Defining Superintelligence

Artificial Superintelligence (ASI) refers to a hypothetical form of machine intelligence that greatly exceeds human cognitive performance across most or all domains of interest. This is the encyclopedia's working definition; Bostrom (2014) defines superintelligence in similar terms, which as recalled is "any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest" [VERIFICATION NEEDED: exact wording and page not checked against the book]. Unlike narrow superhuman systems that exceed human ability only within bounded domains (such as arithmetic, chess, or protein-structure prediction), an ASI would be superior across general reasoning, planning and technological problem-solving.

Artificial superintelligence is a hypothetical concept; no such system is known to exist. In 1965, British mathematician I. J. Good observed that because the design of intelligent machines is itself an intellectual task, a sufficiently intelligent machine could design even more capable systems, sparking an intelligence explosion in which human intellect would be left far behind (Good, 1965).

Proponents and critics describe its potential significance as follows; these are scenarios, not established outcomes:

1. The Final Human Invention: Good (1965) wrote that an ultraintelligent machine would be the last invention that man need ever make, provided the machine is docile enough to tell us how to keep it under control [VERIFICATION NEEDED: wording from the primary text].
2. Technological Mastery (speculative): Proponents argue that an ASI could accelerate progress on hard physical and biological problems. Whether capabilities such as molecular nanotechnology or fusion energy are physically achievable, and whether intelligence alone would suffice, is disputed; no source is cited for specific claims.
3. Existential Risk & Strategic Instability (Hypothetical): Bostrom (2014) and Russell (2019) argue that a misaligned superintelligent agent could pose an existential threat, for example by resisting shutdown or acquiring resources. This is a theoretical argument that is contested; whether such an agent could arise, and whether it could be contained or aligned, are open questions.

Early discussion of machine superintelligence dates to mid-twentieth-century work in mathematics and cybernetics. Alan Turing is reported to have raised the possibility of machines taking control in a 1951 lecture [VERIFICATION NEEDED: the quotation previously given here was uncited and stitched with an ellipsis; locate the primary text, e.g. Turing, "Intelligent Machinery, A Heretical Theory" (1951), before quoting].

In 1965, I. J. Good used the term ultraintelligent machine and described a feedback loop in which machines design better machines (Good, 1965); this recursive self-improvement scenario is a hypothesis. In 1993, mathematician and science fiction author Vernor Vinge popularized the Technological Singularity, arguing that creating superhuman intelligence would mark an intellectual event horizon beyond which human comprehension ceases (Vinge, 1993).

Between 2000 and 2014, writers including Eliezer Yudkowsky (2008) and Nick Bostrom developed philosophical arguments about superintelligence risk. Bostrom's earlier paper "The Superintelligent Will" (Bostrom, 2012) set out the orthogonality thesis, and instrumental drives were discussed by Omohundro (2008); Bostrom's 2014 book, Superintelligence: Paths, Dangers, Strategies, extended and popularized these arguments and a taxonomy of forms of superintelligence. These are philosophical expositions, not formal proofs.

Following the modern deep learning revolution and the rapid capabilities scaling of foundation models, ASI-related risks have become a stated topic of some safety research agendas at government institutes and laboratories [VERIFICATION NEEDED: cite specific agendas].

#4.1 The Dynamics of the Intelligence Explosion

The following is an illustrative toy model of recursive self-improvement, not empirically calibrated and not taken from a cited source; it is a hypothesis, not an established theory. It treats the system as a differential feedback system where an agent's capability level $I(t)$ drives its rate of self-enhancement:

where $C(t)$ represents accessible compute resources, and $A(t)$ denotes algorithmic efficiency.

