In 11 AI-exposed Danish occupations, Humlum and Vestergaard estimate precise null effects on earnings and recorded hours two years after ChatGPT, ruling out effects larger than 2%.

SocioLens project,

Claim LD02-HQ-C02 (v1.1)

In 11 AI-exposed Danish occupations, Humlum and Vestergaard estimate precise null effects on earnings and recorded hours two years after ChatGPT, ruling out effects larger than 2%.

Research opportunities this claim motivates

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

How much of a firm's AI productivity gain reaches the wages of workers who stay?Incumbent workers in 11 exposed Danish occupations show precise null earnings and hours effects two years after ChatGPT; the claim does not license a no-hiring-effect reading, which is why the design separates stayers from new hires.

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with qualifiers: scope
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: Global aggregate null; no hiring effect; no task or occupational change
Limitations: Denmark; selected exposed occupations; self-reported adoption/use; pre-agentic horizon
Source (as cited): Humlum & Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026
Source type: Working paper (recorded as “Academic working paper”)
Institutional position: Independent academic
Maturity of the evidence: Rapidly maturing 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

Humlum & Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026 (Working paper)
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Version

This is the current version, v1.1. Earlier versions stay available, and their records never change.

Versions: v1.0, v1.1 · current

What changed in this version

This version supersedes version v1.0 (LD02-HQ-C02--v1.0), which stays available with its record unchanged.

Changed fields: 19 CONTRADICTORY / SUPPORTING SOURCES; 21 DISTRIBUTIONAL LEDGER; 25 RECORDED DATE
What changed: The distributional ledger no longer says that an early-career earnings and hours null is reported. It now gives what the March 2026 revision of the paper reports for early-career workers (aged 22–25): their employment fell in several of the occupations, but their share of employment did not change differentially at workplaces that encourage chatbot use, with effects above about a third of a percentage point ruled out. The contradictory/supporting field now also notes the paper's workplace-level nulls for employment (total hours), job churn and the early-career share, and that Danmarks Nationalbank Working Paper 223 finds lower employment growth at AI-adopting firms. The public-facing claim, the 2% bound, the 11 occupations and the sample of about 25,000 workers in 7,000 workplaces are unchanged.
Why: A recheck on 14 October 2026 read the full text of NBER Working Paper 33777 as revised in March 2026 (retitled "Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI"; previously "Large Language Models, Small Labor Market Effects"). Version 1.0 was built from the paper's abstract, and the HQ batch notes left "full Humlum/Vestergaard early-career ... details" open. The full text reports early-career results only as the employment share at adopting workplaces (Section 3.4, footnote 21), not as earnings or hours, and the same section reports no differential change in workplace employment (total hours) or job churn. The main result is confirmed in the full text: confidence intervals rule out average earnings and hours effects larger than 2% two years after ChatGPT, in 11 occupations, with 25,000 workers across 7,000 workplaces in the late-2024 survey round. The recheck and this revision were done by an AI agent reading the paper, not by a person.

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 (SCOPE) (as printed: “ELIGIBLE WITH SCOPE 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	LD02-HQ-C02	—
2	PUBLIC-FACING CLAIM	In 11 AI-exposed Danish occupations, Humlum and Vestergaard estimate precise null effects on earnings and recorded hours two years after ChatGPT, ruling out effects larger than 2%.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	E — Wages, hours, employment and transitions	—
5	MATURITY	RAPIDLY MATURING	states: RAPIDLY_MATURING
6	SOURCE	Humlum & Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026	source_ids: SRC-LD02-HUMLUM-VESTERGAARD-NBER-33777
7	SOURCE TYPE	Academic working paper	classes: WORKING_PAPER
8	INSTITUTIONAL POSITION	Independent academic	—
9	SCOPE	Denmark; 11 AI-exposed occupations; roughly two-year post-ChatGPT horizon	—
10	DESIGN	Adoption surveys linked to administrative labour records; difference-in-differences	—
11	SAMPLE / DATASET	Approximately 25,000 workers / 7,000 workplaces linked to Danish administrative records	—
12	MODEL / TOOL / VERSION	ChatGPT and workplace chatbots, including in-house systems	—
13	COMPARATOR	Adopters/users vs relevant non-adopters/non-users under study design	—
14	MAIN RESULT	Earnings and recorded-hours effects are close to zero; effects larger than 2% are ruled out over the study horizon; tasks nevertheless reorganise	—
15	INFERENTIAL TARGET	Short-run incumbent earnings/hours effect	—
16	DOES NOT LICENSE	Global aggregate null; no hiring effect; no task or occupational change	—
17	VERIFICATION STATE	CONTENT VERIFIED	state: CONTENT_VERIFIED_PRIMARY · verified_by: UNKNOWN
18	LIMITATIONS	Denmark; selected exposed occupations; self-reported adoption/use; pre-agentic horizon	—
19	CONTRADICTORY / SUPPORTING SOURCES	Consistent with broad short-run nulls; the same paper also finds no differential change in employment (total hours), job churn or the early-career share at workplaces that encourage chatbot use through December 2024, while Nationalbank 2026 finds lower employment growth at AI-adopting firms by late 2025 with a different adoption measure, unit and horizon	—
20	GEOGRAPHY / LANGUAGE	Denmark / English	—
21	DISTRIBUTIONAL LEDGER	Early-career (22–25) employment fell in several of the occupations, but the early-career share did not change differentially at adopting workplaces (effects above about a third of a percentage point ruled out); adopters move into higher-paying occupations; no separate early-career earnings/hours estimate is reported	—
22	R01 / FIELD-MAP LINKS	D2, L-A, L-B, L-C, R-1	—
23	WHAT WOULD CHANGE THIS CLAIM?	Later waves showing material divergence in earnings/hours	—
24	PUBLICATION STATUS	ELIGIBLE WITH SCOPE QUALIFIER	state: ELIGIBLE · qualifiers: SCOPE
25	RECORDED DATE	2026-10-14	—

