The less formal way conveys the concept better; and it makes sense, the worse the discriminator performs (whether it is overly sensitive or less sensitive when attempting to sus out the validity of assets), the worse the generator performs, at least with regard to the quality of the output for human purposes. If I'm understanding the use of gradient correctly, the generator become trapped in a local minimum because it discovers how to consistently exploit the weaknesses of the discriminator.

I don't know for sure if it always applies; you could apply an evolutionary algorithm where two or more competing populations are tackling the same problem from opposing sides, and have relatively infrequent breeding between members of the populations, motivating avoidance of bottlenecking while enabling the development of unique solutions; over several generations, any short term loss should serve to be a long term gain. But, I guess they'd still be dependent on how the scoring system works (equivalent to loss function?).