The energy cost of AI is becoming a serious constraint. Training a large language model can consume as much electricity as 500 transatlantic flights, and inference at scale is equally demanding. Chipmakers and research labs are exploring neuromorphic designs that process information using spikes of energy similar to biological neurons, potentially achieving AI performance at a fraction of current power requirements.
Early neuromorphic chips from Intel and IBM demonstrate promising results on specific tasks including sensory processing, pattern recognition, and time-series prediction. Commercial viability for large language model inference remains unproven, but venture capital is flowing into the subsector at a record pace. Several national laboratories have partnered with startups to explore neuromorphic computing for scientific simulation workloads.
