Research on AI competition dynamics

We conduct long-term future research on improving cooperation in competition for the development of transformative artificial intelligence (AI).


We aim to gain better insight into the dynamics of competition for transformative AI to reduce the risks they pose or to prevent them entirely. We build upon our AI competition models to analyze ways to shift the competitors’ incentives toward more safety.

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In addition to using game theory, we leverage computational modeling to investigate AI competition dynamics. Therefore, we build reliable research software tools which enable researchers to explore possible future scenarios and the effects of policy proposals.

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We’re a community of mission-aligned freelancers and volunteers. United by our goal of reducing catastrophic risks from competition for transformative AI, we’re passionate about the research we conduct and the software we build. Join our community to help us reach our goal.

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News and updates

Keep informed about our research results, software tools, and community developments.

Announcing the Safety-Performance Tradeoff web app

In collaboration with Associate Professor Robert Trager, we've created an interactive web app implementing the Safety-Performance Tradeoff (SPT) model created by him, Paolo Bova, Nicholas Emery-Xu, Eoghan Stafford, and Allan Dafoe. The web app allows other researchers and decision-makers to explore how safety insights could affect the safety choices of competing AI developers. When exploring the models, one can either set the parameters for two different scenarios or choose one of multiple presets. The effects can then be compared graphically and the web app summarizes the key model insights—e.g. explaining how it is possible that an AI safety breakthrough fails to decrease the risks of AI systems.

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