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Artificial Intelligence in the News How AI Retools, Rationalizes, and Reshapes Journalism and the Public Arena Felix M. Simon AI in the News: Retooling, Rationalizing, and Reshaping Journalism and the Public Arena
Keywords: Artificial Intelligence, AI, Democracy, Journalism, Generative AI, LLMs, News, News Industry, Platform companies This report was funded by the Tow Center for Digital Journalism at Columbia University and received support from Balliol College and the Oxford Internet Institute at the University of Oxford. AI in the News: Retooling, Rationalizing, and Reshaping Journalism and the Public Arena
Executive Summary Despite growing interest, the effects of AI on the news industry and our information environment — the public arena — remain poorly understood. Insufficient attention has also been paid to the implications of the news industry’s dependence on technology companies for AI. Drawing on 134 interviews with news workers at 35 news organizations in the United States, the United Kingdom, and Germany — including outlets such as The Guardian, Bayerischer Rundfunk, the Washington Post, The Sun, and the Financial Times — and 36 international experts from industry, academia, technology, and policy, this report examines the use of AI across editorial, commercial, and technological domains with an eye to the structural implications of AI in news organizations for the public arena. In a second step, it considers how a retooling of the news through AI stands to reinforce news organizations’ existing dependency on the technology sector and the implications of this. Chapter 1 is broken down into three parts, exploring (i) news organizations’ motives for introducing AI into their businesses; (ii) the ways in which AI is currently being used for the production and distribution of journalism; and (iii) the expectations being placed on AI’s scope to deliver efficiency. ● In terms of motivations, news organizations have adopted AI as a result of recent technological advancements, market pressures stemming partially from the industry’s financial challenges, competitive dynamics with a focus on innovation, and the pervasive sense of uncertainty, hype, and hope surrounding AI. ● AI is now applied across an ever greater range of tasks in the production and distribution of news. Contrary to some assertions, many of the most beneficial applications of AI in news are relatively mundane, and AI has often not proved to be a silver bullet in many cases. ● AI’s potential to increase efficiency in news organizations is a central motivator for its adoption. Various examples demonstrate that efficiency and productivity gains have been achieved, including dynamic paywalls, automated transcription, and data analysis tools in news production. ● Such efficiency gains are task- and context-dependent. Potential efficiency gains can be curtailed by factors such as the unreliability of AI outputs, concerns about reputational damage resulting from inaccurate AI outputs, and the difficulty of automating certain tasks. Reflecting on the extent to which AI has impacted news organizations, I argue that it presents a further rationalization of news work through AI, as work processes that traditionally relied on human intuition are increasingly becoming suffused with or replaced by a technology that is imbued with ideas of rationality, efficiency, and speed — and that does indeed provide greater efficiency and effectiveness in some contexts. However, the effects of AI in the news are subject to contextual AI in the News: Retooling, Rationalizing, and Reshaping Journalism and the Public Arena
factors, with professional norms, resistance from news workers, regulations, audience preferences, and existing technological infrastructures all acting as constraints. Chapter 2 explores the questions of how and why news organizations rely on technology companies for AI. Again, it is broken down into three parts, analyzing (i) the contexts in which publishers rely on AI and AI infrastructure from platform companies; (ii) the reasons for this reliance; and (iii) the implications of this relationship. Key takeaways include: ● News organizations make extensive use of AI products and infrastructure from major tech companies like Google, Amazon, and Microsoft across various aspects of their operations. ● Larger, better resourced news organizations are more likely to engage in in-house AI development. The majority of other publishers, especially smaller ones, opt for third-party solutions from platform companies because of the high costs associated with custom AI. ● Publishers turn to platform companies’ AI offerings due to the costs and challenges associated with independent development, including the need for extensive computing power, competition for tech talent, and the scarcity of large datasets. The convenience, scalability, and cost-effectiveness of platform offerings make them attractive, allowing publishers to leverage AI capabilities without the financial burden of in-house development. ● Despite reservations in some quarters of the news industry, the adoption of “platform AI” is largely viewed as a pragmatic choice driven by economic challenges and the competitive landscape for tech talent. ● The complexity of AI increases platform companies’ control over news organizations, creating lock-in effects that risk keeping news organizations tethered to technology companies. This limits news organizations’ autonomy and renders them vulnerable to price hikes or the shifting priorities of technology companies that may not align with their own. ● The lack of transparency in AI systems raises worries about biases or errors creeping into journalistic output, especially as generative AI models gain prominence. There is also a risk that the use of AI undercuts journalists’ autonomy by limiting their discretionary decision- making abilities. The growing use of AI in news work tilts the balance of power toward technology companies, raising concerns about “rent” extraction and potential threats to publishers’ autonomy business models, particularly those reliant on search-driven traffic. As platforms prioritize AI-enhanced search experiences, publishers fear a shift where users opt for short answers, impacting audience engagement and highlighting the increasing control exerted by platform companies over the information ecosystem. Bringing all this together, Chapter 3 interrogates the question of whose interests are being served by the increasing adoption of AI in the news and how this shift stands to reshape the public arena — our information ecosystem. In this chapter I argue:
