What happened
General Intuition, a startup building foundation models for embodied AI, raised $320 million last month at a $2.3 billion valuation. As reported by TechCrunch, the company's model is trained on millions of hours of video game data, including controller inputs. The company demonstrated its model powering a quadrupedal robot after fine-tuning it on only eight minutes of real-world robotics data. General Intuition aims to provide a base model for other robotics companies to build on.
How the room's reading it
The chatter around this frames it as a potential 'ChatGPT moment' for robotics. Proponents, including CEO Pim de Witte, argue that the industry is on the cusp of a shift away from specialised models built on huge, real-world datasets. Their thesis is that foundation models trained on high-quality synthetic data — like video games with player action data — can develop a general 'intuition' about movement and interaction. This approach, they claim, will make much of the current bespoke robotics work redundant, dramatically lowering the barrier to entry for building physical AI.
Sailfish's take
We think the core idea here is right — the future of robotics is foundation models, not bespoke systems built from scratch. Chasing massive real-world datasets has been a bottleneck for years. Using synthetic data from games to build a base intuition for physical space is a smart way to cut that knot. The claim of fine-tuning a robot with just eight minutes of data is the part to watch closely. If that holds up across different tasks and hardware, it's a massive shift. We'd bet this approach works for general navigation, but we're sceptical it solves for high-stakes, precision tasks without much more real-world data.