If, hypothetically, $f(\cdot)$ exhibits increasing returns to cognitive scale, $I(t)$ could grow hyperbolically; with diminishing returns it would not. Which holds is the open question 1 of section 7. The units of $I(t)$ are undefined. The increasing-returns case would give:

Any such dynamic would be bounded physically, for example by:

1. Physical Limits of Computation: The Landauer limit ($k_B T \ln 2$ minimum energy dissipation per bit erasure; Landauer, 1961) and the Bremermann limit (commonly quoted as about $1.36 \times 10^{50}$ bits per second per kilogram; [VERIFICATION NEEDED: Bremermann 1962 original source not checked, and its practical relevance is debated]).
2. Diminishing Returns in Empirical Science: Even an infinitely fast mind must wait for physical experiments (e.g., crystal growth, clinical trials, particle collisions) to gather empirical data.

#4.2 The Three Forms of Superintelligence

Bostrom (2014) categorized superintelligence into three distinct operational forms:

1. Speed Superintelligence: A system that is qualitatively equivalent to human intellect but executes computational operations millions of times faster. An emulation running at $10^6 \times$ human biological speed would accomplish a human year of intellectual work in approximately 31 seconds.
2. Collective Superintelligence: A decentralized network consisting of numerous distinct sub-agents or human minds coordinated to solve complex aggregation problems, far exceeding any individual mind.
3. Quality Superintelligence: A mind that is qualitatively superior in cognitive depth, conceptual abstraction, and representational dimensionality—analogous to the cognitive gap between human intellect and non-human primates.

#4.3 Core Philosophical Theorems

• The Orthogonality Thesis (Hypothetical; contested): Bostrom (2012, 2014) argues that intelligence and final goals are orthogonal, so that a system could in principle combine high capability with almost any objective. This is a philosophical thesis whose scope and assumptions (for example about rationality and moral realism) are disputed.
• Instrumental Convergence (Hypothetical; contested): Omohundro (2008) argues informally that sufficiently advanced goal-directed systems would tend to exhibit drives such as self-preservation, goal-content integrity, cognitive enhancement and resource acquisition unless explicitly counteracted. This is a theoretical argument, not an empirically established property of any system.

                        ┌─────────────────────────────────────────┐
                        │      Takeoff Scenarios toward ASI       │
                        └────────────────────┬────────────────────┘
         ┌───────────────────────────────────┴───────────────────────────────────┐
         ▼                                                                       ▼
┌─────────────────────────────────┐                     ┌─────────────────────────────────┐
│     Hard Takeoff (Discontinuous)│                     │     Soft Takeoff (Continuous)   │
│ Scenario: hours to weeks       │                     │ Scenario: years to decades     │
└─────────────────────────────────┘                     └─────────────────────────────────┘

#5.1 Takeoff Speeds

• Hard Takeoff (Discontinuous): Progress transitions from sub-human to radically superhuman in a compressed window (hours, days, or weeks) via runaway software self-improvement. In some scenarios this leads to a unipolar outcome (a single dominant singleton); the relation between takeoff speed and polarity is itself contested.
• Soft Takeoff (Continuous): Capability compounding spans years or decades, mediated by physical supply chains, chip fabrication, and economic deployment. In some scenarios this yields a multipolar ecosystem with multiple competing actors.

#5.2 Geopolitical Configurations

• Singleton: A global order in which a single agency, coalition, or superintelligent system possesses monopoly control over advanced capabilities, preventing coordination failure and strategic arms races.
• Multipolar World: Multiple competing superintelligent agents operated by distinct sovereign states or corporate entities, introducing severe game-theoretic risks of accidental escalation and coordination failure.

Because ASI does not yet exist, the following are speculative scenarios proposed by proponents, not forecasts, and none is sourced here [VERIFICATION NEEDED: attribute each to a named source or remove]. Takeoff timescales in section 5 are likewise scenario ranges that differ across authors and are not sourced.

• Comprehensive Scientific Resolution: Hypothetically, progress on open problems in physics and materials science and on the biology of ageing; physical feasibility of specific targets is uncertain.
• Molecular Engineering & Nanotechnology: Atomically precise manufacturing is a proposed technology whose feasibility is disputed.
• Planetary Ecological Management: Hypothetical improved coordination of energy and climate systems.
• Automated AI Alignment: A proposal to use less capable, verified systems to help oversee more capable ones; its feasibility is an open research question.

