Many economic forecasts of artificial intelligence rely on task-based models in which AI substitutes for routine activities while complementing higher-level human skills, implying a stable recomposition of work rather than large-scale labor displacement. This paper argues that this assumption is conditional rather than guaranteed, and that the conditions must be deliberately engineered. We identify three structural mechanisms that can undermine the stable complementarity assumed in mainstream models: agentic AI systems that endogenously reorganize task structures rather than substituting within fixed boundaries; a validation asymmetry in which AI scales execution faster than humans can scale meaningful oversight; and an AI expectation trap in which firms restructure around anticipated automation before it has fully materialized, suppressing expertise pipelines and accelerating displacement ahead of actual technical capability. Together with macro feedback dynamics — including a layoff trap that converts individually rational automation decisions into collective demand compression — these mechanisms define a plausible disruptive scenario that task-based models are not designed to capture.
Against this backdrop, the paper develops a multilevel constructive framework for making human centrality economically durable in AI-rich production systems. At the micro level, we draw on the Hybrid Intelligence framework to specify a production architecture in which human–AI workflows are designed to build rather than drain human expertise, embedding judgment at the dynamic prediction–judgment frontier in ways that make human contribution verifiably productive rather than symbolically retained. At the market level, we introduce the human-touch marketplace as a demand-side mechanism grounded in the economics of credence goods and multi-attribute competition: a class of markets in which complement value — trust, provenance, accountability, liability, legitimacy, relational continuity, and meaning — sustains large-scale, durable demand for human involvement even as AI drives the cost of functional performance toward zero. We show that this marketplace is not a new market category but the AI-era sharpening of credence-good markets that already exist across healthcare, law, finance, education, consulting, and professional services. We further argue that complement value alone is insufficient for long-run durability: the human-touch marketplace preserves value, but HI functional superiority — visible, high-profile demonstrations that human–AI combinations outperform AI-only systems in high-stakes consequential domains — is what grows it. We show how complement value and HI functional superiority are mutually reinforcing: the near-term complement-value premium provides the commercial foundation that sustains HI organizations while functional superiority is being developed, and demonstrated superiority in turn deepens complement value economy-wide. Finally, we combine the pro-worker AI framework of Acemoglu, Autor, and Johnson with these market-level mechanisms to show that pro-worker workflow design and complement-driven demand are structurally interdependent rather than parallel strategies. At the institutional level, we develop Sensecurity — an update of the Nordic flexicurity model for the AI era — as a governance roadmap that addresses the specific market failures preventing a human-touch equilibrium from emerging spontaneously, including pipeline externalities, credence-good verification problems, automation-biased capital markets, and race-to-the-bottom competition on functional value. The paper concludes that the long-term positive equilibrium is not human preservation by technological limitation, but human centrality through superior hybrid performance combined with curated complement value — and that achieving it requires treating the institutional architecture through which human judgment is embedded in production as the central design challenge of the AI economy.