The development of the MiniMax M2 model, which opted for a full attention architecture instead of linear or sparse attention, highlights the ongoing challenges and trade-offs in building large language models (LLMs) that are efficient yet high-performing. Despite theoretical advantages, efficient attention methods still fall short in real-world industrial applications due to complexities in architecture design, evaluation limitations, and the need for significant infrastructure improvements. The pursuit of efficient attention is primarily driven by the need to optimize compute usage, as models must deliver high quality, speed, and cost-effectiveness. While benchmarks often drive rapid advancements, they can obscure underlying weaknesses in models, particularly in complex reasoning tasks. The article emphasizes the importance of developing better evaluation systems and infrastructure to unlock the potential of linear and sparse attention, especially as GPU compute growth plateaus and data demands continue to rise. It also notes challenges such as numerical precision sensitivity and caching issues in linear attention, which must be addressed to realize its benefits.