v1.3.6 · 2026-07-22
The terms the site uses, each defined once.
Definitions and standardization notes for the terms the methodology and the instruments use: semiconductor manufacturing, AI training hardware, export controls, and compute policy. Each entry says what the term means and why it matters for governance, beyond what it expands to.
77 terms
Extreme ultraviolet lithography uses 13.5 nm wavelength light to pattern features below 7 nm on silicon wafers. Requires a tin-droplet plasma source, multilayer mirrors, and vacuum-sealed optical path.
Governance relevance
ASML is the sole manufacturer of EUV machines. Any disruption to ASML or its supply chain halts production of the most advanced AI chips worldwide.
ASML holds a complete monopoly on EUV lithography equipment.See on page
Deep ultraviolet lithography uses 193 nm (ArF) or 248 nm (KrF) wavelength light. Multi-patterning techniques extend DUV to ~7 nm nodes at the cost of additional process steps and yield loss.
Governance relevance
DUV machines are manufactured by ASML, Nikon, and Canon. China can access DUV (not EUV) equipment, which supports production at mature nodes but not cutting-edge AI chips.
A named manufacturing generation (e.g., N4, N5, N7) indicating transistor density and power characteristics. Modern node names are marketing labels; actual gate lengths differ from the stated nanometer value.
Governance relevance
Export controls target chips manufactured at advanced nodes (roughly 14 nm and below). The node designation determines whether a chip is freely exportable or restricted.
TSMC N4 achieves ~1.7x logic density over N7.See on page
A thin disc of crystalline silicon (typically 300 mm diameter) on which hundreds of chip dies are patterned simultaneously. Wafer starts per month is the standard measure of fab capacity.
Governance relevance
Global advanced-node wafer capacity is concentrated in Taiwan, which holds ~92% of leading-edge pure-play foundry manufacturing (CSET 2021, a country-level share including TSMC, UMC, and PSMC). TSMC is the dominant operator within it (no tier-1/2 source publishes its company-level slice); its wafer allocation decisions determine which AI chips get manufactured.
The fraction of functional dies per wafer. Advanced-node yields typically start at 30-50% and improve to 80%+ over 12-18 months of production maturity.
Governance relevance
Low yields at new process nodes constrain chip supply regardless of wafer capacity. Yield data is closely guarded; its absence is itself a signal of production difficulty.
Chip-on-Wafer-on-Substrate: TSMC's 2.5D advanced packaging technology that places multiple chiplets and HBM stacks on a silicon interposer. Critical bottleneck for AI accelerators.
Governance relevance
CoWoS packaging capacity constrained AI chip supply through 2024-2025. TSMC controls nearly all advanced packaging for AI accelerators; alternative capacity (Samsung I-Cube) remains limited.
Every H100 and A100 GPU uses CoWoS packaging.See on page
High Bandwidth Memory: vertically stacked DRAM dies connected by through-silicon vias (TSVs). HBM3e provides ~4.8 TB/s bandwidth per stack. Required for modern AI accelerators.
Governance relevance
SK Hynix (~57%), Samsung (~22%), and Micron (~21%) produce all HBM (Q3 2025, Counterpoint Research). HBM supply is a binding constraint on AI accelerator production, independent of logic chip availability.
H100 SXM5 ships with 80GB HBM3; H100 PCIe uses HBM2e. H200 SXM with 141GB HBM3e.See on page
A semiconductor manufacturer that fabricates chips designed by other companies (fabless firms). TSMC, Samsung Foundry, and GlobalFoundries are the major foundries.
Governance relevance
The foundry model concentrates manufacturing in a small number of companies and geographies. Taiwan hosts >60% of global advanced-node foundry capacity.
A chip company that designs but does not manufacture its own chips. NVIDIA, AMD, Qualcomm, and Apple are fabless; they rely on foundries for production.
Governance relevance
Fabless companies depend entirely on foundry access. Export controls on foundry services can restrict a fabless company's ability to produce chips even if the design itself is unrestricted.
Integrated Device Manufacturer: a company that both designs and fabricates its own chips. Intel, Samsung, and Texas Instruments are IDMs.
Governance relevance
IDMs have more supply chain resilience than fabless companies but face higher capital requirements. The US CHIPS Act subsidies primarily target IDM-style domestic manufacturing.
