PROTRACTED FEASIBILITY DRIFT: AI AND THE STRUCTURAL VULNERABILITY OF U.S. BIODEFENSE

The U.S. biodefense enterprise is entering a period in which artificial intelligence (AI) is enhancing its longstanding vulnerability of feasibility drift (i.e., the structural tendency to convert bounded technical successes into premature operational commitments). It is doing this by decoupling digital performance metrics from physical operational reality. This, in turn, allows government programs to mistake inexpensive digital progress for operational progress. This dynamic suppresses the enterprise’s natural failure filter, blinds decision-makers to capability inversion, and treats computational metrics as evidence of operational readiness. As AI becomes embedded in biosurveillance, detection, and threat-assessment workflows, the Feasibility Drift Framework predicts that the enterprise will misinterpret failures as data immaturity, marginalize domain scientists, and fast-track unvalidated systems during crises. These conditions create chronic underperformance and novel adversarial opportunities because a system that cannot perceive its own drift cannot perceive deliberate manipulation of that drift. The paper concludes that these vulnerabilities cannot be addressed through technical improvements alone. Rather, they require an independent, prospective, and binding validation function operating before significant programmatic commitment. DISCLAIMER: The views in this manuscript are those of the author and not the views of Chemring Sensors and Electronic Systems.