Vučko’s argument begins with a simple observation about the structure of human experience. Nearly everything people do, from careers to hobbies to relationships, involves investing psychological or physical resources toward outcomes that are not guaranteed in advance. A surgeon operates without knowing exactly how the patient will recover. A novelist writes without knowing whether the book will find readers. A climber ascends a wall that might defeat them. These two features—effort and uncertainty—are usually treated as costs to be minimized. Technological progress, in its idealized form, promises precisely that: machines that work harder than we do and predict outcomes better than we can. But Vučko asks what remains of an activity once both parameters are optimized away.

To answer that question, he grounds his analysis in self-determination theory, one of the most influential frameworks in contemporary motivation science. Developed by psychologists Edward Deci and Richard Ryan, the theory holds that human well-being depends on satisfying three basic psychological needs: autonomy, competence, and relatedness. Crucially, the sense of competence is not fed by easy successes. It grows from challenges that demand genuine investment and from outcomes that could plausibly have gone otherwise. When an activity requires no effort and carries no risk of failure, the psychological machinery that generates satisfaction from achievement has nothing to work on. Vučko contends that effort and uncertainty are therefore not incidental annoyances but prerequisites for any sufficiently meaningful activity.

The thought experiment at the heart of the paper imagines extreme automation taken to its logical endpoint. Suppose technology advanced to the point where machines outperform humans at every task—diagnosing disease, writing legal briefs, composing music, driving vehicles, even conducting scientific discovery. There are already signs of this trajectory. GPT-4 has been shown to pass the bar exam, and AI systems for breast cancer screening have matched or exceeded human radiologists in international evaluations. Moravec’s paradox, the observation that tasks easy for humans remain hard for machines even as machines conquer tasks humans find difficult, is steadily eroding as artificial intelligence encroaches on domains once considered exclusively human.

What happens to such activities in a fully automated world? Vučko’s answer is that they do not disappear, but they change character. Activities in which humans can no longer compete with technology become, in his terms, recreation. A chess player today already inhabits this condition: since Deep Blue defeated Garry Kasparov in 1997, no human seriously claims to play chess better than a machine, yet millions still play. The game has become something played for enjoyment rather than objective achievement. Vučko argues that this transformation is sufficient to conditionally erode the subjective meaningfulness of activities for some individuals. Knowing that your best effort is objectively trivial compared with what a machine can do changes the texture of the experience, even if the pleasure remains.

The erosion does not stop with obviously competitive pursuits. Vučko emphasizes that no activity exists in isolation. Even autotelic activities—those done for their own sake, such as socializing, self-reflection, or reading fiction for the pleasure of emotional transportation—can be negatively affected by the awareness of one’s diminished capacity for objective contribution. A person might still enjoy a long walk in the mountains, but if they know that no physical accomplishment of theirs carries any weight in a world where machines handle everything, the background sense of being a capable, contributing agent begins to thin. Meaning, on this account, is not generated activity by activity in a vacuum; it depends on a broader self-understanding in which one’s efforts matter.

This leads to what may be the paper’s most striking implication: there is a limit to how much human distress technology can alleviate without producing a new kind of suffering. Much of the motivation for automation is the reduction of toil, risk, and anxiety. But if Vučko is right, the sources of distress he targets—uncertainty about outcomes and the strain of effort—are the same sources from which meaningful activity draws its nourishment. Eliminating all sources of distress also eliminates the potential for meaningful activities. The utopian project, pursued without limit, becomes self-defeating. Limited automation, by contrast, is conducive to meaningful lives: it removes drudgery while leaving humans room to strive, fail, and achieve on their own terms.

The argument engages a rich philosophical tradition. Robert Nozick’s famous experience machine thought experiment anticipated the intuition that pleasurable experiences divorced from genuine achievement feel hollow. Albert Borgmann’s philosophy of technology distinguished between the shallow comforts of the device paradigm and the deeper satisfactions of focal practices that demand engagement. More recently, John Danaher has explored whether life in a world without work can be worth living, and L. Scripter has defended the possibility of meaningful lives alongside artificial intelligence. Vučko’s contribution is to identify a specific mechanism—the joint minimization of effort and uncertainty—and to trace its consequences systematically, rather than relying on general appeals about the dignity of labor.

