Earlier quoted context omitted.
More compute also requires more data - scaling equally with model size, according to the Chinchilla paper. How much more data is available that hasn't already been swept up by AI companies? And will that data continue to be available as laws change to protect copyright holders from AI companies?
It's not just the volume of original data that matters here. From empirics we know performance scales roughly like (model parameters)*(training data)*(epochs). If you increase any one of those, you can be certain to improve your model. In the short term, training data volume and quality has given a lot of improvements (especially recently), but in the long run it was always model size and total time spent training th…
Is there any reason to think the same thing wouldn't happen in billion parameter LLMs?