Generative AI and Investigative Journalism—An Application of Baumol's Cost Disease Across Mass Communication Sectors

Abstract

Generative artificial intelligence (AI) has transformed various industries, automating tasks and enhancing productivity. Yet, its impact varies across sectors. In mass communication, AI has notably benefited entertainment, public relations, and advertising. However, it poses a lesser advantage for investigative journalism, where labor-intensive research and other highly specialized activities depend on highly educated journalists. This situation parallels Baumol's concept of a cost disease, which applies to labor-intensive “stagnant” service sectors such as healthcare, education, and the performing arts. These sectors rely heavily on human labor and struggle to achieve significant productivity gains from technological advances. This means they may require decreasing wages or increasing subsidies to compete with more productive industries. In mass communication, this phenomenon could be especially pertinent to (investigative) journalism, which directly competes for attention with other communication forms. This could exacerbate pressures on wages and working conditions, hindering the attraction of talent to the field and necessitating higher public spending on this vital journalism form. This commentary presents a cross-industry analysis of the impact of generative AI on communication sectors and derives directions for further research that would enhance our understanding of journalism's economic viability in a digital communication landscape and inform policy makers to address the affiliated societal challenges. It closes with a discussion of Guzman and Lewis’s call for more cross-industry research on AI in communication industries.