The Pattern
The definition of artificial general intelligence has been rewritten three times in ten years, and each rewrite arrived precisely when the existing systems were about to fail the previous test. That pattern is not an accident of scientific progress.
The original standard
The original standard, set in 2007, required AI to generalize reliably across genuinely novel environments with no task-specific tuning. None of the systems currently on the market meet that standard. The industry responded by changing the standard.
The redefinitions
The first shift redefined AGI in terms of benchmark performance. If a system could score well on enough tests, it was declared “general.” But benchmarks isolate narrow competencies and abstract away real-world context. Strong benchmark performance often provides little evidence of robustness under novelty, uncertainty, or shifting objectives.
The second shift redefined AGI in economic terms. If a system could perform tasks that previously required human labor, it was declared “general.” This definition has the advantage of appearing rigorous because it can be attached to data: automation studies, occupational surveys, task completion rates. But it measures economic substitution, not cognitive generality.
The third and most recent shift is made directly in Nature. A paper published in the journal in early 2026 by Chen and colleagues argued that success on behavioral tests, including variants of the Turing Test, constitutes evidence that AGI has already been achieved. The Turing Test measures whether a human evaluator, communicating via text, can distinguish a machine from a human over a sustained exchange. It was proposed by Alan Turing in 1950 as a thought experiment about the nature of machine intelligence, not as a certification protocol for general intelligence. Systems have been reported to pass versions of it by exploiting superficial conversational patterns and the limited technical knowledge of non-expert evaluators, not by exhibiting anything the 2007 standard would recognize as reliable generalization.
The critique the redefinition has to answer
The Chen et al. piece is not a rebuttal of the pattern above. It is an instance of it, made in one of the field’s most visible venues. Gary Marcus’s response to claims like it is direct: benchmark success, even across many tasks, is not sufficient evidence of general intelligence. On the properties the 2007 standard actually required, flexibility, generality, and reliability across genuinely novel tasks, current systems remain brittle, sensitive to distributional shift, and inconsistent outside curated evaluation settings.
The redefinition does not need to answer that critique to serve its purpose. Once “AGI has been achieved” can be sourced to a paper in Nature, the claim earns a place in press releases and investor decks, regardless of whether the underlying systems meet the original standard.
Why this matters beyond AGI
This is propaganda because it uses the authority of science, benchmarks, peer-reviewed papers, the word “intelligence,” to make claims that the science does not support. The redefinition pattern is not a neutral evolution of terminology. It is a strategic retreat to definitions the current products can pass, presented as progress rather than concession. The audience, policymakers, investors, journalists, the public, hears “AGI” and understands the 2007 definition: a system that can think generally, like a human. The industry uses the 2026 definition: a system that can pass a text-based imitation game. The gap between those two definitions is where the hype lives.
Verdict: Misleading. AGI has not been achieved by the original standard. The industry redefined the term to claim it has. The redefinition is the propaganda.
