When plotted on a sigmoid activation function, low confidence predictions correspond to a large gradient (closer to 0.5) while high confidence predictions correspond to a progressively diminishing gradient (closer to 1 or 0). A prediction plotted within the large gradient zone of a sigmoid activation function corresponds with more-useful information being given back to the generator about how to improve its weights. Inversely, a prediction plotted within the diminishing gradient zone will provide the generator with less-useful information. The more confident the discriminator becomes with its predictions, the more the gradient and the usability of the information coming from the discriminator will be decreased (vanishing gradient problem). This leads to a repeating cycle where the discriminator continually improves and the performance of the generator ultimately stagnates.