Lately, statements from two prominent figures in the technology and artificial intelligence industries have been circulating in the media and across online discussion forums. Sam Altman, the CEO of OpenAI, and Elon Musk, the founder of xAI and owner of X, have both used similar language to suggest that we are already in the ‘singularity‘. The idea has since been repeated, interpreted, and amplified by others. It is a simple statement, but it carries a dramatic undertone. It sounds as if a decisive threshold has been crossed and that we have entered a fundamentally different phase of technological development. Depending on how one understands the word, the implication can even seem mildly apocalyptic or prophetic. But what does the statement actually mean? We are in the singularity of what? And how much attention should we pay to these grand proclamations, which seem to appear with increasing frequency? Part of the answer may lie in the way the technology industry communicates. Companies and individuals working at the forefront of AI are engaged in a race not only for technical capability, investment, and talent, but also for public attention. Dramatic claims help shape the conversation around a technology, influence how its development is perceived, and reinforce the impression that history is accelerating around us.
There may also be a less strategic explanation. People who work closely with AI systems experience their rapid development directly and continuously. They see new capabilities appear from one model generation to the next, and they are often among the first to encounter tools that are unavailable to the wider public. This proximity can produce a form of tunnel vision. It may lead people to give AI more general capability, autonomy, and transformative power than it has actually demonstrated outside the specific environments in which it performs well. This effect may be particularly strong with AI because the technology communicates through language. When a system produces a fluent explanation, writes a persuasive paragraph, or discusses a technical subject in a seemingly reasoned way, people can instinctively associate its output with understanding and intelligence. The language is visible; the limitations behind it are not always equally visible. As a result, AI systems can appear more capable than they are, especially when their performance is judged through conversation rather than through the complete execution of a complex real-world task. The statement that we have reached the singularity also implies that a certain threshold has been crossed. Yet it is remarkably unclear what that threshold is, who defined it, or how it is supposed to be measured. In most public debates, the singularity is discussed as if it were a technical milestone comparable to the launch of a new model or the achievement of a benchmark. In practice, however, it is a vague and moving target. Depending on who is using the term, it may refer to artificial general intelligence, or AGI; to artificial superintelligence; to the moment when AI systems become capable of improving their own successors; or simply to a general sense that AI progress has become extraordinarily fast. These ideas are related, but they are not interchangeable. AGI refers broadly to a system capable of performing a wide range of intellectual tasks at a human level. Superintelligence refers to a system that would surpass human performance across most or nearly all cognitive domains. The singularity, by contrast, is usually associated with what might happen after one of these milestones: a period of self-reinforcing improvement in which AI systems advance so rapidly that the future becomes difficult to predict. The term can also be confusing because its best-known scientific associations are cosmic. In physics, a singularity refers to a point or region (famously, the centre of a black hole) where existing theories produce extreme or undefined results. The technological singularity borrows this language metaphorically. It does not refer to knowledge contracting into a physical point, but to a possible limit of prediction: a period when AI-driven change becomes so rapid or self-reinforcing that familiar methods of understanding and forecasting no longer work. The analogy concerns the breakdown of prediction, not the literal shape or direction of change. Once these different ideas are blurred together, the word ‘singularity’ begins to sound more sensational than explanatory. It compresses several different claims into one dramatic label: AI is becoming more capable; progress is accelerating; humans may not understand the next phase; and established assumptions may no longer apply. A more careful question is therefore: threshold of what, exactly? Is it the point at which AI matches human capability across most intellectual tasks? Is it the point at which AI exceeds humans in nearly all of them? Is it the point at which an AI system can improve its own successor more effectively than human researchers can? Or is it simply the point after which technological change feels faster than institutions, markets, and societies can absorb? These questions reveal a basic problem. The word ‘threshold’ suggests a definite and measurable boundary, but the criteria commonly proposed to identify it are not equally definite. ‘Most intellectual tasks’ and ‘nearly all cognitive domains’ require us to decide which tasks count, which humans or groups provide the comparison, and what level of performance qualifies as success. The claim that change ‘feels faster’ is even more subjective: it describes a social perception rather than a clearly measurable technical event. This does not mean that such judgements are meaningless. They may be useful for discussing broad social change or strategic risk. But they should not be confused with a universally accepted scientific test. A genuine threshold would require agreed definitions, observable criteria, repeatable evaluations, and a way to determine whether the relevant capability persists outside carefully selected demonstrations. These are very different propositions, and they require different kinds of evidence. A model performing well on a benchmark is not proof of general intelligence. A system that can generate code is not necessarily capable of designing, testing, certifying, manufacturing, and maintaining an entire complex industrial system. A rapid increase in AI capability does not automatically mean that every other domain of science, technology, and human life will change at the same pace.