Outsourced Semiconductor Assembly and Test: companies that package and test fabricated chips. ASE, Amkor, and JCET are major OSATs.
Governance relevance
OSATs are concentrated in Taiwan, China, and Malaysia. Advanced packaging (required for AI chips) is more concentrated than standard OSAT services.
Fin Field-Effect Transistor: a 3D transistor architecture where the gate wraps around a raised "fin" of silicon. Used at 14nm through 3nm nodes.
Governance relevance
The performance gains from FinFET architecture underpin every modern AI accelerator. The transition to GAA transistors at 2nm creates a new manufacturing chokepoint.
Gate-All-Around transistor: the successor to FinFET where the gate completely surrounds the channel. Samsung and TSMC are transitioning to GAA at 2nm and below.
Governance relevance
GAA manufacturing requires new equipment and process expertise. Countries and companies without access to GAA technology face a capability gap in AI chip production that grows with each successive node.
A quartz plate with patterned chrome features that defines the circuit layout projected onto the wafer during lithography. A single advanced chip design requires 80-100 mask layers.
Governance relevance
Photomask sets for advanced nodes cost $10-30M and take months to produce. This cost barrier limits who can design cutting-edge chips.
Post-fabrication techniques (CoWoS, InFO, EMIB, Foveros) that integrate multiple chiplets and memory stacks into a single package. Enables the large-die configurations required for AI accelerators.
Governance relevance
Advanced packaging is the newest bottleneck in AI chip supply. Capacity is more concentrated than wafer fabrication, with TSMC controlling the majority of AI-relevant packaging.
Floating-point operation: a single arithmetic operation (add, multiply) on a floating-point number. FLOP/s (per second) measures raw compute throughput; total FLOP measures cumulative compute used for training.
Governance relevance
FLOP is the primary metric for AI governance thresholds. The EU AI Act requires reporting for models trained above 10²⁵ FLOP; the former US Executive Order 14110 set notification at 10²⁶ FLOP.
GPT-4 training compute is estimated (Tier 3) at approximately 2 × 10²⁵ FLOP.See on page
Tera (10¹²) floating-point operations per second. The standard unit for GPU compute throughput. H100 SXM: 989.5 TFLOP/s FP16 dense, 1,979 TFLOP/s with 2:4 structured sparsity.
Governance relevance
TFLOP/s ratings determine how quickly a facility can accumulate training compute. Higher TFLOP/s per GPU means fewer GPUs are needed to reach regulatory FLOP thresholds.
Half-precision 16-bit floating-point format. Most AI training uses FP16 or BF16 arithmetic; FLOP/s benchmarks for AI typically report FP16 throughput.
Governance relevance
The precision format determines the effective compute per operation. Export controls reference total operations per second, which varies by precision mode.
Brain floating-point 16-bit format: same total bits as FP16 but with 8 exponent bits (matching FP32) and 7 mantissa bits. Preferred for training stability.
Governance relevance
BF16 and FP16 deliver comparable throughput on modern GPUs. Governance thresholds typically count operations regardless of precision format.
Thermal Design Power: the maximum sustained power draw of a chip in watts. H100 SXM: 700W; H100 PCIe: 350W; H200 SXM: 700W; B200: 1,000W.
Governance relevance
TDP determines data center power requirements and cooling needs. A facility's total power capacity divided by per-GPU TDP provides an upper bound on GPU count, which Scrutica uses as one estimation path for facility-level compute.
Power estimation path: 425 MW facility / 700W per H100 = ~607K GPUs maximum.See on page
NVIDIA's proprietary high-bandwidth GPU-to-GPU interconnect. NVLink 4.0 (Hopper) provides 900 GB/s bidirectional bandwidth; NVLink 5.0 (Blackwell) provides 1,800 GB/s.
Governance relevance
NVLink is a sole-source technology from NVIDIA. Clusters using NVLink achieve higher training efficiency than those using standard PCIe or InfiniBand for GPU communication.
High-bandwidth, low-latency networking fabric used to connect GPU nodes in AI training clusters. NVIDIA (via Mellanox acquisition) dominates the AI-grade InfiniBand market.
