  Existing strategies for managing risks from advanced AI systems often focus
on affecting what AI systems are developed and how they diffuse. However, this
approach becomes less feasible as the number of developers of advanced AI
grows, and impedes beneficial use-cases as well as harmful ones. In response,
we urge a complementary approach: increasing societal adaptation to advanced
AI, that is, reducing the expected negative impacts from a given level of
diffusion of a given AI capability. We introduce a conceptual framework which
helps identify adaptive interventions that avoid, defend against and remedy
potentially harmful uses of AI systems, illustrated with examples in election
manipulation, cyberterrorism, and loss of control to AI decision-makers. We
discuss a three-step cycle that society can implement to adapt to AI.
Increasing society's ability to implement this cycle builds its resilience to
advanced AI. We conclude with concrete recommendations for governments,
industry, and third-parties.
