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trailEffective training compute required for AI systems capable of automating all economic tasks (FLOP, in 2025-equivalent effective FLOP)
The lower bound from present frontier shortfall
As of 2025–2026, frontier AI systems trained at 10^26–10^27 FLOPs fall short of automating all economic tasks.
atomic
The anchoring and effective-compute-gap argument
Increasing AI automation from 20% to 100% of economic tasks requires several orders of magnitude more effective compute.
not yet estimated
Log10 ratio of effective compute required to automate 100% versus 20% of economic tasks (the "effective FLOP gap")
The argument from rapid capability gains at current scale
AI agents’ 50%-reliability software-task horizon will continue doubling approximately every seven months.
≈ 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
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Increasing training compute enables AI systems to automate increasingly complex tasks.
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≈ 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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assumed
AI capabilities can be adequately modeled as a function of a single effective-compute variable combining physical compute and algorithmic efficiency.
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Effective training compute required for AI systems capable of automating all economic tasks (FLOP, in 2025-equivalent effective FLOP)
≈ 3,000,000,000,000,000,000,000 trillion eFLOP90% interval 30,000,000,000,000,000 trillion eFLOP to 10,000,000,000,000,000,000,000,000,000 trillion eFLOP, with a 15% chance that Probability that the quantity is ill-defined: full automation is not reached by scaling a single effective-compute axis (e.g. because data, embodiment, or paradigm changes make 2025-equivalent eFLOP an unworkable conversion).
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