AI Governance to Avoid Extinction: The Strategic Landscape and Actionable Research Questions
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Abstract (arXiv)
Humanity appears to be on course to soon develop AI systems that substantially outperform human experts in all cognitive domains and activities. We believe the default trajectory has a high likelihood of catastrophe, including human extinction. Risks come from failure to control powerful AI systems, misuse of AI by malicious rogue actors, war between great powers, and authoritarian lock-in. This research agenda has two aims: to describe the strategic landscape of AI development and to catalog important governance research questions. These questions, if answered, would provide important insight on how to successfully reduce catastrophic risks. We describe four high-level scenarios for the geopolitical response to advanced AI development, cataloging the research questions most relevant to each. Our favored scenario involves building the technical, legal, and institutional infrastructure required to internationally restrict dangerous AI development and deployment (which we refer to as an Off Switch), which leads into an internationally coordinated Halt on frontier AI activities at some point in the future. The second scenario we describe is a US National Project for AI, in which the US Government races to develop advanced AI systems and establish unilateral control over global AI development. We also describe two additional scenarios: a Light-Touch world similar to that of today and a Threat of Sabotage situation where countries use sabotage and deterrence to slow AI development. In our view, apart from the Off Switch and Halt scenario, all of these trajectories appear to carry an unacceptable risk of catastrophic harm. Urgent action is needed from the US National Security community and AI governance ecosystem to answer key research questions, build the capability to halt dangerous AI activities, and prepare for international AI agreements.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 May 2025.
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Citing papers per year: 2025: 4; 2026: 2.
6 papers.
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- 2025Dimensional Characterization and Pathway Modeling for Catastrophic AI Riskscited by 0
- 2025The Stories We Govern By: AI, Risk, and the Power of Imaginariescited by 0
- 2026Are AI Capabilities Increasing Exponentially? A Competing Hypothesiscited by 0
- 2026Falling Behind Drives Unsafe Development in an Idealised AI Race Experimentcited by 0
- 2026Falling Behind Drives Unsafe Development in an Idealised AI Race Experiment28 Jul 2026
- 2026Are AI Capabilities Increasing Exponentially? A Competing Hypothesis4 Feb 2026
- 2025The Stories We Govern By: AI, Risk, and the Power of Imaginaries15 Aug 2025
- 2025Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks8 Aug 2025
- 2025Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape10 Jul 2025
- 2025Evaluating LLM Agent Adherence to Hierarchical Safety Principles: A Lightweight Benchmark for Probing Foundational Controllability Components3 Jun 2025