AGI (Artificial General Intelligence) is a hypothetical AI at human level in most intellectual tasks. ASI (Artificial Superintelligence) is the next step: a system that surpasses the best humans in practically everything.
Neither state has been achieved yet: all systems as of 2026, including the most powerful LLMs, remain narrow AI by any strict definition.
The transition from AGI to ASI, according to common theory, is not a gradual improvement but a sharp phase transition through a recursive self-improvement mechanism.
OpenAI, Anthropic, and Google DeepMind are currently closest to creating AGI — but none of them publicly claims to have reached this level.
AGI, Artificial General Intelligence, is a hypothetical artificial intelligence system capable of performing virtually any intellectual task at a human level, not just a narrow set of tasks for which it was specifically trained. Unlike today's language models, which are essentially highly sophisticated next-token predictors, AGI should possess common sense understanding of the world, the ability for abstract reasoning, and the transfer of knowledge from one domain to another — for example, applying chess game logic to logistics planning.
We will deliberately not repeat the entire breakdown of technical barriers to AGI here — the problem of world modeling, memory and planning limitations, computational limits, as well as the debate between optimists and skeptics regarding timelines. We have already covered all of this in detail in a separate article: AGI Prospects: Are We Truly on the Verge of Creating AI Equal to the Human Mind? Our goal here is different — to understand what comes after AGI, and why this "after" causes even more concern among researchers than AGI itself.
What is ASI
ASI, Artificial Superintelligence, is a fundamentally different category, not just an "improved version of AGI." If AGI aims to equal humans, ASI is described as a system that surpasses the combined intellectual capabilities of all humanity simultaneously in every single domain — science, strategic thinking, creativity, and even social and emotional intelligence (LatentView).
The term itself truly entered widespread circulation thanks to philosopher Nick Bostrom and his 2014 book "Superintelligence: Paths, Dangers, Strategies," which was the first to systematically lay out possible paths to superintelligence and the associated risks. Over the decade, the discussion has moved from a purely philosophical plane to a practical one: today, the topic of ASI is an active research priority for leading AI laboratories, rather than abstract futurology.
It is important to understand: ASI is currently a purely hypothetical concept. No research organization or company in the world as of 2026 has demonstrated or claimed to have created a system that meets this definition (LatentView). At the same time, this is precisely why the discussion about its potential consequences is being held now, rather than postponed until its actual creation — the cost of being late with preparation is considered too high.
Example: How ASI's approach differs from AGI's approach in practice
To make the difference tangible rather than abstract, let's consider the same request — "find a cure for an aggressive type of cancer" — and how different levels of intelligence would theoretically approach it.
An AGI system would handle this task similarly to how a team of very strong human researchers would, only faster: it would study existing scientific literature, propose several hypotheses based on known mechanisms, design experiments to test them, and possibly synthesize a candidate drug — all within the framework of existing scientific models and theories.
ASI, by definition, would act differently. It is not limited by existing scientific theories as a starting point — it is capable of generating entirely new biological hypotheses that human scientists have not even formulated yet, simultaneously calculating millions of molecular interaction variants, and finding solutions that lie beyond the current scientific consensus. According to optimistic estimates, this would theoretically allow for decades of medical research to be compressed into years — not because ASI "thinks faster" in a literal sense, but because it can hold and relate orders of magnitude more variables simultaneously than any human or team of humans (LatentView).
How AGI Differs from Modern LLMs
We've already discussed this difference in detail in a previous article, so here we'll provide only a concise summary — mainly for the context needed further on when transitioning to ASI.
Criterion
Modern LLMs (ANI)
AGI
ASI
Existence status in 2026
Exists and is actively used
Not achieved
Not achieved, purely hypothetical
Knowledge transfer between domains
Limited, requires retraining
Broad, comparable to human
Unlimited within available data
World model and causality
Simulates through statistical patterns
Has its own internal world model
World model inaccessible to human understanding
Self-improvement capability
Absent
Partial, with human involvement
Autonomous, recursive
For a full breakdown of the first column — why LLMs still don't measure up to AGI and what specific technical barriers need to be overcome — read the article "AGI Prospects".
Why ASI is Considered the Next Stage of Development
In my opinion, the key to understanding the logic "AGI inevitably leads to ASI" lies not in optimism or pessimism about AI in general, but in a specific technical mechanism: recursive self-improvement. If a system has reached the AGI level, it is by definition capable of performing intellectual tasks at a human level — and therefore, the task of improving artificial intelligence architecture as well, as it is an intellectual task like any other (Netguru).
