General Intuition lands $134M seed to teach agents spatial reasoning using video game clips | TechCrunch

"Medal, a platform for uploading and sharing video game clips, has spun out a new frontier AI research lab that's using its trove of gaming videos to train and build foundation models and AI agents that can understand how objects and entities move through space and time - a concept known as spatial-temporal reasoning. Called General Intuition, the startup is betting that Medal's dataset - which consists of 2 billion videos per year from 10 million monthly active users across tens of thousands of games"

""When you play video games, you essentially transfer your perception, usually through a first-person view of the camera, to different environments," Pim de Witte, CEO of Medal and General Intuition, told TechCrunch. He noted that gamers who upload clips tend to post very negative or positive examples, which serve as really useful edge cases for training. "You get this selection bias towards precisely the kind of data you actually want to use for training work.""

"This data moat is what reportedly attracted the attention of OpenAI, which late last year attempted to acquire Medal for $500 million, per The Information. (Neither OpenAI nor General Intuition would comment on the report.) It's also what has led to General Intuition's raising a whopping $133.7 million in seed funding, led by Khosla Ventures and General Catalyst with participation from Raine."

General Intuition is a research lab spun out from Medal that leverages Medal's large-scale gaming clip dataset to train foundation models and AI agents for spatial-temporal reasoning. Medal's dataset collects roughly 2 billion videos per year from 10 million monthly active users across tens of thousands of games, with user clips often containing strong negative or positive edge cases useful for training. The dataset reportedly drew acquisition interest and enabled a $133.7 million seed round led by Khosla Ventures and General Catalyst. The lab focuses on building agents that learn from first-person visual input to predict actions and generalize to unseen environments, targeting gaming and search-and-rescue drone applications.

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