AI-native Silicon Engineering

Building Frontier Silicon for embodied intelligence

Where we started

Engineering has always depended on human reasoning. AI is changing that. For the first time, we can build AI-native engineering systems that can reason, orchestrate, and execute complex engineering work alongside them. We believe this marks the beginning of a new era. That belief is why we founded Tayen.

We started with one of the hardest engineering challenges in the world: designing advanced silicon. We reimagined the engineering process around AI. The result is an AI-native silicon design engine that designs chips an order of magnitude faster while using a fraction of the resources required by conventional approaches.

We paired that engine with our own architectural innovations and focused it on one of the most ambitious frontiers in computing: the silicon that will power embodied intelligence.

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.

HIGH LEVEL LOW LEVEL ~1 year 3–5 years 6–8 years Level 3 Child/elder care, travel companion Infrastructure-independent Multi-domain Novel high-consequence settings Many models running Heavy redundancy (airplane level) On-body learning regime Level 2 Cooking, dog walking Untethered Multi-domain Familiar settings Multiple models running Partial redundancy No learning on body Level 1 Making bed, dishes, laundry Tethered Simple tasks Familiar settings Teleoperated No learning on body

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.

1× 20× 100× Sustained compute 1× 10× 50× Memory capacity 1× 20× 100× Memory bandwidth 1× 1.5× 2× Power envelope Level 1 Level 2 Level 3

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.

Physical World Models VLA predicts action JEPA predicts embedding Spatial predicts geometry Generative predicts pixels models structure models dynamics A plausible view and not a settled architecture HIGH LEVEL LOW LEVEL Reasoning & planning imagine consequences in a compact space JEPA World simulation & training data render futures and edge cases to train and evaluate the rest Generative Spatial substrate (the world to act in) explicit, persistent geometry Spatial Fast real-time control turn intent into motor commands at hundreds of Hz VLA

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.

1 The AI engine explores the design space 2 Select winners 3 Tape out the best chips 4 Post-silicon trains the engine. The next generation is better. ~70% digital ~30% analog
  • Architected for world models
  • Built for embodied workload
  • Optimized for Time-to-Learning