这真是一个让人当场破防的早晨。 一位 The Verge 的科技记者起床、走进厨房、对着支持 Alexa 的博世咖啡机说了句，煮杯咖啡。 目前还没有人真正解决这个问题：如何让 LLM 知道什么时候该精确、什么时候可以随机。 没有即兴发挥，也没提什么复杂要求，她只是希望机器老老实实执行一个早就设好的程序，结果，被拒绝了。 而且不是一次。 自从升级到 Alexa Plus（亚马逊的生成式 AI 语音助手）之后，这种对话几乎成了她的晨间固定项目。 每一次要它煮咖啡，Alexa 都能给出不同理由，以惊人的创造力告诉你，不行。 2025 年都快过去了，AI 会写论文、会写代码、会陪人聊天、会教书，却在清晨败给了一句「煮杯咖啡」。 在社区讨论中，类似吐槽场面非常壮观，可谓怨声载道。 开灯这个事儿，完全成了重灾区。 播放歌曲也是艰难。 定个时也这么难了啊。 彻底心灰意冷的也有。 显然，现实与大家对 AI 的直觉预期，构成了鲜明反差。 传统助手虽然笨吧，但高度确定，只要你把（有点傻的）「咒语」念对，结果总是可以预期的。 现在好了，以 LLM 为核心的生成式AI助手，智商是高了，理解是深了，表达是丰富了，却偏偏在它们原本最擅长 ...

Core Viewpoint - The article discusses the challenges faced by generative AI voice assistants, particularly in executing simple commands reliably, highlighting a gap between user expectations and actual performance [14][18]. Group 1: User Experience with AI Assistants - Users have reported frustrations with AI voice assistants like Alexa, which fail to execute basic commands such as brewing coffee or turning on lights, despite their advanced capabilities [4][8]. - The transition to generative AI has led to a situation where users experience inconsistent responses, with the AI providing creative but unhelpful reasons for not executing commands [7][16]. Group 2: Technical Limitations of Generative AI - Generative AI introduces a level of randomness that can lead to misunderstandings in command execution, making it unsuitable for tasks requiring precision and reliability [18][22]. - Traditional voice assistants operated on a template-matching basis, ensuring predictable outcomes, while generative models struggle to maintain consistency in system calls [19][23]. Group 3: Potential and Future Directions - Despite current limitations, there is recognition of the potential of generative AI to understand complex tasks and improve user interactions, suggesting a paradigm shift in capabilities [30][34]. - The article suggests that the chaos observed may not be a failure of generative AI but rather a misalignment of its application in contexts where deterministic execution is critical [44].