This blog post dives into the concept of mixture of experts (MoE) in AI, explaining how it allows large language models to operate more efficiently by utilizing multiple specialized models (experts) and a routing network to select relevant experts based on the input. It discusses the pros and cons of MoE models, particularly in terms of training complexity and cost efficiency during inference. The author also speculates on the future adoption of MoE architecture in both open and proprietary AI models.