Across 74.6 million publications from 1960 to 2019, papers that use AI were more likely to be among the top 5% most cited in their field and year for a majority of disciplines, a premium the authors find stronger in disciplines with a lower propensity to use AI, while disciplines with more women or Black scientists showed less potential benefit.

SocioLens project,

Claim LD09-SL-C08 (v1.0)

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with a qualifier
Verification state: Checked against the primary source; the source is a preprint. 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: Any measure of topic narrowing, or a causal effect of AI use
Limitations: Read in the preprint (arXiv version 2, under an earlier title), not the Nature Human Behaviour version; the journal details come from the Crossref record; AI use identified from field categories and text terms; associational
Source (as cited): Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578)
Source type: Peer-reviewed journal article (recorded as “Peer-reviewed journal article (read in preprint version)”)
Institutional position: Independent academic (Northwestern University)
Maturity of the evidence: Emerging 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

Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578) (Peer-reviewed journal article)
Read the original

Version

This page shows version v1.0. Its record never changes, even when newer evidence arrives; the site may add a note to the page or withhold a figure not cleared for public use.

This pinned version is also the current version.

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 (QUALIFIED) (as printed: “ELIGIBLE WITH QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY (PREPRINT)
Source type (coded): PEER_REVIEWED_JOURNAL_ARTICLE
Maturity (coded): EMERGING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD09-SL-C08	—
2	PUBLIC-FACING CLAIM	Across 74.6 million publications from 1960 to 2019, papers that use AI were more likely to be among the top 5% most cited in their field and year for a majority of disciplines, a premium the authors find stronger in disciplines with a lower propensity to use AI, while disciplines with more women or Black scientists showed less potential benefit.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	A — Science and scholarship	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578)	source_ids: SRC-SL-GAO-WANG-NHB-2024
7	SOURCE TYPE	Peer-reviewed journal article (read in preprint version)	classes: PEER_REVIEWED_JOURNAL_ARTICLE
8	INSTITUTIONAL POSITION	Independent academic (Northwestern University)	—
9	SCOPE	Science and scholarship broadly (19 disciplines, including the social sciences), publications 1960–2019	—
10	DESIGN	Text-based measurement of AI use across publications and patents; comparison of citation outcomes	—
11	SAMPLE / DATASET	74.6 million publications and 7.1 million patents	—
12	MODEL / TOOL / VERSION	AI use identified from AI terms (n-grams) in titles and abstracts, with AI papers defined by Microsoft Academic Graph field categories	—
13	COMPARATOR	Papers in the same field and year that do not use AI	—
14	MAIN RESULT	A citation premium for AI-using papers (higher chance of being in the top 5% by citations) for a majority of disciplines, appearing stronger where AI use is less common; less potential benefit where women or Black scientists are better represented	—
15	INFERENTIAL TARGET	Who gains from the use of AI in research	—
16	DOES NOT LICENSE	Any measure of topic narrowing, or a causal effect of AI use	—
17	VERIFICATION STATE	CONTENT VERIFIED PREPRINT	state: CONTENT_VERIFIED_PRIMARY · qualifiers: PREPRINT · verified_by: UNKNOWN
18	LIMITATIONS	Read in the preprint (arXiv version 2, under an earlier title), not the Nature Human Behaviour version; the journal details come from the Crossref record; AI use identified from field categories and text terms; associational	—
19	CONTRADICTORY / SUPPORTING SOURCES	Individual gains in natural science: LD09-SL-C01	—
20	GEOGRAPHY / LANGUAGE	Global / English	—
21	DISTRIBUTIONAL LEDGER	Potential benefit lower in disciplines with more women or Black scientists	—
22	R01 / FIELD-MAP LINKS	LD-09, D3	—
23	WHAT WOULD CHANGE THIS CLAIM?	Evidence on who adopts AI within disciplines and with what returns	—
24	PUBLICATION STATUS	ELIGIBLE WITH QUALIFIER	state: ELIGIBLE · qualifiers: QUALIFIED
25	RECORDED DATE	2026-10-15	—

Field 22 references resolved against Field Map v0.2: LD-09 (LIVING_DEBATE); D3 (DOMAIN)

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

Pinned Sources (field 6, version-specific)

“Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578)” → Gao & Wang (2024), “Quantifying the use and potential…” (SRC-SL-GAO-WANG-NHB-2024--v1.0) — Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578); class PEER_REVIEWED_JOURNAL_ARTICLE (explicit public provenance pin)

Cited by Living Debate versions

Individual gain, collective narrowing? Does AI make science, creative work and shared knowledge more alike? (v0.1) LD-09

Cite this page

SocioLens project. (2026, October 15). Across 74.6 million publications from 1960 to 2019, papers that use AI were more likely to be among the top 5% most cited in their field and year for a majority of disciplines, a premium the authors find stronger in disciplines with a lower propensity to use AI, while disciplines with more women or Black scientists showed less potential benefit [Claim LD09-SL-C08, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld09-sl-c08/v1.0/

@misc{sociolens_ld09_sl_c08_v1_0,
  author = {{SocioLens project}},
  title = {Across 74.6 million publications from 1960 to 2019, papers that use AI were more likely to be among the top 5\% most cited in their field and year for a majority of disciplines, a premium the authors find stronger in disciplines with a lower propensity to use AI, while disciplines with more women or Black scientists showed less potential benefit.},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld09-sl-c08/v1.0/}},
  note = {Claim LD09-SL-C08, 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 bb8959b46937f86ebd4ffa7c584c20afe43f194765a3bf3d64ecc333fab4478f.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD09-SL-C08 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-09 debate; a new Claim, first version, written from the project's LD-09 source notes, checked against full texts or official abstracts. Block “ENTRY 08 — LD09-SL-C08” SHA-256 88a5c153c01c2999e5cf0dba1b56dca1aaf0cdf7b7457f4fc5442145fa73298a; fixture fixtures/authored/claims/LD09-SL-C08--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD09-SL-C08 v1.0); the first version of a new Claim of the LD-09 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD09-SL-C08 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-09 debate; a new Claim, first version, written from the project's LD-09 source notes, checked against full texts or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-GAO-WANG-NHB-2024--v1.0 — basis: field 6 of ENTRY 08 — LD09-SL-C08 cites "Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578)"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-GAO-WANG-NHB-2024--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578)".