Notably, the paper does not claim that a highly automated future must be bleak. Moderate automation, which relieves people of tedious and dangerous work while preserving domains of genuine human challenge, could expand the space for meaningful activity rather than contract it. The concern is specifically with unlimited automation—the endpoint at which no domain of human striving retains objective significance. Vučko also acknowledges an important boundary condition: transhumanist scenarios in which humans merge with technology warrant separate consideration. If the striving agent is itself augmented, or partially constituted by the machines, the analysis of who is investing effort and bearing uncertainty changes fundamentally, and the existential calculus may differ.

For readers watching generative AI reshape creative and professional landscapes, the analysis offers a framework for anxieties that are often dismissed as mere nostalgia or Luddism. Creative displacement anxiety—the unease felt by artists and writers whose skills seem suddenly replicable—may reflect a genuine psychological injury, not simple economic fear. If competence, effort, and uncertainty are pillars of meaning, then watching machines master one’s craft after years of dedicated practice strikes at something deeper than income. Vučko’s paper suggests that societies racing toward maximal automation should ask not only what tasks can be automated, but what human capacities for meaning they are quietly dismantling—and whether some friction is worth keeping.

Subject of Research: The impact of extreme technological automation on effort, uncertainty, and the meaningfulness of human activities

Article Title: The limits of technological progress: effort, uncertainty, and meaningful activities

Article References: Vučko, D. (2026). The limits of technological progress: effort, uncertainty, and meaningful activities. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03356-4

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03356-4

Keywords: automation, technological progress, meaning in life, self-determination theory, artificial intelligence, human obsolescence, effort, uncertainty, recreation, philosophy of technology, well-being, transhumanism

Anders was writing at a moment of vertiginous technological change, when the first electronic computers were being unveiled to a public still reeling from the atomic bomb. He saw that people standing before these machines experienced an unfamiliar mixture of awe and self-doubt. Humans, he observed, are inferior to their technological appliances in strength, speed and precision, in much the same way that human thinking fares badly against the thinking power of computing machines. T’s response to this realisation was not simply intellectual. It registered in his body, in a sense of standing clumsily and imprecisely under the gaze of perfect devices. Anders described the shame as being about having naturally grown rather than having been made, about owing one’s existence to the blind, uncalculated, archaic process of procreation and birth, in stark contrast to immaculate products that are carefully designed through and through.

The concept remained obscure for decades. The provocative essay in which Anders identified Promethean shame was first translated into English only in 2016, and a full translation of the book in which it appeared, The Obsolescence of the Human, has only just been published by University of Minnesota Press. Sparrow argues this delay is unfortunate, because Promethean shame is all too evident in contemporary social, political and philosophical responses to AI. If the room-sized computers of the 1940s were enough to provoke the emotion in a few sensitive observers, the intelligent machines of today evoke it nearly universally, and the cultural consequences are everywhere to be seen.

Consider the rhetoric that now surrounds artificial intelligence. Breathless claims about superhuman performance are a staple of both popular and academic writing on almost any topic. We are constantly told we must get ready for AI, that we must dedicate ourselves to understanding it, and that it is inevitable humans will have to make way for AI and robots as they take our jobs. Leading philosophers insist there is a not-insignificant chance AI will become thousands of times smarter than us, that we might outsource our conscience to machines, and that we should live in fear of being turned into paperclips. Prominent scientists argue the future belongs to machines and that our only recourse is to leave our bodies behind as mind uploads in virtual worlds, or as digital simulations derived from our social media profiles. Sparrow notes that Marvin Minsky’s famous quip about future computers, that if we are lucky they might decide to keep us as pets, captures the vibe of contemporary AI discussion and stands as pithy an expression of Promethean shame as anything Anders himself coined.

But Anders’s analysis goes deeper than rhetorical excess. He argued that Promethean shame expresses a fundamental change in our sense of what it means to be human. New technologies establish new tasks for us, tasks for which our evolved bodies and minds are poorly suited. A pilot landing a jet must make complex decisions in split seconds. A student who wants to use AI to review the philosophical literature responsibly must, in order to check the results, possess a knowledge of that literature which by hypothesis they lack, and which may in fact be impossible to acquire. In this new world, humans in their natural state appear as faulty constructions when measured against the tasks they face. Yet, as Anders pointedly observed, humans can only be considered constructions, faulty ones even, once they are classed as machines. The comparison itself smuggles in a dehumanising premise.

