Before it
Neural nets processed fixed-size inputs with no notion of order.
The problem
Language arrives as a sequence, and context from earlier words has to persist.
What it introduced
A recurrent hidden layer that feeds its own previous state back in, giving the network a memory of what came before.
Why it matters
It established that sequence can live in a network's internal state rather than in its input window.
What modern models inherit
The idea of a running hidden state summarising everything read so far.
What came next
LSTM fixed the gradient problems this design exposed.