AI 中文总结
本研究提出人类瓶颈框架，分析生成式AI对创新过程各阶段瓶颈的影响，区分瓶颈类型并探讨AI对传统创新流程的挑战。
AI 中文摘要
我们提出一种人类瓶颈视角，用于理解生成式AI如何变革创新过程。核心前提是，传统上困扰创新过程的诸多约束源于认知与社会层面，根植于人们产生创意、评估新颖性以及通过社会系统交流的方式。生成式AI对这些约束的作用并非一致：在每个阶段，它可能加深部分瓶颈，同时缓解其他瓶颈，预测这些结果需要理解约束本身的潜在机制。我们识别出创新过程四个阶段的瓶颈：创意生成、筛选与测试、偏好测量与消费者洞察、扩散及市场学习。通过将分析建立在人类行为而非快速变化的AI能力基础上，我们提供了一个框架，用于评估新进展是缓解还是加剧各阶段最重要的瓶颈。我们还区分了两类瓶颈：一类是随着AI能力提升可能缩小的瓶颈，另一类是源于持久人类约束的瓶颈。此外，我们讨论了贯穿整个创新流程的AI相关问题，这些问题对传统创新过程的存在性与结构构成了挑战。
英文摘要
We propose a human bottleneck perspective for understanding how generative AI transforms the innovation process. The central premise is that many constraints traditionally plaguing the innovation process are cognitive and social in origin, rooted in how people generate ideas, evaluate novelty, and communicate through social systems. Generative AI does not act uniformly on these constraints. At each stage, it can deepen some bottlenecks while alleviating others, and predicting these outcomes requires understanding the underlying mechanisms of the constraint itself. We identify bottlenecks in four stages of the innovation process: ideation, screening and testing, preference measurement and consumer insight, diffusion, and market learning. By grounding analysis in human behavior rather than rapidly changing AI capabilities, we offer a framework for assessing whether new developments alleviate or intensify the bottlenecks that matter most at each stage. We also distinguish bottlenecks likely to narrow as capabilities improve from those rooted in enduring human constraints. We further discuss AI-related issues that cut across the entire innovation pipeline, challenging the very existence and structure of the traditional innovation process.
