6 billion, TechCrunch reported Monday (Aug. 24), citing unnamed sources.
The company’s AI model was developed using hundreds of millions of hours of gameplay from General Intuition CEO Pim de Witte’s video game clip-sharing platform, Medal, and trains generalized AI agents to move through time and space, according to the report.
General Intuition plans to use the new capital to invest in additional compute infrastructure and talent to improve its general model, with a focus on robotic embodiments, per the report.
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General Intuition did not immediately reply to PYMNTS’ request for comment.
General Intuition announced an earlier funding round two months ago, saying in a June 25 post on X that it raised $320 million in a Series A round at a $2.3 billion valuation. The company said the round was led by Khosla Ventures, former Google Executive Chairman and CEO Eric Schmidt and Amazon founder Jeff Bezos.
General Intuition announced in an Oct. 25, 2025, post on X that it raised $133.7 million in a seed round.
In the June 25 post, the company said: “General Intuition is the frontier lab for acting in space and time. We build large action foundation models trained on billions of ground truth action-labeled gameplay clips from [17 million] monthly active users on Medal, and push the frontier of world models to generate infinite training environments.”
Several announcements around world models have been made in recent months.
It was reported June 17 that Amazon invested in and launched a partnership with Odyssey, a startup that builds AI systems designed to simulate the physical world. Odyssey’s world models can create 3D environments. The round raised $310 milion and valued Odyssey at $1.45 billion.
Chinese tech giant Alibaba announced June 15 that it unveiled its first suite of AI models for robots. The suite includes Qwen-RobotNav, which helps robots understand how to navigate physical spaces; Qwen-RobotWorld, a video “world model” that allows a robot to predict how a physical scenario will unfold; and Qwen-RobotManip, which enables cross-embodiment training at scale.
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