In the movie 2001: A Space Odyssey, the murderous artificial intelligence HAL 9000 went into operation on January 12, 1992. In the early Terminator films, Skynet became “a new order of intelligence” on August 29, 1997. And now, in real life, tech company OpenAI says that artificial general intelligence—AGI—may have arrived or become imminent with the release of its new model, Astra, on September 3, 2026.
The claim feels important, but it’s worth asking what AGI even means at this point. The term, once confined to technical science papers, has gripped Silicon Valley, steered trillions of dollars in investment and fanned fears of human displacement, or worse. Along the way, though, its meaning has slipped. By some older definitions, we reached AGI several years ago; by others, AI systems still lack essential faculties. So the only reason anyone would draw a precise line is that they have decided enough is enough.
It used to be that with every advance in AI—the development of programs that could beat humans at chess and Go or the rise of chatbots—the field redefined the boundary between machine and human to keep the two firmly apart. Now, with the pursuit of AGI, frontier AI labs have been trying to collapse the distinction.
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Before business leaders co-opted the term, AGI was the banner of an iconoclastic community of researchers toughing it out during the so-called AI winter of the 1990s, a low point in the boom-bust cycle the field has long been prone to. “If you even talked about building real thinking machines, people thought you were a nutcase,” recalls Ben Goertzel of the SingularityNET Foundation. An AGI pioneer, he and others thought the field needed to dream big again.
These pioneers fully expected that machines would one day surpass humans—that AGI would become ASI, or artificial superintelligence—because there’s no reason to expect that humans are the pinnacle of intelligence. “The human level is an arbitrary point,” Goertzel says.
Lately, though, the idea of generality seems to have fused with that of superhuman intelligence. “Many people nowadays, when they use the ‘AGI’ term, either mean ‘ASI’—or what I would think of as ASI—or they mean something else, like does it have a soul or is it conscious?” says Blaise Agüera y Arcas, vice president of Technology & Society at Google.
Yet the level of intelligence is really a second dimension, independent of generality. Pocket calculators are superhuman but narrow, whereas the first large language models, though barely articulate, were general. “We got AGI when we started to have language,” Agüera y Arcas argues. In a 2023 essay, he and Peter Norvig of Stanford University heralded the arrival of AGI, based on how the large language models (LLMs) of the time could do things they weren’t directly trained to do.
His point is that although LLMs, no less than chess engines and other specialist systems, are trained on a specific task—namely, predicting the next word in a text passage—that task is special. It demands the acquisition not only of grammar but also of considerable world knowledge. Agüera y Arcas suggests that language is an “AGI-complete” task—once mastered, it enables a broader capacity.
In humans, this is not true. For us, language is a distinct faculty. People with brain damage can lose the ability to understand or speak words yet still do math, play chess and compose music. But Agüera y Arcas says LLMs are different. There is no clean separation of their linguistic and reasoning abilities. “The set of things that you could ask the model to do becomes open-ended,” he says. And today’s LLMs are able to call on other computational modules to fill in their weak spots, so language is the gateway to ever-broader capacity.
Still, other AI researchers say today’s machines don’t yet qualify for the “G” in AGI. Joscha Bach of the California Institute for Machine Consciousness, another AGI pioneer, says a truly intelligent system should not need to devour the entire Internet and consume vast amounts of computing power for even the simplest query. “We would have a system that is much more compact than us and has read far less than us—that is learning much better than us and reasoning much better than us,” he says.
Goertzel says he has been using OpenAI’s new Astra model and finds it doesn’t do anything he couldn’t on his own; it just does it faster. That’s not to be sniffed at; it lets Goertzel get more done. But he says the system still lacks a creative spark. Current AI models are also unable to learn on-the-go from experience, a hallmark of human intelligence. “They can learn temporarily, as it were, during the course of a session, but none of that learning gets baked back in,” Agüera y Arcas says.
Maybe the lesson here is that we need less lumping and more splitting. AI is not a monolith, and we need to recognize that. There’s no need to choose between being pro- or anti-AI. It is quite possible to continue developing systems that help scientists cure disease while clamping down on those that launch massive cyberattacks. Perhaps there will be no AGI moment because AI is not one thing. Indeed, “I”—intelligence—is not one thing, and it is seldom the most important thing. AGI, however we define it, will not solve the climate crisis or world hunger or some other great problem—because the stumbling block is not knowing what to do but doing it.
We demand sophistication from machines. Maybe we should start demanding it from humans, too.