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Adaptation · Labour & consumers

Cross-tracker concordance (labour)

How many of the four labour trackers (Canaries payroll, Canaries entry-level, Revelio, CAIT) currently read past their own AI-attributable-break threshold. Derived metric cross_tracker_concordance, 0 to 4.

Reason through this

The reading

Zero of four. The entry-level signal is real but below its threshold; nothing else is close.

Every readingeach dot links to its record
count
0 · 2026-09-30 · estimate
a readingnormal rangefast range
estimate

1 reading drawn from the derived metric cross_tracker_concordance; 7 observations in the evidence.

Derived (cross_tracker_concordance) from: Canaries Dashboard data (by exposure); Revelio Labs AI Labor Market Tracker (US); California AI-Unemployment Tracker (CAIT); Budget Lab at Yale AI labour-market tracker (dissimilarity index chart data). Stanford Digital Economy Lab and ADP Research, AI Economic Indicators: Canaries Dashboard. Revelio Labs, AI Labor Market Tracker. Hyman, von Wachter et al. (2026), Tracking AI-Related Job Loss Using Unemployment Insurance Claims Data in California, California Policy Lab. The Budget Lab at Yale.

The numbers
as ofreadingvaluerecord
2026-09-30—0estimate

Why it matters

The thesis's falsification rule requires three trackers breaking concurrently, not one. This is that count, made explicit.

What cuts against it

Thresholds are ours; the four trackers measure different populations and geographies, so concurrence is a loose test.

How it is measured

Normal = at most one tracker past its threshold; fast = two; falsifying = three or more concurrently, per the thesis rules in the brief.

Applied to metric:cross_tracker_concordance.

Derived rows (1)
as ofdimsvalueinputs
2026-09-300sources123+4 more
Latest readings
  • 2026-09-30 · 0as of 2026-09-30(7 obs)
Why this status, and its history

Zero of four labour trackers past their AI-attributable-break threshold as of July 2026: Canaries y/y -0.2% (threshold -3%), Revelio gap -6% (threshold -10%), CAIT +1.0% (threshold +5%), entry-level shortfall 19% (threshold 25%). The falsification rule needs three concurrent. Initial seed.

  1. 2026-09-10unmeasured to consistent with normalconf — → 70 · initial seed (model-drafted)

    Zero of four labour trackers past their AI-attributable-break threshold as of July 2026: Canaries y/y -0.2% (threshold -3%), Revelio gap -6% (threshold -10%), CAIT +1.0% (threshold +5%), entry-level shortfall 19% (threshold 25%). The falsification rule needs three concurrent. Initial seed.

    sources1234
Why this confidence

Rests on 7 observations from Budget Lab at Yale AI labour-market tracker (dissimilarity index chart data), California AI-Unemployment Tracker (CAIT), Canaries Dashboard data (by exposure), Revelio Labs AI Labor Market Tracker (US); best evidence tier 6, grade B; current.

Confidence is independent of status: 90–95 multiple strong independent sources; 70–89 good evidence, some ambiguity; 50–69 mixed or hard to operationalise; 0–49 limited or vague evidence.

Predictions that lean on this