METAXIS Intelligence Workbench (Operator)

AI_LABOR -- Report report_intel_dbab7b01963396b5_v2

version=2 readiness=CONDITIONALLY_READY generated_at=2026-10-02T02:56:50.510890+00:00

Intelligence Object: intel_dbab7b01963396b5 (IO version 3, readiness CONDITIONALLY_READY)

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Current State

Hypotheses (canonical status)

Claims (Key Claims) AVAILABLE

• {"type": "FACT", "text": "현재 확인되지 않았다.", "claim_id": "claim_4765a68987d86c67", "claim_status": "OPEN", "claim_type": "DESCRIPTIVE"}
• {"type": "FACT", "text": "현재 확인되지 않았다.", "claim_id": "claim_931b08c7ebfc9604", "claim_status": "OPEN", "claim_type": "DESCRIPTIVE"}
• {"type": "FACT", "text": "현재 확인되지 않았다.", "claim_id": "claim_4731da53eae6b843", "claim_status": "OPEN", "claim_type": "DESCRIPTIVE"}
• {"type": "INTERPRETATION", "text": "현재 증거만으로 판단하기 어렵다.", "claim_id": "claim_fe878b52922b46a4", "claim_status": "OPEN", "claim_type": "CAUSAL"}
• {"type": "INTERPRETATION", "text": "현재 확인되지 않았다.", "claim_id": "claim_8fd5da8f27c9fc2b", "claim_status": "OPEN", "claim_type": "ATTRIBUTION"}
• {"type": "INTERPRETATION", "text": "현재 확인되지 않았다.", "claim_id": "claim_30fdf98bc1a9c818", "claim_status": "OPEN", "claim_type": "PREDICTIVE"}
• {"type": "FACT", "text": "현재 확인되지 않았다.", "claim_id": "claim_8e305d0c30d23514", "claim_status": "OPEN", "claim_type": "DESCRIPTIVE"}
• {"type": "FACT", "text": "현재 확인되지 않았다.", "claim_id": "claim_0da20db21fe1463a", "claim_status": "OPEN", "claim_type": "DESCRIPTIVE"}

근거 / Research Evidence

empty / NOT_AVAILABLE

Statistics

Observation

• {"type": "EVIDENCE_SUMMARY", "indicator": "SL.TLF.ACTI.ZS", "source": "src_worldbank_api", "geography": "USA", "period": "2010-2022", "observation_count": 13, "trend_status": "STABLE", "limitations": []}

Forecast

none

반증 자료 / Counterevidence AVAILABLE

• {"direction": "null-result / no-significant-divergence", "status": "EVIDENCE_FOUND", "source": "Kauhanen & Rouvinen (ETLA, Finland, Applied Economics Letters 2025) population-level wage/employment data, 2 years post-ChatGPT"}
• {"direction": "aggregate-labor-market-no-change", "status": "EVIDENCE_FOUND", "source": "US BLS JOLTS Aug 2026 release -- openings/hires/separations each little changed, no AI attribution in release"}

Alternative Explanations AVAILABLE

• GENERAL_POST_2022_TECH_SECTOR_HIRING_SLOWDOWN
• DIFFERING_LABOR_MARKET_INSTITUTIONS_US_VS_FINLAND
• EXPOSURE_NOT_YET_REALIZED_AS_DISPLACEMENT (OECD framing)
• FORECAST_HORIZON_NOT_YET_ELAPSED (KDI 10-year projection)

Geographic Applicability AVAILABLE

• {"geography": "USA", "basis": "statistic geography field (World Bank country code)", "scope": "event_country=UNKNOWN"}
• {"geography": "OECD_CROSS_COUNTRY", "basis": "OECD AI Exposure Measure methodology paper", "scope": "cross-country exposure/capability-gap methodology, not a per-country displacement measurement"}
• {"geography": "USA", "basis": "BLS JOLTS, NBER w31161, Stanford SIEPR, Fed FEDS Note", "scope": "US-specific adoption, productivity (customer-support only), and early-career-cohort employment evidence -- not generalized to the full US labor market"}
• {"geography": "KR", "basis": "KDI macroeconomic forecast", "scope": "South Korea 10-year FORECAST only, not an observed outcome; never merged with US/Finland observed data"}
• {"geography": "FI", "basis": "Kauhanen & Rouvinen / ETLA population-level study", "scope": "Finland national wage-earner population data, 2 years post-ChatGPT; a genuine counterevidence data point to a broad early-career-displacement generalization, not a refutation of the narrower US Stanford SIEPR finding"}

Temporal Applicability AVAILABLE

• {"period_start": "2010", "period_end": "2022", "source": "series_c302bf9e2576ad47"}
• {"period_start": "2022-11", "period_end": "2026-09", "source": "claims: OECD/BLS/NBER/Stanford-SIEPR/ETLA/Fed-FEDS (see key_claims)", "note": "post-ChatGPT (Nov 2022) observed-evidence window; kept explicitly separate from the KDI 2026-2036 FORECAST horizon, which must never be read as already observed"}

Known Gaps AVAILABLE

• {"gap_type": "ATTRIBUTION_GAP", "reason": "no sector/occupation-level decomposition linking the original aggregate labor-force-participation indicator to AI adoption specifically -- still true after this round's additions, which rest on separate, newer claims rather than decomposing this indicator"}
• {"gap_type": "COUNTEREVIDENCE_GAP", "reason": "PARTIALLY_RESOLVED this round -- ETLA/Finland null-result and BLS JOLTS aggregate-no-change evidence now connected as real counterevidence to a broad AI-displacement reading; still no counterevidence specifically targeting the narrow Stanford SIEPR early-career finding itself (e.g. no replication attempt or alternative explanation study of that specific cohort/occupation set)"}
• {"gap_type": "POLICY_RESEARCH_GAP", "reason": "carried over -- still NOT_READY"}
• {"gap_type": "AI_DISPLACEMENT_VS_PRODUCTIVITY_DISAGGREGATION_GAP", "reason": "across all 7 new claims, only NBER w31161 (productivity) and Stanford SIEPR (narrow-cohort employment) give any causal/attribution-level evidence; OECD, BLS, KDI, ETLA, and Fed sources are each explicitly scoped to methodology, aggregate-no-attribution, forecast, null-result, or adoption-only respectively -- no source in this corpus yet measures an economy-wide, AI-attributed net employment effect for the general working-age population in any single geography."}

Sources (출처) -- 8 actually used

Reports (version history)

Version Diffs (via report_engine.diff_reports())

Provenance

Full Report → IO → Hypothesis → Claim → Evidence → Source chain →