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Economic impacts of AI-augmented R&D

Tamay Besiroglu, Nicholas Emery-Xu, Neil Thompson · Research Policy · 8 Jun 2024

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Abstract

Since its emergence around 2010, deep learning has rapidly become the most important technique in Artificial Intelligence (AI), producing an array of scientific firsts in areas as diverse as protein folding, drug discovery, integrated chip design, and weather prediction. As scientists and engineers adopt deep learning, it is important to consider what effect widespread deployment would have on scientific progress and, ultimately, economic growth. We assess this impact by estimating the idea production function for AI in two computer vision tasks that are considered key test-beds for deep learning and show that AI idea production is notably more capital-intensive than traditional R&D. Because increasing the capital-intensity of R&D accelerates the investments that make scientists and engineers more productive, our work suggests that AI-augmented R&D has the potential to speed up technological change and economic growth.

About this publication

Leading field journal. A leading journal in economics or a neighbouring field (on the Tinbergen Institute’s A list or near the top of the RePEc ranking), or a top-rated computer-science venue.

Placed by: Leading field journal. How sources are ranked.

Published
2024 (29 months ago)
Citations
64 (OpenAlex)

Citations in context

median 47 this paper: 64 0 202

64 citations — 74th percentile among the 23 indexed papers from 2024 in this collection. Citation counts favor older papers; this compares only same-year papers here, not the whole literature.

2020202120222023202420252026 published

□ added to this collection in 2026.

Neil Thompson signed the wemustactnow.ai letter · author affiliation matches seed institutions massachusetts institute of technology, university of california — part of why this paper is in the collection.

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