Glawatzki’s core theoretical move is to connect two concepts that researchers have usually treated separately. Anthropomorphism, the attribution of human characteristics to non-human entities, has mostly been studied as a psychological phenomenon occurring in individual users who chat with bots and start to feel a social presence. Sociotechnical imaginaries, a term from science and technology studies, describe collectively held visions of desirable futures made possible by science and technology. The study proposes a synthesis of the two: “anthropomorphising imaginaries,” futures that are structured from the ground up by human-like descriptions of machines. In these imaginaries, generative AI is not merely useful; it is framed as performing at a human level and deserving human-like trust, which positions it as powerful in the societies being imagined.

To reconstruct these imaginaries, the researcher conducted a qualitative content analysis of 35 corporate documents published between late 2024 and spring 2025 by three companies: OpenAI, Meta, and the French startup Mistral AI. The sample included product announcements, research updates, company pages, case studies, and user documentation, selected because they either explained the abilities and features of a model or discussed its contexts of use. The choice of companies was deliberate: OpenAI with roughly 800 million weekly active users, Meta with its openly released Llama models, and Mistral AI, often described as Europe’s most promising competitor to U.S. systems, together represent a diverse but culturally proximate slice of Western AI development. Using a structured qualitative approach, the analysis coded the documents for attributed abilities, characteristics, roles, and the distribution of responsibility and power between humans and machines.

The results show that companies anthropomorphize generative AI along four dimensions: cognition, communication, sociality, and personality. Cognitive attributions are the most prominent. Terms like “chain of thought” frame algorithmic processes with human cognitive metaphors, and models are credited with knowledge, systematic understanding, and reasoning that produce “accurate answers.” Some rhetoric even naturalizes these abilities: Mistral AI claims that large language models, “by nature,” are not good at calculations, a phrasing that positions the technology as an independently evolving, almost organic entity rather than a human-built product with material costs and consequences. Communication skills are anthropomorphized too, with Meta describing Llama as able to switch casually between humor, empathy, intellectualism, creativity, and problem-solving. At the extreme, Meta calls the model “an expert conversationalist” that is “companionable and confident,” granting it a narrated personality that transcends its status as software.

From this material, three distinct imaginaries emerge, each present across all three companies. The first imagines generative AI as a tool with human capabilities. Here, human cognitive abilities—thinking, learning, understanding—are portrayed as technically encodable inside a seemingly neutral, human-controllable product that will boost productivity and democratize skills. Intelligence becomes a commodity sold by private companies, and the vision carries a distinctly neoliberal flavor: humans augmented by AI will remain competitive and productive, while non-use is framed as a competitive liability. The study warns that this framing raises awkward questions about whose intelligence gets encoded into the tool, potentially importing racist and ableist conceptions of intelligence, while concealing the often exploitative human labor of data collection and verification behind the machine.

The second imaginary casts generative AI as an empowering helper, a cooperative assistant integrated into every corner of life. Companies oscillate between attributing competence to the technology and reassuring users that humans remain in control. In this vision, AI manages daily tasks, offers professional support, and even provides emotional care: Meta describes Llama as recognizing whether a user is looking for chit-chat, emotional support, humor, or venting. The study notes that this rhetoric subtly devalues the human, implying that people are exhausted, limited, and in need of technological enhancement—a premise the researcher identifies as transhumanist in spirit. Strikingly, the imaginary also recasts users as teachers: OpenAI compares a GPT model to “a junior coworker” needing explicit instructions, reframing the unpaid labor of training and prompting as pedagogical care that ultimately optimizes corporate products.

The third and most far-reaching imaginary envisions generative AI as an anthropomorphized superintelligence that will reach and surpass human-level intelligence through self-directed evolution. Meta’s claim that one powerful model was the perfect choice to “teach” smaller models constructs a future in which AI progress unfolds independently of humans. Models are framed as experts and “thought partners,” with OpenAI comparing reasoning models to “a senior co-worker” whom you can trust to work out the details. The study argues that this attribution of expertise to a system that generates text through probability calculations devalues situated human knowledge and normalizes the opacity of AI systems. Yet the same imaginary flips into threat mode: OpenAI’s preparedness framework anticipates self-improvement and autonomous replication capabilities, evoking a Frankenstein narrative that conveniently positions the companies themselves as humanity’s protectors.

Each imaginary generates a corresponding construction of AI power. Instrumental power corresponds to the intelligent tool: high performance, but under human control. Cooperative power corresponds to the helper: a shared power emerging from human–AI collaboration, cultivated through both functional and affective trust. Autonomous power corresponds to the superintelligence: power located mainly within the machine itself, justified by narratives of inevitable technical evolution. The three types build on each other, progressively shifting power away from humans, and each promotes distinct forms of trust—functional reliability in the first case, emotional benevolence in the second, and epistemic confidence in the third.

The normative implications are sobering. According to the study, these constructions foster uncritical acceptance of AI, frame its use as inherently desirable while foreclosing discussion of alternatives, depoliticize the technology by hiding the exploitative labor conditions of its creation, normalize post- and transhumanist ideologies, and legitimize AI companies as authoritative shapers of the future. This matters because companies possess what the paper calls infrastructural power: their research ideas spread more contagiously than academic ones, their innovations routinely precede regulation, and in an AI race driven by speed and resources, futures get realized not through democratic negotiation but through corporate momentum. Sociotechnical futures, as prior research quoted in the study puts it, become commodified by hegemonic technology companies and their expanding infrastructures.

The study is candid about its limits: the analysis covers only Western companies, and firms from other cultural contexts, such as China’s DeepSeek, may articulate different imaginaries that increasingly influence Western discourse. But its central warning travels well beyond the sample. Anthropomorphism, the paper concludes, is not merely an exaggerated or misleading metaphor to be corrected by careful speakers. Embedded in sociotechnical imaginaries and deployed strategically, it becomes an instrument of hegemonic power that shapes how AI is researched, developed, appropriated, and regulated. The words “it thinks” may seem trivial, but whoever controls those words is, in a very real sense, writing the future.

Subject of Research: Anthropomorphising sociotechnical imaginaries of generative AI constructed by AI companies

Article Title: Constructing AI power: anthropomorphising imaginaries of generative AI by AI companies

Article References: Glawatzki, A. (2026). Constructing AI power: anthropomorphising imaginaries of generative AI by AI companies. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03367-1

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03367-1

Keywords: anthropomorphism, generative AI, sociotechnical imaginaries, AI power, OpenAI, Meta, Mistral AI, transhumanism, AI hype, large language models, critical AI studies, corporate power