Bridging Distant Ideas: the Impact of AI on R&D and Recombinant Innovation

TL;DR

This paper models how AI influences firms' innovation strategies, showing that AI can promote distant knowledge recombination but may also lead to reduced originality and increased duplication, with effects depending on AI productivity and automation levels.

Contribution

It introduces a Schumpeterian model capturing AI's dual role in facilitating distant recombinations and increasing creative destruction, revealing complex impacts on innovation focus.

Findings

Higher AI productivity encourages more distant recombinations if facilitation outweighs competition effects.

Increasing AI-automated R&D initially promotes radical innovations, then shifts to incremental as AI reliance grows.

Full automation leads to zero recombination distance, potentially undermining knowledge creation.

Abstract

We study how artificial intelligence (AI) affects firms' incentives to pursue incremental versus radical knowledge recombinations. We develop a model of recombinant innovation embedded in a Schumpeterian quality-ladder framework, in which innovation arises from recombining ideas across varying distances in a knowledge space. R&D consists of multiple tasks, a fraction of which can be performed by AI. AI facilitates access to distant knowledge domains, but at the same time it also increases the aggregate rate of creative destruction, shortening the monopoly duration that rewards radical innovations. Moreover, excessive reliance on AI may reduce the originality of research and lead to duplication of research efforts. We obtain three main results. First, higher AI productivity encourages more distant recombinations, if the direct facilitation effect is stronger than the indirect effect due to intensified competition from rivals. Second, the effect of increasing the share of AI-automated R&D tasks is non-monotonic: firms initially target more radical innovations, but beyond a threshold of human-AI complementarity, they shift the focus toward incremental innovations. Third, in the limiting case of full automation, the model predicts that optimal recombination distance collapses to zero, suggesting that fully AI-driven research would undermine the very knowledge creation that it seeks to accelerate.