Working Paper: NBER ID: w24541
Authors: Ajay Agrawal; John McHale; Alex Oettl
Abstract: Innovation is often predicated on discovering useful new combinations of existing knowledge in highly complex knowledge spaces. These needle-in-a-haystack type problems are pervasive in fields like genomics, drug discovery, materials science, and particle physics. We develop a combinatorial-based knowledge production function and embed it in the classic Jones growth model (1995) to explore how breakthroughs in artificial intelligence (AI) that dramatically improve prediction accuracy about which combinations have the highest potential could enhance discovery rates and consequently economic growth. This production function is a generalization (and reinterpretation) of the Romer/Jones knowledge production function. Separate parameters control the extent of individual-researcher knowledge access, the effects of fishing out/complexity, and the ease of forming research teams.
Keywords: Artificial Intelligence; Economic Growth; Knowledge Production
JEL Codes: O30; O33; O40; Z38
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| AI advancements (C45) | enhanced discovery rates (O36) |
| enhanced discovery rates (O36) | economic growth (O49) |
| AI advancements (C45) | economic growth (O49) |
| knowledge access and burden of knowledge (D83) | enhanced discovery rates (O36) |