“The ChatGPT moment for physical AI is here” said Jensen Huang in early 2026.
But it is slow.
Because data is missing.
Training robots’ brains at scale is either a bankruptcy exercise (collecting real-world data can cost billions of dollars), or technologically impossible today (traditional simulators have perfect math but Tetris-like visuals and GenAI has Hollywood grade content but remains a slot machine that randomly hallucinates physics).
To fix that, we’re building the Matrix for Physical AI.
Our Neuro-Kinematic Engine (NKE) is a reality-grade world generator that can "manufacture" reality to provide a hyper-fast training arena for robotic AIs.
To train a robot to fold a shirt or avoid big rocks on Mars, we can generate not just the visuals of these scenarios but also their constitutive physical equations and edge-case variations.
By creating custom and cost-effective “synthetic nervous systems” that can be downloaded into robots, we could provide them with 1,000 days of real-world experience in ~1 hour.
We are also building the quantum primitives to offload procedural world generation to quantum computers when they mature and make our platform exponentially faster.
By helping to make tomorrow’s autonomous hardware safer and smarter with more speed, our technology aims to power global GDP and return human time so we can live richer lives.