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2 claims

Claims about obstacles to estimating AI's causal effect on productivity, especially that widespread adoption leaves few untreated workers, tasks, or firms to serve as counterfactuals, making measured gains hard to attribute to AI. Excludes selection bias among the measured (Self-selection bias in AI productivity estimates), accuracy of self-reported gains, and external-validity transfer across set

Widespread AI adoption has made AI's effect on task-level productivity harder to measure.

SupportedEvidence favors the claim, but the chain is incomplete or the sources are secondary.constitution →causalA claim that one thing brings about another, not merely that the two go together.constitution →AI Productivity Impactimportance · notableImportance 0.45, from 0 to 1 · notable: a contested point in a live debate (also the default before judging). Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

As AI adoption becomes widespread, working without AI ceases to be a realistic control condition in productivity experiments.

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →empirical · derivedA factual claim that rests on inference from other evidence rather than direct observation.constitution →AI Productivity Impactimportance · minorImportance 0.40, from 0 to 1 · minor: narrow or largely settled, cheap to get right. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

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