CC Capabilities

Capabilities / Disconfirming — Attribution

Federal Reserve Bank of New York, Liberty Street Economics (May 2026): “Do Job Postings Show Early Labor-Market Effects of AI?”

Federal Reserve Bank of New York Labour economics score 22/100 confidence 0.87
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.

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