Current AI systems face fundamental limitations that scaling alone cannot solve:

The Von Neumann Bottleneck

Traditional computing separates memory from processing, forcing constant data transfer that consumes 60% of energy and creates the "memory wall" limiting AI performance.

Unsustainable Energy Demands

LLMs already require gigawatt-hours to train. Energy consumption scales exponentially with model size, while performance gains plateau—a fundamental mismatch for sustainable intelligence.

Brittle Intelligence

AI systems lack adaptability, struggle with causal reasoning, and fail when encountering scenarios outside their training distribution—fundamental gaps that more data cannot bridge.

Our Solution

Physics-Native Intelligence

We're building intelligence that operates directly through physical processes—where computation, memory, and adaptation emerge naturally from material properties rather than digital simulation.

Event-Driven Computing

Information flows only when needed, eliminating wasteful continuous processing and achieving brain-like energy efficiency.

In-Memory Computation

Memory and processing are unified at the device level, breaking the von Neumann bottleneck through neuromorphic architectures.

Adaptive Learning

Systems that continuously adapt to new environments without catastrophic forgetting, enabling lifelong learning and robust generalization.

"The brain achieves superintelligence with 20 watts.
We're building systems that work the same way."

The Problem