Right. If the cost of adding more training data remains flat or increases, a logarithmically increasing n will eventually reach a point such that it's more expe

Right. If the cost of adding more training data remains flat or increases, a logarithmically increasing n will eventually reach a point such that it's more expensive to continue increasing training volume. In fact, unless the cost of adding and using training data exponentially decreases over time, it's a mathematical certainty that a logarithmically increasing n will quickly incur expensive, diminishing returns for using more data. So in the context of this Google paper, you could conceive of a situation where training data actually becomes easier to load (albeit subexponentially) and still becomes too expensive to use relatively quickly.