What Is ASI in AI? Artificial Superintelligence Meaning, and AGI vs ASI

ASI in AI stands for artificial superintelligence: a hypothetical AI system that outperforms the best humans at virtually every cognitive task, not just one. The key words are "best" and "every". A calculator beats every person at arithmetic and a chess engine beats every grandmaster, but neither is superintelligent, because each is superhuman in one narrow domain. ASI means a single system ahead of the strongest human experts across science, engineering, strategy, writing, and persuasion at once. No such system exists in 2026, and whether and when one could is among the most argued questions in the field.

The term shows up in news and lab announcements far more than it is defined, which is why searches for "ASI meaning" and "AGI vs ASI" run into the thousands a month. This page gives the plain definition, the difference from AGI, where the idea comes from, how it relates to recursive self-improvement (RSI), and what any of it changes for people who build on today's models.

AGI vs ASI: the difference in one line each

• AGI, artificial general intelligence: a system that can do most cognitive work a capable person can do, at roughly human level. It is the point where an AI could take over a typical knowledge job.
• ASI, artificial superintelligence: a system that does nearly all cognitive work better than the best person in each field. It is the point where an AI beats the leading expert in every expert's own field.

The gap between the two is not small. Human-level at most tasks means an AI comparable to a good generalist employee. Superintelligence means an AI that is a better mathematician than the best mathematician, a better chip designer than the best chip designer, and a better negotiator than the best negotiator, all in one system. The reason the two get discussed together is the argument, examined below, that the first might lead to the second quickly.

ANI, AGI and ASI: the three levels

The common taxonomy has three rungs:

1. ANI, artificial narrow intelligence. Good at one task or a narrow family of tasks. Every AI system deployed so far is ANI in this sense, including systems that are superhuman at their task.
2. AGI, artificial general intelligence. Broadly capable, at or near human level across most tasks, able to learn new ones without being rebuilt.
3. ASI, artificial superintelligence. Broadly capable and beyond the best humans across nearly all tasks.

Google DeepMind's 2023 "Levels of AGI" paper made the scale more precise by separating breadth from performance. Its performance levels run from Emerging (as good as an unskilled person) through Competent, Expert, and Virtuoso to Superhuman (better than every person), and it applies them separately to narrow and general systems. On that grid, superhuman narrow systems already exist: AlphaZero for chess, shogi, and Go, AlphaFold for protein structure prediction, and modern chess engines. Superhuman general systems, which is what ASI means, do not. The paper placed the frontier chatbots of 2023 at the Emerging level of general intelligence, and the debate since has been about how many rungs they have climbed.

That split is the most useful thing to remember when you read a claim about superintelligence. "Superhuman at X" is common and often true. "Superhuman in general" is a different and much stronger claim.

Where the idea comes from

In 1965 the statistician I. J. Good described an "ultraintelligent machine" that could surpass any human at any intellectual activity, and pointed out that since designing machines is an intellectual activity, such a machine could design a better one. The result would be an "intelligence explosion", and the first ultraintelligent machine would be "the last invention that man need ever make", provided it was docile enough to tell us how to keep it under control.

The philosopher Nick Bostrom gave the modern term its standard definition in his 2014 book Superintelligence: Paths, Dangers, Strategies: any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest. Bostrom also split it into three forms, which still help when a claim is vague:

• Speed superintelligence: a mind that thinks like a human but much faster. A thousand years of work done in a year.
• Collective superintelligence: many minds coordinated so well that the whole outperforms any human organisation. Thousands of copies of one model working in parallel are the obvious modern version.
• Quality superintelligence: a mind that is not just faster or more numerous but qualitatively smarter, able to see solutions people could not find with any amount of time.

Today's systems already have a weak claim to the first two in narrow settings: a model answers in seconds what takes a person an hour, and a lab can run thousands of copies at once. The open question is the third.

How ASI could arrive: RSI and takeoff speed

There are two broad stories about the path, and most public disagreement maps onto them.

The fast takeoff story runs through recursive self-improvement. Once a model is good enough at AI research to meaningfully improve its successor, and the successor is better still at the same work, the gains compound. In the strongest version, the jump from roughly human-level to far beyond it takes months or less. The 2025 "AI 2027" scenario by Daniel Kokotajlo and co-authors is a widely read example: a superhuman coding agent speeds up AI research enough to reach superintelligence within about a year.

The slow takeoff story says each step is gated by things intelligence alone does not speed up: compute that takes years to build, experiments that take weeks to run, data that has to be collected from the real world, and evaluations that get harder to design as systems get stronger. Capability keeps climbing, but over many years and with visible warning signs along the way.

