Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Emanuele Bazzichi, Massimo Riccaboni, Fulvio Castellacci

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.

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

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.

Paper Structure

This paper contains 11 sections, 40 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. The model: Baseline Set Up
  3. Knowledge Space
  4. R&D Sector
  5. Extension: A Quality-Ladder Model
  6. Equilibrium and Model's Analysis
  7. Balanced Growth Path Equilibrium
  8. Comparative Statics
  9. Increase in AI productivity, $\mathbf{\Delta \boldsymbol{m}>0}$.
  10. Increase in the share of AI tasks, $\mathbf{\Delta \boldsymbol{\alpha}>0}$
  11. Conclusions

Figures (3)

  • Figure 1: Knowledge space before (above) and after (below) the introduction of the new "Econophysics" node. Link weights indicates the distance among knowledge fields; red links are the new combination possibilities introduced by the new field.
  • Figure 2: Simulation of the quality ladder model with true FOC and free-entry condition (no Taylor approximation). Blue curves for the AI price increasing proportionally to the stock of knowledge $A_t$; green dashed curves for AI price increasing more than proportionally than $A_t$ and orange dotted curves for AI price decreasing over time.
  • Figure 3: Comparative statics with respect to the fraction of tasks AI is applied to $\alpha$ and AI productivity $m$.

Theorems & Definitions (1)

  • proof