mlsanityData to 7 Oct 2026

Natural Selection Favors AIs over Humans

arXiv 2303.16200 ↗First posted 28 Mar 2023cs.CY cs.AI cs.LGcited by 24

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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.

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11 of 95 references.

References · 11

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  4. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  5. 2022The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Modelscited by 312
  6. 2021Unsolved Problems in ML Safetycited by 291
  7. 2019Risks from Learned Optimization in Advanced Machine Learning Systemscited by 250
  8. 2022Is Power-Seeking AI an Existential Risk?cited by 101
  9. 2022Forecasting Future World Events with Neural Networkscited by 63
  10. 2021What Would Jiminy Cricket Do? Towards Agents That Behave Morallycited by 57
  11. 2020Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applicationscited by 16

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Citing papers per year: 2023: 10; 2024: 7; 2025: 3; 2026: 4.

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  6. 2024On the Undecidability of Artificial Intelligence Alignment: Machines that Haltcited by 1
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  8. 2024Joint Scoring Rules: Zero-Sum Competition Avoids Performative Predictioncited by 1
  9. 2026Eigenism: Ethics for a Human-AI Futurecited by 1
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