TL/DR: In this paper, i demonstrate compelling case that the Transformer architecture is a holographic associative memory. The attention mechanism performs the Fourier Transform, the mathematical operation that describes holography. The MLP layer performs the inverse Fourier Transform, retuning information back to its original format. And the trainable parameters inside the Transformer act as a holographic film that records information. Transformers make predictions in a similar manner to how hopfield networks and holographic films perform pattern completions. If transformers work via holography, that opens a huge avenue for optimizing the transformer architecture, for example using physical light waves directly, instead of simulating them, which would result in 1000x increase of the training, processing speeds.