According to @godofprompt, researchers have developed a neuromorphic AI chip that mimics how biological neurons process information, utilizing the Neural Engineering Framework (NEF) to map computations onto spiking neural networks. Published in Nature Communications Scientific Reports, this prototype chip achieves pattern recognition accuracy comparable to traditional deep learning models while consuming just 1% of the energy (source: @godofprompt, Oct 30, 2025). Unlike traditional AI chips that rely on billions of multiply-accumulate operations, this neuromorphic hardware uses sparse, event-driven spikes, dramatically reducing power usage. The breakthrough demonstrates a fundamentally different computing paradigm, opening up new business opportunities in edge AI, IoT, and sustainable large-scale AI deployments by addressing the energy bottleneck in AI applications.