If a model improves after training on data, that provides strong evidence to frontier labs and enterprises fine-tuning their own models that the data is useful for training models.

“Because if the model does climb, then it's very strong proof point to the labs as well as the enterprises who are trying to fine tune their own models that this data is actually good because the purpose of data is to train models, so we, we try to complete the whole loop.”