Artificial General Intelligence: The Milestone That Disappears as We Approach It

By Matthew Parish

Sunday 6 September 2026

There are some expressions in the history of technology that become less precise the more important they become. “Artificial intelligence” is one of them. “Artificial general intelligence” — usually abbreviated to AGI — is another. Indeed AGI may be one of the strangest concepts in the history of science. For decades people have confidently predicted its arrival without agreeing what it is. Now, in September 2026, machines possess abilities that would have been described as artificial general intelligence without hesitation twenty years ago. Yet there remains vigorous disagreement about whether AGI has arrived.

Perhaps this tells us something important. The problem may not principally be that artificial intelligence has failed to become general. It may be that we never decided what “general intelligence” meant in the first place.

What is AGI supposed to mean?

The expression is normally used to distinguish a general-purpose intelligent machine from what used to be called “narrow AI”. A chess computer is the classic example of narrow artificial intelligence. It might defeat every human chess player alive while being incapable of composing a letter, recognising a cat or explaining why Paris is the capital of France. Its intelligence is spectacular but confined.

AGI was supposed to be different. An artificial general intelligence would transfer its abilities between domains. It could reason about mathematics, read literature, write computer programs, interpret photographs, understand legal arguments, learn unfamiliar subjects and solve problems it had not specifically been designed to solve.

That sounds straightforward until one tries to turn it into a definition. OpenAI historically adopted an economic definition: AGI consists of “highly autonomous systems that outperform humans at most economically valuable work”. Google DeepMind has approached the problem somewhat differently, proposing classifications based upon the breadth, depth and autonomy of machine capabilities. More recent attempts have tried to compare machines against human cognitive faculties such as reasoning, memory, learning, perception, attention, executive function and social cognition.

These are all perfectly respectable approaches. But they are not definitions of the same thing. Consider a machine that can outperform 99 per cent of human beings at mathematics, programming, medicine, law, history and engineering but cannot reliably tie a shoelace.

Is it generally intelligent? Or consider a machine possessing encyclopaedic knowledge and extraordinary reasoning abilities that nevertheless occasionally makes an elementary mistake.

Is that AGI? If not, consider a human being who occasionally makes elementary mistakes. Is he therefore not generally intelligent? The conceptual difficulties multiply rapidly.

The problem with the word “human”

Virtually every definition of AGI contains an implicit comparison with human beings. Yet “human-level intelligence” is itself an extraordinarily peculiar standard. Which human? An average adult cannot prove a mathematical theorem, diagnose an unusual disease, translate between twenty languages, write sophisticated computer software or explain the intricacies of international tax law.

Some humans can do each of these things. Virtually no human can do all of them. Human intelligence is profoundly specialised. A brilliant barrister may be hopeless at mathematics. A distinguished mathematician may be incapable of repairing a motorcar. A gifted novelist may understand almost nothing about molecular biology.

We nevertheless describe all three as possessing “general intelligence” because human beings share a collection of underlying faculties — learning, abstraction, memory, planning, linguistic competence, physical interaction and adaptation to unfamiliar circumstances. But once that becomes our definition, another problem appears. Modern artificial intelligence is already vastly more general than almost any previous machine.

A contemporary frontier model can discuss Kant, diagnose errors in computer code, translate Ukrainian, analyse a contract, solve mathematical problems, interpret an image and compose a sonnet — all within the same conversation. No machine remotely resembling this existed twenty years ago. If that is not “general” intelligence, then the word general is doing some rather mysterious work.

Moving the goalposts

There is a recurring phenomenon in the history of artificial intelligence. Whenever a machine acquires an ability previously considered evidence of intelligence, humans cease regarding that ability as particularly intelligent. Chess was once regarded as requiring profound intelligence. Computers became better than humans at chess and chess-playing machines became “narrow AI”. Recognising objects in photographs was once considered extraordinarily difficult. Machines learned to do it and image recognition became ordinary computer vision.

