I completely agree with this and, as I said, it's obvious that more data produces better predictions, even with simple models. My point is that it looks backwards to me putting effort into finding more and better data (creating a corpus for a given subject is a challenge in itself) instead of trying to come up with a model that infers more and produces better predictions with less data. Once you have such a model then you can surely collect and feed it a lot of data to improve the output, but until then, why even bother?
It's not like they aren't trying to improve the model as well, all the time. It's just saying that right now the benefit of getting more data for existing (already very sophisticated) models is greater than the incremental benefits of model improvements given existing data.
