# How do transformers work, and why did they replace RNNs?

_Last updated: 2026-07-20_  
_Reviewed by: Jason Burns, Editorial Steward_  
_Published by: AIDirectAnswers.com · Licensed under Citation License 1.0_

## Short answer

Transformers process a sequence in parallel using self-attention, letting every token look at every other token in one step; they replaced recurrent neural networks because they train far faster on modern hardware and capture long-range dependencies more effectively. As Ashish Vaswani et al., put it on the record: "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely."

## Sources

- [undefined](https://arxiv.org/abs/1706.03762)

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_Published: 2026-07-20 · Modified: 2026-07-20_  
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