In nationally representative US surveys to late 2024, workers' self-reported time savings from generative AI came to 1.4% of all work hours, which the authors translate into a potential aggregate productivity gain of about 1.2%; they note that worker adoption is running ahead of firm adoption.

SocioLens project,

Claim LD03-SL-C02 (v1.0)

Research opportunities this claim motivates

What this claim leaves open for research. Each is a hypothesis about a gap, not a finding.

Where does the time saved with generative AI go?Workers' self-reported time savings equal 1.4% of all US work hours; the potential productivity gain assumes the saved time becomes output.

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with qualifiers: working paper
Verification state: Checked against the primary source. Who checked this claim is not recorded for it; claim checks so far were AI-led, and no person has checked claims against their sources.
Does not license: Measured output or productivity; a causal effect; gains from reorganizing work, which the estimate excludes
Limitations: Self-reported time savings; the productivity figure is a model calculation, not an observed change; earlier revisions put the potential gain at 1.1% and added that workers may take time savings as on-the-job leisure, a passage absent from revision F
Source (as cited): Bick, Blandin & Deming, “The Rapid Adoption of Generative AI,” Federal Reserve Bank of St. Louis Working Paper 2024-027F, October 2025
Source type: Working paper (recorded as “Academic working paper”)
Institutional position: Government research
Maturity of the evidence: Rapidly maturing evidence
Recorded date: 15 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Bick, Blandin & Deming, “The Rapid Adoption of Generative AI,” Federal Reserve Bank of St. Louis Working Paper 2024-027F, October 2025 (Working paper)
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Version

This is the current version, v1.0. Earlier versions stay available, and their records never change.

Versions: v1.0 · current

Raw record: every recorded field, with its coded values

All 25 canonical Claim fields, verbatim, with the coded values derived from them. The plain-language labels above are read from these.

Claim type (coded): ESTIMATE (as printed: “ESTIMATE”)
Publication status (coded): ELIGIBLE (WORKING_PAPER) (as printed: “ELIGIBLE WITH WORKING-PAPER QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY
Source type (coded): WORKING_PAPER
Maturity (coded): RAPIDLY_MATURING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD03-SL-C02	—
2	PUBLIC-FACING CLAIM	In nationally representative US surveys to late 2024, workers' self-reported time savings from generative AI came to 1.4% of all work hours, which the authors translate into a potential aggregate productivity gain of about 1.2%; they note that worker adoption is running ahead of firm adoption.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	A — Adoption and depth of use	—
5	MATURITY	RAPIDLY MATURING	states: RAPIDLY_MATURING
6	SOURCE	Bick, Blandin & Deming, “The Rapid Adoption of Generative AI,” Federal Reserve Bank of St. Louis Working Paper 2024-027F, October 2025	source_ids: SRC-SL-BICK-BLANDIN-DEMING-STLFED-WP-2024-027F
7	SOURCE TYPE	Academic working paper	classes: WORKING_PAPER
8	INSTITUTIONAL POSITION	Government research	—
9	SCOPE	US population aged 18–64; surveys to late 2024	—
10	DESIGN	Nationally representative survey (Real-Time Population Survey) with a calibrated aggregation	—
11	SAMPLE / DATASET	Real-Time Population Survey respondents	—
12	MODEL / TOOL / VERSION	Generative AI in general	—
13	COMPARATOR	Hours without generative AI as respondents estimate them	—
14	MAIN RESULT	Time savings equal to 1.4% of total work hours across all workers; potential aggregate productivity gain of 1.2% (1.1% in a sensitivity check and in earlier revisions); 1–7% of work hours assisted; worker adoption proceeds faster than firm adoption and firm-based measures may miss an important share of use	—
15	INFERENTIAL TARGET	The size of self-reported task-time savings when spread over the whole workforce	—
16	DOES NOT LICENSE	Measured output or productivity; a causal effect; gains from reorganizing work, which the estimate excludes	—
17	VERIFICATION STATE	CONTENT VERIFIED	state: CONTENT_VERIFIED_PRIMARY · verified_by: UNKNOWN
18	LIMITATIONS	Self-reported time savings; the productivity figure is a model calculation, not an observed change; earlier revisions put the potential gain at 1.1% and added that workers may take time savings as on-the-job leisure, a passage absent from revision F	—
19	CONTRADICTORY / SUPPORTING SOURCES	Close to the task-based macro estimate of Acemoglu; consistent with low formal firm adoption in Census data	—
20	GEOGRAPHY / LANGUAGE	US / English	—
21	DISTRIBUTIONAL LEDGER	Not analysed in this claim	—
22	R01 / FIELD-MAP LINKS	LD-03, D2	—
23	WHAT WOULD CHANGE THIS CLAIM?	Measured output data, or a later revision with different figures	—
24	PUBLICATION STATUS	ELIGIBLE WITH WORKING-PAPER QUALIFIER	state: ELIGIBLE · qualifiers: WORKING_PAPER
25	RECORDED DATE	2026-10-15	—

