How did we use "all the data"? New knowledge appears on the internet every day, new scientific articles and videos are published.

At the speeds AI is moving, we've effectively used it all; the high quality data you need to make smarter models is coming in at a trickle. We're not getting 10^5 Principia Mathematicas published every day. Maybe I just don't have the vision to understand it, but it seems like AI-generated synthetic data for training shouldn't be able to make a smarter model than whatever produced that data. I can imagine synthetic data would be useful for making models more efficient (that's what quantized models are, after all), but not pushing the frontier.