The summer that BERT came out I was working at a startup that was using character-based CNN models for classification. We were thinking a lot about alternate representations, other members of the team were keen on word vectors but I wasn't, particularly because it seemed the documents were were working on frequently had out-of-dictionary words, because those words were important, and because discarding them would lead to failure.

(We were working on "foundation models" too, so it's not just being out-of-dictionary in the final model that's a problem but being out-of-dictionary in the foundation model which is more expensive to train.)

We were doing OK with character based models for classification but people believed that storing the "dictionary" inside the neural net was not a good use of the neural net so there was a lot of enthusiasm for tokens.

Meanwhile I felt so sure that schemes like Word2Vec were doomed that I had left an earlier project using RNNs where the goal was text understanding with a foundation model made by training an RNN to write fake abstracts for case reports from PubMed.

When byte-pair encoding was introduced I remember telling people in a meeting that it was the first tokenization scheme we'd looked at that I could endorse.

I have to admit though that I wish we could work at the character label.