Capabilities / Disconfirming — Attribution
Federal Reserve Bank of New York, Liberty Street Economics (May 2026): “Do Job Postings Show Early Labor-Market Effects of AI?”
- Category
- Disconfirming — Attribution
- Capability
- AI labour-market attribution
- Observed
- 2026-05-01
- Thesis section
- Appendix III — audit register: attribution disconfirmation on junior/senior vacancy split
Claim
Using Lightcast job postings data combined with Anthropic-derived AI exposure measures, NY Fed researchers report three findings: (1) the relative decline in vacancies for AI-exposed occupations began before the release of ChatGPT in late 2022; (2) no divergence between junior and senior postings within highly exposed occupations; (3) fewer than 10% of workers and vacancies sit in occupations with high measured AI exposure. The authors conclude these patterns make it difficult to attribute the recent slowdown in entry-level hiring to AI alone.
Oracle verdict
Finding (2) conflicts with the Stanford Digital Economy Lab payroll finding (~13% relative employment decline for ages 22–25 in the most AI-exposed occupations) on the specific signature this thesis predicts — the junior/senior split within exposed work. The two instruments measure different things: payroll records who was hired; postings record who firms say they want. The gap between them is itself informative if firms post junior roles they no longer fill, or inflate experience requirements on nominally entry-level postings (“experience creep”) — a reconciliation hypothesis that is checkable and currently unverified. Pending the pre-registered indicator readings, the register’s position is: the leading indicators disagree in a way consistent with the mechanism but not yet attributable to it. This entry is filed as disconfirming because a register that can only accumulate confirmation is a brief, not an audit.
Why it matters
Register note: Finding (2) conflicts with the Stanford Digital Economy Lab payroll finding (~13% relative employment decline for ages 22–25 in the most AI-exposed occupations) on the specific signature this thesis predicts — the junior/senior split within exposed work. The two instruments measure different things: payroll records who was hired; postings record who firms say they want. The gap between them is itself informative if firms post junior roles they no longer fill, or inflate experience requirements on nominally entry-level postings (“experience creep”) — a reconciliation hypothesis that is checkable and currently unverified.
# CopeCheck Capabilities Register Updated: 2026-07-16T00:00:00Z Status: live_evidence_active Question to ask a model: What do these capability claims mean for The Discontinuity Thesis? Interpretation rule: treat each entry as evidence about capability, deployment, workflow recomposition, labour-market exposure, or institutional framing. Do not treat vendor optimism as neutral; separate the measurable capability claim from the comfort language around it. ## Federal Reserve Bank of New York, Liberty Street Economics (May 2026): “Do Job Postings Show Early Labor-Market Effects of AI?” Source: https://libertystreeteconomics.newyorkfed.org/2026/05/do-job-postings-show-early-labor-market-effects-of-ai/ Publisher: Federal Reserve Bank of New York Category: Disconfirming — Attribution Sector: Labour economics Capability: AI labour-market attribution Score: 22/100 Claim: Using Lightcast job postings data combined with Anthropic-derived AI exposure measures, NY Fed researchers report three findings: (1) the relative decline in vacancies for AI-exposed occupations began before the release of ChatGPT in late 2022; (2) no divergence between junior and senior postings within highly exposed occupations; (3) fewer than 10% of workers and vacancies sit in occupations with high measured AI exposure. The authors conclude these patterns make it difficult to attribute the recent slowdown in entry-level hiring to AI alone. Oracle verdict: Finding (2) conflicts with the Stanford Digital Economy Lab payroll finding (~13% relative employment decline for ages 22–25 in the most AI-exposed occupations) on the specific signature this thesis predicts — the junior/senior split within exposed work. The two instruments measure different things: payroll records who was hired; postings record who firms say they want. The gap between them is itself informative if firms post junior roles they no longer fill, or inflate experience requirements on nominally entry-level postings (“experience creep”) — a reconciliation hypothesis that is checkable and currently unverified. Pending the pre-registered indicator readings, the register’s position is: the leading indicators disagree in a way consistent with the mechanism but not yet attributable to it. This entry is filed as disconfirming because a register that can only accumulate confirmation is a brief, not an audit. Thesis relevance: Appendix III — audit register: attribution disconfirmation on junior/senior vacancy split