[D] How useful is DCN compared to good old MLP?

In industrial large scale search/recommendation context, DCN seems to be still a popular kid on the street, compared to more straightforward MLP. The idea feels more or less like ResNet, where the original raw input keeps showing up at every layer. But is the element-wise product of layer output of raw input really adding any value here? If so why is it not adopted by the transformer architecture?

context element every good industrial kid layer machinelearning mlp popular product raw recommendation resnet scale search street wise