Report · AI & Tech

Americans are split on whether working alongside AI will protect jobs

Reading

Americans are evenly divided on whether becoming more AI-savvy will blunt the threat of automation: 33% agree that workers who learn to work alongside AI face less risk of replacement, including 9% who strongly agree, while an identical 33% disagree, including 10% who strongly disagree, according to a Verasight survey conducted Dec. 3-8, 2025.

The single largest group, though, takes neither side, with 34% landing in the middle.

Topline

response scale

Topline scale

Americans are split on whether working alongside AI will protect jobs.

To what extent do you agree with the following statement: "If employees become more AI-savvy and use AI to boost and optimize their productivity (i.e., work alongside AI), the threat of jobs being replaced by AI will decrease."

  • Neither agree nor disagree 34.2%
  • Agree 23.8%
  • Disagree 23.1%
  • Strongly Disagree 9.6%
  • Strongly Agree 9.3%

2025 · base n 1,000 · +/- 3.2%

tech_behavior

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Methodology

Full methodology
Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting
Field dates
2025-12-03 → 2025-12-08
Base (unweighted)
1,000
Margin of error
+/- 3.2%
Module
tech_behavior

Source

Citation

Verasight Human/LLM Comparison Survey #2025-172, fielded December 3-8, 2025, N=1,000 US adults age 18+, +/- 3.2%.

https://reports.verasight.io/r/verasight-human-v-synthetic

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
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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.