Report · AI & Tech

Should AI-generated content come with a label? Seven-in-ten Americans say yes

Reading

In a Verasight survey of 1,509 U.S. adults conducted July 30 to Aug. 4, 2025, 71% of Americans favored requiring clear labels when content has been generated or heavily modified by AI. Including 50% who strongly favor and 21% who somewhat favor.

Few Americans opposed the labels (6%), with 3% who somewhat oppose and 3% who strongly oppose. Another 23% were neutral or unsure, with 15% who neither favor nor oppose and 8% who said they are not sure.

Topline

response scale

Topline scale

71% of Americans favor requiring labels on AI-generated content.

Do you favor or oppose requiring clear labels when content has been generated or heavily modified by AI?

  • Strongly favor 50.1%
  • Somewhat favor 21.4%
  • Neither favor nor oppose 14.5%
  • Not sure 8.2%
  • Somewhat oppose 3.3%
  • Strongly oppose 2.5%

2025 · base n 1,509 · +/- 3.1%

AI Adoption Survey July 2025

View source

Methodology

Full methodology
Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting
Field dates
2025-07-30 → 2025-08-04
Base (unweighted)
1,509
Margin of error
+/- 3.1%
Module
AI Adoption Survey July 2025

Source

  • 01
    Should AI-generated content come with a label? Seven-in-ten Americans say yesreports.verasight.io/reports/ai-adoption-survey-july-2025

Citation

AI Adoption Survey July 2025, fielded July 30-August 4, 2025, N=1,509 US adults age 18+, +/- 3.1%.

https://reports.verasight.io/reports/ai-adoption-survey-july-2025#do-you-favor-or-oppose-requiring-clear-labels-when-content-has-been-generated-or-heavily-modified-by-ai

Verasight survey methodology

How Verasight conducts surveys.

This page describes the Verasight general survey contract, separate from how the Data Library packages it. Each wave's specific field dates, sample sizes, and module breakdown are listed in that wave's report.

Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting.
Population
US adults age 18+.
Sample design
Surveys are run as omnibus or single-topic waves. Omnibus waves are split into modules with their own respondent set, typically around one thousand respondents per module.
Field window
Each wave specifies its own field dates. Most omnibus waves field across roughly two weeks.
Weighting
Per-module weighting to CPS targets including age, race and ethnicity, sex, income, education, region, and metropolitan status.
Partisanship benchmark
Pew Research Center's NPORS benchmarking surveys, three-year running average.
Vote benchmark
2024 presidential vote population benchmarks.
Margin of error
Typically about plus or minus 3.4 to 3.6 percent per module at standard module sizes. Question-level MoE is recomputed when a base shrinks materially below the module baseline.
Reporting
Every wave is published as a standalone report at verasight.io/reports with full instrument and methodology.
Transparency
AAPOR transparency standards.

Wave-specific methodology, full weighting variable lists, and verbatim instrument text live in each report at verasight.io/reports.