AIdhd: Emergent Cognitive Phenotypes in Sustained Human-AI Collaboration Among Neurodivergent Operators

Attention-deficit/hyperactivity disorder (ADHD) research has documented the paradox of hyperfocus — the capacity for total task absorption in a population defined by inattention — yet clinical literature has not examined what occurs when ADHD-architecture cognition intersects with sustained, intensive artificial intelligence collaboration. This paper introduces the AIdhd phenotype: a distinct cognitive operating mode that emerges when operators with ADHD engage in extended human-AI co-production. Drawing on a 120-day longitudinal self-study (N=1) in which a single operator produced over one million words of cross-domain output across eight professional disciplines, we document five novel features of this phenotype — context offloading, scatter channeling, validation circuit activation, metacognitive amplification, and sleep/rest resistance — alongside five novel risks, including substrate masking, dependency formation, and imposter amplification. The paper formalizes the divergence between behavioral performance metrics (Human Symbiosis Score 2.0) and physiological substrate metrics (HSS 3.0) during AIdhd episodes, demonstrating that ADHD-architecture operators can sustain output that behavioral metrics classify as Competent while physiological metrics indicate Critical depletion. We propose the AIdhd Protocol, a five-component governance checklist for neurodivergent operators entering sustained AI collaboration, and outline a prospective clinical study design for replication. The central argument is that AIdhd is not ADHD worsened by technology, but a qualitatively new cognitive operating mode requiring its own governance architecture. We did not cure ADHD. We gave it a flight plan. ---