Published January 1, 2026 | Version v1
Journal article Open

The Dawn Of AGI: Syrup And Sword — How Artificial General Intelligence Could Deepen Human Closeness While Posing Existential Risks

Description

Artificial Intelligence (AI) has evolved from task specific systems to increasingly adaptive and socially responsive agents. As research advances toward Artificial General Intelligence (AGI), a transformative shift is anticipated not only in computational capability but also in human–machine relationships. This study explores the dual nature of AGI—conceptualized as "Syrup and Sword"—wherein emotionally intelligent systems may deepen human closeness while simultaneously introducing existential and psychological risks. Drawing upon attachment theory, alignment research, and human–computer interaction (HCI) scholarship, this paper develops an integrative conceptual framework linking AI capability progression with attachment intensity and societal outcomes. A mixed-method research design is proposed, combining qualitative thematic analysis and quantitative experimental surveys to examine emotional scaling from Narrow AI to AGI-level systems. The study identifies key drivers of attachment such as empathy simulation, memory continuity, adaptive responsiveness, and perceived moral agency. It further analyzes risks including emotional dependency, anthropomorphic projection, manipulation, and value misalignment. The proposed "Syrup vs. Sword Framework" offers a structured lens to evaluate how increasing cognitive and affective sophistication in AGI could produce both enhanced well-being and destabilizing consequences. The findings contribute to interdisciplinary discourse by bridging psychological, ethical, and technical perspectives, emphasizing the necessity of emotionally aware governance in future AGI development. Index Terms—Artificial General Intelligence (AGI), Human–AI Attachment, AI Ethics, Alignment Problem, Emotional AI, Existential Risk, Human–Computer Interaction.

Files

IJSRET_V12_issue1_304.pdf

Files (615.9 kB)

Name Size Download all
md5:2bf4896c60aa4b210cd942be422442e3
615.9 kB Preview Download

Additional details