桥(图论)
桥接(联网)
机制(生物学)
计算机科学
人工智能
价值(数学)
图层(电子)
人工智能应用
预测值
认知科学
机器学习
作者
Xinying Qu,J. P. Eggers,M. V. Shyam Kumar
摘要
Abstract Research Summary Complementing the role of AI in facilitating search and identifying combinations of high value in inventive activity, we argue that AI fundamentally alters the innovation landscape by unlocking new combinations that were previously infeasible. This effect arises because AI acts as a powerful shared layer due to its predictive capabilities and its ability to transmit solutions across domains, thereby creating a bridge between previously unconnected elements. Utilizing a matched sample of patents, we show that inventions incorporating AI exhibit a greater degree of novel recombinations compared to those without AI, and that our proposed bridging mechanism is consistent with these novel recombinations. Our study contributes by identifying a new mechanism by which recombinations emerge in inventive activity while also highlighting the role of enabling technologies such as AI in facilitating such recombinations. Managerial Summary How does AI impact inventive activity? We examine the question by studying the patenting activity of US firms over the period 2005–2023. We argue that AI fundamentally alters the innovation landscape by unlocking new combinations that were previously infeasible. This effect arises because AI acts as a powerful shared layer due to its predictive capabilities and its ability to transmit shared solutions, thereby creating a bridge between previously unconnected technological elements. Our analysis demonstrates that inventions that build on AI involve novel recombinations to a greater degree compared to inventions that don’t, and that AI bridges and connects knowledge domains that were hitherto disparate. These findings indicate that AI is more than just an invention of a new method of invention, and that it fundamentally reshapes the nature of inventive activity.
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