The rise of SLMs also challenges the traditional AI development paradigm. Instead of throwing more parameters at problems, developers are focusing on architectural efficiency and targeted training. This shift has led to innovations in model compression, knowledge distillation, and specialized architectures that squeeze maximum performance from minimal resources.