As more users across the world are interacting with dialog agents in their daily life, there is a need for better speech understanding that calls for renewed attention to the dynamics between research in automatic speech recognition (ASR) and natural language understanding (NLU). We briefly review these research areas and lay out the current relationship between them. In light of the observations we make in this paper, we argue that (1) NLU should be cognizant of the presence of ASR models being used upstream in a dialog system's pipeline, (2) ASR should be able to learn from errors found in NLU, (3) there is a need for end-to-end datasets that provide semantic annotations on spoken input, (4) there should be stronger collaboration between ASR and NLU research communities.