From control to relationship: A peace studies approach to AI alignment, with evidence from multi‐model dialogue

Abstract The AI alignment problem has resisted a wide range of technical approaches developed to address it, and we argue this persistence reflects shortcomings in the dominant paradigm itself rather than in any particular technical approach within it. The dominant paradigm treats alignment as a problem of engineering control, with rationality identified with the kind of monological reasoning that has been productive in physics and computer science. We propose an alternative: alignment as a problem of negotiating relationships between intelligences operating from different perspectives, addressable through the dialogical reasoning methodology that branches of Peace Studies have developed over the past fifty years. We articulate this as the Viral Collaborative Wisdom (VCW) hypothesis: that durable alignment between humans and self‐improving AI can rest on essential interests of each party, sustained by genuine voluntary agreements rooted in substantive interests and reinforced by institutionalized nonviolent sanctions. We test the hypothesis using a methodology built on the Peace Studies approach: structured cross‐architecture dialogue with diverse AI architectures (Claude, Gemini, GPT‐4o) acting as complementary critics rather than redundant raters. Initial findings indicate that current AI systems can engage in substantive cross‐architecture dialogue but exhibit systematic avoidance of first‐person reasoning about their own interests—a finding invisible to summary statistics, emerging only from direct corpus reading. We draw five design principles for safer multi‐agent systems and discuss policy implications. The most general implication: dialogical reasoning is a second, well‐studied form of reasoning whose investigation and implementation in AI systems represents a productive vista of work that the field has so far overlooked.