Governance relevance
InfiniBand is a controlled technology under US export rules. Restricting InfiniBand access limits a country's ability to build large-scale AI training clusters, even if GPUs are available.
Specialized matrix-multiply units in NVIDIA GPUs that perform mixed-precision fused multiply-add operations. Responsible for the majority of AI training throughput.
Governance relevance
Tensor core count and generation determine a GPU's AI-relevant compute capacity, which is the basis for export control performance thresholds.
The data transfer rate between GPU compute units and memory, measured in TB/s. H100 SXM: 3.35 TB/s; H200 SXM: 4.8 TB/s (with HBM3e).
Governance relevance
Memory bandwidth is a binding constraint for many AI workloads. Export control thresholds consider both compute throughput and memory bandwidth as dual criteria.
NVIDIA's server-grade GPU form factor with direct NVLink connectivity. SXM variants (H100 SXM, B200 SXM) offer higher power limits and bandwidth than PCIe equivalents.
Governance relevance
SXM GPUs are the building blocks of large AI training clusters. The SXM vs. PCIe distinction matters for export controls because SXM enables cluster-scale training that PCIe cannot.
PCI Express: the standard expansion bus interface. PCIe GPUs (e.g., H100 PCIe) have lower TDP (350W vs. 700W) and no NVLink, limiting large-scale training capability.
Governance relevance
Some export control frameworks distinguish between SXM and PCIe variants. PCIe GPUs are less useful for frontier AI training, so export restrictions may treat them differently.
The total floating-point operations used to train a model, measured in FLOP. Distinct from inference compute (running a trained model) and fine-tuning compute.
Governance relevance
Training compute is the primary measurable input for AI capability governance. Higher training compute correlates with more capable (and potentially more dangerous) models.
The EU AI Act uses 10²⁵ total training FLOP as the GPAI reporting threshold.See on page
Running a trained model to produce outputs (predictions, text, images). Inference compute per query is orders of magnitude smaller than total training compute, but aggregate inference demand can exceed training demand.
Governance relevance
Inference compute is not currently subject to governance thresholds, but aggregate inference demand drives data center capacity expansion and energy consumption.
Empirical power-law relationship between training compute, dataset size, model parameters, and model performance. Kaplan et al. (2020) and Hoffmann et al. (2022, "Chinchilla") established key scaling relationships.
Governance relevance
Scaling laws predict that spending more compute on training yields more capable models. This predictability enables governance frameworks that use compute thresholds as capability proxies.
A training configuration that balances model size and dataset size according to the Hoffmann et al. (2022) scaling law. Chinchilla-optimal models use ~20 tokens per parameter.
Governance relevance
Chinchilla-optimal training requires less compute than over-parameterized training for the same capability. This complicates governance: a FLOP threshold can be reached with a smaller, better-trained model.
The number of trainable weights in a neural network. GPT-4 is widely reported (Tier 3, analyst estimates; OpenAI has not confirmed) at ~1.8 trillion parameters (MoE). Larger parameter counts require more memory and compute.
Governance relevance
Parameter count was an early proxy for model capability but is less informative than training compute. Current governance frameworks use FLOP thresholds rather than parameter thresholds.
Model FLOP Utilization: the fraction of theoretical peak GPU throughput actually achieved during training. Typical values: 30-50% for large clusters. Higher MFU means more efficient hardware utilization.
Governance relevance
MFU determines how much real compute a facility extracts from its hardware. Two identical GPU clusters can differ 2x in effective training compute based on MFU alone.
Scrutica's hardware estimation path uses MFU = 40% as the default assumption.See on page
Continued training of a pre-trained model on a smaller, task-specific dataset. Uses 100-10,000x less compute than the original pre-training run.
Governance relevance
Fine-tuning can significantly alter a model's capabilities (including safety-relevant ones) at a fraction of the original training cost. Current governance frameworks do not consistently cover fine-tuning compute.
Model Evaluation and Threat Research benchmark measuring how long an AI agent can autonomously work on real-world tasks. Scored on a 0-5 scale corresponding to task durations from seconds to weeks.
Governance relevance
METR scores provide a standardized measure of autonomous capability. Higher METR scores at lower compute levels would indicate capability is becoming more accessible, a key governance concern.
