The human brain performs roughly 10^15 synaptic operations per second — and it does this on approximately 20 watts of power. A single A100 GPU, by comparison, draws 400 watts while performing far simpler pattern-matching tasks. This is not an engineering gap. It is a paradigm gap.
The problem with scaling
The last decade of AI progress has been built on one insight: scale works. More data, more parameters, more compute — and the results improve. But scaling is not free. Training large language models consumes energy equivalent to thousands of transatlantic flights. Inference costs are accumulating at a rate that no energy grid was designed to absorb. The trajectory is unsustainable.
What neuromorphic computing actually does
Neuromorphic chips don't process information the way conventional processors do. Instead of clocked, synchronous operations on dense matrices, they use spiking neural networks — sparse, asynchronous, event-driven computation that only activates when there is something to process. Silence costs nothing. This is how biology handles the problem: the brain doesn't run all its neurons at full power all the time. It spikes selectively.
Why this matters now
We are at an inflection point. The hardware exists. The theoretical foundations — spike-timing dependent plasticity, integrate-and-fire neuron models, memristive synapses — are mature enough to build on. What has been missing is a focused engineering effort to close the gap between biological plausibility and deployable intelligence. That is what we are building.
The question is no longer whether neuromorphic computing can work. The question is who builds it first — and what they build it for.
