For generative AI to succeed, how engaging a conversationalist must it be? For almost sixty years, some conversational agents have responded to any question or comment to keep a conversation going. In recent years, several utilized machine learning or sophisticated language processing, such as Tay, Xiaoice, Zo, Hugging Face, Kuki, and Replika. Unlike generative AI, they focused on engagement, not expertise. Millions of people were motivated to engage with them. What were the attractions? Will generative AI do better if it is equally engaging, or should it be less engaging? Prior to the emergence of generative AI, we conducted a large-scale quantitative and qualitative analysis to learn what motivated millions of people to engage with one such 'virtual companion,' Microsoft's Zo. We examined the complete chat logs of 2000 anonymized people. We identified over a dozen motivations that people had for interacting with this software. Designers learned different ways to increase engagement. Generative conversational AI does not yet have a clear revenue model to address its high cost. It might benefit from being more engaging, even as it supports productivity and creativity. Our study and analysis point to opportunities and challenges.

翻译：生成式AI要想成功，需要具备多大程度的对话吸引力？近六十年来，部分对话智能体已能针对任何提问或评论作出回应以维持对话进程。近年来，多个系统采用机器学习或复杂语言处理技术，例如Tay、小冰、Zo、Hugging Face、Kuki和Replika。与生成式AI不同，这些系统专注的是参与感而非专业知识，却吸引了数百万用户与其互动。其吸引力究竟何在？生成式AI若同样具备高参与度是否会表现更佳，还是应当降低参与度？在生成式AI兴起前，我们通过大规模定量与定性分析，探究了数百万用户与微软Zo这类"虚拟伴侣"的互动动机。我们研究了2000名匿名用户的完整聊天记录，识别出十余种驱动用户与软件交互的动机，设计人员由此习得多种提升参与度的方法。当前生成式对话AI尚未形成明确盈利模式以覆盖其高额成本，但若能在提升参与度的同时兼顾生产力与创造力支持，或将从中获益。本研究的分析与发现揭示了相关机遇与挑战。