Long Tails & the Impact of GPT on Labor

Kausik, B.N. (2023): Long Tails & the Impact of GPT on Labor.

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.

• Item Type: MPRA Paper
• English Title: Long Tails and the Impact of GPT on Labor
• Language: English
• Keywords: AI, labor
• Subjects: J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J21 - Labor Force and Employment, Size, and Structure; J - Labor and Demographic Economics > J3 - Wages, Compensation, and Labor Costs > J30 - General; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O33 - Technological Change: Choices and Consequences ; Diffusion Processes

References

Fowler, Gregory, A. (2023): “Say what, Bard? What Google’s new AI gets right, wrong and weird” https://www.washingtonpost.com/technology/2023/03/21/google-bard/

Roose, Kevin, (2023): “A Conversation With Bing’s Chatbot Left Me Deeply Unsettled” https://www.nytimes.com/2023/02/16/technology/bing-chatbot-microsoft-chatgpt.html

Korinek, Anton and Stiglitz, Joseph, (2020): “Steering Technological Progress,” https://conference.nber.org/conf_papers/f143989.pdf

Brynjolfsson, Erik, (2022), “The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence,”https://digitaleconomy.stanford.edu/news/the-turing-trap-the-promise-peril-of-human-like-artificial-intelligence/

Acemoglu, Daron, (2022): “The Harms of AI,” https://economics.mit.edu/files/21848

Autor, D., Mindell, D., and Reynolds, E. (2020). “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines” Cambridge: MIT Press. https://workofthefuture.mit.edu/wp-content/uploads/2021/01/2020-Final-Report4.pdf

Basu, Kaushik, (2022), “An inclusive future? Technology, New Dynamics and Policy Challenges” Brookings Institute, https://www.brookings.edu/wp-content/uploads/2022/05/Inclusive-future_Technology-new-dynamics-policy-challenges.pdf

Acemoglu, D., and Restrepo, P., (2019), “Automation and New Tasks: How Technology Displaces and Reinstates Labor.” Journal of Economic Perspectives—Volume 33, Number 2.

Autor, D., Chin, C., Salomons, A.M., & Seegmiller, B., (2022), “New Frontiers: The Origins and Content of New Work, 1940–2018,” https://www.nber.org/papers/w30389

Eloundou, T., Manning, M., Mishkin, P., and Rock, D. (2023). “​​GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models,” https://arxiv.org/pdf/2303.10130.pdf

Anderson, C., (2004) “The long tail.” Wired Magazine, 12(10):170–177, 2004.

Anderson, C., (2006) “The Long Tail: Why the Future of Business is Selling Less of More.” Hyperion.

Brynjolfsson, E., Hu, Y.J., and Smith, M.D., (2007), “Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers.” MIT Center for Digital Business Working Paper.

Goel, S., Broder, A, Gabrilovich, E., Pang, B., (2010). “Anatomy of the long tail: ordinary people with extraordinary tastes.” Proceedings of the third ACM international conference on Web search and data mining.

Bessen, J.E., (2019), “Automation and Jobs: When Technology Boosts Employment” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2935003

Frey, C. and M.A. Osborne (2013). “The Future of Employment: How Susceptible are Jobs to Computerization?” https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf

Arntz, M., Gregory, T. & Zierahn, U., “Revisiting the Risk of Automation,” July 2017 Economics Letters 159:157-160

Bubeck, S., Chandrasekharan, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y-T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribiero, M.T., Zhang, Y., (2023). “Sparks of Artificial General Intelligence: Early experiments with GPT-4.” https://arxiv.org/pdf/2303.12712.pdf

Harskamp, R.E., and De Clercq, L., (2023), “Performance of ChatGPT as an AI-assisted decision support tool in medicine: a proof-of-concept study for interpreting symptoms and management of common cardiac conditions (AMSTELHEART-2),” https://www.medrxiv.org/content/10.1101/2023.03.25.23285475v1.full.pdf

Noy, S. and Zhang, W., (2023). “Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence,” https://economics.mit.edu/sites/default/files/inline-files/Noy_Zhang_1.pdf

Chafkin, (2022), “Even After $100 Billion, Self-Driving Cars Are Going Nowhere” https://www.bloomberg.com/news/features/2022-10-06/even-after-100-billion-self-driving-cars-are-going-nowhere