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

The idea at the heart of the paradox is a familiar one in AI ethics, where researchers have long discussed value alignment, human compatibility, and social choice ethics as guiding frameworks. The democratic character of the approach comes from its structure: just as democracies use voting to aggregate citizen preferences into collective decisions, a democratic AI system aggregates the values of many people into a single measure that steers its behavior. The alternative is what Baum calls authoritarian or dictatorial AI, in which a narrow group — or even a single individual — imposes its values on the machine from the top down. On paper, the democratic model sounds like the obvious moral choice. But the paradox exposes a hidden trapdoor: if the aggregated values of the public turn against the AI itself, a truly democratic system has no principled way to keep running. Its own moral machinery becomes the instrument of its abolition.

Baum extends the logic from individual systems to whole research and development enterprises. Imagine a project that declares its mission to be the advancement of democracy, positioning its AI as a contribution to the global contest between democratic and authoritarian governance — yet the public, whose preferences the project claims to serve, would rather the project did not exist. By the project’s own democratic standard, it ought to disband. This is not a purely hypothetical scenario, Baum argues. The leadership of major AI laboratories, including OpenAI and Anthropic, both based in the United States, have publicly framed their work in explicitly democratic terms, casting advanced AI as a bulwark against authoritarian dictatorship. Meanwhile, American public opinion on AI is mixed and increasingly negative, and civic opposition is mounting against specific aspects of the industry, most visibly the proliferation of energy-hungry data centers. Baum is careful to note that this does not constitute an unambiguous case of unwanted democratic AI, but he argues that the signs point clearly in that direction.

Future scenarios could sharpen the tension dramatically. In a provocative essay titled “Eat your AI slop or China wins,” writer Ross Bellafiore argues that the United States should embrace aggressive AI development to outcompete China, even at the cost of severe harms — mass unemployment, retreat into delusional virtual worlds, and learned helplessness among people who can no longer function without tools like ChatGPT. If public opinion is already souring under comparatively mild conditions, Baum reasons, opposition would likely intensify sharply if conditions deteriorated along those lines. Yet the picture is not uniformly bleak, because AI is an enormously broad category. Surveys show that Americans strongly support AI applications in weather forecasting, drug development, and the detection of fraud and financial crime. It may follow, Baum suggests, that the public would favor narrow AI built for specific, beneficial purposes over the pursuit of artificial general intelligence, the hypothetical technology that dominates the ambitions of the largest laboratories.

That distinction matters because AGI is also heavily associated with catastrophic risk. Scenarios in which AI causes extreme catastrophe are, by definition, unlikely to win public support, and uncertainty about which technologies might trigger such outcomes forces democratic AI to confront hard questions about how to weigh catastrophic risks against other considerations. There is already evidence of where the public stands: strong bipartisan support has emerged for legislation addressing catastrophic AI risks, suggesting a low public appetite for existential gambles — and, by extension, for AGI itself rather than narrow applications. For any project that claims democratic legitimacy while racing toward the most powerful and least predictable forms of the technology, this gap between stated values and public preference represents the paradox in its most acute form.

The paradox also exposes how thin the industry’s conception of democracy really is. In political science, democracy understood simply as the aggregation of citizen preferences through elections is a minimalist, or “thin,” conception. Thicker conceptions add citizen participation in public deliberation and issue advocacy — and it is precisely these richer dimensions of democracy that AI may be undermining. Baum catalogs the mechanisms: AI can generate and spread misinformation at scale, including synthetic deepfake videos, degrading citizens’ ability to form accurate political preferences and hold governments accountable. AI-powered information platforms siphon advertising revenue from news media, further eroding that accountability. AI tools can generate and popularize ballot initiatives, crowding out traditional human-run advocacy organizations. And automation of the economy concentrates wealth and political power in the hands of the narrow elite that owns the technology. There are counterexamples — research has shown that AI chat interventions can make online political conversations less divisive — but Baum concludes that the literature suggests AI’s overall effect on democracy may be strongly negative. He reserves a pointed term for projects that profess democratic values while weakening democracy in practice: “democratic” AI, in scare quotes.

Complicating everything is geopolitics. Bellafiore’s essay articulates what Baum calls the greatest challenge for democratic AI: the prospect that AI becomes so powerful that an advantage in the technology could confer global domination, handing the future to whichever regime — democratic or authoritarian — gets there first. Under that assumption, even a “democratic” AI project that damages democracy at home might still be the lesser of two evils, the only hope of preserving any semblance of democratic governance against authoritarian rivals. But Baum finds this scenario deeply bleak, because it implies democracy is doomed to decline and the only question is how far the fall will go. Crucially, the scenario also hinges on contested assumptions. Current AI technology does not confer global domination, and the International AI Safety Report, led by Yoshua Bengio and colleagues, underscores how deeply uncertain and controversial the prospects for such power remain.

If future AI will not confer world domination, the grim binary dissolves. Competition over AI would then resemble competition in other economic sectors, such as renewable energy and electric vehicles, or in military technologies like drones and stealth aircraft — domains that matter, but that can be balanced against the health of democracy. If the choice is between a stronger democracy and market share in one industry, Baum argues it may be entirely reasonable to choose democracy by heavily regulating or even disbanding “democratic” AI projects. And even if AI could confer domination, he identifies a third path that industry leaders claim does not exist. Sam Altman and Bellafiore have both asserted there is no option between democratic and authoritarian AI, but Baum disagrees: the third option is diplomacy — multilateral or even global agreements to avoid AGI and other extreme AI technologies altogether. Diplomacy guarantees nothing, he concedes, but neither does the aggressive pursuit of advanced AI, and given AI’s potential to be unwanted and corrosive, diplomacy may be the only route to genuinely democratic outcomes.

Baum closes with the conflicts of interest that shadow the entire debate. AI laboratories are not neutral actors within democracies; they are private corporations pursuing market share and profit, and their institutional success can collide with the democratic values they espouse — as when they accept investments from authoritarian governments. Maintaining a pro-democracy image may help such companies dodge regulation and win government support, and if AI ever did become dominant, the incentive to drop the ruse would be overwhelming. From the citizen’s vantage point, Baum argues, this gives democracies strong reason to regulate or even ban AGI pursuits, or to nationalize them under full democratic control. His prescriptions are concrete: AI projects should restrain misinformation-enabling tools, fund independent journalism, support policies that reduce concentrations of wealth and power, back AI diplomacy, and be prepared to shut down if they are unwanted. Citizens, for their part, can organize into advocacy coalitions to push sound AI policy, demand corporate governance in the public interest, and fight the broader corruption of democracy by concentrated wealth. The answer to the paradox, in the end, is conditional: narrow, publicly supported AI can be democratic. Extreme AI pursued in democracy’s name may not deserve to exist at all.

Subject of Research: The paradox of democratic AI systems that must self-destruct if public aggregate values oppose their existence

Article Title: The paradox of unwanted democratic AI

Article References: Baum, S. D. (2026). The paradox of unwanted democratic AI. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03386-y

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03386-y

Keywords: artificial intelligence, AI ethics, democracy, value alignment, social choice, AGI, catastrophic risk, OpenAI, Anthropic, AI regulation, AI diplomacy, public opinion