In a field experiment with 640 Kenyan small-business owners, a GPT-4 business adviser on WhatsApp had no average effect on revenue or profit, but its effect for owners who started below the median was more than 0.20 standard deviations lower than for those above it.

SocioLens project,

Claim LD04-SL-C02 (v1.0)

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with a qualifier
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: A general rule that AI widens gaps; the high-performer gain as established (the authors hedge it)
Limitations: One country, one tool and one period; the paper frames the low-performer loss as nearly 10% in the abstract and about 0.08 in the body; the authors find no difference in questions asked or advice received, only in what owners did with it
Source (as cited): Otis, Clarke, Delecourt, Holtz & Koning, “The Uneven Impact of Generative AI on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya,” Management Science, 2026
Source type: Peer-reviewed journal article (recorded as “Peer-reviewed journal article”)
Institutional position: Independent academic
Maturity of the evidence: Emerging evidence
Recorded date: 14 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Otis, Clarke, Delecourt, Holtz & Koning, “The Uneven Impact of Generative AI on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya,” Management Science, 2026 (Peer-reviewed journal article)
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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
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	LD04-SL-C02	—
2	PUBLIC-FACING CLAIM	In a field experiment with 640 Kenyan small-business owners, a GPT-4 business adviser on WhatsApp had no average effect on revenue or profit, but its effect for owners who started below the median was more than 0.20 standard deviations lower than for those above it.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	A — Performance while the tool is in use	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Otis, Clarke, Delecourt, Holtz & Koning, “The Uneven Impact of Generative AI on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya,” Management Science, 2026	source_ids: SRC-SL-OTIS-CLARKE-DELECOURT-HOLTZ-KONING-MNSC-2026
7	SOURCE TYPE	Peer-reviewed journal article	classes: PEER_REVIEWED_JOURNAL_ARTICLE
8	INSTITUTIONAL POSITION	Independent academic	—
9	SCOPE	Small-business owners in 44 of Kenya’s 47 counties; May–November 2023	—
10	DESIGN	Randomized field experiment, about five months	—
11	SAMPLE / DATASET	640 entrepreneurs	—
12	MODEL / TOOL / VERSION	GPT-4 assistant delivered through WhatsApp	—
13	COMPARATOR	Entrepreneurs without the AI assistant (control-arm details not reported here)	—
14	MAIN RESULT	No average effect on revenue or profit; below-median baseline performers did more than 0.20 SD worse relative to above-median ones; the abstract describes low performers as nearly 10% worse and high performers as possibly over 15% better	—
15	INFERENTIAL TARGET	Heterogeneity of business performance by baseline performance	—
16	DOES NOT LICENSE	A general rule that AI widens gaps; the high-performer gain as established (the authors hedge it)	—
17	VERIFICATION STATE	CONTENT VERIFIED	state: CONTENT_VERIFIED_PRIMARY · verified_by: UNKNOWN
18	LIMITATIONS	One country, one tool and one period; the paper frames the low-performer loss as nearly 10% in the abstract and about 0.08 in the body; the authors find no difference in questions asked or advice received, only in what owners did with it	—
19	CONTRADICTORY / SUPPORTING SOURCES	Runs against compression findings on well-specified tasks; consistent with other widening results on open-ended tasks	—
20	GEOGRAPHY / LANGUAGE	Kenya / Not checked	—
21	DISTRIBUTIONAL LEDGER	High performers made tailored changes; low performers adopted generic advice such as price cuts and more advertising	—
22	R01 / FIELD-MAP LINKS	LD-04, D2	—
23	WHAT WOULD CHANGE THIS CLAIM?	Replications in other countries or with other tools that find compression on comparable open-ended business decisions	—
24	PUBLICATION STATUS	ELIGIBLE WITH QUALIFIER	state: ELIGIBLE · qualifiers: QUALIFIED
25	RECORDED DATE	2026-10-14	—

Field 22 references resolved against Field Map v0.2: LD-04 (LIVING_DEBATE); D2 (DOMAIN)

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

Pinned Sources (field 6, version-specific)

“Otis, Clarke, Delecourt, Holtz & Koning, “The Uneven Impact of Generative AI on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya,” Management Science, 2026” → Otis et al. (2026), “The Uneven Impact of Generative AI…” (SRC-SL-OTIS-CLARKE-DELECOURT-HOLTZ-KONING-MNSC-2026--v1.0) — Otis, Clarke, Delecourt, Holtz & Koning, “The Uneven Impact of Generative AI on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya,” Management Science, 2026; class PEER_REVIEWED_JOURNAL_ARTICLE (explicit public provenance pin)

Cited by Living Debate versions

Cite this page

SocioLens project. (2026, October 14). In a field experiment with 640 Kenyan small-business owners, a GPT-4 business adviser on WhatsApp had no average effect on revenue or profit, but its effect for owners who started below the median was more than 0.20 standard deviations lower than for those above it [Claim LD04-SL-C02, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld04-sl-c02/v1.0/

@misc{sociolens_ld04_sl_c02_v1_0,
  author = {{SocioLens project}},
  title = {In a field experiment with 640 Kenyan small-business owners, a GPT-4 business adviser on WhatsApp had no average effect on revenue or profit, but its effect for owners who started below the median was more than 0.20 standard deviations lower than for those above it.},
  year = {2026},
  date = {2026-10-14},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld04-sl-c02/v1.0/}},
  note = {Claim LD04-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 72621f480a54521e605b1a5ee15527d77b34bdb96b36fdd7be9a1bcc4dde93e8.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD04-SL-C02 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-14; 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 14 October 2026 to build the LD-04 debate; a new Claim, first version, written from the project's LD-04 source notes, checked against full texts or official abstracts. Block “ENTRY 02 — LD04-SL-C02” SHA-256 648c11722b33738a84434cb405ddbf3d601ffde874cc6dd24fe15e58c6d42713; fixture fixtures/authored/claims/LD04-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 — LD04-SL-C02 v1.0); the first version of a new Claim of the LD-04 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD04-SL-C02 v1.0" (Project Lead (Claude, AI), 2026-10-14); 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 14 October 2026 to build the LD-04 debate; a new Claim, first version, written from the project's LD-04 source notes, checked against full texts or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-OTIS-CLARKE-DELECOURT-HOLTZ-KONING-MNSC-2026--v1.0 — basis: field 6 of ENTRY 02 — LD04-SL-C02 cites "Otis, Clarke, Delecourt, Holtz & Koning, “The Uneven Impact of Generative AI on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya,” Management Science, 2026"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-OTIS-CLARKE-DELECOURT-HOLTZ-KONING-MNSC-2026--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Otis, Clarke, Delecourt, Holtz & Koning, “The Uneven Impact of Generative AI on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya,” Management Science, 2026".