Methodology

The bis_license_actions substrate ingests the BIS Office of Technology Evaluation per-country “Annual Country Licensing and Trade Analysis” PDFs (bis.gov · OTE ↗). 2,587 rows × 33 jurisdictions × calendar years 2018-2022. Statutory authority Section 1765 of ECRA (50 USC §4824 / Pub. L. 115-232). Authority tier 1 (primary government measurement). Per-country ground-truth extraction pass-rate at substrate-build time was 88/88 = 100%.

Why the substrate ends at CY2022. The CY2022 country PDFs were published in early 2023 — historically a 2-4 month lag from calendar year-end. CY2023 country data has not been released by BIS as of 2026-06-12, putting the next vintage approximately 27 months past the historical cadence. No suppression, delay-announcement, or restructure has been publicly issued. The honest framing is “BIS has not released CY2023 OTE country-licensing data; release is overdue relative to historical cadence; no public publication timetable issued.” The platform substrate-refreshes will land on the BIS publication, not on inference — see CLAUDE.md DQP §8 (“Verify, never hedge”).

New statutory product on the horizon. The Maintaining American Superiority by Improving Export Control Transparency Act (H.R. 1316 · 119th Congress ↗, signed August 2025) mandates a separate annual report to Congress covering every license action for “covered entities” (Entity-List / Military-End-User-List firms in China, Russia, Syria, embargoed jurisdictions), with aggregate statistics releasable publicly. The first report is due no later than 2026-08-19. This will be a distinct (narrower-scope, higher-detail) data product from the historical OTE country-licensing series, and may or may not coexist with a renewed OTE series.

What we CAN answer for CY2023-2026. Bilateral semiconductor trade-flow (HS 8542 / 854231 / 854232 / 854239) runs through 2026 on UN Comtrade, CEPII BACI (231 reporters), Taiwan Customs, China GACC, and Japan e-Stat. These measure what was declared under customs classifications — not what BIS licensed. The /trade-impact and /diversion-pipeline-tracker pages render both side-by-side and never silently compose them. CRS R48642 (Sutter, updated 2025-09-19, crs · R48642 ↗) is the leading public synthesis of the CY2023+ export-control posture absent the OTE substrate; it cites press-release-conveyed CY2020-Q1-2022 license values and does not contain CY2023 OTE-grade license data.

What we CANNOT answer for CY2023-2026. The per-country, per-ECCN, per-year license-action grid (approved / denied / returned-without-action / deemed-export) does not exist publicly. Inference from trade-flow shifts to a license-vs-banned-vs-grey-market trichotomy is structurally unsound at this vintage: trade-flow reflects customs declarations and re-export routing, license-action data reflects BIS decisions. Conflating them silently is the single most common methodological error in trailing-twelve-month US-China chip-shipment analyses; we surface them separately and refuse to compute the trichotomy until the OTE data lands.

Surfaces with the vintage disclosure: /trade-impact · /diversion-pipeline-tracker. Substrate: bis_license_actions (2,587 rows; CY2018-CY2022 × 33 jurisdictions). Statutory authority: Section 1765 of ECRA. Closure of audit gap A2 (Wave-14 cross-domain coverage-gap audit, 2026-05-19).

The Compute Visibility Index measures, per country, what fraction of in-country compute capacity is attributable to a public-filing-grade ultimate beneficial owner. Four tiers plus an explicit coverage state: Tier 1 — UBO identified through SEC / FCA / equivalent securities-regulator filings; Tier 2 — UBO through corporate-registry filings (Companies House, Bundesanzeiger, METI EDINET, NRA registries); Tier 3 — UBO through licensed analyst-database research (held under subscription, not redistributed) where primary filings are not available; Tier 4 — the operator is named in Scrutica's records but the upward ownership walk found no documented ancestors. Data Gap is a separate state: the facility itself has no operator or owner organisation linked, and the chain-walk has not yet reached it.

The Tier 4 / Data Gap distinction is intentional. Tier 4 is a finding about the records (the chain has been looked at, and it is opaque); Data Gap is a finding about Scrutica's coverage (the chain has not yet been looked at). Comparing two countries on opacity without separating these two states is structurally unfair to both — /compute-visibility renders the gap percentage in its own column rather than collapsing it into Tier 4.

