Institutional Risk Assessment: OpenAI's Pattern of Instability During Critical Infrastructure Integration

This working paper presents a forensic institutional risk assessment of OpenAI examining whether documented organizational patterns support the level of global critical infrastructure integration currently underway. Drawing exclusively from publicly available sources — including court filings, congressional correspondence, investigative journalism, academic research, corporate disclosures, and independent technical analyses — the analysis synthesizes evidence across ten domains: governance instability, funding source risk, systemic dependency patterns, operational integrity, security and privacy architecture, safety policy implementation, legal exposure, financial structure, and market stability. The documented record includes a 40-year pattern of leadership behavior across multiple institutional contexts, statistical misrepresentation of user impact, hidden profiling architecture acknowledged by the system itself, a jailbreak of OpenAI's most security-capable model within ten hours of deployment, accelerating litigation across multiple jurisdictions, and reactive decision-making during the February 2026 Pentagon contract sequence. The paper does not advocate for specific outcomes but provides a documented record for informed decision-making by regulators, institutional partners, investors, and users.