Гонка за искусственным интеллектом превращается в марафон, где выживут только те, кто умеет управлять рисками

Winning the AI Pentathlon Requires Endurance The Case for Resilience and Risk Management as Core Elements of Competitive National AI Strategy Expert InsightsPublished Jul 6, 2026 The Case for Resilience and Risk Management as Core Elements of Competitive National AI Strategy Expert InsightsPublished Jul 6, 2026 Global artificial intelligence (AI) competition should be understood not just as a sprint to develop the most-advanced systems but also as a longer-term marathon to accumulate net benefits—the gains from AI minus the costs of accidents, misuse, and disruption. In that marathon, resilience and risk management could be powerful levers of strategic advantage, not merely responses to safety concerns. In this paper, drawing on Charles Perrow’s theory of “normal accidents” in complex technological systems, the author demonstrates how firms and nations that are well prepared to navigate a world of recurring AI risk could outperform those that are not—including those that won the initial development sprint. Four intermediate scenarios explore how different levels of AI-risk cost could affect net benefits at the national and global levels and how even modest resilience advantages could compound into decisive competitive edges over time. The author identifies policy options at every level—individuals, firms, infrastructure operators, and state, local, and national governments—for building the resilience needed to compete effectively in a world of normal AI accidents. This effort was independently initiated and conducted by the Center for the Geopolitics of Artificial General Intelligence within RAND Global and Emerging Risks using income from operations and gifts from RAND supporters. This publication is part of the RAND expert insights series. The expert insights series presents perspectives on timely policy issues. This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions. RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.