The development of artificial general intelligence (AGI) presents a paradoxical challenge where competitive dynamics might push even safety-conscious companies toward dangerous acceleration. While individual actors may prioritize safety, game-theoretic pressures could lead to a race where each believes their approach is superior, while profit-driven players exacerbate risks. This creates a need to understand and mitigate these competitive pressures through modeling and strategic coordination.

Understanding the Competitive Landscape

One way to approach this problem is by combining game theory with behavioral economics to model different scenarios:

• When all major AI companies prioritize safety but have differing confidence in their alignment strategies
• When some prioritize profit over safety
• How defensive actions might be misinterpreted as offensive threats (and vice versa)

The models would incorporate psychological factors in how actors perceive competitors' efforts, economic incentives driving the race, and potential coordination mechanisms that could reduce dangerous dynamics. This could help explain why even well-intentioned organizations might end up in unsafe competition.

Potential Applications and Stakeholders

The insights from this modeling could benefit several groups:

• AI safety researchers needing to navigate competitive pressures
• Policymakers crafting regulations for responsible AI development
• Company leaders making strategic decisions about research priorities

Each stakeholder faces unique challenges in balancing progress with safety, and the models could help identify where coordination might be most effective.

Execution and Implementation

A phased approach might start with theoretical modeling before moving to empirical validation:

1. Developing game-theoretic models of "altruistic races" where actors believe their approach is safest
2. Testing assumptions through experiments and surveys with industry participants
3. Proposing coordination mechanisms based on findings, potentially inspired by historical analogs

A simpler starting point could focus just on the game-theoretic aspects before expanding to more complex behavioral components.

This approach could fill a critical gap in understanding how competition shapes AI development, even among safety-conscious actors, and suggest ways to structure the ecosystem for safer outcomes. By combining rigorous modeling with empirical validation, it might offer actionable insights for all parties involved in AGI development.