Instead of manually collecting benchmark data on-site like competitors, Juxta simulates millions of movement paths in a 3D model of any space. This 'synthetic fingerprinting' approach allows them to make any location trackable remotely in under an hour, enabling massive scalability.

The primary challenge in robotics AI is the lack of real-world training data. To solve this, models are bootstrapped using a combination of learning from human lifestyle videos and extensive simulation environments. This creates a foundational model capable of initial deployment, which then generates a real-world data flywheel.

To overcome the data bottleneck in robotics, Sunday developed gloves that capture human hand movements. This allows them to train their robot's manipulation skills without needing a physical robot for teleoperation. By separating data gathering (gloves) from execution (robot), they can scale their training dataset far more efficiently than competitors who rely on robot-in-the-loop data collection methods.

Large language models are insufficient for tasks requiring real-world interaction and spatial understanding, like robotics or disaster response. World models provide this missing piece by generating interactive, reason-able 3D environments. They represent a foundational shift from language-based AI to a more holistic, spatially intelligent AI.

The push toward physical AI and spatial intelligence is primarily a strategy to overcome data scarcity for training general models. By creating simulated 3D environments, researchers can generate the novel, complex data that is currently unavailable but crucial for advancing AI into the real world.

Instead of simulating photorealistic worlds, robotics firm Flexion trains its models on simplified, abstract representations. For example, it uses perception models like Segment Anything to 'paint' a door red and its handle green. By training on this simplified abstraction, the robot learns the core task (opening doors) in a way that generalizes across all real-world doors, bypassing the need for perfect simulation.

Current multimodal models shoehorn visual data into a 1D text-based sequence. True spatial intelligence is different. It requires a native 3D/4D representation to understand a world governed by physics, not just human-generated language. This is a foundational architectural shift, not an extension of LLMs.

Beyond its primary positioning service, Juxta's operations will create a massive, proprietary dataset of labeled floor plans and satellite imagery. The founder envisions this byproduct becoming a hugely valuable asset, potentially sold to AI labs and creating a powerful, secondary business model.

Game engines and procedural generation, built for entertainment, now create interactive, simulated models of cities and ecosystems. These "digital twins" allow urban planners and scientists to test scenarios like climate change impacts before implementing real-world solutions.