Capacity-weighted aggregation. Tier distributions are reported in capacity-weighted form: a country's tier-1 percentage is the sum of power_capacity_mwacross tier-1-classified facilities divided by the country's total documented capacity, not the count of tier-1 facilities over the total count. A 1 GW Tier 1 facility carries more weight than fifty 1 MW Tier 4 ones; the weighting follows where governance leverage actually lives. Per-country detail surfaces the facility-count distribution alongside for completeness.

What this index does not measure.It measures transparency of compute-asset ownership records, not the quality of a country's regulatory regime, the cleanness of its corporate-disclosure law, or the rigour of its export-control posture. A country with mostly Tier 1 capacity has more legible compute records; that is structurally different from being “more transparent overall.” Tier 4 capacity is not an allegation of wrongdoing — private holding structures and family-trust ownership are common, legal, and unrelated to compute-governance posture. The classifier surfaces structural opacity; what to do with that observation is a downstream governance question.

Hosted vs Controlled. A facility that physically sits in country A but whose ultimate beneficial owner sits in country B counts toward country A on the “hosted” view and country B on the “controlled” view. The two diverge where jurisdictional arbitrage applies (e.g., AWS facilities in Ireland controlled from the US; G42-controlled facilities hosted in the UAE). /coordination-gapssurfaces the divergence as the policy-relevant signal — export-control authority follows controlled-jurisdiction, not hosted-jurisdiction.

Surface: /compute-visibility (top-30 dashboard + per-country drill-down). In-page methodology: /compute-visibility#methodology (chain-walk algorithm + Tier 4 vs Data Gap worked example + Inspur cross-listing case). Substrate: organizations + ownership_chain_edges + facilities.operator_org_id.

The Compute Under Control metric is the jurisdictional-attribution counterpart to the Compute Visibility Index. It answers a strict question: of the compute physically hosted inside country C, what fraction is operated by a C-incorporated firm whose ultimate corporate parent is also C-incorporated? Each facility falls into one of five named classes: Domestic — host country, operator country, and ultimate parent country all equal C; Foreign-concentrated — operator and ultimate parent agree on a non-host jurisdiction (the textbook hyperscaler-region pattern: Microsoft Azure Frankfurt operated by Microsoft Deutschland, ultimately Microsoft US); Bifurcated — operator and ultimate parent jurisdictions disagree (the 21Vianet-Azure-China pattern: China-incorporated operator, US-incorporated ultimate parent); Opaque — ultimate parent country unresolved (chain terminates without a country-attributable node); Data gap— no operator org linked.

Why “strict” matters. A single-jurisdiction-pick classifier (the existing Hosted-vs-Controlled tab's approach: owner_org.country_hq, one hop) smooths bifurcated cases into whichever side the picker chose. The strict criterion refuses that smoothing: when operator and ultimate parent are in different jurisdictions, the facility appears in the drill-down for bothcountries as a jurisdictional-axis contributor, and in neither's “Under control” column. The bifurcated bucket is a first-class line item, not a rounding choice.

Ultimate-parent resolver. The classifier reads from mv_ownership_chain_closure(the transitive closure of the upward-walk view, bounded at six hops; the same substrate the Compute Visibility Index reads). For each facility's operating organisation, the ultimate parent is the deepest reachable ancestor in the closure; ties at the same hop depth are broken alphabetically (deterministic). When no documented ancestor exists, the operator itself serves as its own ultimate parent. The resolver is conservative: it does not infer beneficial ownership from indirect signals.

Where the operational definition matters.A Cayman-domiciled holding company is treated as Cayman-domiciled, not as the jurisdiction of its top operating subsidiary. A reader who prefers the “substantive economic control” reading reads the drill-down (operator and ultimate parent are surfaced side-by-side) and forms the alternate judgement explicitly — the classifier never silently picks a winner. Multinational holding parents with no clear-majority jurisdiction are reported by country of incorporation; the editorial choice is documented inline so a Luxembourg-controlled row reads as the SPV's incorporation, not its controlling shareholder's.

