Today we explore whether mechanistic interpretability could hold the key to building leaner, more transparent—and perhaps even smarter—large language models. From knowledge distillation and pruning to low-rank adaptation, we examine cutting-edge strategies to make AI models both smaller and more explainable. Join Arshavir as he breaks down the surprising challenges of making models efficient without sacrificing understanding.