According to God of Prompt, the major breakthrough in scalable mixture-of-experts (MoE) AI models came with the introduction of load balancing losses and expert capacity buffers, which resolved the critical training instability that plagued the original 1991 approach. Previously, gradients collapsed when using hundreds of experts, causing some experts to never activate while others dominated. By implementing these simple yet effective mechanisms, modern AI systems can now efficiently utilize large numbers of experts, leading to more robust, scalable, and accurate models. This advancement opens significant business opportunities for deploying large-scale, cost-efficient AI systems in natural language processing, recommendation engines, and enterprise automation (Source: @godofprompt, Jan 3, 2026).