urn:uuid:281f2aa4-6161-51d5-8f09-5bc911281897SocioLens — LD-03: Organizational absorption: Why micro gains do not become macro outcomesNew versions of this debate, revisions of its claims, Radar updates and research opportunities.2026-10-18T00:00:00ZSocioLensurn:uuid:cbb60bc8-d42c-5f7c-8984-87b07c8f4b22New research opportunity: RO-LD03-08 — In small Greek firms, does generative AI stay a personal tool or change how teams work?2026-10-18T00:00:00ZIn small firms in Greece outside tourism (for example accounting offices, shops, small manufacturers and software companies), who uses generative AI and for which tasks, and has any firm changed how work is divided, checked or handed over because of it?urn:uuid:daa9dde0-956d-5aff-9995-e4049d387f79New research opportunity: RO-LD03-07 — Is the time AI saves paid for with weaker checking?2026-10-16T00:00:00ZWhere public services and clinics turn the time saved by generative-AI drafting into higher caseloads (social-work case records, clinical notes), does the time spent checking each output fall and the share of errors that survive into the signed record rise, and how much of any rise comes from time pressure rather than from a decline in the checkers' own skill?urn:uuid:04536b98-8d79-5e0d-94eb-37bf543b75d1New debate: LD-03 v0.1 — Organizational absorption: Why micro gains do not become macro outcomes2026-10-15T00:00:00ZExperiments find that generative AI saves workers time on many tasks. Why does so little of that show up in firm performance and national productivity, at least so far?urn:uuid:8c471811-d0c7-5dfe-a88e-03f5364c333dNew research opportunity: RO-LD03-01 — Does deploying AI to whole teams change the work that individual licences did not?2026-10-15T00:00:00ZIn real organizations, when generative AI is deployed to whole teams or units, with and without a redesign of the team's workflow, do coordination-dependent work (meetings, handoffs, jointly written documents) and the unit's measured output change, compared with units where only some individuals have the tool?urn:uuid:de61975e-ebcb-54a9-9f71-dc5d1e8908b5New research opportunity: RO-LD03-02 — Where does the time saved with generative AI go?2026-10-15T00:00:00ZWhen workers save time with generative AI, how much of it goes to more of the same output, to new tasks, to checking AI output, to on-the-job leisure or to shorter hours, measured with time-use diaries or telemetry linked to the same workers' measured output?urn:uuid:b7865a7c-16f8-5a1f-bd48-91436aea5a80New research opportunity: RO-LD03-03 — How much of the time AI saves its users do colleagues spend checking and fixing?2026-10-15T00:00:00ZIn organizations where some staff produce work with generative AI, how much time do reviewers, managers and recipients spend checking, correcting and redoing that work, and once both are counted, does the downstream cost offset the time the author saved?urn:uuid:10a03f6d-2d64-5a03-9956-30acc2d23e98New research opportunity: RO-LD03-04 — Is there a generative-AI productivity J-curve inside firms?2026-10-15T00:00:00ZIn a multi-year panel of firms, does deeper integration of generative AI, together with measured complementary investment (process redesign, training, data and capitalized software), show an early dip and a later rise in measured productivity, and do firms that adopt without that investment show neither?urn:uuid:8fe59e22-1f91-554e-96a8-5ecd7488f1d3New research opportunity: RO-LD03-05 — Do generative-AI deployments in government improve services, not just staff time?2026-10-15T00:00:00ZWhen a government agency deploys generative AI to caseworkers, adjudicators or reviewers, do service outcomes recorded in administrative data (processing time, error and appeal rates, outcomes for citizens) improve against a credible comparison group, beyond staff time savings?urn:uuid:72421d5e-a6ec-5d5a-8bd8-867ac29ae790New research opportunity: RO-LD03-06 — Do organizations' AI policies match what their staff actually do with AI?2026-10-15T00:00:00ZDo organizations' formal generative-AI policies, mandates and announcements match measured use and work outcomes, and where they diverge, is the divergence ceremonial adoption (policy without practice) or shadow use (practice without policy)?urn:uuid:d3f7d835-fdba-5de5-91d0-f9667ff8f8e7Claim LD02-HQ-C02 revised to v1.12026-10-14T00:00:00ZWhat 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.