Long Tails & the Impact of GPT on Labor

Nat Kausik ()

MPRA Paper from University Library of Munich, Germany

Abstract: Recent advances in AI technologies renew urgency to the question whether automation will cause mass unemployment and reduction in standards of living. While prior work analyzes historical economic data for the impact of automation on labor, we seek a test to predict the impact of emerging automation technologies such as Generative Pre-trained Transformers (GPT). Towards that goal, we observe that human needs favor long tail distributions, i.e., a long list of niche items that are substantial in aggregate popularity. In turn, the long tails are reflected in the products and services that fulfill those needs. Technologies that address a small portion of the distribution, typically the head, free up human labor to focus on more complex tasks in the long tail, thereby improving productivity and potentially lifting wages. In contrast, technologies that cover substantial portions of the long tail can squeeze wages or displace humans entirely. With this in mind, we propose a long tail test for automation technologies to predict their impact on labor. We find that popular GPTs perform poorly on such tests in that they are erratic on straightforward long tail tasks, hence absent breakthroughs, will augment human productivity rather than cause mass displacement of human labor. Going forward, we believe that to have a broad impact on displacing or devaluing human labor, AI must at least be capable of long-tail tasks that humans perform with ease.

Keywords: AI; labor (search for similar items in EconPapers)
JEL-codes: J21 J30 O33 (search for similar items in EconPapers)
Date: 2023-03-29
New Economics Papers: this item is included in nep-tid
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Citations: View citations in EconPapers (3)

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https://mpra.ub.uni-muenchen.de/117104/1/MPRA_paper_117104.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/117135/1/Long%20Ta ... GPT%20on%20Labor.pdf revised version (application/pdf)

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