Generative artificial intelligence has pushed the marginal cost of content production toward zero, and public knowledge production now faces seven interlocking risks: misinformation outpacing correction, irreversible deepfake harm, generation platforms judging their own output, nominally independent checks failing from shared origins, knowledge bases contaminated through citation loops, responsibility displaced onto individual users, and the dilemma between openness and gatekeeping. These risks share one root: the absence or failure of a refutability structure. Effective governance requires two load-bearing components: calibration points independent of the generation process, error-correction bandwidth proportionate to generated output, and heterogeneous redundancy, together with a floor that intercepts irreversible harms in advance. Through two-pole analysis and counterexample testing, the thesis is revised: floor and bandwidth are mutually conditional—the floor delimits domains where trade-offs are barred, the bandwidth bears routine correction elsewhere; lacking either, the structure destabilizes. The study is structured qualitative deduction without empirical data, offering a testable framework for future research.
