It's important to understand the difference and difficulties that surround generative and discriminative modeling. In recent years, discriminative modeling has seen some great successes. Typically requiring Markov decision processes in order for the generative modeling process to work, these techniques suffered from a lack of flexibility without heavy design tuning. That is, until the advent of the GANs architecture that we're discussing today. Goodfellow adequately summed up the issues surrounding discriminative and generative models in his paper in 2014:

What are Goodfellow and his fellow authors getting at in this screenshot? Essentially, prior generative models were painful to train/build. GANs can have their challenges in terms of training and design, but represent a fundamental shift in flexibility in output given the ease of setup. In the Chapter 3, My First GAN in Under 100 Lines, we'll build a GAN network in under 100 lines of code.