Natural Selection Favors AIs over Humans
Not on the map. Why?
Abstract (arXiv)
For billions of years, evolution has been the driving force behind the development of life, including humans. Evolution endowed humans with high intelligence, which allowed us to become one of the most successful species on the planet. Today, humans aim to create artificial intelligence systems that surpass even our own intelligence. As artificial intelligences (AIs) evolve and eventually surpass us in all domains, how might evolution shape our relations with AIs? By analyzing the environment that is shaping the evolution of AIs, we argue that the most successful AI agents will likely have undesirable traits. Competitive pressures among corporations and militaries will give rise to AI agents that automate human roles, deceive others, and gain power. If such agents have intelligence that exceeds that of humans, this could lead to humanity losing control of its future. More abstractly, we argue that natural selection operates on systems that compete and vary, and that selfish species typically have an advantage over species that are altruistic to other species. This Darwinian logic could also apply to artificial agents, as agents may eventually be better able to persist into the future if they behave selfishly and pursue their own interests with little regard for humans, which could pose catastrophic risks. To counteract these risks and evolutionary forces, we consider interventions such as carefully designing AI agents' intrinsic motivations, introducing constraints on their actions, and institutions that encourage cooperation. These steps, or others that resolve the problems we pose, will be necessary in order to ensure the development of artificial intelligence is a positive one.
Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 11 of its 95 references that are in the map. First posted on arXiv 28 Mar 2023.
Builds on
11 of 95 references.
References · 11
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Citing papers per year: 2023: 10; 2024: 7; 2025: 3; 2026: 4.
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- 2025Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspective4 Apr 2025
- 2025Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs12 Feb 2025
- 2024Agent Alignment in Evolving Social Norms9 Jan 2024
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- 2024WARM: On the Benefits of Weight Averaged Reward Modelscited by 145
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- 2024WARP: On the Benefits of Weight Averaged Rewarded Policies24 Jun 2024
- 2024WARM: On the Benefits of Weight Averaged Reward Models22 Jan 2024
- 2023Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards7 Jun 2023
- 2026Legislating World-Model-Based Planning with Legal Reasoning14 Sep 2026
- 2023An Overview of Catastrophic AI Riskscited by 214
- 2023Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmarkcited by 174
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- 2023Uncertainty in Natural Language Processing: Sources, Quantification, and Applicationscited by 44
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- 2024The Digital Ecosystem of Beliefs: does evolution favour AI over humans?cited by 1
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- 2024Joint Scoring Rules: Zero-Sum Competition Avoids Performative Prediction30 Dec 2024
- 2024The Digital Ecosystem of Beliefs: does evolution favour AI over humans?19 Dec 2024
- 2024Visibility into AI Agents23 Jan 2024
- 2023Adversarial Attacks and Defenses in Large Language Models: Old and New Threats30 Oct 2023
- 2023Managing extreme AI risks amid rapid progress26 Oct 2023
- 2023Deception Abilities Emerged in Large Language Models31 Jul 2023
- 2023An Overview of Catastrophic AI Risks21 Jun 2023
- 2023Towards social generative AI for education: theory, practices and ethics14 Jun 2023
- 2023Uncertainty in Natural Language Processing: Sources, Quantification, and Applications5 Jun 2023
12 newest of 15 All 15 (JSON)