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A detailed critique of the METR AI time horizons graph reveals numerous severe methodological errors, including biased human baselines, unmeasured data, and test-training contamination, undermining its conclusions about AI capabilities.
The article argues that AGI timelines based on scaling current architectures may be misguided, as true general intelligence requires rationality—dealing with open-ended real-world contexts—which current systems lack due to the frame problem and a low correlation between intelligence and rationality.