This line of thinking doesn't really correspond to the reason Transformers were developed in the first place, which was to better utilize how GPUs do computation. RNNs were too slow to train at scale because you had to sequentially compute the time steps, Transformers (with masking) can run the input through in a single pass.
It is worth noting that the first "LLM" you referring to was only 300M parameters, but even then the amount of training required (at the time) was such that training a model like that outside of a big tech company was infeasible. Obviously now we have models that are in the hundreds of billions / trillions of parameters. The ability to train these models is directly a result of better / more hardware being applied to the problem as well as the Transformer architecture specifically designed to better conform with parallel computation at scale.
The first GPT model came out ~ 8 years ago. I recall when GPT-2 came out they initially didn't want to release the weights out of concern for what the model could be used for, looking back now that's kind of amusing. However, fundamentally, all these models are the same setup as what was used then, decoder based Transformers. They are just substantially larger, trained on substantially more data, trained with substantially more hardware.