DeepMind has developed a system that creates efficient sorting algorithms, particularly for small datasets of 2 to 8 elements, optimizing for minimal execution time and memory usage. This advancement allows for the generation of highly customized sorting functions that can outperform traditional library sorts in specific scenarios. The discussion highlights the historical context of sorting algorithms, noting that while hand-optimized assembly code was once common, modern compilers can achieve similar efficiencies. However, the practicality of such optimizations is questioned, as the time spent on fine-tuning may not justify the marginal gains in performance. Overall, the innovation represents a significant step in algorithm efficiency, particularly for cases where the number of elements is known at compile time.