Heuristic Stacks: Information Competition in AI-Mediated Knowledge Systems

This article introduces the concept of heuristic stacks to describe how shortcut dynamicsoperating at cognitive, model, and platform levels converge within AI-mediated knowledgesystems. Beginning with a reframing of hallucination in large language models, understoodhere not as isolated error but as heuristic-like completion under answer-mandatedconstraints, the analysis develops a framework for examining how layered shortcut systemsreshape the conditions under which knowledge is produced, circulated, and stabilized.Drawing on empirical research into alt-tech platform ecosystems, including Telegram andParler during the 2021-2022 period, the article demonstrates how heuristic stack dynamicsoperated in human-curated information environments prior to widespread AI integration,and what this pre-AI baseline reveals about the structural challenges generative AI nowintroduces at greater scale and at earlier stages of knowledge production. The article furtherargues that generative AI does not occupy a single position within information ecosystemsbut can function simultaneously across production, cognitive, and platform layers, and thatits routine integration risks degrading the ambient epistemic environment independently ofany specific piece of generated content. Design choices governing AI platforms are identifiedas primary sites of epistemic intervention, with friction profiles, defined as the degree towhich a system interrupts or accelerates shortcut accumulation, functioning as keydeterminants of whether AI systems act as stack accelerants or stack interrupters. The articleconcludes by reframing AI governance as epistemic infrastructure governance, attending notonly to output accuracy but to the aggregate conditions that AI-mediated knowledge systemscollectively produce.