This is where the phase transition lies, not gradual improvement. An AGI system capable of modifying its own weights, training procedures, and architectural decisions can, in principle, create a successor smarter than itself. That successor, in turn, can improve the next iteration even more effectively — and so on. This is precisely why researchers describe the transition from AGI to ASI not as a linear improvement in benchmark performance, but as a qualitative leap into a completely different category of system (Netguru).
Why it's a "Phase Transition" and Not Just Faster Progress
The difference is worth explaining with a simple analogy from physics: heating water from 20°C to 90°C is a gradual change, each degree taking approximately the same effort. But transitioning from 99°C to steam at 100°C is already a qualitative change in the state of matter, not just "one more degree." Researchers describe the AGI → ASI transition precisely this way: as long as the system improves itself with human involvement, progress is limited by the speed of the human team. But the moment AGI itself becomes a full participant in this process, the limitation of human speed disappears — and the system enters a fundamentally different mode of operation, not just "works a bit faster."
This directly echoes the distinction that Vernor Vinge introduced back in 1993, speaking of "hard takeoff" and "soft takeoff" scenarios for the singularity: the former takes hours or days, the latter stretches over years. The question "how sharp will the AGI → ASI transition be" is essentially the same question, only applied specifically to this one step of technological development. More on the history of the concept itself — in our guide Technological Singularity: A Complete Guide in Simple Terms.
Where the Weak Point in This Logic Lies
Frankly, I believe it's important to immediately name the vulnerability of this argument: it relies on the assumption that improving AI architecture is a task of the same type as other intellectual tasks that AGI can already solve. This is not self-evident. Designing neural networks, selecting hyperparameters, and making architectural decisions might turn out to be a task with fundamentally different properties — for example, one where empirical testing on real "hardware" and time are resources no less important than the intelligence designing the solutions. How much this hypothesis is already confirmed by practice — and what specific limitations researchers at Anthropic and OpenAI have discovered in real experiments — is discussed in detail in the technical analysis: Recursive Self-Improvement: How Artificial Intelligence Begins to Improve Itself.
How AGI is Related to Singularity
If we put all the pieces of this puzzle together, a logical sequence emerges: modern LLMs → AGI → recursive self-improvement → ASI → technological singularity. In this model, AGI acts not as the singularity itself, but as its probable trigger — the moment when the complex intellectual work of creating even more powerful AI becomes accessible to AI itself for the first time, not just to humans.
Example: Why AGI is a "Trigger" and Not a "Result"
The difference between a "trigger" and a "result" is well illustrated by the domino analogy. Imagine a long row of dominoes, where each subsequent one is larger than the previous — just as each subsequent generation of AI is theoretically more powerful than the last. AGI in this analogy is not the last, largest domino (that's the role of ASI or singularity itself), but the first domino that can be pushed at all with a force comparable to human strength. Before AGI, the entire row of dominoes simply stands still, no matter how long and potentially powerful it might be: no previous, weaker model is capable of pushing the next domino independently, without human intervention. This is precisely why, in the logic of the singularity cluster, AGI is not the ultimate goal of the discussion, but a condition under which the chain reaction described above becomes physically possible at all.
More on the origin of the singularity concept itself, the history of the term from Vernor Vinge to Ray Kurzweil, and the full spectrum of researchers' predictions — in our basic cluster guide: Technological Singularity: A Complete Guide in Simple Terms.
Does AGI Exist Today
The most honest answer is: no, and by any strict definition. Even the most advanced models of 2026 remain narrow AI — they are impressively effective in specific measurable tasks, but do not demonstrate the universal knowledge transfer and causal understanding of the world that the definition of AGI requires (LatentView).
A telling benchmark is the results of the ARC-AGI Semi-Private Eval from the ARC Prize Foundation: OpenAI's o3 model scored 87.5% on this benchmark, specifically designed to measure general, not memorized, reasoning. This is a significant leap compared to previous models, but the result still falls short of the threshold that researchers consider an indicator of AGI (Netguru).
Another interesting organizational nuance: according to the partnership agreement between OpenAI and Microsoft, an independent committee of experts must officially confirm the achievement of AGI — and, according to industry insiders, such a conclusion is unlikely to appear before 2030 even in an optimistic scenario. Who is currently closest to this threshold and by what criteria it is being evaluated — we will discuss in the next section. We have already covered the broader picture of the debate between optimists (Musk, Altman) and skeptics (Marcus, LeCun) regarding AGI timelines in the article "AGI Prospects".
