Scientific autonomy in the structural bubble: from institutional bias to AI-mediated consensus

Most professional science is produced inside institutions whose survival depends on competitive funding, political legitimacy, and reputation management. Under these conditions, knowledge production does not unfold in a neutral space of ideas but within a structurally constrained environment—a structural bubble —in which only some questions, methods, and conclusions are likely to be funded, published, and disseminated at scale. This article models four interacting layers: (1) institutional funding and conflicts of interest; (2) publication systems, peer review, and metrics as selection and valuation mechanisms; (3) consensus infrastructures (policy-governed encyclopedic and secondary synthesis platforms) that stabilize dominant framings under non-scientific governance rules; and (4) artificial intelligence (AI) -mediated access systems that operate as recursive second-order filters by reproducing prestige-weighted patterns through citation bias, over-generalization, and automation bias. The manuscript's incremental contribution is to theorize how AI-mediated synthesis and discovery reshape epistemic authority and legitimacy as workflow infrastructure (not merely as efficiency tools), to make this mechanism explicit in a minimal formal sketch, and to specify safeguards (validation loops, traceability, and oversight) that condition when AI strengthens vs. weakens scientific autonomy. Governance implications are derived from social epistemology and the sociology of quantification rather than presented as normative add-ons, and boundary conditions are sharpened with explicit falsifiers. Finally, the paper proposes a minimal empirical program to quantify agenda alignment, consensus lock-in, and amplification effects in AI-mediated discovery, summarization, and gatekeeping.