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Orthodox models of innovation governance—most notably the classical Triple Helix of university–industry–government relations and linear Schumpeterian catch-up frameworks—exhibit severe explanatory and prescriptive deficits when applied to the contemporary Global South. Designed around the institutional assumptions of advanced industrial economies, these paradigms presuppose symmetrical actor bargaining power, mature intellectual property regimes, and frictionless value capture within domestic borders. In the age of artificial intelligence (AI) and platform capitalism, however, developing economies confront a structural "New Triangular Trade" characterized by data colonialism: domestic behavioral, industrial, and trade data are extracted as uncompensated raw inputs to train proprietary foundation models in the Global North, only to be re-imported as rent-extracting, black-box application programming interfaces (APIs). Drawing upon the operational architecture developed by Mamun for the Bangladesh AI Business Summit 2026, and synthesizing it with Teeceian complementary asset theory, Schumpeterian capability-failure economics, and decolonial digital statecraft, this article proposes a novel macro-micro theoretical paradigm: The Sovereign Co-Specialized Helix (SCH) Framework. The SCH framework reconceptualizes Southern innovation systems through four interlocking mechanisms: the macro-level co-specialization of academic, industrial, and diplomatic-state asset portfolios; the enforcement of a Triple-Lock Sovereignty Frontier spanning data, compute/models, and governance/applications; micro-institutional orchestration via time-boxed Minimum Viable Innovation Engines (MVIEs), purpose-built sovereign engines, the Trade Asset Digital Verification (TADV) protocol, and a Governed Compute Bank; and a dual-core Schumpeterian accounting system pairing the Export/Economic Performance Index (EPI) with the Economic/Export Vulnerability Index (EVI). The resulting theory demonstrates how trading nations in the Global South can achieve rapid technological upgrading while systematically reducing external algorithmic dependency. Written in a personal capacity; the views expressed are those of the author. Published in the KSCCN series.
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Syed Muntasir Mamun (2026) studied this question.
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