I don’t think that’s fair to say, there are two principle sources of data that paint a fairly consistent picture - one is scaling laws, where we found years ag

I don’t think that’s fair to say, there are two principle sources of data that paint a fairly consistent picture - one is scaling laws, where we found years ago that pretraining validation loss scales in an almost miraculously predictable way with data volume and compute. There are apparently theoretical bases for this that I don’t quite understand but this property alone is holding at every scale we’ve ever tested. There is not just “one” scaling law but the point is there are scaling laws and they continue to faithfully predict the performance gains we see - one is benchmarks, which I always point to epoch capability index as a good summary of them in aggregate which makes it nice to plot on one curve the capability improvement over time To me either one without the other is substantially weaker, the fact that theory and empirical measurements give you a very good scaling law on a more unintuitive quantity (pretraining validation loss) that’s only indirectly related to the downstream performance you care about, benchmarks (in aggregate) are more direct measures of downstream performance but are harder to nail down clean and well motivated “laws” from theory (as far as I can tell). Nevertheless we do in fact see a clear trend that is not slowing. That doesn’t mean there aren’t a whole host of benchmark problems that don’t impact the numbers involved here (leakage from training data, fundamental flaws in the design, benchmaxxing) but they don’t change the larger story. These problems don’t plausibly explain the clean trends we see.