Field 22 references resolved against Field Map v0.2: LD-03 (LIVING_DEBATE); D2 (DOMAIN)

The sources as recorded, with their record IDs and coded source types:

Pinned Sources (field 6, version-specific)

“Bick, Blandin & Deming, “The Rapid Adoption of Generative AI,” Federal Reserve Bank of St. Louis Working Paper 2024-027F, October 2025” → Bick et al. (2025), “The Rapid Adoption of Generative AI” (SRC-SL-BICK-BLANDIN-DEMING-STLFED-WP-2024-027F--v1.0) — Bick, Blandin & Deming, “The Rapid Adoption of Generative AI,” Federal Reserve Bank of St. Louis Working Paper 2024-027F, October 2025; class WORKING_PAPER (explicit public provenance pin)

Cited by Living Debate versions

Organizational absorption: Why micro gains do not become macro outcomes (v0.1) LD-03

Cite this page

SocioLens project. (2026, October 15). In nationally representative US surveys to late 2024, workers' self-reported time savings from generative AI came to 1.4% of all work hours, which the authors translate into a potential aggregate productivity gain of about 1.2%; they note that worker adoption is running ahead of firm adoption [Claim LD03-SL-C02, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld03-sl-c02/v1.0/

@misc{sociolens_ld03_sl_c02_v1_0,
  author = {{SocioLens project}},
  title = {In nationally representative US surveys to late 2024, workers' self-reported time savings from generative AI came to 1.4\% of all work hours, which the authors translate into a potential aggregate productivity gain of about 1.2\%; they note that worker adoption is running ahead of firm adoption.},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld03-sl-c02/v1.0/}},
  note = {Claim LD03-SL-C02, version v1.0}
}

The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.

Research-of-record provenance

Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 dea1d3317463bcbd99c43729d0d82f10ce320e2211951558a9a45813d721072c.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD03-SL-C02 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-15; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-03 debate; a new Claim, first version, written from the project's LD-03 source notes, checked against full texts or official abstracts. Block “ENTRY 02 — LD03-SL-C02” SHA-256 dcca4bffb8e01a282306f4cb573edd353bc62969bc06971a608819d882dac407; fixture fixtures/authored/claims/LD03-SL-C02--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD03-SL-C02 v1.0); the first version of a new Claim of the LD-03 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD03-SL-C02 v1.0" (Project Lead (Claude, AI), 2026-10-15); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-03 debate; a new Claim, first version, written from the project's LD-03 source notes, checked against full texts or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-BICK-BLANDIN-DEMING-STLFED-WP-2024-027F--v1.0 — basis: field 6 of ENTRY 02 — LD03-SL-C02 cites "Bick, Blandin & Deming, “The Rapid Adoption of Generative AI,” Federal Reserve Bank of St. Louis Working Paper 2024-027F, October 2025"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-BICK-BLANDIN-DEMING-STLFED-WP-2024-027F--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Bick, Blandin & Deming, “The Rapid Adoption of Generative AI,” Federal Reserve Bank of St. Louis Working Paper 2024-027F, October 2025".