  Software developers use natural language to interact not only with other
humans, but increasingly also with chatbots. These interactions have different
properties and flow differently based on what goal the developer wants to
achieve and who they interact with. In this paper, we aim to understand the
dynamics of conversations that occur during modern software development after
the integration of AI and chatbots, enabling a deeper recognition of the
advantages and disadvantages of including chatbot interactions in addition to
human conversations in collaborative work. We compile existing conversation
attributes with humans and NLU-based chatbots and adapt them to the context of
software development. Then, we extend the comparison to include LLM-powered
chatbots based on an observational study. We present similarities and
differences between human-to-human and human-to-bot conversations, also
distinguishing between NLU- and LLM-based chatbots. Furthermore, we discuss how
understanding the differences among the conversation styles guides the
developer on how to shape their expectations from a conversation and
consequently support the communication within a software team. We conclude that
the recent conversation styles that we observe with LLM-chatbots can not
replace conversations with humans due to certain attributes regarding social
aspects despite their ability to support productivity and decrease the
developers' mental load.
