This paper analyzes three recent lawsuits against Open AI (the makers of Chat GPT) and two licensing agreements between Open AI and news organizations to argue that generative AI represents a further exacerbation of the precarious relationship between news publishers and information intermediaries. The growth of search engines and digital platforms as the primary means of news consumption has increased contestations around lost traffic and revenue for news publishers. Through the analysis of the lawsuits and licensing agreements, this essays shows three changes in the relationship with significant consequences for the future of the news industry. First it shows that the increasing use of generative AI chatbots (e.g. Chat GPT) will further reduce traffic to publishers' websites.

Secondly, it shows a further opacity in the relationship between publishers and information intermediaries given the lack of disclosure about what is used to train their AI models. Lastly, the analysis shows that the content of news sites will no longer be used "as is" but mixed and merged with other content to erase journalistic labor and contribution. These findings point to worrisome disruptions ahead in the digital news ecosystem where devoid of the precious currency of traffic, news outlets could be pushed further into a precarious position of decreased revenue and content and increasing layoffs. The consequent dismantling of established institutions of the news media also means the loss of rigorous reporting, fact checking, archival knowledge and adversarial journalism that is the lifeblood of democracies.

Economic precarity and the competitive environment of the news industry have resulted in pressures for news professionals to become early adopters and foster AI in journalism. At the same time, practitioners have voiced concerns over overblown expectations of AI and ethical challenges specific to journalistic contexts. Against this backdrop, this study asks: What role do expectations play in realizing journalistic AI projects in small newsrooms and with what implications? Empirically, we draw on eight months of ethnographic fieldwork following the Associated Press’ efforts to develop AI tools for five local newsrooms in collaboration with data scientists from U.S. universities. Previous research has conceptualized AI hype as the gap between possibilities and realities, or as a form of stimulation, amplification, and magnification. This study examines hype through a theoretical lens of a sociology of expectations. It understands hype as a cultural resource that coordinates actors and mobilizes resources that can be strategically leveraged. Instead of painting news professionals caught in between AI hype with little power, or as uncritical actors, this study sheds light on the complex role of their involvement and investment in shaping expectations around AI.

This paper examines the ethico-political negotiations surrounding "world models" in the news media industry. Utilizing ethnography, it explores how data workers in a news organization integrate AI-driven solutions into the editorial process, navigating the construction of world models. Drawing from six months of fieldwork, the study focuses on a concrete AI project involving collaboration between an in-house data science team, universities, and industrial PhD students. Observation primarily occurred in the development department, with journalists implicitly present as end-users. Methodologically, the paper incorporates insights from the anthropologies of technology and algorithmic systems, framing digitization as multifaceted. It also considers the anticipatory practices of participants and theoretical frameworks from political geography, history of science, and media studies. Theoretical underpinnings are complemented by insights from science and technology studies, particularly regarding "science frictions" that emerge when disparate domains intersect. The findings contribute to critical data studies and AI research, providing ethnographic insights into machine learning projects' mundane work practices and understanding how machine learning projects aim to model the world while also negotiating tensions between reflecting existing realities and shaping future trajectories.

Platformization of news, and the resultant concerns for publishers, have led to the rise and formalization of a network of intermediaries that mediate between news businesses and large distribution platforms in India. These intermediaries facilitate and broker interactions between news publishers and platforms by providing AI/ML tools and algorithmic expertise on news production, distribution, and monetization. This paper locates and conceptualizes them as platformization intermediaries, highlighting their role in reshaping cultural economies and practices into platform-optimized models. Platformization intermediaries leverage certified partnerships with major platforms thereby assisting in integrating platform infrastructure, funding and governance models into the digital news industry.

Taking a political economy approach, this study critically examines the services and market dynamics of a range of platformization intermediaries in the news industry in India. Further, interviews with these actors in India unpack the implications on news publishers and the public interest value of news. This paper draws focus onto these emerging and influential actors aiding the platformization of news. It highlights their role in translating platforms' algorithmic and economic priorities into the human and social practices of news-making, and in reinforcing platform dependencies within the digital news industry.

A recent development in the field of fact-checking is what some scholars call the “debunking turn” in which fact-checking organisations move from fact-checking expressions of politicians and public figures to checking claims made on social media. A main driver of this change is the proliferation of a paid program initiated by Meta, where fact-checkers check and label claims on the platform in exchange for monetary remuneration. This paper draws on interviews with and fieldwork amongst fact-checkers who are or have been part of the Meta partnership. Based on the empirical insights we argue that the human-machine assemblage in fact-checking is (1) enabling a move beyond the ‘debunking turn’ by turning journalists into ‘machine learners’ and (2) cements a ‘politics of demarcation’ in which public contestation over public facts is diminished and moved into networked infrastructures.

With this argument, the paper highlights an additional aspect of the platformisation of journalism, as the labelling and claim-checking work of journalists now also enables large tech platforms to expand technical infrastructures that commodify journalistic work by turning it into training data aimed at improving their ML systems and algorithms. This enables platforms to move further beyond their current market role, as they also participate in the further industrialisation and standardisation of fact-checking. As large tech companies become industry leaders in the provision of ML systems, for example, for fact-checking, the need to understand what politics they produce equally increases, as they become integral in the production of democratic ideals of citizens and public debate.