Sparrow finds this logic at work in the way computational metaphors have colonised human self-understanding. Machine intelligence must be possible, we are told, because we are just machines made of meat. He detects it too in a subtle rhetorical manoeuvre: complex, multidimensional human activities are redescribed in narrow, quantifiable terms that machines can perform, and then we are told the machines do it better than we do. And he perceives it in proposals to hand over roles once considered quintessentially human, requiring touch and physical comfort, to machines, whether as sexual partners, digital friends, or caregivers for the elderly. Each case involves quietly shrinking the definition of a human task until a machine can be declared its superior.

Perhaps the most striking part of Anders’s argument concerns freedom. He noticed that the world of fabricated products presents itself as indefinite, open and malleable, daily intent on reforming itself and adapting to new situations, while the human body remains what it always was, still yesterday’s body, still the body of our parents and ancestors, indistinguishable from that of a troglodyte. Seen from the perspective of machines, the body looks conservative, anti-progressive, obsolete and unchangeable, a dead weight in the rise of machines. The result is a strange inversion: the machine’s potentials are nurtured, giving it a free hand becomes a duty, and stifling its potential becomes an immoral deed. The machine presents itself as the subject of desire and demand, expecting humans to exert themselves to come up with ever better offers. Anders summarised the inversion bluntly: things are free, and human beings are unfree.

This inversion, Sparrow suggests, is even more evident today than when Anders was writing. Where people once believed in the progressive force of human reason, it is now widely believed that technology makes revolutions, not people. The future, we are told, belongs to spiritual machines, with humans along for the ride if we are included at all. Our most urgent task, on this view, becomes ensuring that the superintelligences destined to control humanity’s fate will be friendly to us. Anders also linked Promethean shame to what he called human engineering, the project we now call transhumanism. Humans, he wrote, are deserting to the camp of their machines, having convinced themselves that machines are ontologically superior to beings who are born. They want to make themselves because they no longer want to be something not-made, feeling indignant not that they were made by others but that they were not made at all. Sparrow observes that one would be hard pressed to find a better description of today’s tech billionaires and their transhumanist enablers, with their obsession with designer babies, robots, mind uploading and immortality. In Anders’s arch formulation, the experiments of human engineering are the initiation rites of the robotic age.

There is a final, darker twist. Like shame more generally, the lack of self-confidence associated with Promethean shame leads us to behave in ways that make things worse. The more miserable humans become in trying to keep pace with their creations, the more frantically they multiply the bureaucracy of machines, sub-machines and sub-sub-machines, which in turn deepens their misery. What appears to be liberation from the tyranny of nature may result in an unprecedented subjugation to technology, as Oliver Rachello has suggested in drawing lessons from Anders for AI ethics. Anders himself put it starkly: humans turned themselves into masters only to find a new way to become slaves.

Escaping this trap, Sparrow concludes, will require serious cultural self-work, some of which he hopes will take place in the pages of AI & Society. The remedy he proposes is disarmingly simple in principle. We are not machines, and we need feel no shame when lifeless things perform exactly as we have designed them to perform. That we are born rather than made is, as Hannah Arendt argued, a condition of our freedom and our agency, not a defect to be regretted. The urgent task of building a world in which our creations serve us, rather than us serving them, begins with recognising and overcoming Promethean shame. Seventy-five years after T stood blushing before a wall of vacuum tubes, the emotion he felt has become the unacknowledged background hum of the AI age, and naming it may be the first step toward thinking clearly about the machines we have made.

Subject of Research: Promethean shame and the philosophical response to artificial intelligence

Article Title: AI and Promethean shame

Article References: Sparrow, R. (2026). AI and Promethean shame. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03371-5

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03371-5

Keywords: artificial intelligence, Promethean shame, Gunther Anders, philosophy of technology, transhumanism, AI & Society, Robert Sparrow, human obsolescence, machine intelligence, mind uploading, AI ethics, human dignity

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