The article explores the ongoing experiment of scaling deep neural networks, examining how increased parameters, data, and compute affect their learning and performance. It discusses the lack of a mature theoretical framework for understanding these dynamics and introduces the concept of "quanta" as a way to analyze neural scaling. The author reflects on a recent model they developed, considering its implications and limitations.