Producing fluent prose, translating languages, writing computer programs and passing professional examinations were subsequently proposed as indicators of advanced intelligence. Machines learned to do those things as well. So the frontier moved again. This phenomenon is sometimes called the “AI effect”: once computers can do something, we cease calling the activity intelligence. AGI risks becoming the ultimate manifestation of this effect — a horizon that recedes whenever we approach it.

Intelligence is not consciousness

A further confusion must be removed. AGI does not necessarily mean consciousness. Nor does it mean sentience, emotions, desires, self-awareness or a soul. These are philosophical questions concerning subjective experience. They may ultimately prove related to intelligence or they may not. Nobody presently knows.

A machine might therefore possess extraordinarily broad intellectual abilities while having no subjective experiences whatsoever. This distinction matters because popular culture has conditioned us to imagine AGI anthropomorphically. We expect the intelligent machine eventually to wake up, look around and announce that it exists.

There is no reason technological development should proceed in this theatrical fashion. Artificial intelligence may instead become progressively more capable until one morning humanity discovers that machines perform most cognitive activities better than human beings — without there ever having been a particular Tuesday upon which somebody could sensibly announce that AGI had arrived.

Has AGI therefore already been achieved?

As of September 2026, there are three defensible answers. The first is yes. If AGI means a machine capable of competent intellectual performance across an extraordinarily broad range of domains, then the argument that AGI has already arrived is increasingly powerful.

The latest frontier systems can reason across disciplines, use computers, analyse images and documents, write sophisticated software and undertake extended professional tasks. They possess stores of accessible information enormously exceeding those of any individual human being.

By historical standards this looks remarkably like what people once meant by artificial general intelligence. Indeed the controversy became particularly acute with OpenAI’s September 2026 release of GPT-6 Astra. OpenAI President Greg Brockman has publicly expressed the view that the system may represent the arrival of AGI.

The second defensible answer is no. Current systems still exhibit a peculiarly uneven — or “jagged” — intelligence. They can display astonishing sophistication and then make bizarrely elementary mistakes. Their long-term learning, persistent memory, autonomous operation, physical competence and reliability remain different from those of human beings. They also remain heavily dependent upon technological infrastructure constructed and maintained by humans. A human being can wake in an unfamiliar town, discover that he has no money, find employment, negotiate with strangers, acquire food, locate accommodation and reorganise his life. An AI model generally cannot independently do this. That distinction may matter enormously.

The third answer is the most interesting. AGI may not be a binary condition at all.

Intelligence has dimensions

Suppose intelligence has several dimensions: knowledge; reasoning; learning; memory; perception; creativity; social understanding; planning; physical competence; autonomy; and adaptability.

Machines may exceed humans dramatically on some dimensions while remaining inferior on others. There need therefore be no moment at which artificial intelligence “becomes general”.

Instead two irregular curves — human and machine competence — progressively cross one another.

Machine intelligence surpasses humanity first at calculation, then chess, then information retrieval, then translation, then programming, then scientific reasoning, then professional analysis, then perhaps autonomous research. Other abilities may follow later.

Under this conception AGI is not a technological event comparable to the first atomic explosion. It resembles industrialisation. Nobody woke one morning in Manchester in 1787 and announced that the Industrial Revolution had occurred. Only afterwards did historians draw a line around a sprawling process and give it a name. Future historians may treat AGI similarly.

When will it arrive?

Predictions about AGI should therefore be treated with caution because the answer depends almost entirely upon the definition. Under a permissive definition — broad intellectual competence comparable to or exceeding that of ordinary human beings — one can reasonably argue that we are already in the early AGI era.

Under a stronger definition — reliable human-level or superhuman competence across virtually all cognitive tasks — the answer is probably that we are close but not demonstrably there. Under the strongest definition — a genuinely autonomous artificial agent capable of learning continuously, pursuing long-term objectives, operating economically and socially in the world and replacing an intelligent human worker across essentially arbitrary occupations — substantial problems remain.