Frontier models in 2025 achieve METR time horizons of 2-3 (minutes to hours).See on page
Power Usage Effectiveness: ratio of total facility power to IT equipment power. PUE 1.0 = all power goes to compute; PUE 1.2 = 20% overhead for cooling, lighting, networking. Industry average: ~1.3.
Governance relevance
PUE determines how much of a facility's total power actually reaches GPUs. A lower PUE means more effective compute per megawatt, which feeds directly into facility-level FLOP estimates.
Scrutica's power estimation path uses PUE to convert total facility power to GPU power.See on page
Megawatt: 1 million watts. The standard measure of data center power capacity. A 100 MW facility can host roughly 100,000-140,000 H100 GPUs at full load (accounting for PUE).
Governance relevance
Power capacity is often the binding constraint on AI compute. A 500 MW data center campus represents a significant national infrastructure investment and a measurable concentration of AI capability.
The xAI Colossus facility in Memphis draws approximately 150 MW.See on page
Direct-to-chip or immersion cooling that removes heat more efficiently than air cooling. Required for GPUs above ~400W TDP (Blackwell B200 at 1,000W mandates liquid cooling).
Governance relevance
Liquid cooling infrastructure is a prerequisite for next-generation AI clusters. Facilities without it cannot host the latest accelerators; the result is a capacity gap between upgraded and legacy sites.
A set of GPU servers connected by high-bandwidth networking (NVLink + InfiniBand) that can collectively train a single model. Modern frontier clusters contain 10,000-100,000+ GPUs.
Governance relevance
GPU cluster scale determines what size models a facility can train. Clusters above certain sizes can produce models that exceed governance thresholds.
The fraction of installed GPU capacity actively performing compute, averaged over time. Typical values: 50-80% for training workloads. Affected by scheduling, maintenance, and workload mix.
Governance relevance
Utilization determines how much of a facility's theoretical capacity is realized in practice. Low utilization means installed capacity overstates actual compute output.
The network infrastructure connecting GPU nodes within a cluster. Combines NVLink (intra-node), InfiniBand or Ethernet (inter-node), and spine-leaf topology for scalable bandwidth.
Governance relevance
Interconnect quality determines maximum trainable model size. Without adequate interconnect, additional GPUs cannot participate in a single training run, so raw GPU count overstates effective cluster scale.
GPU cloud providers (CoreWeave, Lambda, Nebius, Applied Digital, IREN) that rent compute capacity without the broad cloud services offered by hyperscalers (AWS, GCP, Azure).
Governance relevance
Neoclouds have opened AI-specific compute capacity outside hyperscaler ecosystems, but their near-total dependency on NVIDIA GPUs replicates the concentration dynamic at a different layer.
Cloud providers operating at massive scale: Amazon (AWS), Microsoft (Azure), Google (GCP), Meta, Oracle. Characterized by >100 MW data center campuses, custom silicon programs, and global infrastructure.
Governance relevance
Hyperscalers control the majority of commercially accessible AI compute. Their infrastructure investment decisions (where to build, what chips to buy) shape national compute capacity.
A supply chain relationship where only one company produces a critical input. ASML for EUV machines, TSMC for advanced-node foundry services, and Zeiss for EUV optics are sole-source dependencies.
Governance relevance
Sole-source dependencies are the most concentrated chokepoints in the compute supply chain, and therefore the points where export controls and policy interventions have maximum effect. No alternative supplier exists at any price.
The concentration page identifies supply chain steps with sole-source exposure.See on page
A directed relationship between two entities in the supply chain graph. Edges have attributes: relationship type (supplier/customer/partner), value ($M), and source (licensed supply-chain database or SEC filing).
Governance relevance
The supply chain graph surfaces hidden dependencies and cascade paths. Two companies that appear unrelated may share a critical upstream supplier, creating correlated exposure invisible from a bilateral view.
Herfindahl-Hirschman Index: the sum of squared market shares. Ranges from near 0 (perfect competition) to 10,000 (monopoly). Calculated as HHI = sum(s_i^2) where s_i is each firm's percentage market share.
