Infrastructure for world models, simulation and embodied intelligence
World Labs · Spatial Intelligence
World models learn geometry, physics, motion and time from massive multimodal datasets. Training them—and simulating persistent 3D environments for robots, science and design—turns compute into the core production resource.
Explore the world-model thesisWhy it matters for Cubed: physical intelligence needs continuous training, video generation and simulation at a scale that makes compute the market.
Match workloads to GPU capacity
Generate persistent 3D environments
Learn physics, motion and causality
Feed real-world outcomes back into training
Cubed coordinates the full physical-AI workload: multimodal data processing, GPU simulation, world-model training, evaluation and low-latency deployment. Builders pay for verified compute outcomes while providers earn from useful capacity.
Physical AI needs more than an API endpoint. Cubed turns fragmented GPU supply into a verifiable compute network for simulation, multimodal training and real-time inference—priced by workload and measured by results.
// Closed Source Approach
class ProprietaryAI {
private blackBoxModel;
private restrictedAccess;
moveFastBreakThings() {
// Rapid deployment
// Unknown safety implications
// Limited oversight
}
}
// Compute-Native Cubed Approach
class Cubed {
public worldModel: SpatialIntelligence;
public computeNetwork: VerifiedGPUCapacity;
defensiveAcceleration() {
// Simulate before deployment
// Verify every compute job
// Learn from real-world feedback
return this.trainInSimulation();
}
}
Cubed is creating a transparent compute marketplace where world-model builders can access, price and scale the infrastructure required for simulation and physical AI.
