This paper examines human–AI interaction through the lens of reflective-loop governance rather than machine consciousness. Building on the architectural findings of Attention Is Not Enough: Loop-Consciousness Conditions in Neural Network Architectures (Zenodo 19440321), it argues that large language models function not as faithful amplifiers like telescopes or mathematics, but as high-gain reflective instruments that amplify insight and error alike. The paper develops a two-dimensional model of reflective-loop operation, distinguishing productive reflection from capture and whipsaw failure modes. It introduces the distinction between the generation and adjudication of difference, arguing that while AI systems may assist in generating objections, alternatives, and critiques, the adjudication of those differences remains dependent on externally grounded human judgement. The framework is applied to AI governance, labour, epistemics, and automation risk, introducing the concepts of captured judgement nodes, vacated judgement nodes, causal loops, and audit trails. The paper concludes that the central challenge of advanced AI systems is not machine rebellion but the preservation of meaningful human adjudication within increasingly amplified decision loops. Related works The present paper forms part of a broader research programme examining feedback, cognition, governance, and human–AI interaction. • Ryder, J.F. (2025). Loop-Consciousness: Coherence Regimes and the Emergence of Awareness in Feedback Systems. Zenodo. DOI: 10.5281/zenodo.19438797 • Ryder, J.F. (2025). Attention Is Not Enough: Loop-Consciousness Conditions in Neural Network Architectures. Zenodo. DOI: 10.5281/zenodo.19440321 • Ryder, J.F. (2026). The New Alexandria: AI, Independent Research, and the Knowledge Commons. Zenodo. DOI: 10.5281/zenodo.20287535 The present paper extends the governance implications of these earlier works. Whereas Loop-Consciousness examined the conditions under which awareness may emerge in coupled systems, and Attention Is Not Enough examined whether contemporary neural architectures satisfy those conditions, The High-Gain Reflective Instrument shifts attention to the preservation of human judgement within human–AI reflective loops. The focus therefore moves from the emergence of awareness to the governance of amplification, adjudication, and agency. Future extensions of this work will examine conceptual rather than merely lexical movement in reflective loops; the distinction between instrumental and epistemic grounding in tool-assisted AI systems; the relationship between structural safeguards and reflective-loop literacy; and the governance of systems whose builders may be incentivised to induce capture rather than preserve adjudication.