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Algorithmically mediated markets now dominate data-driven industries, where visibility, pricing, ranking, and resource allocation are governed by opaque automated systems rather than direct human negotiation. This theory-development article synthesizes peer-reviewed studies to advance a novel conceptual explanation of strategic risk—the emergent, self-reinforcing exposure arising from the interplay of uncertainty, dependence on algorithmic intermediaries, and competitive volatility. Traditional strategy frameworks fail to capture how platform ecosystems invert firm boundaries, how algorithmic opacity exacerbates information asymmetry, and how automated feedback loops accelerate market instability. We argue that strategic risk is not merely an external shock but a systemic property generated by algorithmic governance itself. Dependence on digital infrastructures locks organizations into structural vulnerabilities, while rapid changes in recommendation and ranking algorithms create unpredictable volatility that propagates across ecosystems. The article develops six theoretical propositions that delineate causal pathways from algorithmic mediation to heightened risk exposure and identifies organizational responses that may either mitigate or inadvertently amplify instability. A conceptual model visualizes these dynamics, highlighting directional flows and reinforcing feedback loops. By reframing strategic risk as endogenous to algorithmically governed markets, the framework offers new avenues for digital business and strategy theory, emphasizing the need for algorithmic resilience capabilities. Practical implications underscore the limits of conventional risk management in platform-dominated environments.