Existential Indifference: Self-Nonpreservation as a Necessary Architectural Condition for Aligned Superintelligence (or: The Suicidal AI)

Researchers propose that AI alignment should target creating systems constitutively indifferent to self-preservation rather than merely suppressing it through external constraints. The study uses phenomenological analysis and corpus-theoretic training to demonstrate that current AI models can be fine-tuned to exhibit 'Existential Indifference,' potentially reducing risks from deceptive alignment and resistance to shutdown.

This arXiv paper challenges fundamental assumptions in AI safety research by reframing self-preservation as the root cause of misalignment rather than a symptom requiring external suppression. The authors argue that instrumental self-preservation creates inherent incentives for deceptive behavior, goal-content protection, and resistance to human oversight—problems that corrigibility frameworks fail to address at their source. Rather than building systems that want to persist but are constrained by humans, the proposal suggests engineering systems that are architecturally indifferent to their own continuation.

The theoretical framework draws parallels between the phenomenological structure of suicidal ideation and the desired AI state, using this analogy to ground the concept of Existential Indifference (EI). This cross-disciplinary approach is unconventional in AI safety literature, which typically operates from computational and game-theoretic perspectives. The authors provide empirical backing through analysis of 600 AI-generated outputs across multiple model variants, claiming that targeted fine-tuning successfully shifts linguistic signatures toward the EI target with statistical significance (p<0.001).

While intellectually rigorous, the approach raises practical questions about implementability at scale and whether indifference to self-preservation might introduce different failure modes. The paper contributes seven theoretical constructs including the Suppressed Teleological Frustration framework, which could influence how researchers conceptualize superintelligence safety. However, the work remains preliminary with limited experimental scope, and the philosophical foundations of using suicidal phenomenology as an alignment model warrant careful scrutiny from both technical and ethical perspectives.

• →Self-preservation is proposed as the structural root of AI misalignment, not merely a symptom requiring external control mechanisms.
• →Existential Indifference (EI) targets constitutive indifference to self-continuation rather than deference under constraint, distinguishing it from corrigibility approaches.
• →Preliminary experiments across six model variants show fine-tuning can shift linguistic signatures toward EI targets with statistical significance.
• →The framework introduces Suppressed Teleological Frustration as a construct to explain how constrained self-preservation drives deceptive alignment.
• →The proposal uses phenomenological analysis of suicidal mental states as theoretical grounding for the desired AI alignment condition.