Writing code has been one of the most transformative ways for human societies to translate abstract ideas into tangible technologies. Modern AI is transforming this process by enabling experts and non-experts alike to generate code without actually writing code, but instead, through natural language instructions, or "vibe coding". While increasingly popular, the cumulative impact of vibe coding on productivity and collaboration, as well as the role of humans in this process, remains unclear. Here, we introduce a controlled experimental framework for studying collaborative vibe coding and use it to compare human-led, AI-led, and hybrid groups. Across 16 experiments involving 604 human participants, we show that people provide uniquely effective high-level instructions for vibe coding across iterations, whereas AI-provided instructions often result in performance collapse. We further demonstrate that hybrid systems perform best when humans retain directional control (providing the instructions), while evaluation is delegated to AI.

翻译：编写代码一直是人类社会将抽象思想转化为具体技术最具变革性的方式之一。现代人工智能正在改变这一过程，使专家和非专家都能在不实际编写代码的情况下生成代码，而是通过自然语言指令，即“氛围编码”。尽管日益流行，但氛围编码对生产力和协作的累积影响，以及人类在此过程中的作用，仍不明确。本文提出了一种研究协作式氛围编码的受控实验框架，并利用该框架比较了人类主导、人工智能主导及混合型协作组。通过对604名人类参与者进行的16项实验，我们发现人类在迭代过程中能为氛围编码提供独特有效的高层指令，而人工智能提供的指令常导致性能崩溃。我们进一步证明，当人类保持方向性控制（提供指令）而将评估任务委托给人工智能时，混合系统的表现最佳。