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trailExtrapolating exponential AI capability trends is a sound basis for short AI timelines.
Artificial general intelligence will be developed by 2028.
The argument from robust, persistent trends· for
The task-completion time horizon of frontier AI models began growing faster around 2024.
≈ 103 days
Doubling time, in days, of the 50%-task-completion horizon of frontier AI models released from 2024 onward on METR's software task suite
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≈ 203 days
Doubling time of the 50%-task-completion time horizon of frontier AI models on METR's software task suite, 2019–2025, in days
METR's apparent post-2024 time-horizon acceleration reflects measurement artifacts rather than faster AI capability growth.
Benchmarks beyond METR's software suite show AI time horizons or capabilities growing faster since 2024.
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AI time horizons grow roughly exponentially across many task domains beyond software engineering, with similar doubling times.
atomic
Growth in AI task-completion time horizons on benchmarks tracks progress toward AI that can automate AI research or most cognitive work.
The ability to sustain autonomous work on long tasks is the main capability bottleneck preventing AI from automating AI research.
Time-horizon trends measured on benchmark software tasks generalize to real-world software work.
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AI agents’ 50%-reliability software-task horizon will continue doubling approximately every seven months.
Increasing training compute enables AI systems to automate increasingly complex tasks.
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The argument from fragile extrapolation· against
Growth in frontier AI training compute will slow substantially before 2030 because of cost, power, and chip constraints.
atomic
AI benchmarks overestimate models' capabilities on real-world tasks.
shared · shown elsewhere
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Extrapolating exponential AI capability trends is a sound basis for short AI timelines.
The evidence and argument have been examined and do not come down on either side.constitution →
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