The answer, then, is that there is no universally accepted threshold. No official body has defined a singularity test. There is no standard measurement that says: here is the line, and on this side is ordinary progress, while on the other side begins the singularity. What exists instead is a family of overlapping futurist ideas, some of them old, some of them more recent, and all of them somewhat elastic in popular use. That elasticity is precisely why the term is so often invoked in a loose and somewhat mystical way. It sounds rigorous, but it often functions as a vibe. This is not entirely accidental. The idea of an AI singularity has roots in older speculation about an intelligence explosion and recursive self-improvement, in which a machine capable of improving itself could create a feedback loop of accelerating capability. The intelligence-explosion scenario is often traced to statistician I. J. Good, who described it in 1965, while science-fiction writer Vernor Vinge later helped popularise the term ‘technological singularity‘ in AI and futurist discussions. From there, the concept became entangled with later notions of AGI and superintelligence. In public discourse, however, those distinctions are rarely maintained. The result is a single umbrella term that can mean different things to different people without ever being pinned down. If the threshold cannot be clearly defined, then it is difficult to say with confidence whether it has been crossed. And yet that is exactly what some high-profile AI figures seem to be doing. Musk’s ‘we are in the singularity‘ and Altman’s ‘we are past the event horizon‘ style of language are not neutral descriptions. They are interpretations. They suggest that AI progress has reached a phase where it is no longer merely incremental. They imply a qualitative shift, a before and after. But they do not by themselves prove that a formal singularity has been reached, because no formal singularity test exists in the first place. What they are really expressing, I think, is a strong sense that AI capability growth has become unusually fast and unusually broad. That is still significant. AI models can now write code, summarise information, generate images, assist with analysis, and increasingly act as a kind of general-purpose cognitive infrastructure. In many domains, the rate of improvement really does feel striking. For those who work close to the technology, the change can feel less like gradual progress and more like standing inside a moving machine. But there is an important difference between ‘this is moving quickly’ and ‘the singularity has arrived’. The world is not made of model benchmarks alone. It is not enough for software to become better at narrow or even broad cognitive tasks. A civilisation is not transformed simply because a computer program can outperform humans in text generation or coding. The real world is full of friction: regulation, procurement, engineering constraints, hardware limitations, organisational inertia, legal liability, safety testing, labour relations, capital costs, and plain old human unpredictability. Those things do not disappear because one domain of AI improves rapidly. Consider an aeroplane, for example. Designing one is clearly an intellectual task. It involves aerodynamics, structures, propulsion, control systems, materials, certification requirements, manufacturability, maintenance, economics, and risk management. AI can already help with many parts of that process. It can propose shapes, accelerate simulation, optimise parameters, and support engineers in exploring design space. But helping with parts of a process is not the same thing as autonomously producing a safe, certifiable, economically viable aircraft from start to finish. That realisation reveals a broader truth about AI hype. A system can be impressive, even transformative, without already being total. A language model that writes competent code is useful. A model that helps generate aircraft concepts is useful. But usefulness in a subtask is not identical to mastery of the whole messy chain from idea to deployment. And human civilisation is mostly made of messy chains. This is where singularity talk often overreaches. It takes a real but partial acceleration in one domain and turns it into a story about the whole of reality. It can make the leap from ‘AI is changing knowledge work’ to ‘we have entered a new historical phase’ sound more conclusive than it really is. That leap is especially seductive in a sector driven by exponential charts, dramatic demonstrations, and winner-takes-all narratives. Still, it would also be too easy to dismiss the entire concept as hype. That would miss the real point. AI is genuinely changing how people work, how software is built, how information is processed, and how organisations think about productivity. It is not imaginary. The question is not whether there is change, but how to describe it responsibly. This is where the singularity term becomes politically and intellectually tricky. For some, it is a warning. For others, a provocation. For others still, a marketing device. It can be used to suggest inevitability, to create urgency, to attract investment, to frame strategic competition, or to imply that all scepticism is simply behind the curve. In that sense, the word becomes powerful precisely because it is vague enough to travel across audiences. But policymakers, regulators, and institutions cannot afford to operate on vibes. They need concrete distinctions. They need to know what AI systems can do now, where they are improving, where they fail, which sectors are exposed first, and which governance problems are emerging. If we talk about a singularity without defining the threshold, we risk replacing analysis with atmosphere. That is why a more useful framing would be ‘What exactly is changing, in which domains, and at what speed?‘ rather than ‘Are we in the singularity?‘ This is a far more practical question for governance. It lets us separate hype from evidence, capability from deployment, and technical progress from societal transformation. It also keeps us from mistaking a powerful software trend for a total explanation of human development. In the end, the singularity remains a useful word only if we remember that it is not a measurement. It is a metaphor. It can help describe the feeling that AI progress is accelerating beyond familiar expectations, but it cannot by itself tell us what has been crossed, who decided that a boundary exists, or how the boundary should be verified. And that, perhaps, is the main takeaway. Claims that the singularity has arrived deserve neither automatic dismissal nor fearful acceptance. Their value depends on whether they encourage careful questions about what is actually changing, where the change is occurring, and at what speed. Without that grounding, the word can become a dramatic label that replaces explanation, leaving audiences impressed, anxious, or both. Author: Slobodan Kovrlija
The problem of the threshold
From futurist idea to public narrative
Capability reality
A more useful question
