In S&P 500 annual reports, 11% of firms had AI deeply integrated into their business processes in 2025 and a further 10% used it in production, against 5% in 2022; among non-technology firms profit margins dipped at the pilot stages and rose with deep integration, while revenue per employee showed no difference at the advanced stages.

SocioLens project,

Claim LD03-SL-C01 (v1.0)

Research opportunities this claim motivates

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

Is there a generative-AI productivity J-curve inside firms?In S&P 500 annual reports, profit margins of non-technology firms dipped at pilot stages and rose with deep integration, while revenue per employee showed no difference at advanced stages.

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with qualifiers: preprint
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: A causal effect of adoption on profits or productivity; total factor productivity; firms outside the S&P 500
Limitations: Descriptive, not causal (the authors say so and raise reverse causality); adoption is coded by a language model from what firms choose to write in filings; productivity is proxied by revenue per employee; the conclusion's 1% (2022) to 5.7% (2025) series is deep integration among non-technology, non-financial firms (the appendix 'All Other' row), against 11.4% for all firms in 2025; not peer reviewed
Source (as cited): Yu, Fleming, Hampton, Combemale & Thompson, “AI Adoption in S&P 500 Firms,” arXiv:2607.08920, July 2026
Source type: Preprint (recorded as “Preprint”)
Institutional position: Independent academic
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

Yu, Fleming, Hampton, Combemale & Thompson, “AI Adoption in S&P 500 Firms,” arXiv:2607.08920, July 2026 (Preprint)
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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 (PREPRINT) (as printed: “ELIGIBLE WITH PREPRINT QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY (PREPRINT)
Source type (coded): PREPRINT
Maturity (coded): EMERGING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD03-SL-C01	—
2	PUBLIC-FACING CLAIM	In S&P 500 annual reports, 11% of firms had AI deeply integrated into their business processes in 2025 and a further 10% used it in production, against 5% in 2022; among non-technology firms profit margins dipped at the pilot stages and rose with deep integration, while revenue per employee showed no difference at the advanced stages.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	A — Adoption and depth of use	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Yu, Fleming, Hampton, Combemale & Thompson, “AI Adoption in S&P 500 Firms,” arXiv:2607.08920, July 2026	source_ids: SRC-SL-YU-FLEMING-HAMPTON-COMBEMALE-THOMPSON-ARXIV-2607-08920
7	SOURCE TYPE	Preprint	classes: PREPRINT
8	INSTITUTIONAL POSITION	Independent academic	—
9	SCOPE	S&P 500 firms, 2016–2025; adoption as described in 10-K filings	—
10	DESIGN	Descriptive panel; adoption scored from 10-K text on a five-level rubric by a language model and linked to accounting data	—
11	SAMPLE / DATASET	S&P 500 firms' annual reports, 2016–2025	—
12	MODEL / TOOL / VERSION	AI in general as firms describe it (rubric applied by GPT-5-mini)	—
13	COMPARATOR	Firms at lower adoption levels; firm and year fixed effects	—
14	MAIN RESULT	2025: 11% deep integration plus 10% production use (21%), from 5% in 2022; technology firms are about two-thirds of firms at the two highest levels; non-technology profit margins 2–3 points lower at exploration and pilot stages and 12.6 points higher with deep integration; revenue per employee significantly lower at exploration and pilot stages and not different at the advanced stages	—
15	INFERENTIAL TARGET	How deeply large listed firms integrate AI, and how that correlates with profitability and revenue per employee	—
16	DOES NOT LICENSE	A causal effect of adoption on profits or productivity; total factor productivity; firms outside the S&P 500	—
17	VERIFICATION STATE	CONTENT VERIFIED PREPRINT	state: CONTENT_VERIFIED_PRIMARY · qualifiers: PREPRINT · verified_by: UNKNOWN
18	LIMITATIONS	Descriptive, not causal (the authors say so and raise reverse causality); adoption is coded by a language model from what firms choose to write in filings; productivity is proxied by revenue per employee; the conclusion's 1% (2022) to 5.7% (2025) series is deep integration among non-technology, non-financial firms (the appendix 'All Other' row), against 11.4% for all firms in 2025; not peer reviewed	—
19	CONTRADICTORY / SUPPORTING SOURCES	Consistent with Census survey evidence of narrow firm adoption; the profit pattern matches the J-curve argument	—
20	GEOGRAPHY / LANGUAGE	US / English	—
21	DISTRIBUTIONAL LEDGER	Technology firms adopt far more deeply than others	—
22	R01 / FIELD-MAP LINKS	LD-03, D2	—
23	WHAT WOULD CHANGE THIS CLAIM?	A peer-reviewed version with different figures, or a measure of productivity beyond revenue per employee	—
24	PUBLICATION STATUS	ELIGIBLE WITH PREPRINT QUALIFIER	state: ELIGIBLE · qualifiers: PREPRINT
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)

“Yu, Fleming, Hampton, Combemale & Thompson, “AI Adoption in S&P 500 Firms,” arXiv:2607.08920, July 2026” → Yu et al. (2026), “AI Adoption in S&P 500 Firms” (SRC-SL-YU-FLEMING-HAMPTON-COMBEMALE-THOMPSON-ARXIV-2607-08920--v1.0) — Yu, Fleming, Hampton, Combemale & Thompson, “AI Adoption in S&P 500 Firms,” arXiv:2607.08920, July 2026; class PREPRINT (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 S&P 500 annual reports, 11% of firms had AI deeply integrated into their business processes in 2025 and a further 10% used it in production, against 5% in 2022; among non-technology firms profit margins dipped at the pilot stages and rose with deep integration, while revenue per employee showed no difference at the advanced stages [Claim LD03-SL-C01, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld03-sl-c01/v1.0/

@misc{sociolens_ld03_sl_c01_v1_0,
  author = {{SocioLens project}},
  title = {In S\&P 500 annual reports, 11\% of firms had AI deeply integrated into their business processes in 2025 and a further 10\% used it in production, against 5\% in 2022; among non-technology firms profit margins dipped at the pilot stages and rose with deep integration, while revenue per employee showed no difference at the advanced stages.},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld03-sl-c01/v1.0/}},
  note = {Claim LD03-SL-C01, 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 8d48b09dc67cd7aca949eec7c65979f0d5184ad80f3804a1ecaae5934e594caa.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD03-SL-C01 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 01 — LD03-SL-C01” SHA-256 c9aa86cced8c55e097a09f452402e71ed53b902b50d31f0090b54ea097b3bb8b; fixture fixtures/authored/claims/LD03-SL-C01--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-C01 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-C01 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-YU-FLEMING-HAMPTON-COMBEMALE-THOMPSON-ARXIV-2607-08920--v1.0 — basis: field 6 of ENTRY 01 — LD03-SL-C01 cites "Yu, Fleming, Hampton, Combemale & Thompson, “AI Adoption in S&P 500 Firms,” arXiv:2607.08920, July 2026"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-YU-FLEMING-HAMPTON-COMBEMALE-THOMPSON-ARXIV-2607-08920--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Yu, Fleming, Hampton, Combemale & Thompson, “AI Adoption in S&P 500 Firms,” arXiv:2607.08920, July 2026".