Before it
Models had a fixed word vocabulary and an unknown token for everything else.
The problem
Rare words, names, morphology and other languages fell outside the vocabulary entirely.
What it introduced
Byte-pair encoding: merge frequent character pairs to build a subword vocabulary that can spell anything.
Why it matters
This is why your tokenizer splits words into pieces, and why models count letters badly and handle numbers oddly.
What modern models inherit
Subword tokenization, still the input layer of essentially every LLM.
What came next
WordPiece in GNMT, SentencePiece, and the byte-level BPE in GPT-2 onwards.