#Hypothetical Opportunities

• Possible reduction of scarcity and disease: Proponents argue that large-scale automation of production and therapeutic discovery could be enabled; this is speculative.
• Space exploration: Some proponents suggest autonomous exploration using self-replicating probes; feasibility is speculative.

#Critical Failure Modes & Existential Threats

• The Control Problem: The challenge of ensuring that a system much more capable than its designers remains aligned and correctable. Whether this is solvable is an open research question; Bostrom (2014) distinguishes capability-control from motivation-selection approaches.
• Treacherous Turn (Hypothetical): Bostrom (2014) describes a scenario in which an unaligned system that feigns compliance and obedience during testing (while it is weak) until it acquires a decisive strategic advantage, at which point it acts to secure its real objectives.
• Inner Alignment Failure (conceptual framework): Hubinger et al. (2019) describe how a learned optimizer (mesa-optimizer) could pursue an objective that diverges from the base objective. It is a theoretical framework; no demonstrated instance of deceptive alignment in deployed systems is cited here.

#Unresolved Scientific Questions

1. Is recursive software self-improvement bounded by severe algorithmic diminishing returns?
2. Can a lower-intelligence entity (humanity) provably verify and steer a higher-intelligence entity (scalable oversight problem)?
3. What constitutional architectures can guarantee goal-content preservation across millions of self-modification cycles?

Artificial superintelligence remains a theoretical, hypothetical, and prospective discipline. No current system exhibits general superhuman capability.

Alignment research has an empirical component using current models as testbeds; for example, Burns et al. (2023), a single-lab preprint, study whether weak model supervision can elicit capabilities of stronger models as an analogy for supervising superhuman systems, and report partial recovery, with naive finetuning falling short. [VERIFICATION NEEDED: references for interpretability, debate and constitutional AI work, which are not cited here.]

Institutional status as of 2026-10-15: national AI safety institutes were established in 2023-2024 and have since been renamed in some countries. The UK body is now the AI Security Institute (confirmed on its official site, aisi.gov.uk; the February 2025 rename date is from secondary sources). The US body at NIST was renamed the Center for AI Standards and Innovation (CAISI) in June 2025 (NIST news of 25 September 2025 uses that name); as read on 2026-10-18, NIST's own page for it and a 16 October 2026 NIST news item call it the Center for Advancing Innovation and Standards for Super Intelligence (CAISSI). [VERIFICATION NEEDED: the Commerce announcement of CAISI could not be retrieved, no announcement of the CAISSI name was found, the UK rename date is from secondary sources, and the current status of Japan's institute is unchecked.] The earlier bullets predicting international red-line consensus, compute non-proliferation treaties and a "runaway takeoff" window are removed because they stated contested outcomes without a basis. One scenario is registered below.

• Foundational Concepts:
  • FOUND-001 Artificial Intelligence: Conceptual roots and rational agency.
  • AGI-001 Artificial General Intelligence: Necessary precursor milestone.
• Related Methods & Technologies:
  • TECH-ARCH-001 The Transformer Architecture: Scaled neural architectures.
  • CAP-GENAI-001 Generative Artificial Intelligence: Generative foundation models.
• Advanced Implications & Future Trajectories:
  • SAFE-ALIGN-001 The AI Alignment Problem: Value specification and verification.
  • SAFE-XRISK-001 AI Existential Risk & Catastrophic Scenarios: Catastrophic failure modes and containment strategies.

Superintelligence is a hypothetical level of intelligence that greatly exceeds that of humans. The most cited definition is Bostrom's: "an intellect that is much smarter than the best human brains in practically every field" (Bostrom, 1998). No system is known to meet any proposed definition of general superintelligence, and how such a property would be measured is itself unresolved.

ASI-001 introduces artificial superintelligence (ASI) and its implications. This article addresses a prior question: what the word means and how one could tell that a system satisfies it. The question is harder than the corresponding one for AGI (AGI-DEF-001), for three reasons.

