TL;DR sort of yes. But they're also useful for reasons not related to computational "power".
An example here with an actual algorithm, although it's been a couple of years so my explanation might be a bit wrong in places. and/or i might have gotten the completely wrong end of the stick with the current thread.
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The CTC (Connectionist Temporal Classification [0]) algorithm maps a sequence x with length X -> sequence y with length Y.
i.e. in speech to text we might have some audio features that correspond to the following class predictions (post softmax classification)
x -> hellllloooooooooo wwwooorrrllld
we want to get this as the output
y -> hello world
we have the alphabet as classes we try to predict for each sequence item in x.
we could just removed all the duplicate in the first long sequence, but we would end up with `helo world` ... we need to preserve one of the early `l` characters in `hello` somehow
CTC uses a blank token (aka dummy) token to handle potentially deliberately repeated items in sequence x.
By adding the blank token to the classes predictions, we can get the model to predict something like this (post softmax classification)
y* -> hel~l~~oooo~~~~~~ w~~o~~r~~l~~d
The CTC decoder (non-ML decoding algo) heuristically removes repeated tokens. Turning the above into ...
y -> hello world
... the duplicate `o` and `~` characters are removed.
It was a decent enough algorithm for speech-to-text prior to attention/transformers etc.
However, it makes CTC vulnerable to well designed adversarial example attacks because there is a massive bias within models to predict the blank token -- meaning it's very easy to modify input sequence x to switch the output sequence y to include blank tokens for nefarious purposes (the subject of my unfinished phd).
[0]: www.cs.toronto.edu/~graves/preprint.pdf