Back to AI & Tech

What worries people about AI

Source reportMethodology

Overview

AI concern is broad across multiple risk areas. Misinformation, bias, and job loss each draw at least 80% at the somewhat-concerned level or higher.

The emotional measure points the same way. About 50% agree that they feel anxious about the rise of AI, compared with 22% who disagree.

Topline

86% are concerned about AI misinformation, 81% about bias, and 82% about job loss.

Misinformation in AI output

  • Extremely concerned 33.9%
  • Somewhat concerned 27.9%
  • Very concerned 25.4%
  • Not at all concerned 7.1%
  • I’m unfamiliar with this topic 5.6%

2025 · base n 1,509 · +/- 3.1%

AI Adoption Survey July 2025

View source data

Misinformation is the highest concern

About 86% are at least somewhat concerned about misinformation in AI output.

The strongest responses are substantial too: 33% are extremely concerned and 26% are very concerned.

Bias and job loss are close behind

Bias in AI model training and output draws about 81% at least somewhat concerned.

AI taking jobs from humans is similarly high at about 82%.

Stacked breakdown

50% agree that they feel anxious about the rise of AI.

I feel anxious about the rise of AI

Strongly agree
18.0%
Somewhat agree
31.8%
Neutral
26.8%
Somewhat disagree
14.8%
Strongly disagree
8.7%

2025 · base n 1,509 · +/- 3.1%

AI Adoption Survey July 2025

View source data

Anxiety is common, but not universal

About 50% agree that they feel anxious about the rise of AI, while 22% disagree and 29% are neutral.

Concern about humans developing relationships with AI is lower than misinformation or jobs but still broad, with about 67% at least somewhat concerned.

Methodology

Full methodology
Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting
Population
US adults age 18+
Field dates
2025-07-30 → 2025-08-04
Base (unweighted)
1,509
Margin of error
+/- 3.1%
Module
AI Adoption Survey July 2025
Sponsor
Verasight
Weight variable
weight
Weighting targets
age, race/ethnicity, sex, income, education, region, metropolitan status

Sources

[5]
  • 01
    How concerned are you about the following issues surrounding AI? - Misinformation in AI outputShows concern about misinformation in AI output.reports.verasight.io/reports/ai-adoption-survey-august-2025
  • 02
    How concerned are you about the following issues surrounding AI? - Bias in AI model training and outputShows concern about bias in AI training and output.reports.verasight.io/reports/ai-adoption-survey-august-2025
  • 03
    How concerned are you about the following issues surrounding AI? - AI taking away jobs from humansShows concern about AI taking jobs from humans.reports.verasight.io/reports/ai-adoption-survey-august-2025
  • 04
    Please indicate how much you agree or disagree with the following: - I feel anxious about the rise of AIAdds a direct anxiety measure about the rise of AI.reports.verasight.io/reports/ai-adoption-survey-august-2025
  • 05
    How concerned are you about the following issues surrounding AI? - Humans developing relationships with AIAdds concern about humans developing relationships with AI.reports.verasight.io/reports/ai-adoption-survey-august-2025

Citation

AI Adoption Survey August 2025, fielded September 3-8, 2025, N=1,519 United States adults, +/- 3.3%.

https://reports.verasight.io/reports/ai-adoption-survey-august-2025#how-concerned-are-you-about-the-following-issues-surrounding-ai-misinformation-in-ai-output

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.