US graduates from the most AI-exposed tenth of college majors experienced a 5 percentage-point decline in initial employment and a 13% decline in initial earnings relative to less-exposed majors after ChatGPT.

SocioLens project,

Claim LD02-HQ-C06 (v1.0)

Research opportunities this claim motivates

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

Do AI-exposed graduates' early penalties fade or persist beyond the first two years?Graduates from the most AI-exposed tenth of US majors saw a 5-point fall in initial employment and a 13% fall in initial earnings; the claim explicitly does not license long-run scarring, which is exactly what this design would measure.

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with qualifiers: working paper, exposure
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: Long-run career scarring; universal graduate effect; independent replication of Tucker
Limitations: Very recent working paper; shares author/data infrastructure with Tucker; exposure not use; partner-institution sample
Source (as cited): Orr, Tucker & Warren, “Graduating into Disruption,” US Census CES-WP-26-56, September 2026
Source type: Working paper (recorded as “Government working paper”)
Institutional position: Government research
Maturity of the evidence: Emerging evidence
Recorded date: 25 September 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Orr, Tucker & Warren, “Graduating into Disruption,” US Census CES-WP-26-56, September 2026 (Working paper)
Read the original

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, EXPOSURE) (as printed: “ELIGIBLE WITH WORKING-PAPER / EXPOSURE QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY
Source type (coded): WORKING_PAPER
Maturity (coded): EMERGING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD02-HQ-C06	—
2	PUBLIC-FACING CLAIM	US graduates from the most AI-exposed tenth of college majors experienced a 5 percentage-point decline in initial employment and a 13% decline in initial earnings relative to less-exposed majors after ChatGPT.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	D — Hiring, entry and career ladders	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Orr, Tucker & Warren, “Graduating into Disruption,” US Census CES-WP-26-56, September 2026	source_ids: SRC-LD02-ORR-TUCKER-WARREN-2026
7	SOURCE TYPE	Government working paper	classes: WORKING_PAPER
8	INSTITUTIONAL POSITION	Government research	—
9	SCOPE	US graduates from PSEO partner institutions; entry outcomes after ChatGPT	—
10	DESIGN	Event-study using major-level AI exposure	—
11	SAMPLE / DATASET	PSEO linked to LEHD administrative records	—
12	MODEL / TOOL / VERSION	Major-level AI exposure index	—
13	COMPARATOR	Less-exposed majors / lower exposure deciles	—
14	MAIN RESULT	Initial employment −5pp; initial earnings −13%; part of earnings loss reflects movement into lower-wage sectors such as restaurants/retail	—
15	INFERENTIAL TARGET	Entry penalty for graduates from highly exposed majors	—
16	DOES NOT LICENSE	Long-run career scarring; universal graduate effect; independent replication of Tucker	—
17	VERIFICATION STATE	CONTENT VERIFIED	state: CONTENT_VERIFIED_PRIMARY · verified_by: UNKNOWN
18	LIMITATIONS	Very recent working paper; shares author/data infrastructure with Tucker; exposure not use; partner-institution sample	—
19	CONTRADICTORY / SUPPORTING SOURCES	Supports US entry-margin signal; Norway differs geographically	—
20	GEOGRAPHY / LANGUAGE	US / English	—
21	DISTRIBUTIONAL LEDGER	New graduates; sectoral downshifting	—
22	R01 / FIELD-MAP LINKS	D2, D4b, L-A, R-1	—
23	WHAT WOULD CHANGE THIS CLAIM?	Later cohorts recover rapidly or revised PSEO estimates materially shrink the entry penalty	—
24	PUBLICATION STATUS	ELIGIBLE WITH WORKING-PAPER / EXPOSURE QUALIFIER	state: ELIGIBLE · qualifiers: WORKING_PAPER, EXPOSURE
25	RECORDED DATE	2026-09-25	—

Field 22 references resolved against Field Map v0.2: D2 (DOMAIN); D4b (SUB_AREA); L-A (LENS); R-1 (LENS)

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

Pinned Sources (field 6, version-specific)

“Orr, Tucker & Warren, “Graduating into Disruption,” US Census CES-WP-26-56, September 2026” → Orr et al. (2026), “Graduating into Disruption” (SRC-LD02-ORR-TUCKER-WARREN-2026--v1.0) — Orr, Tucker & Warren, “Graduating into Disruption,” US Census CES-WP-26-56, September 2026; class WORKING_PAPER (explicit public provenance pin)

