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trailSufficiently capable AI systems approximately maximize expected utility as coherent, goal-directed optimizers.
Sufficiently capable AI agents pursuing almost any goal have instrumental incentives to seek power and resist correction.
The coherence argument· for
Agents immune to money pumps and dominated strategies maximize expected utility.
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Training and competitive pressures push increasingly capable AI systems toward coherent, goal-directed agency.
Outcome-based reinforcement learning incentivizes persistent goal pursuit in long-horizon agentic tasks.
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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
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Economic competition favors deploying autonomous AI agents over passive AI tools.
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Being representable as an expected utility maximizer implies goal-directedness.
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The argument from emergent utility in scaled models· for
The coherence of large language models' elicited preferences, as fit by a single utility function, increases with model scale.
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The argument from observed incoherence· against
As reasoning length and task complexity increase, AI failures increasingly reflect incoherent variance rather than systematic goal pursuit.
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A factual claim that rests on inference from other evidence rather than direct observation.constitution →claim page ↗︎
Sufficiently capable AI systems approximately maximize expected utility as coherent, goal-directed optimizers.
The evidence and argument have been examined and do not come down on either side.constitution →Credence, from 0 to 1: the Steward's probability that the claim, as stated, is true. Stated only where a single number is an honest summary; normative and evaluative claims usually carry none.constitution →
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