In my unpopular opinion, it wasn't transformers or attention, but pretraining on language data that kicked off the LLM Boom. Alec radford in his little jupyter notebook trained a very small non-transformer to predict simply the next-character on Amazon reviews. He noticed emergence of a neuron which when toggled controlled the sentiment of the remaining text. This is published as the sentiment neuron paper.

Till then, transformers were being used primarily for stuff like translation and such and no one was even pretraining at scale, even tho transformers and attention existed.

Openai and google if you count T5 persisting with pretrained generative models was what led to the LLM boom. Yes they used transformers, but that's just one IMO minor aspect.