Earlier sequence models read text one step at a time, which degraded information over distance and could not be parallelised. The transformer discarded that, letting every position look at every other position simultaneously.
That change made the computation a few large matrix multiplications — exactly what accelerators are built for. Transformers did not only perform better; they could absorb far more compute, which is what made scaling exploitable.