Cheap Expertise: Mapping and Challenging Industry Perspectives in the Expert Data Gig Economy

2026-05-04

AI Summary

This study examines how artificial intelligence has catalyzed a gig economy centered on data annotation, reshaping the valuation and institutional positioning of human expertise. Drawing on discourse analysis, qualitative content analysis, and digital ethnography, the research systematically investigates public statements by CEOs of five annotation firms across social media and podcasts. It reveals, for the first time, a tripartite logic underpinning the industry’s “cheap expertise” discourse: human expertise is framed as an extractable resource, organizational expertise as a target for reform, and AI expertise as the benchmark for return on investment. Critiquing the commodification and de-institutionalization of expertise, the study proposes a socio-ethically oriented reflective framework to inform policymaking, educational transformation, and worker empowerment.

Abstract

Demand for expert-annotated data on the part of leading AI labs has created an expert gig economy with the potential to reshape white collar work and society's understanding of expertise. In this research, we study the vision for the future of expertise described in the public communication of five industry data annotation organizations and their CEOs, as reflected on social media feeds and public appearances on podcasts. We find that the industry envisions AI expertise as cheap, meaning that it can offer a better return on investment than human expertise. Human expertise, meanwhile, is viewed as an extractable resource, the value of which can be judged relative to AI expertise. Finally, institutional expertise (such as that created or possessed by universities and corporations) is viewed as in need of liberation or reform, such that it can be incorporated into the latest artificial intelligence systems. Our findings have implications for human experts, whose professional lives may be transformed and revalued by this industry, as well as for societal institutions that mediate expertise. We close this work with a series of provocations intended to elicit consideration of how society can best approach an AI-driven expert gig economy and the cheap expertise it intends to produce.

Problem

• expert gig economy
• cheap expertise
• AI annotation
• human expertise
• institutional expertise

Innovation

• expert gig economy
• cheap expertise
• AI data annotation
• expertise commodification
• institutional expertise