Report · AI & Tech

Few Americans worry about backlash for using AI at work or school

Reading

Concern about being judged for using AI on the job or in class is far from widespread; roughly three-in-ten Americans (29%) agree they worry about a backlash, including 9% who strongly agree.

Opinion leans the other way for most: 36% disagree that they are concerned, including 14% who strongly disagree, and about a third (35%) neither agree nor disagree, according to a January 2025 Verasight survey.

Topline

response scale

Topline scale

Few Americans worry about backlash for using AI at work or school.

To what extent do you agree with the following statement: “I am concerned about facing backlash for using Artificial Intelligence (AI) in my professional or school work.”

  • Neither Agree or Disagree 34.8%
  • Strongly Disagree 14.2%
  • Disagree 13.4%
  • Somewhat Agree 12.8%
  • Strongly Agree 8.8%
  • Somewhat Disagree 8.1%
  • Agree 7.7%

2025 · base n 1,000 · +/- 3.3%

Verasight AEA Omnibus Survey #2025-002

Source report pending

Methodology

Source report pending
Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting
Field dates
2025-01-16 → 2025-01-27
Base (unweighted)
1,000
Margin of error
+/- 3.3%
Module
Verasight AEA Omnibus Survey #2025-002

Source

  • 01
    Source report pending

Citation

Verasight AEA Omnibus Survey #2025-002, fielded January 16-27, 2025, N=1,000 United States adults, +/- 3.3%.

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.