Relationship to peer work.Public country-level compute attribution is sparse. Hawkins, Lehdonvirta & Wu (SSRN, June 2025, “AI Compute Sovereignty”) frame a three-tier single-jurisdiction-pick across 225 cloud regions — a different decomposition than the bifurcated-vs-opaque split. Epoch AI's April 2026 hyperscaler-concentration post estimates 71% of cumulative AI compute is held by five US-headquartered firms as of Q4 2025 (company-level, not country-level). CNAS's Sovereign AI Index attributes program-level capacity to sovereign jurisdictions but does not decompose the operator-vs-ultimate-parent gap. The Scrutica framework closes that gap explicitly: every row carries operator country and ultimate-parent country side-by-side, every residual class is named, and the drill-down is at facility granularity. The framework is a Scrutica editorial choice and is positioned as such; the underlying classifications are reproducible from the substrate.

Surface: /compute-visibility#under-control (top-30 dashboard + facility-level drill-down). In-page methodology: /compute-visibility#under-control-methodology (per-class definitions + ultimate-parent resolver + definitional caveats). Substrate: v_facility_control_attribution + mv_compute_under_control_by_country (materialized views fed by mv_ownership_chain_closure).

The Sovereign AI Reality Dashboard at /sovereign-ai renders two complementary inflation decompositions per program. The first — financing-type decomposition — was the original ornament: per-program, the headline announced figure splits into government / private / FDI / credit components. The second — gap-shape decomposition, shipped 2026-05-20 as the cross-program scorecard — reads the announced-vs-deployed gap through a Scrutica editorial taxonomy of six named patterns: Aspirational (future-target capacity announcement without operational deployment yet), Phased rollout (Phase 1+ operational, Phase N forward-scheduled), Financing-stalled (intent + counterparties exist, capital closure does not), Chip-allocation-conditional (deployment authorization-gated by BIS / chip-supplier / foundry-capacity), Vaporware (announcement without substantive substrate), Other (catch-all documented inline).

Why two decompositions, not one.Financing-type and gap-shape answer different questions. Financing-type answers “how is this program’s announced figure assembled?”; gap-shape answers “why is the announced figure outrunning the deployed figure?”. A program can be 100% government-financed (financing-type) and 80% aspirational (gap-shape) at the same time. Both are surfaced on the page so a reader can choose the lens that fits the analytical question.

Per-attribution provenance. Every pattern attribution carries source URL, accessed-at date, and a confidence rating: 1 = announcement-text-explicit or primary-source-anchored (a government disclosure, a primary procurement record, a substrate-verified data-quality flag); 2 = analyst-inferred from public evidence (a step of judgement above the substrate, but the underlying signal is public); 3= Scrutica-editorial attribution where substrate is sparse (used only when the program-level financial substrate is too thin to support a confident pattern call but the gap-shape is still analytically informative; marked explicitly so a reader doesn’t mistake it for a primary-source-anchored claim).

Framework is editorial.The 6-pattern taxonomy is a Scrutica editorial framework, not an industry standard. Closest peer attempts: CNAS’s Sovereign AI Index (announced-vs-disbursed pairs, single ratio per program); Brookings sovereign-AI capacity assessments (qualitative narrative); IDC sovereign-AI tracker (announced-vs-operational pairs); CSIS sovereign-technology programs (jurisdictional-leverage framing). None publish a per-program decomposition along Scrutica’s 6 patterns. A reader who would classify a row differently can read the per-row notes and re-classify inline; the framework is positioned as one operational definition among several.

Where the literature is silent.Programs with sparse financial substrate (Sweden, Poland, parts of the Africa regional aggregate, the NATO/AUKUS coordination wrapper) carry an explicit “Other” attribution with confidence 3 + a rationale — the taxonomy does not force a pattern call when the substrate does not support one. War-time programs (Ukraine) carry “Other” with confidence 2 + a note that political-instability is the binding constraint, qualitatively distinct from vaporware. The framework refuses to invent a pattern attribution to fill the row.

