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2 claims

Claims about whether a task has objective, verifiable outcome measures (machine-checkable rewards, ground-truth answers, agreed quality metrics) and how the presence or absence of such measures affects AI learnability or automatability of the task. Excludes benchmark contamination and occupational AI exposure ratings.

Early evidence on AI productivity gains, drawn from easy-to-learn tasks, overstates the gains achievable on hard-to-learn tasks.

SupportedEvidence favors the claim, but the chain is incomplete or the sources are secondary.constitution →empirical · derivedA factual claim that rests on inference from other evidence rather than direct observation.constitution →AI Productivity Impactimportance · notableImportance 0.55, 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 →

Hard-to-learn tasks lack the objective outcome measures AI models need to learn them effectively.

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 →Artificial intelligenceimportance · notableImportance 0.50, 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 →

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