The 2026 evidence fits the early part of both stories. Anthropic reported in June 2026 that Claude authored about 80% of its code and laid out three scenarios, the most likely in its view being AI-aided engineering that keeps accelerating while humans keep control of the research. At the same time, the best agent on AI4AI-Bench, a direct test of whether agents can rewrite a training algorithm and improve on it, closed under a fifth of the distance from the shipped method to the optimum. Models are doing more of the work of building models; nothing yet shows that work compounding on its own. Our RSI glossary entry has the numbers and the five reasons the loop might stall.

Who is talking about superintelligence in 2026

The word moved from philosophy into company strategy over a few years:

• OpenAI published "Governance of superintelligence" in May 2023, arguing that AI systems could exceed expert skill in most domains within ten years and calling for an international oversight body. Two months later it launched a Superalignment team with a pledge of 20% of its compute; the team was dissolved in May 2024. In September 2026 the company named RSI its central research focus, which we covered in OpenAI's RSI push.
• Safe Superintelligence Inc., founded in June 2024 by OpenAI co-founder Ilya Sutskever with Daniel Gross and Daniel Levy, put the word in its name and said it would ship nothing before a safe superintelligence.
• Meta reorganised its AI work as Meta Superintelligence Labs in 2025, after a large investment in Scale AI and the hiring of its founder Alexandr Wang to lead it.
• The critics organised as well. In October 2025 the Future of Life Institute published a Statement on Superintelligence calling for a prohibition on developing it until there is broad scientific consensus that it can be done safely and controllably, with strong public support; Geoffrey Hinton and Yoshua Bengio were among the early signatories.

The pattern to notice: "superintelligence" is now a recruiting and fundraising word as well as a technical one. That is a reason to read each use of it carefully, not a reason to dismiss it.

How to read a superintelligence claim

When a headline, launch post, or investor letter uses the word, four questions sort the signal from the slogan:

1. Narrow or general? "Superhuman at protein design" or "at competitive programming" can be true and checkable. "Superintelligent" with no domain attached is a claim about everything and needs much more evidence.
2. Better than whom? Beating the average person, the average professional, and the best expert are three very different results. ASI means the last one, in nearly every field.
3. On what test, and is it saturated? A benchmark score is evidence about that benchmark. Benchmarks that were hard a year ago are often near the ceiling now, which says as much about the test as about the model. Our LLM evals page covers why scores and real performance drift apart.
4. Over how long a task? Models that beat experts on hour-long problems still fail at multi-day projects. Long-horizon reliability, not peak score, is where the distance to general superhuman performance shows up. For the agent version of that gap, see AI agents getting weird, which pairs unexpected agent behaviour with a theoretical physics benchmark that shows where current reasoning runs out.

A prompt that applies the same checklist to any claim you paste in:

You are a sceptical AI research editor.
<claim>[paste the sentence, paragraph, or announcement]</claim>
1. Restate the claim in one sentence. Is it about one domain (narrow) or about general ability?
2. Who is the comparison group: average people, average professionals, or the best experts?
3. What evidence is given: a named benchmark, a demo, an anecdote, or none? Is that benchmark known to be near saturation?
4. What task length does the evidence cover (seconds, hours, days)?
5. Rate the claim: supported as stated, supported only in a narrower form (write that form), or unsupported.
Quote the exact words you are judging. Do not add facts that are not in the claim.

The last instruction matters: without it, a model will helpfully fill gaps with what it believes about the lab or the model, and the check stops being a check.

What ASI talk changes if you build on LLMs

Very little about Monday's work, and a few things about how you plan. The practical effects of the superintelligence race are already here, whether or not superintelligence ever is:

• Faster model turnover. Labs that believe they are in a race ship more often. Pin model versions in production and rerun a regression set before every upgrade, the same advice our RSI page gives.
• More capability in agents, and more ways to fail quietly. Stronger models take on longer tasks, and longer tasks fail in ways a final answer does not show. The silent agent failures checklist and a proper agent harness with permissions and spending limits matter more as models get more capable, not less.
• Policy that follows the vocabulary. Superintelligence statements, safety frameworks, and capability thresholds increasingly shape what models will and will not do on the API. Watch the system cards and usage policies of the models you depend on.

ASI is a definition worth knowing precisely, because the word is used loosely. The working rule: superhuman narrow systems are real and multiplying, superhuman general systems are not here, and the distance between the two is what every serious argument about the future of AI is about.