dailyai.report

Human brains operate roughly 10,000x more energy-efficiently than modern AI training runs. This efficiency stems from sparse, local activation rather than the dense matrix multiplies used by frontier models. While mixture-of-experts architectures attempt to mimic this sparsity, the trade-off remains a hurdle for interpretability. Practitioners must balance computational efficiency against model transparency.