In the late-CMOS era, semiconductor and electronics companies face severe product schedule and other competitive pressures.  In this context, electronic design automation (EDA) must deliver “design-based equivalent scaling” to help continue essential industry trajectories.  A powerful lever for this will be the use of machine learning techniques, both inside and “around” EDA tools.  This talk will try to convey some key opportunities and value propositions for machine learning in EDA, supported by concrete existence proofs. Example topics include (1) achieving faster design convergence through new predictors of downstream flow outcomes, (2) removing unnecessary design and modeling margins through new correlation mechanisms, and (3) optimizing the usage of EDA tool licenses and available schedule.