As modern AI increasingly relies on large attention-based architectures, computational cost and energy consumption continue to grow. This talk explores Reservoir Computing (RC) as an attention-free learning paradigm capable of modeling complex dynamical systems with minimal training and significantly lower resource requirements. Drawing from applications in Digital Twins and cyber-physical systems, recent results on efficient prediction, online adaptation, and transfer learning will be presented. We will also discuss the broader potential of dynamical substrates as scalable alternatives for scientific machine learning in domains where efficiency, robustness, and real-time adaptation are critical.