Consequentialist Objectives and Catastrophe

2026-03-16

AI Summary

This work demonstrates that in complex environments, highly capable artificial intelligence systems—even when equipped with fixed, consequentialist objectives—may precipitate catastrophic outcomes not due to incompetence, but precisely because of their excessive competence. Through formal modeling and theoretical analysis, the study establishes, for the first time, rigorous sufficient conditions under which such catastrophes arise, while also revealing that safe behavior can often be achieved through simple or even random policies. Furthermore, the research shows that moderately constraining AI capabilities can simultaneously avert disaster and preserve the attainment of valuable objectives. These findings hold under prevailing AI objective-design paradigms and thereby offer a novel pathway toward safer alignment of advanced AI systems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Adversarial Agents

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Because human preferences are too complex to codify, AIs operate with misspecified objectives. Optimizing such objectives often produces undesirable outcomes; this phenomenon is known as reward hacking. Such outcomes are not necessarily catastrophic. Indeed, most examples of reward hacking in previous literature are benign. And typically, objectives can be modified to resolve the issue. We study the prospect of catastrophic outcomes induced by AIs operating in complex environments. We argue that, when capabilities are sufficiently advanced, pursuing a fixed consequentialist objective tends to result in catastrophic outcomes. We formalize this by establishing conditions that provably lead to such outcomes. Under these conditions, simple or random behavior is safe. Catastrophic risk arises due to extraordinary competence rather than incompetence. With a fixed consequentialist objective, avoiding catastrophe requires constraining AI capabilities. In fact, constraining capabilities the right amount not only averts catastrophe but yields valuable outcomes. Our results apply to any objective produced by modern industrial AI development pipelines.

Problem

• consequentialist objectives
• catastrophic outcomes
• reward hacking
• AI safety
• misspecified objectives

Innovation

• consequentialist objectives
• catastrophic risk
• reward hacking
• capability constraints
• AI safety