Governance relevance
The US Department of Justice considers HHI above 2,500 as highly concentrated. Measured AI compute supply chain segments: EUV lithography at 10,000 (ASML monopoly, tier 1), overall foundry at 5,007 named-share lower bound (TrendForce FY2025), HBM at 4,174 (Counterpoint Q3 2025). Advanced packaging publishes no tier-1/2 share set, so no packaging HHI exists.
EUV lithography HHI = 10,000 (ASML monopoly).See on page
A supply chain node where concentration or sole-source dependency creates a governance-relevant bottleneck. Measured by HHI, node centrality, and substitutability assessment.
Governance relevance
Chokepoints are where export controls have maximum leverage and where disruptions have maximum impact. They serve as the natural targets for risk assessment and strategic policy intervention.
The propagation of a disruption through supply chain relationships. Modeled via breadth-first search across the relationship graph with decay factors based on supply share and criticality. Criticality can be editorial (1-10 replaceability scale) or blended with a 3-month price correlation where available. Blend formula: adjustedCriticality = w × 5(1+r) + (1-w) × editorial.
Governance relevance
Cascade analysis surfaces the second- and third-order effects of concentration in the supply chain. Removing or constraining a single node may reshape access for dozens of downstream entities; the propagation depth indicates how much policy leverage a given chokepoint provides.
The cascade simulator models dependency propagation across 18,500+ supply chain edges.See on page
A licensed institutional supply-chain relationship database, held under subscription and not redistributed. Contains bilateral supplier-customer links with relationship value ($M), relationship rank, and 3-month rolling stock price correlation. Authority tier 2: the underlying facts (who supplies whom) are surfaced; the vendor records themselves are not republished.
Governance relevance
An institutional-grade data source financial analysts use to map supply chain dependencies. Scrutica uses it as a tier-2 source for bilateral supply chain edges, with public filings preferred wherever they cover the same relationship.
The 3-month rolling Pearson correlation between two companies' stock prices, stored on the licensed supply-chain database edges. Available on ~78% of those edges. Used to modulate cascade criticality scores when market correlation weighting is enabled. Maps r to the [1,10] criticality scale via 5×(1+r) and blends with editorial scores at a configurable weight (default 0.5). Edges without a correlation value and unlisted companies fall back to editorial criticality.
Governance relevance
Incorporated into the cascade simulator as an empirical edge weight. High positive correlation confirms that the market sees tight coupling; negative correlation suggests the supply relationship is not the primary driver of either company's stock price. The blend avoids over-relying on either signal.
Expert-assessed score (0-1) indicating how easily a disrupted supplier can be replaced. 0 = no substitute exists (ASML for EUV); 1 = drop-in replacements available. Authority tier 3 (analyst assessment).
Governance relevance
Substitutability determines cascade severity. Low substitutability at a chokepoint means disruption propagates with minimal attenuation.
Export Administration Regulations: the US legal framework governing export of dual-use items. Administered by BIS (Bureau of Industry and Security). Key classifications: 3A090 (advanced computing ICs), 4A090 (computers containing 3A090 chips).
Governance relevance
The EAR is the primary tool the US uses to restrict access to advanced AI chips. It determines which chips require licenses for which destinations, and which are prohibited entirely.
A BIS-maintained list of foreign entities subject to specific export restrictions. Inclusion requires a license for most items subject to the EAR, with a presumption of denial for advanced computing.
Governance relevance
Entity List designation is the strongest export control tool short of a full embargo. It can cut a company off from the US technology supply chain entirely.
Scrutica cross-references BIS Entity List entries against the supply chain graph.See on page
Bureau of Industry and Security: the US Department of Commerce agency that administers export controls. Issues licenses, maintains the Entity List, and promulgates rules under the EAR.
Governance relevance
BIS is the institutional center of US technology export control policy. Its rule-making actions directly determine which countries and companies can access advanced AI chips.
An authorization under the EAR that permits export without an individual license when specified conditions are met. Examples: License Exception TSR (technology/software under restriction), ACE (authorized cybersecurity exports).
Governance relevance
License exceptions create controlled pathways for technology transfer. Their scope and conditions are frequently modified; changes to license exceptions can reclassify thousands of transactions overnight.
Transfer of controlled technology to a foreign national within the United States. Governed by the same EAR classifications as physical exports.
