Report · AI & Tech

Many think AI investment advice can be useful for solo investors

Reading

About six-in-ten Americans (61%) think AI-generated investment advice can be at least moderately useful to people managing their own money without a bank or advisor, according to a May 2026 Verasight survey.

That includes 6% who call it very useful. The remaining 39% see little use in it, including 15% who say it offers none at all.

Topline

single choice

Topline distribution

Many think AI investment advice can be useful for solo investors.

To what extent do you think investment or stock advice generated by AI can be useful for individual retail investors who are operating independently without the support of financial institution or professional advisors?

  • 4 26.8%
  • 5 20.7%
  • 1 - Not at all 15.0%
  • 3 14.6%
  • 2 9.6%
  • 6 7.1%
  • 7 - Very much 6.2%

2026 · base n 496 · +/- 4.7%

beliefs

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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
2026-05-11 → 2026-05-11
Base (unweighted)
496
Margin of error
+/- 4.7%
Module
beliefs

Source

Citation

Verasight SPSP Omnibus Survey #2026-045, fielded May 11-11, 2026, N=496 US adults age 18+, +/- 4.7%.

https://reports.verasight.io/r/spsp26

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.