Stop treating `AGI' as the north-star goal of AI research
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Abstract (arXiv)
The AI research community plays a vital role in shaping the scientific, engineering, and societal goals of AI research. In this position paper, we argue that focusing on the highly contested topic of `artificial general intelligence' (`AGI') undermines our ability to choose effective goals. We identify six key traps -- obstacles to productive goal setting -- that are aggravated by AGI discourse: Illusion of Consensus, Supercharging Bad Science, Presuming Value-Neutrality, Goal Lottery, Generality Debt, and Normalized Exclusion. To avoid these traps, we argue that the AI research community needs to (1) prioritize specificity in engineering and societal goals, (2) center pluralism about multiple worthwhile approaches to multiple valuable goals, and (3) foster innovation through greater inclusion of disciplines and communities. Therefore, the AI research community needs to stop treating `AGI' as the north-star goal of AI research.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Feb 2025.
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Citing papers per year: 2025: 7; 2026: 4.
11 papers.
- 2025Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigorcited by 7
- 2025AI for Scientific Discovery is a Social Problemcited by 3
- 2025AI for Just Work: Constructing Diverse Imaginations of AI beyond "Replacing Humans"cited by 2
- 2025Epistemic diversity across language models mitigates knowledge collapsecited by 2
- 2026What if AI systems weren't chatbots?cited by 2
- 2025NLP Meets the World: Toward Improving Conversations With the Public About Natural Language Processing Researchcited by 0
- 2025Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?cited by 0
- 2025On the Measure of a Model: From Intelligence to Generalitycited by 0
- 2026AI Meets Mathematics Education: A Case Study on Supporting an Instructor in a Large Mathematics Class with Context-Aware AIcited by 0
- 2026Position: Reasoning is a Learnable Rule-Based Processcited by 0
- 2026An Investigation of the NeurIPS and ICML 2025 Position Trackscited by 0
- 2026An Investigation of the NeurIPS and ICML 2025 Position Tracks6 Jul 2026
- 2026Position: Reasoning is a Learnable Rule-Based Process29 May 2026
- 2026What if AI systems weren't chatbots?8 May 2026
- 2026AI Meets Mathematics Education: A Case Study on Supporting an Instructor in a Large Mathematics Class with Context-Aware AI9 Mar 2026
- 2025Epistemic diversity across language models mitigates knowledge collapse17 Dec 2025
- 2025On the Measure of a Model: From Intelligence to Generality14 Nov 2025
- 2025Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?13 Sep 2025
- 2025AI for Scientific Discovery is a Social Problem8 Sep 2025
- 2025NLP Meets the World: Toward Improving Conversations With the Public About Natural Language Processing Research2 Jul 2025
- 2025Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor17 Jun 2025
- 2025AI for Just Work: Constructing Diverse Imaginations of AI beyond "Replacing Humans"10 Mar 2025