Spatial AI: the world as a living field of points.
"Spatial AI" is the term the field reached for after spatial computing, and it is still a buzzword in search of a definition. The lab proposes a concrete one. A spatial model should hold the world as an explicit field of points — Gaussian splats, the representation that captures real geometry and appearance — and it should also be a world model that knows how that field moves: the true dynamics of the scene, not a frozen scan. Spatial AI is the mash-up of the two, a living splat field that predicts. Most of the field builds this in the datacenter; the Swap-2C constraint is the opposite — it has to run at the edge, on the low-power device that lives in the world it is modeling. The representation stays explicit and inspectable, the dynamics stay grounded, and the whole thing is accounted in joules on the MathGround substrate. A world model the size of a robot, not a server farm.
A live field of Gaussian splats — depth-sorted and rendered in real 3D on the device, explicit and inspectable, and predicting its own motion: the orange ghost is the field's forecast +Δt ahead. Drag to orbit. Synthetic scene, illustrative.
In the field · World Labs frames a world model as renderer → simulator → planner; the running Gaussian-splat world models (GaussianWorld, GEM) already predict an evolving volume, and on-device splatting is now real (Mobile-GS, ~1000 FPS at a few MB). The open axis is dynamics at the edge — a splat field that predicts, on the low-power body. That is what Spatial AI targets.
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