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

Ajay Agrawal, Joshua Gans and Avi Goldfarb

No 31767, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: J2 O3 (search for similar items in EconPapers)
Date: 2023-10
New Economics Papers: this item is included in nep-ain, nep-cmp, nep-hrm and nep-tid
Note: PR
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