AI Consciousness and Existential Risk
Not on the map. Why?
Abstract (arXiv)
In AI, the existential risk denotes the hypothetical threat posed by an artificial system that would possess both the capability and the objective, either directly or indirectly, to eradicate humanity. This issue is gaining prominence in scientific debate due to recent technical advancements and increased media coverage. In parallel, AI progress has sparked speculation and studies about the potential emergence of artificial consciousness. The two questions, AI consciousness and existential risk, are sometimes conflated, as if the former entailed the latter. Here, I explain that this view stems from a common confusion between consciousness and intelligence. Yet these two properties are empirically and theoretically distinct. Arguably, while intelligence is a direct predictor of an AI system's existential threat, consciousness is not. There are, however, certain incidental scenarios in which consciousness could influence existential risk, in either direction. Consciousness could be viewed as a means towards AI alignment, thereby lowering existential risk; or, it could be a precondition for reaching certain capabilities or levels of intelligence, and thus positively related to existential risk. Recognizing these distinctions can help AI safety researchers and public policymakers focus on the most pressing issues.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 39 of its 120 references that are in the map. First posted on arXiv 24 Nov 2025.
Builds on
39 of 120 references.
References · 39
- 2024The Platonic Representation Hypothesiscited by 362
- 2025The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexitycited by 298
- 2024Deliberative Alignment: Reasoning Enables Safer Language Modelscited by 292
- 2023Role-Play with Large Language Modelscited by 290
- 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 257
- 2023An Overview of Catastrophic AI Riskscited by 214
- 2025Agentic Misalignment: How LLMs Could Be Insider Threatscited by 136
- 2017The Consciousness Priorcited by 105
- 2025Agentic Large Language Models, a surveycited by 105
- 2023Consciousness in Artificial Intelligence: Insights from the Science of Consciousnesscited by 99
- 2021Coordination Among Neural Modules Through a Shared Global Workspacecited by 83
- 2022Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolutioncited by 78
27 more
- 2025Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluationcited by 57
- 2020Word meaning in minds and machinescited by 51
- 2025KGGen: Extracting Knowledge Graphs from Plain Text with Language Modelscited by 49
- 2025An Approach to Technical AGI Safety and Securitycited by 42
- 2021Why AI is Harder Than We Thinkcited by 40
- 2020Deep Learning and the Global Workspace Theorycited by 35
- 2024Taking AI Welfare Seriouslycited by 28
- 2023TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AIcited by 25
- 2021A Theory of Consciousness from a Theoretical Computer Science Perspective: Insights from the Conscious Turing Machinecited by 22
- 2023The feasibility of artificial consciousness through the lens of neurosciencecited by 18
- 2025Large Language Models Report Subjective Experience Under Self-Referential Processingcited by 16
- 2023Semi-supervised Multimodal Representation Learning through a Global Workspacecited by 11
- 2024Is artificial consciousness achievable? Lessons from the human braincited by 11
- 2020A Theoretical Computer Science Perspective on Consciousnesscited by 8
- 2024Does Thought Require Sensory Grounding? From Pure Thinkers to Large Language Modelscited by 7
- 2024Dissociating Artificial Intelligence from Artificial Consciousnesscited by 7
- 2025Benchmarking is Broken -- Don't Let AI be its Own Judgecited by 7
- 2024Agnosticism About Artificial Consciousnesscited by 6
- 2025Principles for Responsible AI Consciousness Researchcited by 6
- 2025Palatable Conceptions of Disembodied Beingcited by 5
- 2024Preliminaries to artificial consciousness: a multidimensional heuristic approachcited by 4
- 2025Subjective Experience in AI Systems: What Do AI Researchers and the Public Believe?cited by 4
- 2024Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global Workspacecited by 3
- 2025From Symbolic to Neural and Back: Exploring Knowledge Graph-Large Language Model Synergiescited by 3
- 2020Functionally Effective Conscious AI Without Sufferingcited by 2
- 2025Quantifying Genuine Awareness in Hallucination Prediction Beyond Question-Side Shortcutscited by 2
- 2025Hypothesis on the Functional Advantages of the Selection-Broadcast Cycle Structure: Global Workspace Theory and Dealing with a Real-Time Worldcited by 1
Built on by
Not cited yet.