TY - JOUR AU - Agrawal, Ajay AU - McHale, John AU - Oettl, Alex TI - Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth JF - National Bureau of Economic Research Working Paper Series VL - No. 24541 PY - 2018 Y2 - April 2018 DO - 10.3386/w24541 UR - http://www.nber.org/papers/w24541 L1 - http://www.nber.org/papers/w24541.pdf N1 - Author contact info: Ajay K. Agrawal Rotman School of Management University of Toronto 105 St. George Street Toronto, ON M5S 3E6 CANADA Tel: 416/946-0203 Fax: 416/978-5433 E-Mail: ajay.agrawal@rotman.utoronto.ca John McHale 108 Cairnes Building School of Business and Economics National University of Ireland, Galway Ireland E-Mail: john.mchale@nuigalway.ie Alexander Oettl Scheller College of Business Georgia Institute of Technology 800 West Peachtree Street NW Atlanta, GA 30308 Tel: 404/385-4570 E-Mail: alexander.oettl@scheller.gatech.edu M1 - published as Ajay Agrawal, John McHale, Alexander Oettl. "Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth," 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 - 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. ER -