The row-lookup explanation makes the static token embedding easy to understand. I would qualify the claim that magnitude carries no meaning, though: whether vector norm is useful depends on the training objective and the similarity function the particular model was trained to use.
For a retrieval model, normalizing a dot-product-trained representation can change rankings rather than merely speed up the same calculation. A small example with two vectors sharing a direction but having different lengths would show that difference directly. Keeping token-table embeddings, contextual hidden states and pooled sentence embeddings separate would also help readers avoid applying the lookup-table explanation unchanged to the vectors they store in a retrieval index.