We'd love to hear from you
Whether for partnerships, support, or general inquiries, you can reach us below
Palo Alto, CA
London, GB
Our team is recruited from
We're backed by
Elad Gil
Jeff Dean
Guillermo Rauch
Garry Tan
Our team is recruited from
We're backed by
Elad Gil
Jeff Dean
Guillermo Rauch
Garry Tan
Our team is recruited from
We're backed by
Elad Gil
Jeff Dean
Guillermo Rauch
Garry Tan
World Model
Odyssey-2
Our most powerful general purpose world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Starchild-1
A step beyond world models that learn only from visual observation, toward systems that learn from richer multimodal interaction with the world
World Model
Agora-1
A multi-agent world model, enabling multiple participants—human or AI—to share and interact within the same world simulation in real-time
Reinforcement Learning
PROWL
A novel RL-driven adversarial framework where an RL agent explores game environments with the objective to improve world model performance
World Model
Odyssey-2
Our most powerful general purpose world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Starchild-1
A step beyond world models that learn only from visual observation, toward systems that learn from richer multimodal interaction with the world
World Model
Agora-1
A multi-agent world model, enabling multiple participants—human or AI—to share and interact within the same world simulation in real-time
Reinforcement Learning
PROWL
A novel RL-driven adversarial framework where an RL agent explores game environments with the objective to improve world model performance
World Model
Odyssey-2
Our most powerful general purpose world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Starchild-1
A step beyond world models that learn only from visual observation, toward systems that learn from richer multimodal interaction with the world
World Model
Agora-1
A multi-agent world model, enabling multiple participants—human or AI—to share and interact within the same world simulation in real-time
Reinforcement Learning
PROWL
A novel RL-driven adversarial framework where an RL agent explores game environments with the objective to improve world model performance
World Model
Odyssey-2
Our most powerful general purpose world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Starchild-1
A step beyond world models that learn only from visual observation, toward systems that learn from richer multimodal interaction with the world
World Model
Agora-1
A multi-agent world model, enabling multiple participants—human or AI—to share and interact within the same world simulation in real-time
Reinforcement Learning
PROWL
A novel RL-driven adversarial framework where an RL agent explores game environments with the objective to improve world model performance