Superintelligence cannot be contained: Lessons from Computability Theory
Not on the map. Why?
Abstract (arXiv)
Superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. In light of recent advances in machine intelligence, a number of scientists, philosophers and technologists have revived the discussion about the potential catastrophic risks entailed by such an entity. In this article, we trace the origins and development of the neo-fear of superintelligence, and some of the major proposals for its containment. We argue that such containment is, in principle, impossible, due to fundamental limits inherent to computing itself. Assuming that a superintelligence will contain a program that includes all the programs that can be executed by a universal Turing machine on input potentially as complex as the state of the world, strict containment requires simulations of such a program, something theoretically (and practically) infeasible.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jul 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 4; 2025: 5; 2026: 1.
13 papers.
Sort:
By area:
- 2021Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)cited by 55
- 2024Exploring and steering the moral compass of Large Language Modelscited by 15
- 2021Impossibility Results in AI: A Surveycited by 13
- 2025Distributional AGI Safetycited by 8
- 2025Principles for Responsible AI Consciousness Researchcited by 6
- 2024Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligencecited by 2
- 2021AI Risk Skepticismcited by 1
- 2024On the Undecidability of Artificial Intelligence Alignment: Machines that Haltcited by 1
- 2026Safety from Honesty in a Disinterested AI Predictorcited by 1
- 2024On Formally Undecidable Traits of Intelligent Machinescited by 0
- 2025The Einstein Test: Towards a Practical Test of a Machine's Ability to Exhibit Superintelligencecited by 0
- 2025Limitations on Accurate, Trusted, Human-level Reasoningcited by 0
1 more
- 2026Safety from Honesty in a Disinterested AI Predictor28 Jun 2026
- 2025Distributional AGI Safety18 Dec 2025
- 2025Take Goodhart Seriously: Principled Limit on General-Purpose AI Optimization3 Oct 2025
- 2025Limitations on Accurate, Trusted, Human-level Reasoning25 Sep 2025
- 2025Principles for Responsible AI Consciousness Research13 Jan 2025
- 2025The Einstein Test: Towards a Practical Test of a Machine's Ability to Exhibit Superintelligence12 Jan 2025
- 2024On the Undecidability of Artificial Intelligence Alignment: Machines that Halt16 Aug 2024
- 2024Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligence4 Jul 2024
- 2024Exploring and steering the moral compass of Large Language Models27 May 2024
- 2024On Formally Undecidable Traits of Intelligent Machines14 Feb 2024
- 2021Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)25 Nov 2021
- 2021Impossibility Results in AI: A Survey1 Sep 2021
1 more
- 2021AI Risk Skepticism2 May 2021
- 2021Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)cited by 55
- 2024Exploring and steering the moral compass of Large Language Modelscited by 15
- 2021Impossibility Results in AI: A Surveycited by 13
- 2025Distributional AGI Safetycited by 8
- 2025Principles for Responsible AI Consciousness Researchcited by 6
- 2024Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligencecited by 2
- 2021AI Risk Skepticismcited by 1
- 2026Safety from Honesty in a Disinterested AI Predictorcited by 1
- 2024On Formally Undecidable Traits of Intelligent Machinescited by 0
- 2025The Einstein Test: Towards a Practical Test of a Machine's Ability to Exhibit Superintelligencecited by 0
- 2025Limitations on Accurate, Trusted, Human-level Reasoningcited by 0
- 2025Take Goodhart Seriously: Principled Limit on General-Purpose AI Optimizationcited by 0
- 2026Safety from Honesty in a Disinterested AI Predictor28 Jun 2026
- 2025Distributional AGI Safety18 Dec 2025
- 2025Take Goodhart Seriously: Principled Limit on General-Purpose AI Optimization3 Oct 2025
- 2025Limitations on Accurate, Trusted, Human-level Reasoning25 Sep 2025
- 2025Principles for Responsible AI Consciousness Research13 Jan 2025
- 2025The Einstein Test: Towards a Practical Test of a Machine's Ability to Exhibit Superintelligence12 Jan 2025
- 2024Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligence4 Jul 2024
- 2024Exploring and steering the moral compass of Large Language Models27 May 2024
- 2024On Formally Undecidable Traits of Intelligent Machines14 Feb 2024
- 2021Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)25 Nov 2021
- 2021Impossibility Results in AI: A Survey1 Sep 2021
- 2021AI Risk Skepticism2 May 2021