Why do we need social singularity? A mechanism-based critique of gradual scenarios in AI existential-risk discourse

• URL: http://arxiv.org/abs/2608.03904v1
• Date: Tue, 04 Aug 2026 16:30:12 GMT
• Title: Why do we need social singularity? A mechanism-based critique of gradual scenarios in AI existential-risk discourse
• Abstract summary: This paper critiques recent gradual and cumulative AI existential-risk scenarios.<n>It argues that, despite their substantive contributions, they remain insufficiently sociologically specified.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper critiques recent gradual and cumulative AI existential-risk scenarios, arguing that, despite their substantive contributions, they remain insufficiently sociologically specified. In particular, these scenarios lack a reflexive perspective, retain a largely technologically deterministic structure, and underestimate the role of collective agency and other social processes. As a result, they also discount the possibility of major social conflict accompanying AI diffusion, which limits their overall plausibility. The paper further identifies a set of social mechanisms likely to become significant before or during AI deployment, including interpretive and performative dynamics, mobilisation and countermobilisation, path dependence and lock-in, cross-regime divergence and multi-speed diffusion, and social-psychological mechanisms such as attachment and reactance. It argues that these mechanisms will interact recursively with AI diffusion and reshape future trajectories, which may subsequently branch, reverse, or undergo discontinuous shifts, thereby expanding the space of plausible AI futures. On this basis, the paper also proposes a novel analytic distinction between technological singularity and social singularity. Whereas the former refers to a putative technological threshold in AI development, the latter denotes a social discontinuity produced by the anticipated approach of that threshold. The central implication is that the most consequential disruptions may emerge not only from advanced AI itself, but also from the social dynamics provoked by the expectation of its arrival.

Related papers

• The Boundaries of Automation: A Theory of Persistent Human Participation [107.84677954791773]
  We argue that human participation may persist even with highly capable AI systems.<n>In some activities, the target is not fully specified in advance but instead emerges through the interaction itself.<n>This perspective has important implications for the limits of automation.
  arXiv  Detail & Related papers  (2026-07-23T17:33:43Z)
• AI Value Alignment for Evolving Social Norms [8.71949046003755]
  We introduce a flexible and mathematical modelling framework, rooted in social physics, aimed at answering macro-level questions regarding the evolving social norms in human populations under the assumption of frequent AI use.<n>We highlight the risk of value lock-in, and normative mode collapse, prominently featured in non-adaptive alignment formulations.
  arXiv  Detail & Related papers  (2026-07-20T21:02:39Z)
• From AGI to ASI [65.04985565452489]
  Building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations.<n>Report investigates how AI itself might continue to develop in a post-AGI world along the continuum of machine intelligence.
  arXiv  Detail & Related papers  (2026-06-10T21:14:10Z)
• Stochastic, Dynamic, Fluid Autonomy in Agentic AI: Implications for Authorship, Inventorship, and Liability [0.2209921757303168]
  Agentic AI systems autonomously pursue goals, adapting strategies through implicit learning.<n>Human and machine contributions become irreducibly entangled in intertwined creative processes.<n>We argue that legal and policy frameworks may need to treat human and machine contributions as functionally equivalent.
  arXiv  Detail & Related papers  (2025-04-05T04:44:59Z)
• AI Automatons: AI Systems Intended to Imitate Humans [54.19152688545896]
  There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness.<n>The research, design, deployment, and availability of such AI systems have prompted growing concerns about a wide range of possible legal, ethical, and other social impacts.
  arXiv  Detail & Related papers  (2025-03-04T03:55:38Z)
• Emergence of human-like polarization among large language model agents [79.96817421756668]
  We simulate a networked system involving thousands of large language model agents, discovering their social interactions, result in human-like polarization.<n>Similarities between humans and LLM agents raise concerns about their capacity to amplify societal polarization, but also hold the potential to serve as a valuable testbed for identifying plausible strategies to mitigate polarization and its consequences.
  arXiv  Detail & Related papers  (2025-01-09T11:45:05Z)
• AI and Social Theory [0.0]
  We sketch a programme for AI driven social theory, starting by defining what we mean by artificial intelligence (AI) We then lay out our model for how AI based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power.
  arXiv  Detail & Related papers  (2024-07-07T12:26:16Z)
• Emergent Explainability: Adding a causal chain to neural network inference [0.0]
  This position paper presents a theoretical framework for enhancing explainable artificial intelligence (xAI) through emergent communication (EmCom) We explore the novel integration of EmCom into AI systems, offering a paradigm shift from conventional associative relationships between inputs and outputs to a more nuanced, causal interpretation. The paper discusses the theoretical underpinnings of this approach, its potential broad applications, and its alignment with the growing need for responsible and transparent AI systems.
  arXiv  Detail & Related papers  (2024-01-29T02:28:39Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Conservative AI and social inequality: Conceptualizing alternatives to bias through social theory [0.0]
  Societal issues can no longer be out of scope for AI and machine learning, given the impact of these systems on human lives. Conservatism refers to dominant tendencies that reproduce and strengthen the status quo, while radical approaches work to disrupt systemic forms of inequality. This requires engagement with a growing body of critical AI scholarship that goes beyond biased data to analyze structured ways of perpetuating inequality.
  arXiv  Detail & Related papers  (2020-07-16T21:52:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.