One way to make a large AI model efficient is to build it from many specialised parts and, for each request, use only the few parts that are relevant rather than the whole thing. This design has become widespread in 2026 because it allows a model to be very large in total knowledge while keeping the cost of each answer manageable.
The approach lets developers grow a model's capacity without a matching jump in running cost. The engineering is tricky, since the system must decide which parts to use for each input. When it works, users get the benefit of a big model at closer to the cost of a small one, which is part of why running capable AI has grown cheaper.