This paper proposes a form of AI critique that differs from linear, progress-focused corporate histories of technology, politicoeconomic analysis of power concentration (Bode and Goodlad, 2023), or traditional literature reviews. Instead, we approach artificial intelligence (AI) as operating across multiple temporalities and discourses by turning to media archaeology on Wikipedia. We propose that media archaeology can aid in the sensemaking of AI because it favors spaciousness and complexities, and Wikipedia can serve that purpose given its quest for universalization. Through this approach, AI does not emerge as a single technology but rather as a sociotechnical assemblage of ideas and objects, shaped by varied modalities of traction and rupture. AI has a history in this sense, evolving both horizontally (as the topic bifurcates over and again) and vertically (as it changes across time). We study Wikipedia’s ‘See also’ link ecology of “Artificial intelligence” article across Wikipedia’s spatial field and temporal field to show the non-linear history of AI genealogies as they are mediated on Wikipedia. Rather than claiming to tell the ultimate ‘Truth’ story of AI or attempting to ‘correct’ the definitions and terms, this intervention aims to map out a specific knowledge space on Wikipedia, with its controversies and gaps. Since AI exists as an issue that is being made and unmade, Wikipedia allows us to see how the knowledge space about AI exists outside of the AI industry’s direct control, and as a technology that is continuously moving and transforming.

The attention economy has been increasingly examined in internet studies to explain sociotechnical and communicational dynamics on digital platforms. However, the commercialization of attention predates digital platforms, having shaped mass media since the 19th century. While not a new phenomenon, the attention economy assumes new contours in the digital era. This study, adopting a Foucauldian genealogical approach, investigates the ruptures between past and present logics of attention economy, analyzing their conceptual, historical, and phenomenological dimensions within critical platform studies. We identify three major ruptures in the attention economy from the mid-19th century to the present: (1) Technologies of attention management, shifting from centralized, mass-media models to distributed, algorithmic personalization; (2) The political economy of attention merchants, transitioning from advertising practices on Madison Avenue to platform-based datafication in Silicon Valley; and (3) The attentional regime, going from the modern consumer’s continuous attention crisis to a hyper-fragmented, screen-addicted user-subject. This genealogical approach highlights that historical ruptures in the attention economy are not merely technological but also involve new relationships between science, the market, and society, shaping regimes of power, knowledge, and subjectivity. By tracing these historical discontinuities, this study contributes to platform studies by offering a theoretical and historical perspective on the digital attention economy, emphasizing its specificities in the contemporary context.

Histobots (Author 2024b), AI-driven chatbots that simulate historical figures, are marketed as tools for education and engagement. They promise immersion but operate within a system of algorithmic mediation that flattens complexity and reinforces dominant narratives. Unlike traditional historical interpretation, which involves deliberate source selection and critical framing, histobots generate responses based on probabilistic patterns, presenting history as seamless, neutral, and objective. This illusion of neutrality conceals deep biases embedded in training data, filtering mechanisms, and corporate imperatives.

This paper examines histobots as algorithmic reenactments. It explores how AI reshapes historiography through feminist and queer theoretical lenses. I reflect on my experience developing a histobot of Hedy Lamarr and analyse AI-generated representations of figures such as Anne Frank, Harriet Tubman, and Marsha P. Johnson. These chatbots erase political agency, neutralise rhetorical power, and homogenise voices. They produce a form of historical negationism that tokenises rather than represents marginalised figures.

I draw on feminist standpoint theory (Haraway 1988; Harding 1991) and critical AI scholarship (Crawford 2021; Felkner et al. 2024) and argue that histobots reproduce epistemic injustices by encoding archival silences and structural biases. They inherit the exclusions of the historical record while reinforcing contemporary inequalities. This paper interrogates whether histobots can be reclaimed for feminist storytelling or whether, as Audre Lorde (1979) cautions, “the master’s tools will never dismantle the master’s house.” Can AI-driven history ever be ethical, or must we build new tools entirely?

This paper critiques the prevailing language used to describe “Artificial Intelligence” (AI) and its impact. It argues that the term "AI" is ambiguous, misleading, and overused, obscuring the complex human-machine relationships at play. The research emphasizes that "AI is neither artificial nor intelligent." Instead, it is part of an ongoing techno-capitalist system that accelerates the commodification of data and exacerbates existing economic, labor, and environmental issues.

The paper advocates for moving beyond anthropomorphic language that attributes human-like qualities to AI systems, such as the term "hallucinations." Instead, it suggests considering AI as "alien intelligence," acknowledging the fundamental differences between machine and human cognition. By understanding what AI does through a cybernetic framework, the paper aims to clarify the relationships between humans and machines and hold creators accountable.

Drawing on early cybernetics research and figures like Norbert Wiener, the paper frames AI as a system of control with material effects, particularly when owned by large organizations. It argues that AI is not a rupture in media systems but an acceleration of techno-capitalism. By adopting better language and frameworks, the research promotes critical analysis, accountability, and transparency in AI development and governance, addressing threats to labor, data, and the environment.