From multi-head to latent attention: The evolution of attention mechanisms

The Transformer was only ever designed to be a better seq-2-seq architecture, so "all you need" implicitly means "all you need for seq-2-seq" (not all you need for AGI), and was anyways more backwards looking than forwards looking.

The preceding seq-2-seq architectures had been RNN (LSTM) based, then RNN + attention (Bahdanau et al "Jointly Learning to Align & Translate"), with the Transformer "attention is all you need" paper then meaning you can drop use of RNNs altogether and just use attention.

Of course NOT using RNNs was the key motivator behind the new Transformer architecture - not only did you not NEED an RNN, but they explicitly wanted to avoid it since the goal was to support parallel vs sequential processing for better performance on the available highly parallel hardware.