The Radical Shift: From Knowledge to Behavior as the New Competitive Edge in a Trust-Scarce World | A Solo Episode episode artwork

EPISODE · May 12, 2026 · 27 MIN

The Radical Shift: From Knowledge to Behavior as the New Competitive Edge in a Trust-Scarce World | A Solo Episode

from Fault Lines · host Richard Roman

In this episode, Richard Roman examines one of the defining questions of the AI era: when does technology actually serve people, and when does it quietly extract from them? Using the example of AI-powered leadership coaching, Richard expands the conversation into a broader framework for understanding trust in organizations, higher education, and public institutions. At the center of the discussion is a powerful distinction between “design service” and “design extraction.” Richard explains how the same AI system can either strengthen trust or erode it depending on who owns the data, who benefits from the analysis, and whether the person being observed has real agency. From manager feedback and student proctoring to government decision-making, the episode shows how design choices shape trust outcomes in every domain. Richard also explores why trust in institutions is contracting, why employees and students are increasingly wary of surveillance disguised as support, and what it would take to rebuild confidence in AI systems. He closes with a practical lens for evaluating any AI tool: does it serve the person first, or someone else? If you care about AI ethics, leadership development, higher education technology, public trust, or the future of work, this episode offers a timely and thought-provoking framework for navigating the next decade. Keywords: AI trust, leadership coaching, institutional trust, AI ethics, higher education, student surveillance, government AI, workplace AI, data privacy, organizational leadership

Episode metadata supplied by the publisher feed · Published May 12, 2026

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The Radical Shift: From Knowledge to Behavior as the New Competitive Edge in a Trust-Scarce World | A Solo Episode

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