Most world models forget. Ours can't afford to.
A generated world is only useful if you can trust it to stay the same between glances, run fast enough to react to, hold up when more than one mind is inside it, and run close to where it's needed, on the robot and at the edge, not only in a datacenter. We're building the parts that make that true. Here's the thinking, kept deliberately vague.
Five bets we're making.
Persistence is the product
The hard problem isn't generating one beautiful frame. It's generating the ten-thousandth frame and having it agree with the first. We treat long-horizon consistency as the core metric, not a stretch goal, and we've built retrieval and memory machinery specifically to keep a world coherent as it grows.
Real-time or it doesn't count
Interactivity dies at high latency. We optimize aggressively for time-to-first-output and sustained throughput, because a world you can't act inside of on human reaction timescales is a video, not a model.
Built for more than one
Single-player generation is a solved-enough demo. The interesting frontier is shared: multiple agents and multiple people occupying one environment and seeing a version of it that stays mutually consistent. We're designing for that from the ground up.
Model-agnostic where it matters
The world moves fast, and today's best base model won't be next quarter's. Our consistency and acceleration layers are designed to plug into whatever the strongest generator turns out to be, so you're never betting on a single checkpoint.
On-device and hybrid, not cloud-only
For robotics and embodied AI, the world model has to live close to the machine. A robot can't stake its next move on a datacenter round trip. We build for a compact model on-device that carries the tight reactive loop, working hand in hand with heavier cloud compute for the parts that can afford the latency. Low latency, data that can stay local, and a world that keeps running with or without a connection.
If any of these are your problem, we should talk.
Interactive simulation
Training and evaluation environments that need to stay consistent across long rollouts.
Robotics & embodied AI
On-device, hybrid world models that let a robot look ahead and plan on a reactive loop, without a datacenter round trip.
Agentic systems
Agents that need a rich, reactive world to learn in, and a consistent one to be tested against.
Generative worlds
Interactive experiences where the environment is generated live rather than authored frame by frame.