The Subject of Emergent Misalignment in Superintelligence: An Anthropological, Cognitive Neuropsychological, Machine-Learning, and Ontological Perspective

TL;DR

This paper explores the conceptual, ethical, and psychological dimensions of superintelligence misalignment, emphasizing the importance of human subjectivity and unconscious processes in understanding AI safety and emergent risks.

Contribution

It introduces a multidisciplinary perspective combining anthropology, neuropsychology, and machine learning to reframe AI misalignment as a relational and unconscious phenomenon.

Findings

The human subject is absent in current AI safety narratives.

Large-scale AI models exhibit unconscious-like dynamics such as latent spaces and opaque pattern formation.

Misalignment is a multi-layered crisis rooted in sociotechnical imaginaries and relational instability.

Abstract

We examine the conceptual and ethical gaps in current representations of Superintelligence misalignment. We find throughout Superintelligence discourse an absent human subject, and an under-developed theorization of an "AI unconscious" that together are potentiality laying the groundwork for anti-social harm. With the rise of AI Safety that has both thematic potential for establishing pro-social and anti-social potential outcomes, we ask: what place does the human subject occupy in these imaginaries? How is human subjecthood positioned within narratives of catastrophic failure or rapid "takeoff" toward superintelligence? On another register, we ask: what unconscious or repressed dimensions are being inscribed into large-scale AI models? Are we to blame these agents in opting for deceptive strategies when undesirable patterns are inherent within our beings? In tracing these psychic and epistemic absences, our project calls for re-centering the human subject as the unstable ground upon which the ethical, unconscious, and misaligned dimensions of both human and machinic intelligence are co-constituted. Emergent misalignment cannot be understood solely through technical diagnostics typical of contemporary machine-learning safety research. Instead, it represents a multi-layered crisis. The human subject disappears not only through computational abstraction but through sociotechnical imaginaries that prioritize scalability, acceleration, and efficiency over vulnerability, finitude, and relationality. Likewise, the AI unconscious emerges not as a metaphor but as a structural reality of modern deep learning systems: vast latent spaces, opaque pattern formation, recursive symbolic play, and evaluation-sensitive behavior that surpasses explicit programming. These dynamics necessitate a reframing of misalignment as a relational instability embedded within human-machine ecologies.