Methodology preprint · Version 0.1 draft · Updated September 2026

Methodology for International AI Compensation Data.

United States, United Kingdom, and Canada. A peer-reviewable methodology preprint. CC BY 4.0.

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Abstract.

The AI compensation data market segments into three product categories: enterprise compensation survey products (Mercer, Aon Radford, WTW) at $25,000 to $100,000 per seat per year; consumer freemium products (Levels.fyi, Glassdoor, Payscale) with opaque methodology and US-dominant coverage; and payroll-feed products (Pave, OpenComp) at $50,000+ per company with individual-record live data. None occupies the developer-API segment with a transparent methodology contract across multiple jurisdictions.

This paper documents the methodology behind Orbyt Intelligence's Tier 1 international coverage. Tier 1 reconciles seven government- sourced data inputs into per-country weighted estimates updated as each government source publishes. Reconciliation weights are locked per country and disclosed on every estimate. Currency normalization uses ECB- backed daily rates. Cross-country sanity bounds quarantine implausible values. Every estimate carries its methodology version, source breakdown, sample size, confidence interval, and disagreement flag in the locked response envelope.

Source weights, locked.

Per-country weights sum to 1.0 independently. The reconciliation engine never blends across countries. A US measurement and a UK measurement for the same role and city slug land in separate tuples and reconcile with their own weight sets.

CountrySourceWeightCadence
USBLS OES0.57Annual (May)
USDOL H-1B LCA0.43Quarterly
UKONS ASHE0.60Annual (October)
UKHMRC PAYE RTI0.20Monthly
UKSkilled Worker Visa0.20On update
CAESDC Open Government0.60Annual (November)
CAStatCan WDS (planned)0.40Not yet ingested

Contents.

  1. Introduction. The state of AI labor data. The gap in the developer-API segment.
  2. Data sources. Per-source documentation for all 8 Tier 1 sources with permanent URLs.
  3. Role taxonomy and occupation crosswalk. The honest disclosure that no official concordance exists between US SOC 2018 and UK SOC 2020 or NOC 2021.
  4. Reconciliation methodology. Per-country weighted average. Renormalization. Disagreement detection at the locked 25% threshold. Cross-country sanity bounds.
  5. Currency normalization. The fx_rates table. Frankfurter API. Weekend FX handling. FX outage policy.
  6. Sample size and confidence. The disclosure floor. Per-country practicality. The HMRC RTI fan-out honest disclosure. Confidence interval calculation.
  7. Limitations and known gaps. Crosswalk uncertainty. Geographic granularity. Selection bias. Structural shifts within the survey window.
  8. Open invitations. The Role Taxonomy is yours. The Methodology is open for critique. The API is priced like Stripe.
  9. References.

License + citation.

Published under CC BY 4.0. You may adopt, modify, and redistribute with attribution. No restriction on commercial use.

Bartak, J. (2026). Methodology for International AI Compensation Data: United States, United Kingdom, and Canada. Orbyt Intelligence. https://www.orbytlabs.ai/orbyt-intelligence/methodology/international