Maximizing the delivered capacity of optical transponders by mining the Signal-to-Noise Ratio (SNR) margin to a near-zero level is critical for the economic viability of future optical networks. This paper examines this trend from the equipment design point of view and applies Artificial Intelligence (AI) to the problem of capacity maximization. This bottom-up approach is focused on providing the key metrics built on the fundamentals of network performance, whereas a top-down approach uses machine learning and policy-driven actions that have the promise to achieve an unprecedented level of control. These methods show promise in delivering a practical near-zero margin network deployment.