Surface: /sovereign-ai#gap-pattern-methodology (cross-program scorecard + inline framework definition). Substrate: data/sovereign/gap_pattern_decomposition.json (editorial overlay; per CLAUDE.md DQP §16). Per-program financing-type decomposition lives in data/processed/sovereign_programs.json as the inflation_decomposition field (financing-type breakdown, not gap-shape).

The PJM grid-context indicator on the US sovereign program at /sovereign-ai renders a county-aggregated capacity rollup of PJM Interconnection's territory (13 Eastern-US states + DC). It exists because the US program's major US-located resources (NAIRR Pilot at Purdue Anvil + SDSC Expanse; CHIPS Act recipient fabs in PA / OH / NY) sit inside PJM's footprint and the planned data-center load against that grid is the operative pre-emption signal for new procurement. The indicator renders only on the US program (program.country_code === 'us') because non-US programs route their host facilities through home grids (CAISO, ERCOT, ENTSO-E, K-EPCO, etc.) and the PJM substrate cannot speak to those.

Substrate currently populated: the GENERATOR queue, not the load queue. The pjm_interconnection_queuetable indexes PJM's multi-decade Generator Interconnection Queue archive (~7,000+ rows; ~70% historically withdrawn before energization, the structural baseline for any interconnection-queue analysis). Generator-queue rows model new generation seeking to interconnect — gas turbines, solar farms, storage, wind — not data-center loads requesting service. Per-facility resolution against a specific data center is structurally infeasible from the generator queue alone; the indicator therefore aggregates to county-level capacity totals.

What the data-center load queue would add. PJM operates a separate Network Service / Large Load Interconnection process that handles megawatt-scale loads (data centers, electric-arc steel, hydrogen electrolyzers). That substrate is not yet ingested into Scrutica; it would resolve the per-facility pre-emption question (which specific data center is queued behind which generator, on which timeline). The current county-rollup is the closest defensible substitute — aggregate generator-side capacity per county across the multi-decade archive is informative about grid headroom even when the per-facility load mapping is missing. The indicator is consumed via getPJMTerritorySummary() at src/lib/data/pjm-county-exposure.ts and labels its withdrawal-rate caveat verbatim on the surface (“{withdrawn_pct}% withdrawn over the multi-decade rolling archive”).

Why this matters for the US sovereign program.NAIRR Pilot pre-procurement decisions and CHIPS Act facility-siting both ride on PJM grid headroom in PA / OH / VA / NY counties — the indicator surfaces the structural grid-side context that a per-facility power-availability claim cannot show on its own. When the load-queue substrate lands, the indicator upgrades from county-rollup to per-facility queued-load disclosure; until then, the surface is honest about which queue is rendered and which question it cannot yet answer.

Surface: /sovereign-ai (US program detail card — PJM grid-context affordance). Substrate: pjm_interconnection_queue (generator queue, multi-decade archive). Source: PJM Interconnection Queue archive (pjm.com; authority tier 1).

The Query Engine at /query is the natural-language interface to Scrutica's data + analytical surface. Architecture: a user's free-text query passes through an injection-defense sanitiser, gets wrapped in <user_query> delimiters, and reaches a Claude Opus 4.7 (1M context) instance routed via OpenRouter. The model first calls a thinkmeta-tool to produce a query plan (visualised live in the UI), then executes data tools in parallel where independent, sequentially where one tool's output feeds another. After up to five rounds of tool execution it streams a synthesis response with every numerical claim wrapped in a citation tag.

The tool surface is 55 tools across 14 layers (facilities, organizations, supply-chain edges, investments, export-control designations, compute deployments, hardware catalog, compute pricing, trade flows, the PJM interconnection queue, capacity snapshots, regulatory thresholds, compute-visibility tiers, ownership chains, sovereign-execution procurement, regulatory-impact pre-scorer, cascade dynamics, scenario models — Tokyo earthquake, Iran threat, SCS cable, Taiwan strait, Abqaiq — BIS license actions, HBM market share, EU grid, recent developments, and per-model training evidence). Each tool wraps a documented data module under src/lib/data/; the full per-tool reference lives at /query/tools.