Nevertheless the direction of travel is difficult to misunderstand. Stanford’s 2026 AI Index records frontier systems meeting or exceeding human baselines on an expanding range of demanding tests, while also observing that benchmarks intended to remain difficult for years have sometimes been overtaken remarkably quickly. The sensible prediction is therefore not a date but an interval. During the late 2020s, systems are likely to become increasingly difficult to distinguish from AGI under ordinary economic definitions. Large portions of professional cognitive work will become technically automatable, although economic, legal and institutional adoption will proceed more slowly.

During the early 2030s, assuming that progress continues and there is no fundamental technological obstacle, the distinction between “AI” and “AGI” may become largely semantic.

But these forecasts contain enormous uncertainty. Technological progress does not obey timetables.

The harder problem: autonomy

There is another possibility. The decisive development may have little to do with raw intelligence. It may instead be agency. A machine that knows more than any human being but waits patiently for somebody to type a question into a box is extraordinarily powerful but fundamentally passive. A machine capable of establishing its own intermediate objectives, operating computers, conducting research, communicating with people, purchasing services, coordinating other machines and pursuing projects lasting months is something categorically different.

This distinction between intelligence and autonomy may ultimately prove more consequential than the distinction between AI and AGI. We already possess machines capable of extraordinary intellectual performance. What we are increasingly constructing are machines capable of doing things. The combination matters. Knowledge plus reasoning produces an adviser. Knowledge plus reasoning plus agency produces an actor. And actors change history.

Beyond AGI

There is yet another expression: artificial superintelligence, or ASI. This normally means an intelligence substantially exceeding humanity across essentially every important cognitive dimension. Here again the definition is troublesome because humanity itself is not a single intelligence. What should the machine outperform? An average person? The cleverest person alive? Every specialist simultaneously? Or the collective intellectual capacity of an entire university, corporation or government?

DeepMind has recently suggested thinking about the development from AGI towards superintelligence as a continuum rather than an instantaneous transition. This is probably the better way to think about the entire subject. There may be no AGI moment. There may instead be a prolonged crossover during which machines become superior to humans at one intellectual activity after another. We are already living through part of that crossover.

The philosophical joke

There is an irony hidden within the expression artificial general intelligence. For seventy years computer scientists have struggled to define when a machine becomes as intelligent as a human. But we have never satisfactorily defined human intelligence either.

Psychologists debate it. Philosophers dispute its nature. Neuroscientists cannot fully explain its mechanisms. We cannot agree how much intelligence is inherited, how much is learned or even precisely what intelligence tests measure. Yet we propose to establish a crisp threshold at which machines acquire this mysterious property.

Perhaps that expectation was always unrealistic. The arrival of machine intelligence may therefore force us to confront an uncomfortable proposition: intelligence was never the singular, mystical faculty we imagined it to be. It may simply be a collection of abilities. Machines acquired some of them long ago. They are acquiring others extraordinarily quickly.

The milestone that vanishes

Hence the question “When will AGI arrive?” may ultimately resemble asking when a child becomes an adult. The law can specify a birthday. Biology cannot. Development occurs continuously until the accumulated changes become impossible to deny. Something similar may now be happening with artificial intelligence.

We can invent benchmarks. We can establish definitions. Companies can announce milestones and academics can dispute them. But the economically and historically important question is becoming different. It is not: Has AGI arrived? It is: What remains that human beings can do intellectually that machines cannot? Twenty years ago the answer was almost everything. Ten years ago it was still an enormous amount. Today the list is considerably shorter.

If present trends continue, sometime during the next several years we may discover that arguments about whether AGI has technically been achieved have become rather like medieval arguments about precisely where one kingdom ended and another began. The map will matter less than the landscape. And the landscape is already changing beneath our feet.