The rise of super-emotional intelligence

Abstract

The debate about the future of artificial intelligence (AI) runs along a single axis: cognitive capability and its projected culmination in superintelligence. We argue that this framing leaves a complementary axis unexamined and propose the goal of Super-Emotional Intelligence (SEI): systems that perceive, model, regulate, and respond to affect better than skilled human experts, while remaining human-centered. SEI is a performance construct realized by a functional architecture, not a claim about machine experience, and we give measurable criteria for each of its four capacities. Today's affect-aware AI, from multimodal emotion recognition to empathic chatbots, recognizes, or simulates emotion at or below human level and is judged against human ceilings. Three things would set SEI apart: perception that adds peripheral and contactless physiological channels, none of them emotion-specific, but some, like the vestigial ear muscles, resolving affective processing at the timescale of the events that evoke it; a generative model of affect grounded in active inference rather than pattern matching; and functional allostatic self-regulation. The decisive gap in current systems is architectural. A sensed state conditions a prompt, but nothing carries a state estimate across turns, models the effect of the system's own responses, or selects actions under an explicit objective. We specify such a closed loop, state the conditions under which it is stable and steerable rather than manipulative, propose a preregistered validation plan whose failure would falsify our claims, and treat governance as a design constraint of the loop rather than an afterthought.