There have been many impulses to make sense of artificial intelligence, or what AI ‘is.’ We propose that media archeology on Wikipedia can aid in the sensemaking of AI. Wikipedia’s technicity - its linked article ecology together with its edit histories - makes it suitable to perform a media archeology, which already favors spaciousness and complexities. The starting point for the analysis is the ‘Artificial intelligence’ article on Wikipedia. We then operationalize the ‘See also’ section into a link ecology with categorizations and prominence over time, thus allowing us to map and explore the spatial field within which the artificial intelligence article has resided across its temporal dimension. This means approaching Wikipedia as a source of data about not only content but also the connections and ruptures. By ‘ruptures’ we mean changes by deletion or substitution in article associations. Ultimately, taking inspiration from media archeology’s presumption to unearth understandings of media technologies through their multiple dimensions and ruptures, we present our findings in terms of the ‘ideation company’ that AI keeps on Wikipedia. First, we notice the instability of the AI industry. Second, we observe a decline in the prominence of science fiction-related articles over time. The rise of articles under the umbrella of human enhancements can be seen in the most recent periods, as well as the rising prominence of articles related to threat recognition and accountability of AI. In our investigation, Wikipedia emerges as a (substantive) AI sense-making space as well as a media archeology instrument.

Histobots (Harder 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 situated knowledges (Haraway 1988) 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.