I took care to point out deployed models. I did not say the models did no learning. In fact, one can draw an analogy from evolution and instinct to offline training/hyperparameter search/architecture tweaks and deployed model.

And, technically, the manner by which most nets are trained, via (minibatched) stochastic gradient descent is online. But the architectures are so ill-adapted to learning continuously and backprop is so data inefficient that retraining on new data is often just better done from scratch (or at best, freezing weights).