Which companies are closest to creating AGI
It is impossible to directly assess the "distance to AGI" — there is no clear and universally recognized test. However, one can rely on indirect indicators: the scale of investment in research, results on specialized benchmarks, and the laboratories' own public statements about their ambitions.
Company
Valuation Estimate (2026)
Key Benchmark
OpenAI
~$850 billion (March 2026)
Goal: "Intern-level AI researcher" by September 2026, fully autonomous researcher by 2028
Anthropic
~$965 billion (May 2026, after Series H)
Over 80% of production code is already written by Claude; RSI research
Google DeepMind
Integrated into Alphabet's capitalization
The industry's deepest research legacy; CEO Hassabis — "foothills of the singularity"
xAI
Not publicly disclosed
Elon Musk publicly states "early stages" of singularity
It is noteworthy that none of these companies publicly claim to have achieved AGI itself — even those CEOs who make loud statements about the proximity of the singularity (more on this in our analytical cluster article: The Wave of Singularity Statements in 2026: What Experts Really Think). This highlights a significant gap between marketing rhetoric and formal recognition of a specific technical threshold.
Frequently Asked Questions
What is the main difference between AGI and ASI?
AGI is artificial intelligence at a human level across most tasks. ASI is intelligence that surpasses the combined capabilities of all humanity in every single field simultaneously. It is not a quantitative, but a qualitatively different category of system.
In my opinion, the most common mistake in popular explanations is to imagine the difference as a simple "smarter / even smarter" scale, as if ASI were AGI with improved characteristics. Personally, I find it easier to explain this difference using a chess analogy. AGI can be compared to a grandmaster who is capable not only of playing chess at the highest level but also of transferring their strategic thinking to other areas — business, negotiations, planning. This is impressive, but it is still one mind, limited by one brain and one experience. ASI, on the other hand, would be as if that same grandmaster were simultaneously the author of all chess theories that have ever existed and will be discovered, the coach of all other grandmasters in the world, and at the same time could analyze every possible game simultaneously, in real-time. This is no longer a "stronger player," but a player of a completely different order.
Does ASI exist today?
No. As of 2026, no research organization or company has demonstrated or claimed to have created a system of this level — it is purely a hypothetical concept.
I would add an important nuance here that is often lost in news headlines: even CEO statements about the "arrival of the singularity" (as in Sam Altman's July statement) are not claims of ASI's existence — these are fundamentally different assertions. Singularity, by the definition we use in this cluster of articles, is a moment of accelerating change, not the fact of a specific superintelligent system's existence. Therefore, even the loudest headlines of 2026, which might have created the impression that "superintelligence is already here," are not actually talking about it — and, in my opinion, it's worth keeping this distinction in mind when reading news on this topic.
Will AGI necessarily lead to ASI?
This is a common theory, not a proven fact. The logic is based on the mechanism of recursive self-improvement: if an AGI system is capable of improving AI architecture as well as a human, it can theoretically create a successor smarter than itself — and this cycle can repeat with increasing speed.
Personally, I would compare this uncertainty to the situation before the first human space flight: the laws of physics theoretically allowed for exiting the atmosphere long before it was technically feasible. Similarly, here: the fact that the recursive self-improvement mechanism is theoretically possible does not automatically mean it will work in practice as smoothly as on paper. For example, a scenario is entirely possible where AGI systems hit real limitations — lack of computing power, energy costs, physical limits of hardware — long before the self-improvement cycle has a chance to accelerate to ASI levels. How serious these limitations are already manifesting in practice is discussed in detail in the article Recursive Self-Improvement.
Who is currently closest to creating AGI?
OpenAI, Anthropic, and Google DeepMind are most often mentioned in this context — due to the scale of investment, results on specialized benchmarks, and public research ambitions. However, none of them officially claim to have actually achieved AGI.
In my opinion, more telling than the rankings themselves is how cautiously these companies formulate their statements specifically about AGI, in contrast to how freely the same CEOs talk about "singularity" or "superintelligence" in the media. For example, the partnership between OpenAI and Microsoft explicitly states that the achievement of AGI must be confirmed by an independent committee of experts, not by the company itself — meaning even the race leader has consciously relinquished the right to declare victory independently. I would consider this an indirect but eloquent signal: within the industry, the threshold of AGI is perceived much more seriously and cautiously than it appears in headlines about the singularity.