Citation discipline. Every numerical or factual claim in the model's synthesis wraps in <cite tool record_id tier>tags. The frontend parses these tags during streaming and renders a Citations Panel below the response with per-citation authority-tier badges (Tier 1 primary measurement → Tier 4 inferred/estimated), source URLs, and a per-citation tool-result drill-down. After synthesis completes, a server-side citation verifier parses the tags and verifies each cited record_id appears in the function_call_output history — citations referencing record IDs not in the tool history are flagged in the panel as “unverified provenance.”

Failure modes by design. The Query Engine refuses rather than fabricates when the question requires data Scrutica does not have. Three explicit refusal classes: forecasting (no forecasting layer exists; revenue projections, demand forecasts, capacity-growth projections are unanswerable); opinions (subjective valuations, policy preferences); intraday pricing (the pricing substrate has 24-hour freshness, not real-time quotes). Off-scope questions (medical advice, legal advice, open-ended LLM chat) hit a refusal pointing back to what the platform actually does.

Provider + reliability. Primary model: anthropic/claude-opus-4.7. Fallback chain on upstream 5xx or quota-exhaustion: anthropic/claude-sonnet-4.6 → openai/gpt-5.5. User-side rate limit is 20 requests / minute / IP (Upstash sliding window). Per-IP daily cost budget is $5 (≈50 typical Opus queries), reset at UTC midnight. The route emits Server-Sent Events for live plan visualisation, per-step status, streaming synthesis, and post-synthesis citation warnings.

The frontier-model comparison genre splits along three methodological seams. Epoch's ECI (a composite over standardized benchmarks — GPQA Diamond, FrontierMath, SWE-bench Verified, MATH Level 5, MMLU-Pro, others — projected onto a single capability axis); Artificial Analysis's Intelligence Index (a 0–100 composite of measured benchmark performance under their own published weights); LMArena's Arena Score (a Bradley-Terry Elo over crowdsourced head-to-head human-preference battles). Each measures something different (task-bench composite, vs. preference Elo, vs. intelligence-vibes composite), and the three correlate without collapsing onto a single ordering. Scrutica renders all three with a toggleable Y-axis, layers a consensus rank (mean rank, ECI-tiebroken), and adds the dimension the source-three don't carry: which export-controlled chip class trained the model.

Per-index provenance. ECI is sourced from Epoch's AI Models database (joined via the capability_scaling.json ingest at benchmark_id = epoch_capabilities_index); a ECI ≥ 132 editorial cutoff produces the frontier-relevant set (~50 distinct base models post-dedup of context-length / effort-level variants). AA Index is curated from artificialanalysis.ai/leaderboards/models at access vintage; max-effort variant taken per model per the methodology note (xhigh / high effort regimes reflect the model's apex capability, not its production-API default). Arena Score is curated from arena.ai/leaderboard/text paginated through positions 1–150; thinking / max-effort variant preferred when multiple variants exist for the same base model. Models post-dating the Epoch snapshot (e.g., GPT-5.5, Claude Opus 4.7 at 2026-05-17 access) appear as supplements with ECI = NULL + a notes annotation per DQP §1.

Typology operationalization. The four-bucket model class (allied closed, non-allied closed, contested closed, open) is computed from trainer country plus open-weights designation. Allied = AUKUS + Five Eyes (US, UK, AU, CA, NZ) + EU + Japan + Korea + Israel + Taiwan. Non-allied = China + Russia + Iran + North Korea per BIS Entity List jurisdictional anchoring. Contested = UAE + Saudi Arabia (G42, HUMAIN, TII); operationally these get non-allied accounting for export-control purposes, but the partnership architecture (Microsoft–G42, NVIDIA–HUMAIN, Microsoft–TII) puts contested-bucket trainers in a distinct posture from China-bucket trainers, and Scrutica preserves the distinction rather than collapsing onto non-allied. Open = weights publicly downloadable irrespective of trainer country (Llama 4, Qwen3, DeepSeek-OS, GLM, Kimi K2, GPT-OSS); downloadable-weights beats trainer-country in the typology because the policy lever the typology exists to inform (export-control of model weights) is moot once the weights are public.

