A short history:
1986: Geoffrey Hinton publishes the backpropagation algorithm as applied to neural networks, allowing more efficient training.
2011: Jeff Dean starts Google Brain.
2012: Ilya Sutskever and Geoffrey Hinton publish AlexNet, which demonstrates that using GPUs yields quicker training on deep networks, surpassing non-neural-network participants by a wide margin on an image categorization competition.
2013: Geoffrey Hinton sells his team to the highest bidder. Google Brain wins the bid.
2015: Ilya Sutskever founds OpenAI.
2017: Google Brain publishes the first Transformer, showing impressive performance on language translation.
2018: OpenAI publishes GPT, showing that next-token prediction can solve many language benchmarks at once using Transformers, hinting at foundation models. They later scale it and show increasing performance.
The reality is that the ideas for this could have been combined earlier than they did (and plausibly future ideas could have been found today), but research takes time, and researchers tend to focus on one approach and assume that another has already been explored and doesn’t scale to SOTA (as many did for neural networks). First mover advantage, when finding a workable solution, is strong, and benefited OpenAI.