The Valuation Paradox of Generative AI: Evidence from Gig Workers

53 Pages Posted: 14 May 2024

See all articles by Chen Liang

Chen Liang

University of Connecticut - School of Business

Jing Peng

University of Connecticut - Department of Operations & Information Management

Zhuoyan Li

Purdue University - Computer Science Department

Ming Yin

Purdue University

Date Written: May 8, 2024

Abstract

The rapid advances in generative AI technologies like large language models raise both excitement and concerns about the future of human-AI collaboration in content creation. Gig workers, pivotal players in content creation tasks such as writing and logo design, are particularly influenced by the adoption of generative AI. Leveraging a randomized online experiment in the real employment process for various writing tasks in a gig economy platform, we quantify gig workers’ willingness to pay (WTP) for AI assistance and examine its impact on their writing performance and experience. Our finding indicates that workers’ valuation of AI assistance as a novel form of job amenity is heavily contingent on its role in the human-AI co-creation process. When the AI assistance is limited to text editing, workers’ WTP for AI assistance amounts to only 5.7% of their earnings from completing the task independently, whereas this figure sharply rises to 29.7% when the AI assistance extends to content generation. Moreover, our findings reveal a higher WTP among workers assigned to creative writing tasks, those with more prior AI experience, and those with lower self-confidence in writing. Nevertheless, these workers do not experience significantly greater performance improvements from the use of AI, and there is little evidence of AI significantly enhancing their writing experience either. Overall, our findings highlight an intriguing valuation paradox: those who attach a higher financial value to AI assistance do not derive significantly greater benefits from it in terms of work performance or experience. This study contributes to our understanding of human-AI co-creation and offers valuable implications for the applications of generative AI in the evolving landscape of gig work.

Keywords: generative AI, human-AI collaboration, willingness to pay, AI-powered job amenity

Suggested Citation

Liang, Chen and Peng, Jing and Li, Zhuoyan and Yin, Ming, The Valuation Paradox of Generative AI: Evidence from Gig Workers (May 8, 2024). Available at SSRN: https://ssrn.com/abstract=4825716 or http://dx.doi.org/10.2139/ssrn.4825716

Chen Liang

University of Connecticut - School of Business ( email )

2100 Hillside Road, Unit 1041
UConn School of Business OPIM
Storrs, CT Connecticut 06269
United States
06269 (Fax)

Jing Peng (Contact Author)

University of Connecticut - Department of Operations & Information Management ( email )

368 Fairfield Road
Storrs, CT 06269-2041
United States

Zhuoyan Li

Purdue University - Computer Science Department ( email )

610 Purdue Mall
West Lafayette, IN 47907
United States

HOME PAGE: http://https://xfleezy.github.io/zhuoyanli/

Ming Yin

Purdue University ( email )

610 Purdue Mall
West Lafayette, IN 47907
United States

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