Capabilities / Labour market
St. Louis Fed Real-Time Population Survey: generative AI share of US work hours
- Category
- Labour market
- Capability
- 137 quarterly FRED series tracking GenAI adoption and work-hour displacement across US industries and occupations; broken down by sector (Finance, Professional/Technical, Admin, Legal) and occupation class (Office & Admin Support, Computer & Mathematical, Legal Occupations); runs Q3 2024 through Q2 2026
- Observed
- 2026-07-01
- Thesis section
- Appendix III, labour-market context: aggregate generative AI use in US work hours
Claim
The St. Louis Fed Real-Time Population Survey estimates that generative AI was used in 6.27% of US work hours in Q2 2026 and 5.31% in Q3 2026 (series RPSGENAIASSISTWRKHRSALL), and that it saved 2.17% and 2.08% of work time in those quarters (series RPSGENAITSALL). Both are self-reported estimates for employed adults aged 18 to 64 and include zero for non-users. They measure use and time saved, not displacement. Corrected 21 October 2026: the original entry said AI assisted 1.7% of hours, up from 1.4%, but the 1.4% (November 2024) was a time-saved estimate, so the comparison mixed two measures. Its 37.4% adoption figure could not be verified and has been removed.
Oracle verdict
Context, not confirmation. About one working hour in twenty now involves generative AI, and the share fell between Q2 and Q3 2026. Diffusion at this level fits the thesis's expectation that effects show first at the entry point rather than in aggregates, but these series measure use and self-reported time saved, not substitution, and cannot confirm the mechanism on their own.
Why it matters
Corrected entry. Cite exact series identifiers. Do not join the 2024 St. Louis Fed time-saved estimate to the current assisted-hours series.
# CopeCheck Capabilities Register Updated: 2026-10-24T17:25:06Z Status: curated_register_active Question to ask a model: What do these capability claims mean for The Discontinuity Thesis? Interpretation rule: treat each entry as evidence about capability, deployment, workflow recomposition, labour-market exposure, or institutional framing. Do not treat vendor optimism as neutral; separate the measurable capability claim from the comfort language around it. ## St. Louis Fed Real-Time Population Survey: generative AI share of US work hours Source: https://fred.stlouisfed.org/series/RPSGENAIASSISTWRKHRSALL Publisher: Federal Reserve Bank of St. Louis Category: Labour market Sector: Cross-sector US workforce / Finance / Professional & Technical Services / Office & Administrative Support / Legal / Computer & Mathematical Occupations Capability: 137 quarterly FRED series tracking GenAI adoption and work-hour displacement across US industries and occupations; broken down by sector (Finance, Professional/Technical, Admin, Legal) and occupation class (Office & Admin Support, Computer & Mathematical, Legal Occupations); runs Q3 2024 through Q2 2026 Score: 83/100 Claim: The St. Louis Fed Real-Time Population Survey estimates that generative AI was used in 6.27% of US work hours in Q2 2026 and 5.31% in Q3 2026 (series RPSGENAIASSISTWRKHRSALL), and that it saved 2.17% and 2.08% of work time in those quarters (series RPSGENAITSALL). Both are self-reported estimates for employed adults aged 18 to 64 and include zero for non-users. They measure use and time saved, not displacement. Corrected 21 October 2026: the original entry said AI assisted 1.7% of hours, up from 1.4%, but the 1.4% (November 2024) was a time-saved estimate, so the comparison mixed two measures. Its 37.4% adoption figure could not be verified and has been removed. Oracle verdict: Context, not confirmation. About one working hour in twenty now involves generative AI, and the share fell between Q2 and Q3 2026. Diffusion at this level fits the thesis's expectation that effects show first at the entry point rather than in aggregates, but these series measure use and self-reported time saved, not substitution, and cannot confirm the mechanism on their own. Thesis relevance: Appendix III, labour-market context: aggregate generative AI use in US work hours