As our computational infrastructure races gracefully forward into increasingly parallel multi-core and clustered systems, our ability to easily produce software that can successfully exploit such systems continues to stumble. For years, we've fantasized about the world in which we'd write simple, sequential programs, add magic sauce, and suddenly have scalable, parallel executions. We're not there. We're not even close. I'll present a radical, potentially crazy approach to automatic scalability, combining learning, prediction, and speculation. To date, we've achieved shockingly good scalability and reasonable speedup in limited domains, but the potential is tantalizingly enormous.