This article discusses the importance of monitoring the internal reasoning of AI models, rather than just their outputs. It outlines methods for evaluating how effectively this reasoning can be supervised, especially as models become more complex. The authors call for collaborative efforts to enhance the reliability of this monitoring as AI systems scale.