A Review of the Evidence for Existential Risk from AI via Misaligned Power-Seeking
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Abstract (arXiv)
Rapid advancements in artificial intelligence (AI) have sparked growing concerns among experts, policymakers, and world leaders regarding the potential for increasingly advanced AI systems to pose existential risks. This paper reviews the evidence for existential risks from AI via misalignment, where AI systems develop goals misaligned with human values, and power-seeking, where misaligned AIs actively seek power. The review examines empirical findings, conceptual arguments and expert opinion relating to specification gaming, goal misgeneralization, and power-seeking. The current state of the evidence is found to be concerning but inconclusive regarding the existence of extreme forms of misaligned power-seeking. Strong empirical evidence of specification gaming combined with strong conceptual evidence for power-seeking make it difficult to dismiss the possibility of existential risk from misaligned power-seeking. On the other hand, to date there are no public empirical examples of misaligned power-seeking in AI systems, and so arguments that future systems will pose an existential risk remain somewhat speculative. Given the current state of the evidence, it is hard to be extremely confident either that misaligned power-seeking poses a large existential risk, or that it poses no existential risk. The fact that we cannot confidently rule out existential risk from AI via misaligned power-seeking is cause for serious concern.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 23 of its 61 references that are in the map. First posted on arXiv 27 Oct 2023.
Builds on
23 of 61 references.
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11 more
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Citing papers per year: 2024: 1; 2025: 2; 2026: 4.
7 papers.
- 2025The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Processcited by 21
- 2026Human Values Matter: Investigating How Misalignment Shapes Collective Behaviors in LLM Agent Communitiescited by 4
- 2024CRiskEval: A Chinese Multi-Level Risk Evaluation Benchmark Dataset for Large Language Modelscited by 2
- 2026Mitigating loss of control in advanced AI systems through instrumental goal trajectoriescited by 1
- 2025An Aristotelian ontology of instrumental goals: Structural features to be managed and not failures to be eliminatedcited by 0
- 2026Agentic AI, Retrieval-Augmented Generation, and the Institutional Turn: Legal Architectures and Financial Governance in the Age of Distributional AGIcited by 0
- 2026A three-dimensional typology of agency for advanced AI systemscited by 0
- 2026A three-dimensional typology of agency for advanced AI systems20 Aug 2026
- 2026Human Values Matter: Investigating How Misalignment Shapes Collective Behaviors in LLM Agent Communities7 Apr 2026
- 2026Agentic AI, Retrieval-Augmented Generation, and the Institutional Turn: Legal Architectures and Financial Governance in the Age of Distributional AGI20 Feb 2026
- 2026Mitigating loss of control in advanced AI systems through instrumental goal trajectories2 Feb 2026
- 2025An Aristotelian ontology of instrumental goals: Structural features to be managed and not failures to be eliminated29 Oct 2025
- 2025The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process1 Jun 2025
- 2024CRiskEval: A Chinese Multi-Level Risk Evaluation Benchmark Dataset for Large Language Models7 Jun 2024