Marcus claims dataset scores AI skepticism at scale

// 83d agoPRODUCT LAUNCH

Marcus claims dataset scores AI skepticism at scale

This open-source project extracts and scores 2,218 testable claims from 474 Gary Marcus Substack posts using two independent LLM pipelines plus a reconciliation layer. The published results show strong support for specific technical critiques, weaker support for market-crash predictions, and clear caveats that all labels are LLM-scored rather than human-verified.

// ANALYSIS

Useful meta-research, but the strongest value is methodological transparency rather than definitive truth claims.

–Dual-pipeline scoring (Claude and Codex) plus reconciliation is stronger than single-model judgment and makes disagreement visible.
–The dataset highlights a key pattern for AI discourse: specific, falsifiable technical claims age better than broad market narratives.
–The repo includes methods and outputs, which makes this reproducible for auditing other public AI commentators.
–Because scoring is automated, downstream users should treat labels as evidence-weighted signals, not final adjudications.

// TAGS

llmresearchbenchmarkopen-sourcesafety

DISCOVERED

83d ago

2026-03-05

PUBLISHED

84d ago

2026-03-04

RELEVANCE

7/ 10

AUTHOR

davegoldblatt

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