Report · AI & Tech

Clear labels for AI-generated content have broad support

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. This includes 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

Source report pending

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
    Source report pending

Citation

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

Source report pending

Verasight survey methodology

How Verasight conducts surveys.

This page describes the Verasight general survey contract, separate from how the Data Library packages it. When a wave report is published, its field dates, sample sizes, and module breakdown are listed in that 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
The canonical report hub is reports.verasight.io. Verified legacy reports remain on their original hosts, and unpublished reports are marked pending.
Transparency
Verasight is a member of the American Association for Public Opinion Research Transparency Initiative.

The canonical report hub is reports.verasight.io; older reports may remain on verified legacy hosts.