I'd argue both areas are successfull because they found good ways to solve the data problem and go "big data" and the use case while nice and shiney now is not

I'd argue both areas are successfull because they found good ways to solve the data problem and go "big data" and the use case while nice and shiney now is not as relevant. Big models trained on text work, because there's a lot of text availabe and the task of predicting the next tokens is nice because you get autolabeled data just by collecting a lot of text. Similarily RL solves this issue by simulations of sorts (supersimplified play a lot of games against eachother, judge somehow cleverly what was better, update stuff, rinse, repeat). I distinctly remember this lightbulb going off in my head when I first played with NLP. I came from ML for computer vision and trained ULMFiT on the entire Wikipedia and was thinking...oh neat no labeling that's magical, if I had more compute I could basically feed this infinite data. I'm not following synthetic data closely (I've used it to train models before) but I see a bright future for this space (like game engines used to simulate big worlds simply to generate data or training in said worlds and transfering).