Embodied general intelligence
We’re building foundation models that break neural scaling laws to make robots efficient, explainable, and general
Our Vision
Scale-is-all-you-need doesn’t scale
Solution
We see a future where advanced intelligence is seamlessly integrated into daily life—powering robotics, autonomous vehicles, generative media, and scientific discovery. But today’s transformer-based models are power-hungry, data-intensive, and hard to control—limiting their ability to scale into real-world, embodied intelligence.
Scaling today’s models requires power-plant-level energy, hundreds of millions of dollars, and months of training. Recent results now cast doubt on whether scaling laws will continue to hold at all.
We need a new path to intelligence.
We’re building next-generation frontier AI built on new mathematical principles discovered in neuroscience and geometric deep learning. These structures unlock orders of magnitude better scaling than the transformer-based architectures and enable a future of embodied intelligence.
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Our Vision
We see a future where advanced intelligence is seamlessly integrated into daily life—powering robotics, autonomous vehicles, generative media, and scientific discovery. But today’s transformer-based models are power-hungry, data-intensive, and hard to control—limiting their ability to scale into real-world, embodied intelligence.
Scale-is-all-you-need doesn’t scale
Scaling today’s models requires power-plant-level energy, hundreds of millions of dollars, and months of training. Recent results now cast doubt on whether scaling laws will continue to hold at all.We need a new path to intelligence.
Solution
We’re building next-generation frontier AI built on new mathematical principles discovered in neuroscience and geometric deep learning. These structures unlock orders of magnitude better scaling than the transformer-based architectures and enable a future of embodied intelligence.
We’re building it
We are building structured models inspired by the adaptation of brains to the natural world. These models have geometrically efficient architectures that allow for state of the art performance on robotic tasks.