Field 22 references resolved against Field Map v0.2: D2 (DOMAIN); L-A (LENS); L-B (LENS); L-C (LENS); R-1 (LENS)

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

Pinned Sources (field 6, version-specific)

“Humlum & Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026” → Humlum & Vestergaard (2026), “Still Waters, Rapid Currents” (SRC-LD02-HUMLUM-VESTERGAARD-NBER-33777--v1.0) — Humlum & Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026; class WORKING_PAPER (explicit public provenance pin)

Cited by Living Debate versions

Cite this page

SocioLens project. (2026, October 14). In 11 AI-exposed Danish occupations, Humlum and Vestergaard estimate precise null effects on earnings and recorded hours two years after ChatGPT, ruling out effects larger than 2% [Claim LD02-HQ-C02, version v1.1]. SocioLens. https://sociolens-web.pages.dev/claims/ld02-hq-c02/v1.1/

@misc{sociolens_ld02_hq_c02_v1_1,
  author = {{SocioLens project}},
  title = {In 11 AI-exposed Danish occupations, Humlum and Vestergaard estimate precise null effects on earnings and recorded hours two years after ChatGPT, ruling out effects larger than 2\%.},
  year = {2026},
  date = {2026-10-14},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld02-hq-c02/v1.1/}},
  note = {Claim LD02-HQ-C02, version v1.1}
}

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 08a743dbf02d72aac7b62c6c4f3b7583423cc1738274c6955819e6fc79c708bb.

Repository-authored inputs (not Drive exports)

SocioLens claim revision — LD02-HQ-C02 v1.1 (v1.1) — 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 docs/PROJECT.md "Research revisions" (a published claim changes only through a new version that states what changed and why). Block “ENTRY 02 — LD02-HQ-C02” SHA-256 ae3a0b088c9f80e16cd3d5ea4b350c6f7728833bc9d03d8dc43bcc13ad3ee743; fixture fixtures/authored/claim-revisions/LD02-HQ-C02--v1.1.

Builder decisions (not Drive data)

VERSION = v1.1 — basis: Claim version taken from the repository-authored revision fixture version (SocioLens claim revision — LD02-HQ-C02 v1.1); a new version of LD02-HQ-C02 v1.0, which stays routable (AGENTS.md research integrity rule 3)
CURRENT_POINTER = true — basis: repository-authored revision "SocioLens claim revision — LD02-HQ-C02 v1.1" (Project Lead (Claude, AI), 2026-10-14); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and docs/PROJECT.md "Research revisions" (a published claim changes only through a new version that states what changed and why)
FIELD_6_SOURCE_REFERENCE = SRC-LD02-HUMLUM-VESTERGAARD-NBER-33777--v1.0 — basis: field 6 of ENTRY 02 — LD02-HQ-C02 cites "Humlum & Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026"; the first field-6 segment of this ClaimVersion's repository-authored ledger block; the same Source record LD02-HQ-C02 v1.0 cites.
SOURCE_PROVENANCE_PIN = SRC-LD02-HUMLUM-VESTERGAARD-NBER-33777--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Humlum & Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026".