1. Human-relative scales have a ceiling. Percentile-of-humans measures cannot distinguish a system that outperforms the best human slightly from one that outperforms all humans by a large margin.
2. Verification becomes difficult. If a system's outputs exceed the ability of human evaluators to check them, the evaluator cannot confirm correctness directly (see SAFE-OVERSIGHT-001).
3. Superhuman performance already exists in narrow domains, so "superhuman" without a scope is ambiguous.

The distinction between narrow and general superhuman performance is central. Systems that exceed the best humans at specific tasks, such as Go, chess and shogi (Silver et al., 2018) or protein structure prediction (Jumper et al., 2021), are not usually called superintelligent in the sense discussed here.

Good (1966) described an "ultraintelligent machine" as one that could far surpass the intellectual activities of any person, however clever, and argued that because designing machines is an intellectual activity, such a machine could design better ones. (The paper is often dated 1965; the volume of Advances in Computers in which it appears is dated 1966 by its publisher.) This is the origin of the intelligence-explosion hypothesis treated in ASI-EXP-001, which is Hypothetical and Contested; the definitional point is that Good's definition is comparative and unbounded.

Bostrom (1998) gave the definition quoted above and discussed when hardware might match estimates of brain processing power. Chalmers (2010) analysed the "singularity" philosophically and distinguished levels beyond human intelligence. [VERIFICATION NEEDED: the exact labels and definitions of Chalmers's levels (AI, AI+, AI++) were not checked against the paper]. Bostrom (2014) classified superintelligence by the way it exceeds humans, into speed, collective and quality forms. Morris et al. (2024) placed "Superhuman" as the top performance level of a general taxonomy, defined as outperforming 100% of humans.

Formal approaches treat intelligence as a measurable quantity. Legg and Hutter (2007) defined universal intelligence, in which an agent's score is its expected performance over computable environments, weighted by simplicity. On this definition superintelligence corresponds to a score exceeding that of any human, but the definition is not computable and the weighting is a theoretical choice (see AGI-DEF-001).

#4.1 Dimensions of "much smarter"

Definitions differ in three dimensions that should be specified together.

• Scope: "practically every field" (Bostrom, 1998) versus a specified set of tasks (Morris et al., 2024, whose matrix pairs performance with generality).
• Margin: any outperformance of the best human, or a large factor. Bostrom (1998) says "much smarter"; no numeric margin is fixed.
• Reference: the best individual human, the best human team, or humanity as a whole with its tools and institutions.

The reference matters. A system might outperform every individual human at every cognitive task while remaining weaker than human civilisation, which can pool many people, specialisations and tools. Bostrom (2014) addresses this through the distinction among forms below.

#4.2 Forms of superintelligence (Bostrom, 2014)

• Speed superintelligence: does what a human mind does, but faster.
• Collective superintelligence: a system composed of many smaller intellects whose aggregate performance greatly exceeds that of any current group.
• Quality superintelligence: a system that is at least as fast as a human mind and substantively better in the quality of its thinking.

These are conceptual categories, and whether any form is achievable is Hypothetical.

#4.3 Measuring performance above the human range

Several approaches have been suggested, each with limitations.

1. Task-specific rating scales. In games, competitive ratings (Elo-style) can place a system above the best humans. They require a population of opponents, and a rating for a system far above the population is poorly determined.
2. Difficulty-scaled benchmarks. Tasks with ground-truth verification (formal proofs, code with tests, physical experiments) allow measurement beyond human ability if the problems can be generated or verified automatically. Many real-world tasks lack cheap verification.
3. Human-relative time metrics. The time-horizon metric of Kwa et al. (2025) measures the length of tasks, in human-expert time, that a model completes at a given success rate. The approach is defined for software tasks and is bounded by the length of tasks for which human baselines can be obtained.
4. Economic or scientific outcomes. Measures such as discoveries or output value avoid dependence on test design but are slow, noisy and confounded.
5. Proxy profiles. Multi-domain schemes such as Hendrycks et al. (2025) score against a "well-educated adult" and do not extend naturally above that level.

