TY  - JOUR
AU  - Cockburn, Iain M
AU  - Henderson, Rebecca
AU  - Stern, Scott
TI  - The Impact of Artificial Intelligence on Innovation
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 24449
PY  - 2018
Y2  - March 2018
DO  - 10.3386/w24449
UR  - http://www.nber.org/papers/w24449
L1  - http://www.nber.org/papers/w24449.pdf
N1  - Author contact info:
Iain M. Cockburn
Questrom School of Business
Boston University
595 Commonwealth Ave
Boston, MA 02215
Tel: 617/588-1486
E-Mail: cockburn@bu.edu

Rebecca Henderson
Heinz Professor of Environmental Management
Harvard Business School
Morgan 445
Soldiers Field
Boston, MA 02163
Tel: 617/495-8014
Fax: 617/496-4072
E-Mail: rhenderson@hbs.edu

Scott Stern
MIT Sloan School of Management
100 Main Street, E62-476
Cambridge, MA 02142
Tel: 617/253-3053
Fax: 617/253-2660
E-Mail: sstern@mit.edu
M1  - published as Iain M. Cockburn, Rebecca Henderson, Scott Stern. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," in Ajay Agrawal, Joshua Gans, and Avi Goldfarb, editors, "The Economics of Artificial Intelligence: An Agenda" University of Chicago Press (2019)

M3  - presented at "Economics of Artificial Intelligence", September 13-14, 2017
AB  - Artificial intelligence may greatly increase the efficiency of the existing economy. But it may have an even larger impact by serving as a new general-purpose “method of invention” that can reshape the nature of the innovation process and the organization of R&D.  We distinguish between automation-oriented applications such as robotics and the potential for recent developments in “deep learning” to serve as a general-purpose method of invention, finding strong evidence of a “shift” in the importance of application-oriented learning research since 2009. We suggest that this is likely to lead to a significant substitution away from more routinized labor-intensive research towards research that takes advantage of the interplay between passively generated large datasets and enhanced prediction algorithms.  At the same time, the potential commercial rewards from mastering this mode of research are likely to usher in a period of racing, driven by powerful incentives for individual companies to acquire and control critical large datasets and application-specific algorithms.  We suggest that policies which encourage transparency and sharing of core datasets across both public and private actors may be critical tools for stimulating research productivity and innovation-oriented competition going forward.
ER  -