It's definitely part of many intro explanations, but it sits in a weird position where you have to know MLPs well but not Transformers for this to be enlightening, which is a shrinking set of people nowadays. Either someone is a novice to all this, or know both, so beginner Transformer explanations can rarely assume deep familiarity with MLPs.

But yes, a Transformer block can be thought of as basically input-dependently deciding the weight vector of a dense layer. In classical MLP-like networks there was never any multiplication between input-dependent values (there are exceptions, like Squeeze-and-Excitation layers). Multiplication is always between parameters (that don't depend on the input) and activations that depend on the input. With Transformers attention provides a lot of multiplicative interactions between input dependent activations.

Also obligatory: Schmidhuber talked about this a long time ago.