Machine learning (ML) and Generative AI (GAI) is changing the way we build, operate, and use data systems. Arguably, industry is leading many of these efforts and they are happening at unprecedented speed - almost every week there is a new product announcement. Yet, a lot of the work feels ad-hoc and many challenges remain to make ML/GAI for systems in all these areas really practical despite all the product announcements. In this talk I will provide an overview of some of these recent developments and outline how the academic solution often differs from the ones deployed in industry. Finally, I will list several opportunities for academia to not only contribute but also build a better, more grounded foundation.