To save effort and schedule in IC implementation, fundamental challenges must be solved. First, the need for (expensive) humans in RTL-to-GDSII implementation must be removed. Humans are very skilled at predicting downstream flow failures, evaluating key early decisions such as RTL floorplanning, and deciding tool/flow options to apply to a given design. Achieving human-quality prediction, evaluation and decision-making will require new machine learning-centric models of both tools and designs. These models will enable future auto-tuning, adaptive flows. Second, to reduce design schedule, focus must return to the long-held dream of single-pass design. Future design tools and flows that never require iteration (i.e., that never fail, but without undue conservatism) demand new paradigms and core algorithms for parallel, cloud-based design automation. Third, learning-based models of tools and flows must continually improve with additional design experiences. Therefore, the EDA and design ecosystem must develop new infrastructure for ML model development and sharing.