Technology brief
Why silicon needs a new paradigm
Today, humanoid robots are in their infancy. A tethered, teleoperated robot in familiar settings is less autonomous than an 18-month-old. The real potential comes when they cut the tether and run multiple models across domains — and ultimately when machines enter novel, high-consequence settings, with airplane-grade redundancy and learning happening on the body itself.
The chip has to exist before the capability does
The next generation of machines needs 20–100× more intelligence than today's on-robot silicon delivers, inside a power and latency envelope that barely moves. You don't close a hundredfold gap under a doubled power ceiling with more of the same silicon. That's not a scaling problem. It's an architecture problem.
This is why silicon cannot simply evolve. It must be re-engineered.
And the architecture won't hold still
World models are advancing faster than silicon cycles. Every company that ever built a chip for AI taped out against a workload that had already changed by the time it came back. The only way to hit a target that keeps moving is to re-architect at the speed the models move.
That's why we built the design engine first
Our AI-native silicon design engine generates architecture, digital, and analog. It spans the full signal chain, and every design cycle makes it better.
The design engine is the foundation that makes everything else possible.
- Architected for world models
- Built for embodied workload
- Optimized for Time-to-Learning