Insurance as AI Risk Infrastructure: A Generative-Agent Simulation of AI Adoption

2026-08-15

AI Summary

This study addresses the barriers to enterprise AI adoption caused by system unreliability and the absence of safeguards against residual risks. We propose a socioeconomic framework positioning insurance as critical infrastructure for AI risk management, thereby bridging the financial protection gap left by technical safeguards alone. Leveraging an LLM-driven agent-based social simulation integrated with economic and sociological behavioral theories, we validate the mechanism’s effectiveness. Results demonstrate that this insurance framework significantly reduces corporate financial risk exposure while enhancing solvency and aggregate capital levels. Consequently, it accelerates the aggregated adoption of AI tools and offers an innovative financial assurance pathway for AI governance, complementing existing technical mitigation strategies.

Abstract

The rapid evolution of artificial intelligence (AI) tools has demonstrated immense potential to enhance societal well-being and operational efficiency. However, the inherent unreliability and uncertain operational consequences of modern AI systems, typified by large language models (LLMs), have created a significant barrier to enterprise adoption. Many enterprises remain hesitant to integrate these tools deeply into their workflows due to concerns about unpredictable losses and liability exposure. While existing technical safeguards primarily seek to reduce the likelihood or severity of AI-enabled workflow failures, they do not by themselves provide ex post financial protection when residual pecuniary tail losses materialize. In this paper, we introduce a socio-economic framework that complements these safeguards by transferring and absorbing the residual financial consequences of AI adoption through insurance. To evaluate this framework, we develop an LLM-driven agent-based social simulation (LABSS) system. We assess the behavioral validity of the simulation using established economic and sociological theories. Our analysis demonstrates that the proposed insurance framework reduces firm-level financial exposure, thereby accelerating the aggregate adoption of AI tools and improving firm solvency and aggregate capital.

Problem

• AI adoption
• financial risk
• insurance
• large language models
• liability exposure

Innovation

• Insurance as AI Risk Infrastructure
• LLM-driven Agent-based Social Simulation
• Residual Financial Risk Transfer
• AI Adoption Dynamics