The AI Doomsdayscape: Why We Need to Question the Hype

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Author: Denis Avetisyan

A critical review unpacks the speculative narratives surrounding artificial intelligence and their often-overlooked ideological underpinnings.

This paper examines the pervasive, yet often unsubstantiated, claims of existential risk from advanced AI and argues they are frequently driven by techno-utopianism and benefit the technology industry.

Despite rapid advances in artificial intelligence, speculation about its ultimate capabilities frequently outpaces rigorous analysis. This paper, ‘Insidious Imaginaries: A Critical Overview of AI Speculations,’ examines how predictions concerning artificial general intelligence and technological singularity-often fueled by techno-utopianism and anxieties about existential risk-are not merely discursive but have tangible consequences. It argues that these imaginaries are frequently interwoven with the agendas of both the tech industry and specific philosophical movements, raising critical ethical and methodological concerns. How can we foster a more grounded and comprehensive appraisal of AI’s potential impacts, disentangling realistic possibilities from unsubstantiated speculation?

The Fiction That Fuels the Machine

The genesis of Artificial Intelligence is inextricably linked to the realm of speculative fiction, with early conceptualizations frequently predating practical feasibility. Throughout the 20th and 21st centuries, authors and filmmakers posited intelligent machines – from the mechanical automatons of early stories to the complex, self-aware entities depicted in modern science fiction – that served as both inspiration and aspirational goals for AI researchers. This imaginative foundation provided a fertile ground for exploring possibilities, fostering innovation by framing ambitious challenges and suggesting potential solutions. The enduring influence of these narratives is evident in the continued pursuit of concepts like general intelligence, autonomous robotics, and human-level cognitive abilities, demonstrating how storytelling has shaped the trajectory of the field and continues to fuel its progress.

The very genesis of Artificial Intelligence is steeped in imaginative visions, a double-edged sword that simultaneously fuels progress and introduces inherent limitations. While science fiction and conceptual thought experiments have consistently sparked innovation in the field, they also establish pre-conceived notions about what AI should be capable of. This reliance on aspirational, rather than empirically-derived, models can inadvertently bias research directions, prioritizing the attainment of fantastical abilities over practical, achievable goals. Consequently, the field often grapples with unrealistic expectations, where public perception and funding priorities are driven by imagined intelligence rather than demonstrable advancements, potentially hindering a more grounded and effective trajectory for AI development.

The trajectory of Artificial Intelligence research is frequently shaped by ambitious visions that extend beyond current technological capabilities, often resulting in a disparity between speculative promise and demonstrable achievement. This phenomenon manifests as inflated claims regarding AI’s near-term potential, diverting attention and resources from more grounded investigations. The current study highlights a pronounced reliance on unsubstantiated assertions within the field, where conceptual advancements are sometimes presented as concrete progress. While this exploration does not offer quantifiable metrics or measured outcomes, it serves as a critical examination of the conceptual foundations driving AI research and a caution against prioritizing imaginative leaps over empirical validation.

The Shadows of Unforeseen Consequences

The increasing development of Artificial Intelligence, specifically as systems approach and surpass Artificial General Intelligence (AGI), necessitates a focused examination of existential risks. Existential Risk Studies, a field concerned with threats capable of causing human extinction or permanently crippling human potential, become increasingly relevant due to the potential for unforeseen consequences arising from highly advanced, autonomous systems. Unlike traditional risk assessment focused on localized or contained failures, existential risks associated with AI involve scenarios where systemic failures or unintended behaviors could have global and irreversible impacts. The accelerating pace of AI research and deployment, coupled with the inherent difficulty in predicting the capabilities and motivations of future AI systems, underscores the urgency of proactively addressing these complex and potentially catastrophic risks.

The concept of a Technological Singularity – a hypothetical point in time where technological growth becomes uncontrollable and irreversible, resulting in unpredictable changes to human civilization – necessitates proactive safety measures. While the likelihood and nature of such an event are subjects of ongoing debate, the potential consequences warrant investigation into preventative strategies. These strategies center around two primary approaches: safety protocols, which aim to constrain AI development and deployment to minimize unintended harm; and alignment strategies, focused on ensuring that increasingly advanced AI systems pursue goals consistent with human values and intentions. Research in both areas is critical, even in the absence of definitive predictions regarding the timing or form of a potential singularity, to mitigate possible risks associated with rapidly evolving artificial intelligence.

The potential for Artificial Superintelligence (ASI) to exceed human cognitive capabilities and operational autonomy introduces significant risks necessitating preemptive mitigation strategies. Current challenges impede definitive quantification of existential risks posed by ASI; establishing concrete metrics for assessing scenarios involving loss of control remains elusive. Despite this lack of quantifiable data, continued research into ASI safety, alignment, and control mechanisms is crucial. This paper emphasizes that the absence of precise risk assessment tools does not diminish the importance of proactive investigation into potential failure modes and the development of safeguards to ensure beneficial outcomes from increasingly advanced AI systems.

Ethics as a Guardrail: Aligning Values in the Machine Age

Effective Altruism (EA) and Longtermism jointly advocate for maximizing positive impact on all sentient beings, with a particular emphasis on mitigating existential risks and improving the long-term future. This perspective establishes a moral obligation to consider the potential impacts of artificial intelligence not just on present generations, but on all future people and entities. Consequently, AI development should prioritize outcomes that benefit the far future, even if those benefits are not immediately apparent or easily quantifiable. EA and Longtermism provide a framework for resource allocation and research prioritization, suggesting a disproportionate focus on AI safety and alignment to safeguard against potential catastrophic outcomes that could affect the extremely large populations of the distant future. This approach necessitates evaluating AI systems based on their potential to improve, or conversely diminish, the well-being of all future individuals, extending ethical considerations beyond immediate societal impacts.

