Where this term shows up Mixture-of-experts architectures appear in this channel's material in the context of conversations about how modern AI systems are built and scaled — part of the broader exploration of intelligence, machine capability, and human meaning captured in How WIRED's Kevin Kelly Predicts the Future After 40 Years in Tech and the related topic AI and Human Meaning. How to understand the idea The core move of an MoE design is division of labor. Rather than every parameter responding to every input, the model routes each input to the experts best suited to it. This lets a system hold far more total capability while staying economical per request — a trade-off worth understanding when weighing claims about how powerful or efficient a given AI system really is. - An MoE architecture splits a model into specialized experts plus a router/gating network that activates only a subset per input.- It increases a model's total capacity while keeping per-input compute modest — the trade-off behind many large-scale AI systems.- Use the term when discussing scaling and efficiency of modern AI models, as this channel's AI-and-human-meaning material does.