Report · AI & Tech

Few Americans use large language models to get political information

Reading

A majority of Americans (53%) say they never use large language models such as ChatGPT to inform themselves about politics, according to a September 2025 Verasight survey of 3,000 U.S. adults.

The rest turn to LLMs at least occasionally: 14% use them several times a week and about as many less than once a month, while 10% do so once a week and 9% one to three times a month.

Topline

single choice

Topline distribution

Few Americans use large language models to get political information.

How often do you use large language models (LLMs, ex: ChatGPT) to inform yourself about politics?

  • Never 53.0%
  • Several times a week 14.3%
  • Less than once a month 13.9%
  • Once a week 10.1%
  • One to three times a month 8.7%

2025 · base n 1,000 · +/- 3.3%

Verasight APSA Omnibus Survey #2025-119

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
2025-09-22 → 2025-09-29
Base (unweighted)
1,000
Margin of error
+/- 3.3%
Module
Verasight APSA Omnibus Survey #2025-119

Source

Citation

Verasight APSA Omnibus Survey #2025-119, fielded September 22-29, 2025, N=1,000 US adults age 18+, +/- 3.3%.

https://reports.verasight.io/r/apsa-2025

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.