The forecasts stopped converging some time last year. Dario Amodei said at Davos in January that AGI was probably a couple of years out, maybe 2027, on the argument that coding and AI research now accelerate each other. Demis Hassabis, at the same event, put it at roughly 50% by 2030 and named scientific discovery as the unsolved part. Two thousand Metaculus forecasters landed in February on 25% by 2029, 50% by 2033. None of that would be strange if they were estimating the same event. The Atlantic's reporting the same month is the giveaway: two years ago these people broadly agreed on the late 2020s, and what has come apart since is not just the date but the thing being dated.
Helen Toner has the framing that explains it. AGI was never a threshold, it's a fuzzy cloud of adjacent concepts, and for twenty years the fuzziness cost nothing because everything we could build was far outside it. We're inside it now. Serious people have declared AGI achieved three separate times since April 2025, while equally serious people put it a decade out, and they can all be looking at the same models.
I can show you what that looks like without a benchmark. A model reads a screenshot of a stack trace, finds the bug in a codebase it has never seen, writes the patch and runs the tests. In 2015 I'd have called that general intelligence and not thought hard about it. The same model, an hour later, loses track of a five-step plan it wrote itself. Both facts are true of one system, so which one you weight decides your answer, and there's no principled way to choose.
The part that should bother people more than it does is where the working definitions now come from. OpenAI's operational bar, systems that outperform humans at most economically valuable work, is contract language carried over from its Microsoft agreement, and leaked criteria reportedly attached a number to it: $100 billion in profits. That's a commercial trigger, not a claim about cognition, and it wandered into the scientific conversation anyway. So when Sam Altman told Forbes the company had basically built AGI and Satya Nadella said the industry was nowhere near, neither had to be wrong. Gary Marcus and two colleagues spent February arguing in Nature that the arrival claim mistakes benchmark scores for real-world flexibility, against opponents whose definition excluded understanding and agency by construction. That fight can't be settled by better evals, particularly when the labs keep grading their own homework.
Toner's suggestion is the only useful one I've seen: say what you actually mean. Fully automated AI research. Systems that learn as efficiently as a child. Enough labour displacement to break the employment model. Each of those can be argued about with evidence. None of them needs the word.
Sources:
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The term "AGI" is almost useless at this point — Helen Toner
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Do You Feel the AGI Yet? — The Atlantic
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Rumors of AGI's arrival have been greatly exaggerated — Marcus on AI
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Shrinking AGI timelines: a review of expert forecasts — 80,000 Hours
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AGI/Singularity: 9,800 Predictions Analyzed — AIMultiple
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The AGI Mirage: Why the Definitional Chaos Is Diagnostic — LessWrong
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Artificial General Intelligence in 2026 — TimeTrex