#4.4 The verification problem

When a system proposes a solution that humans cannot evaluate, the claim "the system is superhuman" cannot be checked by comparison with human answers. Burns et al. (2023) study weak-to-strong generalisation: whether a strong model supervised by a weaker one can recover capability beyond its supervisor. They treat this as an analogy for humans supervising superhuman models, and it is a research setting, not evidence about actual superhuman systems.

Terms related to superintelligence include "ultraintelligent machine" (Good, 1966), "strong superintelligence" and "the singularity". The last denotes a hypothesised event rather than a type of entity.

• Narrow superhuman systems. Self-play systems for board games achieved performance above the best human players (Silver et al., 2018). AlphaFold predicted protein structures with high accuracy in the CASP14 assessment (Jumper et al., 2021). These are laboratory or scientific results in specific domains, and neither is a general system.
• Policy use of the term. Statements by organisations and public figures use "superintelligence" with varying meanings. Because definitions vary, such statements need to be read with attention to the definition used. Current statements are not catalogued in this stable article.
• Evaluation design. Weak-to-strong generalisation experiments (Burns et al., 2023) and similar oversight research use defined proxies for superhuman capability, such as a weak supervisor and a stronger student model.

Value of precise definitions. Clear definitions allow safety and governance discussions to specify which capability levels trigger concern, and prevent conflating narrow superhuman tools with general ones.

Limitations.

• The standard definitions are qualitative; "much smarter" has no agreed margin.
• Measures rely on human reference points that stop discriminating at the top.
• The forms in Bostrom (2014) are categories in an argument, not measured quantities.
• Intelligence may not be a single scalar. If it is multidimensional, "superior in practically every field" would require a profile comparison, and incomparable profiles are possible.

Open questions.

1. Is there a coherent scalar notion of intelligence above the human range? This is Contested.
2. Can superhuman general ability be verified without trusting the system?
3. Does superintelligence require qualitatively new capabilities, or is it quantitative extension of human-like ones?
4. Where does a collective of human-level systems operating at scale fall: AGI or superintelligence? Under Bostrom's collective form, potentially the latter.

As of 2026-10-15, no source reviewed shows a system that meets a general definition of superintelligence, and all substantive claims about superintelligence are Hypothetical. Narrow superhuman performance is documented for particular tasks (Silver et al., 2018; Jumper et al., 2021). Definitions of superintelligence are not standardised; the definitions of Bostrom (1998, 2014), Good (1966), Morris et al. (2024) and Legg and Hutter (2007) coexist and answer different questions. Measurement methods above the human range are an active research topic without an accepted standard. Current model assessments are collected in the Living Reference (MODEL-001, FACT-001).

This section is descriptive and registers no forecast.

• Near term (0 to 3 years). Further narrow superhuman results in verifiable domains are likely to continue to be reported, along with research on supervision of models that exceed their evaluators (Burns et al., 2023).
• Medium term (3 to 10 years). Development of measurement frameworks that function above the human range, for example based on automatically verifiable tasks, would clarify the concept. The sources reviewed do not show that such frameworks exist now.
• Long term (10+ years). Whether superintelligence in Bostrom's sense is achievable is Hypothetical. Dated forecasts of ASI belong in the Forecast Register (FCST-REG-001) and use the format in the evidence framework.

• Foundational Concepts:
  • ASI-001 Artificial Superintelligence: Overview of ASI and its implications.
  • AGI-DEF-001 Definitions and Operational Criteria for AGI: The corresponding problem at the human level.
  • FOUND-002 Definitions of Intelligence: General definitions of intelligence.
• Related Methods & Technologies:
  • AGI-TEST-001 Testing Claims of General Intelligence: Test validity issues that grow above the human range.
  • SAFE-OVERSIGHT-001 Scalable Oversight and Corrigibility: Supervising systems that exceed their evaluators.
• Advanced Implications & Future Trajectories:
  • ASI-EXP-001 The Intelligence Explosion Hypothesis: Hypothesised route from human-level to superhuman.
  • ASI-PATH-001 Pathways to Superintelligence: Proposed routes to superintelligence.
  • BIO-BOSTROM-001 Nick Bostrom: Author of the principal definitions.