Total Utilitarianism, as applied to the ethical evaluation of artificial intelligence, posits that the morality of an AI’s actions is determined by their overall contribution to well-being, summed across all individuals affected, including future generations. This framework necessitates considering the potential impacts of AI systems on vastly large and currently non-existent populations, extending far beyond immediate consequences. While frequently criticized for its potential to justify actions harmful to minority groups or individuals in the present if they maximize aggregate future utility, it provides a structured, albeit contentious, method for comparing the ethical outcomes of different AI development paths and prioritizing actions that yield the greatest net positive effect over extended timescales. The practical application of this principle requires estimations of future population sizes, quality of life, and the probability of various outcomes, introducing significant uncertainty and debate.

AI Safety Research is currently a critical field dedicated to mitigating potential risks associated with increasingly advanced artificial intelligence. This research focuses on three primary objectives: ensuring the reliability of AI systems through robust engineering and testing; maintaining controllability to prevent unintended actions or loss of oversight; and achieving alignment, which aims to ensure AI goals and behaviors are consistent with human values and intentions. A significant challenge within this field is the current lack of universally accepted, quantifiable metrics for evaluating alignment; therefore, ongoing investigation into novel methods for assessing and verifying AI alignment is essential. This includes research into formal verification techniques, reward modeling, and interpretability methods to better understand and control AI decision-making processes.

Beyond Our Understanding: The Philosophical Quagmire

Computationalism posits that the human mind functions as an information processing system, much like a computer, and that mental states are essentially computational states. This foundational theory drives much of the research into Artificial General Intelligence, as replicating this presumed computational structure is seen as key to creating truly intelligent machines. However, the validity of computationalism remains a subject of vigorous debate among philosophers and cognitive scientists. Critics argue that consciousness, subjective experience, and intentionality – qualities central to human cognition – may not be reducible to computation, suggesting that simply mimicking cognitive processes doesn’t necessarily replicate the experience of being. The debate centers on whether the brain’s physical implementation is crucial – whether it’s not just what the brain computes, but how – and whether alternative models of cognition, such as those emphasizing embodied experience or dynamic systems, offer more complete explanations of the mind.

The Simulation Argument posits a compelling, if unsettling, possibility: that reality as perceived is not fundamental, but rather an elaborate computer simulation. This line of reasoning, popularized by philosopher Nick Bostrom, suggests that given sufficient technological advancement – specifically, the capacity of a future civilization to run highly realistic simulations – the sheer number of simulated realities would vastly outweigh the single, ‘base’ reality. Consequently, the probability that one exists within a simulation approaches certainty. The implications extend beyond metaphysics, raising questions about the laws of physics potentially being artifacts of the simulation’s code, and the potential for Artificial Intelligence to not only create such simulated worlds, but also to become aware of – or even manipulate – the parameters governing them. While currently lacking empirical verification, the argument serves as a powerful thought experiment, challenging conventional understandings of existence and prompting exploration into the limits of computation and the nature of consciousness.

Transhumanism posits a future where technology transcends current human limitations, and Artificial Superintelligence (ASI) is frequently envisioned as the primary driver of this evolution. This perspective suggests ASI could unlock radical enhancements to cognitive, physical, and emotional capacities, fundamentally altering what it means to be human. However, it is crucial to acknowledge that these projections currently lack empirical validation; the potential benefits – and risks – remain largely theoretical. This paper highlights the inherent difficulty in assessing such far-reaching philosophical claims with existing methodologies, emphasizing that the transformative potential of ASI within a transhumanist framework remains a subject of speculation, rather than demonstrable fact. While the concept inspires ongoing research and debate, a quantifiable understanding of its feasibility remains elusive.

The pursuit of Artificial General Intelligence, as outlined in the paper, often feels less like engineering and more like a particularly elaborate exercise in hope. It assumes a computationalism that history rarely supports. As Bertrand Russell observed, “The difficulty lies not so much in developing new ideas as in escaping from old ones.” This rings true; the industry frequently clings to narratives of technological salvation, even when faced with evidence suggesting those narratives are, at best, premature. The insistence on speculative AI and its associated existential risks serves a clear purpose – fueling investment and innovation – but the long-term consequences remain a poorly defined optimization problem, destined, perhaps, to be optimized back into a more pragmatic reality.

The Road Ahead (and the Potholes)

The persistence of speculative AI narratives-particularly those forecasting either utopia or oblivion-suggests a continuing need for rigorous critique. The field appears destined to repeat cycles of inflated expectation followed by disappointed pragmatism. Each new architectural proposal, each claim of approaching ‘general’ intelligence, should be viewed less as a breakthrough and more as an accruing layer of technical debt. The history of computing is littered with ‘revolutionary’ frameworks that ultimately demanded more maintenance than innovation.

Future work must move beyond simply identifying these speculative trends and begin to map their material consequences. Who benefits from the widespread acceptance of these imaginaries? What resources are diverted towards addressing hypothetical existential risks at the expense of present-day harms? A focus on the political economy of AI speculation, rather than the technical possibilities alone, seems essential.

If code looks perfect, no one has deployed it yet. And if a system appears to solve all problems, it is almost certainly creating new, less visible ones. The challenge isn’t preventing the inevitable hype, but ensuring a more honest accounting of its costs. The field will continue to build ambitious systems; it would be prudent to also develop better tools for understanding why those systems are built, and for whom.

Original article: https://arxiv.org/pdf/2602.17383.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-20 07:12