Cited by Living Debate versions

Aggregate null or distributional shift? Employment (v0.1) LD-02
Raw record: where this debate places the claim, with its coded facets
HQ placement: SC-LD02-L04, SC-LD02-T08, SC-LD02-T09, SC-LD02-T10; facets: horizon=INITIAL_POST_GRADUATION
Aggregate null or distributional shift? Employment (v0.2) LD-02
Raw record: where this debate places the claim, with its coded facets
HQ placement: SC-LD02-L04, SC-LD02-T08, SC-LD02-T09, SC-LD02-T10; facets: horizon=INITIAL_POST_GRADUATION

Cite this page

SocioLens project. (2026, September 25). US graduates from the most AI-exposed tenth of college majors experienced a 5 percentage-point decline in initial employment and a 13% decline in initial earnings relative to less-exposed majors after ChatGPT [Claim LD02-HQ-C06, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld02-hq-c06/v1.0/

@misc{sociolens_ld02_hq_c06_v1_0,
  author = {{SocioLens project}},
  title = {US graduates from the most AI-exposed tenth of college majors experienced a 5 percentage-point decline in initial employment and a 13\% decline in initial earnings relative to less-exposed majors after ChatGPT.},
  year = {2026},
  date = {2026-09-25},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld02-hq-c06/v1.0/}},
  note = {Claim LD02-HQ-C06, 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 83fc0dc1f2e117cff05de8b8cdf18a940036f402132bdb703b28af2102275f71.

Frozen Drive-derived inputs

R05 — LD-02 Canonical Claim Ledger v1.0 — LOCKED (v1.0) — Drive ID 1XS3OTYh1ZGBSlD3Dev0-5KtGds0abk1qX_24-8lVDqM; revision: NOT_EXPOSED_BY_DRIVE_TEXT_EXPORT; Drive modified 2026-09-25T18:38:36.597Z; block “ENTRY 06 — LD02-HQ-C06” SHA-256 1924402a17b9e926b6838b047fd4aeb0dc386068759672765314776b8d57e195; SocioLens — Programme Status & Authority Register v0.6 status “CURRENT.”; frozen fixture fixtures/frozen/r05-ld02-claim-ledger-v1.0.
SocioLens — Proof Source Registry v0.4 (v0.4) — Drive ID 1_-GLmj6PfetWDQH2KldMv-61vmSmzdAyyxgZZZCFnbE; revision: NOT_EXPOSED_BY_DRIVE_TEXT_EXPORT; Drive modified 2026-09-27T19:04:00.374Z; block “REGISTRY ENTRY SRC-LD02-ORR-TUCKER-WARREN-2026” SHA-256 6a800685626da9a06e0ea97f78fe30beeeb6c2241ad877c19321615d32dd4caf; SocioLens — Programme Status & Authority Register v0.6 status “CURRENT PROOF SOURCE REGISTRY.”; frozen fixture fixtures/frozen/proof-source-registry-v0.4.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the canonical ledger artifact version (R05 — LD-02 Canonical Claim Ledger v1.0); Implementation Architecture Selection §5 logical_id/version convention.
CURRENT_POINTER = true — basis: SocioLens — Programme Status & Authority Register v0.6 status for "R05 — LD-02 Canonical Claim Ledger v1.0 — LOCKED": "CURRENT."
FIELD_6_SOURCE_REFERENCE = SRC-LD02-ORR-TUCKER-WARREN-2026--v1.0 — basis: field 6 of ENTRY 06 — LD02-HQ-C06 cites "Orr, Tucker & Warren, “Graduating into Disruption,” US Census CES-WP-26-56, September 2026"; Builder identification: the cited work is Proof Source Registry v0.4 identity SRC-LD02-ORR-TUCKER-WARREN-2026 (canonical label "Orr, Tucker & Warren, “Graduating into Disruption,” US Census CES-WP-26-56, September 2026"), Registry-linked to this logical Claim; SocioLens record v1.0 is its sole record.
SOURCE_PROVENANCE_PIN = SRC-LD02-ORR-TUCKER-WARREN-2026--v1.0 — basis: Issue #5 proof rendering (Architecture Proof Protocol v0.1 AP-08 / Amendment v1.2 §6): explicit version-pinned public provenance of this ClaimVersion — the pinned field-6 Source record.