The surge in generative AI capabilities has affected sectors such as drug discovery and creative text generation, fueling widespread enthusiasm about its potential to revolutionize scientific discovery through efficient exploration of knowledge combinations. But is this belief well-founded? This belief is rooted in the recombinant growth theory, which posits that innovation accelerates when existing ideas are iteratively combined. However, the theory encounters two significant challenges in understanding the nature of breakthroughs. First, breakthroughs such as relativity replacing Newtonian physics drive progress through competition, because they are fundamentally substitutive of older ones. Second, the recombinant strategy often only generates different ideas rather than better ones. Building on these, our study indicates the limitation of combinatorial view of innovation and point to the role of idea competition rather than combination in advancing science, even in the age of AI. Our results suggest that breakthroughs occur when ideas compete, not when they combine, and that combining more ideas tends to result in smaller innovations. This challenges the combinatoric metaphor of innovation that has captivated academia for three decades and complements subsequent studies equating content novelty with transformative innovation. Policymakers and researchers should focus on fostering environments that encourage idea competition and the development of AI systems capable of generating novel, disruptive ideas.

翻译：生成式人工智能能力的激增已影响药物发现和创意文本生成等领域，激发了人们对其通过高效探索知识组合来革新科学发现的广泛热情。但这一信念是否合理？该信念源于重组增长理论，该理论认为当现有思想被迭代组合时，创新会加速。然而，该理论在理解突破性本质时面临两个重大挑战。首先，相对论取代牛顿物理学等突破通过竞争推动进步，因其本质上是对旧有思想的替代。其次，重组策略往往仅产生不同而非更优的思想。基于此，我们的研究表明了创新组合观的局限性，并指出思想竞争而非组合在推动科学进步中的作用——即使在人工智能时代亦然。我们的结果表明：突破发生在思想竞争而非组合时，且组合更多思想往往导致更小的创新。这挑战了三十年来吸引学界的创新组合隐喻，并补充了将内容新颖性等同于变革性创新的后续研究。政策制定者和研究者应聚焦于营造鼓励思想竞争的环境，并开发能产生新颖、颠覆性思想的人工智能系统。