When AI Disclosure Intensifies: Nonlinear Effects on Governance-Risk Disclosures in Selected U.S. Public Firms

Artificial intelligence (AI) has become increasingly prominent in corporate disclosure, yet its relationship with governance-risk disclosure remains unclear. This study examines whether AI disclosure intensity is nonlinearly associated with governance-risk disclosures among selected U.S. public firms. Drawing on competing governance mechanisms, it argues that rising AI disclosure may initially coincide with heightened control and accountability concerns during periods of organizational and technological transition, but at higher levels may be associated with more stable governance-reporting environments. Using a balanced panel of 53 selected large U.S. public firms observed from 2020 to 2024, the study measures AI disclosure intensity through dictionary-based counts of AI-related terminology in annual Form 10-K filings and captures governance-risk disclosure through references to internal-control weaknesses, restatements, non-reliance statements, and regulatory investigations. Firm and year fixed-effects models with a quadratic specification indicate a robust inverted U-shaped association: governance-risk disclosures rise at low to moderate levels of AI disclosure intensity and decline at higher levels. The findings support a stage-dependent interpretation of AI-related disclosure patterns while underscoring that the evidence is disclosure-based rather than a direct measure of AI governance capability or implementation quality.