To reduce time and effort in IC implementation, fundamental challenges must be solved. First, the need for (expensive) humans must be removed wherever possible. Humans are 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. 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.