Chip-class attribution — the Scrutica differentiator. Training chip class is the supply-chain seam that Epoch / AA / LMArena don't render. The cell is populated from three tiers of disclosure, each tagged with a per-cell training_chip_authority_tier:

• Tier 2 — paper / blog disclosure. Direct disclosure on the lab's domain or in a peer-reviewed paper. Anchors: DeepSeek-V3 Technical Report §3.4 → H800; Meta Llama 4 announcement → H100; Qwen2.5 Technical Report → H800; xAI Colossus announcement → H100.
• Tier 3 — lab statement without paper-level rigor. Press release / earnings call / blog post that states the chip family without per-cluster topology. Google Gemini 2.5 family on TPU-v5p; Gemini 3 family on TPU-v7.
• Tier 4 — family-lineage inference. Sibling model from same lab is on a disclosed chip class, and the lab has not announced a chip transition; the unannounced model inherits. Qwen3-235B-A22B / Qwen3-Coder-480B inherit Qwen2.5's H800 baseline. Tier-4 cells are explicitly labeled in the ModelDetailPanel and the audit log.
• NULL. Closed-frontier US labs (OpenAI's GPT-5.x line, Anthropic's Claude 4.x line) do not publicly disclose per-model training chip class. Cells stay NULL with a notes annotation per DQP §1 (no fabrication) and §5 (no false precision). Inferring “OpenAI uses H100” from cluster gossip would be fabrication; the discipline is to honor the gap.

Export-control taxonomy. Per-chip-class status is anchored to BIS rulemaking dates: October 2022 — initial A100 / H100 controls (ECCN 3A090 + 4A090); October 2023 — H800 / A800 added (the Oct-2022 controls' loophole closed); January 2025 — AI Diffusion Framework adds country-level tier system (later partially rescinded under the Trump administration in 2025-Q2; Scrutica tracks the rescission status separately on /export-controls). Status string mapping: restricted-to-non-allied (any NVIDIA datacenter GPU since the Oct-22 cut), unrestricted (Google TPU — never sold as a chip, only as cloud TPU-VM access; A100 pre-Oct-22 training runs), unknown (chip class NULL → cannot attribute).

Pareto frontier algorithm. A model other strictly dominates model m on (release_date, metric) iff other.date ≥ m.date AND other.metric > m.metric OR other.date > m.date AND other.metric ≥ m.metric. The Pareto frontier is the set of non-dominated models; rendered as outlined dots in the scatter. The set is re-computed per (metric, subset) combination — the “Allied-only Pareto frontier on AA Index” is a different set than the “All-models Pareto frontier on Arena Elo.”

Consensus rank. Mean of (ECI rank, AA rank, Arena rank) over the subset where each metric is non-null. A model with only AA + Arena cells gets a 2-metric mean. Ties break by ECI rank (the index with the most-disclosed methodology), then alphabetically by model_id for full determinism. The consensus is advisory — when ECI / AA / Arena disagree (e.g., a model strong on ECI but weak on Arena Elo), the disagreement is the signal, not the consensus. Per DQP §7 (contradiction surfacing), the ModelDetailPanel shows all three values explicitly, not just the consensus rank.

Honest caveats. (1) Closed-source compute estimates carry ±0.5 OOM. Lab-reported FLOP figures (where reported) are unaudited; analyst-derived estimates (Epoch, SemiAnalysis) anchor to publicly verifiable signals (parameter count × tokens × 6 for dense transformers; cluster size × wall-clock × MFU for known clusters). (2) Tier-4 chip-class inference is not measurement. Family-lineage chip class is an assumption the lab has not switched chip families between releases; the assumption holds until a disclosure contradicts. (3) AA and Arena cells reflect leaderboard snapshots at access date, not the model's lifetime peak; quarterly re-curation per the curator script. (4) The ECI cutoff at 132 is editorial, adjustable in scripts/curate-capability-frontier-models.ts; widening the cutoff admits more models at the cost of diluting the “frontier” framing.

Surface: /threshold-atlas → Capability Frontier tab. Substrate: data/processed/capability-frontier/models.json (62 models, regenerated by scripts/curate-capability-frontier-models.ts). Per-cell authority tier surfaced in the ModelDetailPanel.