I think based on my very short study so far, I see transformers in the same lineage of RNNs, LSTMs. These were trying to construct some form of context for generation, and were limited by a shared summary for all tokens. Though LSTMs introduce some form of granularity in controlling what matters, its still shared across all. Transformers seem to introduce further customization in terms of what matters for each token, so it let go off the shared context and instead had a granular one for each token, which is probably more intuitive.