The Turing Transformation: Artificial Intelligence, Intelligence Augmentation, and Skill Premiums

15 Pages Posted: 9 Oct 2023 Last revised: 20 Apr 2025

See all articles by Ajay K. Agrawal

Ajay K. Agrawal

University of Toronto - Rotman School of Management; National Bureau of Economic Research (NBER)

Joshua Gans

University of Toronto - Rotman School of Management

Avi Goldfarb

University of Toronto - Rotman School of Management

Date Written: October 2023

Abstract

We ask whether a technical objective of using human performance of tasks as a benchmark for AI performance will result in the negative outcomes highlighted in prior work in terms of jobs and inequality. Instead, we argue that task automation, especially when driven by AI advances, can enhance job prospects and potentially widen the scope for employment of many workers. The neglected mechanism we highlight is the potential for changes in the skill premium where AI automation of tasks exogenously improves the value of the skills of many workers, expands the pool of available workers to perform other tasks, and, in the process, increases labor income and potentially reduces inequality. We label this possibility the “Turing Transformation.” As such, we argue that AI researchers and policymakers should not focus on the technical aspects of AI applications and whether they are directed at automating human-performed tasks or not and, instead, focus on the outcomes of AI research. In so doing, our goal is not to diminish human-centric AI research as a laudable goal. Instead, we want to note that AI research that uses a human-task template with a goal to automate that task can often augment human performance of other tasks and whole jobs. The distributional effects of technology depend more on which workers have tasks that get automated than on the fact of automation per se.

Suggested Citation

Agrawal, Ajay K. and Gans, Joshua and Goldfarb, Avi, The Turing Transformation: Artificial Intelligence, Intelligence Augmentation, and Skill Premiums (October 2023). NBER Working Paper No. w31767, Available at SSRN: https://ssrn.com/abstract=4596067

Ajay K. Agrawal (Contact Author)

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Joshua Gans

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

Avi Goldfarb

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada
416-946-8604 (Phone)
416-978-5433 (Fax)

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