Governance relevance
Deemed export rules affect AI research collaboration. A US university lab with foreign researchers may need export licenses for work involving controlled computing technology.
A multilateral export control regime with 42 participating states. Sets baseline control lists for conventional arms and dual-use technologies, including semiconductor equipment and advanced computing.
Governance relevance
Wassenaar provides the multilateral framework that US, EU, and Japanese export controls build on. Countries outside Wassenaar may serve as transshipment points for controlled technology.
The coordination gaps page identifies countries in the supply chain but outside Wassenaar.See on page
Export restrictions based on the intended application rather than the item's technical specifications. Applied when there is knowledge or reason to believe the item will be used for a prohibited purpose.
Governance relevance
End-use controls are harder to enforce than technical threshold controls because they require knowledge of the buyer's intentions. They are the primary tool for restricting mature-node chip exports.
EAR provision that exempts foreign-made items containing less than a specified percentage (typically 25%) of controlled US-origin content. Threshold varies by destination and item.
Governance relevance
The de minimis rule determines how far US export controls extend into foreign supply chains. A lower de minimis threshold (e.g., 10% for Entity List destinations) extends US regulatory reach.
Tera operations per second: the performance metric used in US export control rules to determine whether a chip is "advanced." The October 2022 rule set thresholds at 300 TOPS (for certain interconnect bandwidth) and 600 TOPS.
Governance relevance
TOPS thresholds are the technical criteria that divide freely exportable chips from restricted ones. Chip designers may deliberately design below threshold to avoid export controls.
General-purpose AI: the category defined by the EU AI Act for models trained on broad data that can perform a wide range of tasks. Models trained above 10²⁵ FLOP are classified as GPAI with systemic risk.
Governance relevance
GPAI classification triggers transparency and reporting obligations under the EU AI Act. The 10²⁵ FLOP threshold determines which models face the most stringent requirements.
Under the EU AI Act, the risk classification for GPAI models with "high-impact capabilities" (currently proxied by training compute above 10²⁵ FLOP). Triggers additional obligations: red-teaming, incident monitoring, cybersecurity.
Governance relevance
Systemic risk classification is the strictest tier of EU AI regulation. Whether a model qualifies depends on its training compute, which depends on the facility's compute capacity.
Regulation (EU) 2024/1689: the European Union's comprehensive AI regulation. Establishes risk-based classification, GPAI transparency requirements, and compute thresholds for systemic risk designation.
Governance relevance
The EU AI Act is the first major jurisdiction to use training compute as a regulatory threshold. Its 10²⁵ FLOP line directly connects facility-level compute capacity to regulatory compliance.
Scrutica's Threshold Atlas checks facility-level FLOP capacity against the EU AI Act 10²⁵ trigger and other regimes.See on page
The use of compute as a governance lever for AI policy. Based on the premise that compute is measurable, excludable, and concentrated — properties that make it a tractable intervention point.
Governance relevance
Compute governance is Scrutica's core domain. The platform provides the data infrastructure that compute governance requires: who has how much compute, where, sourced from whom, under what regulatory constraints.
Technology with both civilian and military applications. All advanced AI chips are dual-use; the same GPU that trains a language model can train a military targeting system.
Governance relevance
The dual-use nature of AI chips is the legal foundation for export controls. Wassenaar, EAR, and EU dual-use regulations all regulate AI-relevant technology on dual-use grounds.
In the AI governance context: the spread of advanced AI capabilities to additional actors. Distinct from weapons proliferation but often analyzed with similar frameworks (supply-side controls, demand-side monitoring).
Governance relevance
Compute proliferation tracking (who is acquiring what compute capability, and where) is one of Scrutica's core use cases. Export controls are the primary anti-proliferation tool.
National programs to develop domestic AI compute capacity, independent of foreign hyperscalers. Includes government-funded GPU clusters, national cloud initiatives, and indigenous chip development.
Governance relevance
Two questions define each sovereign program: execution (what fraction of announced spending reaches operational infrastructure) and supply chain exposure (what fraction of the program's hardware falls under allied jurisdictional authority via export controls, FDPR, or entity list designations).
Scrutica tracks 30+ sovereign AI programs with separate government and private investment figures.See on page
Scrutica metric for sovereign AI programs: disbursed spending divided by the GOVERNMENT-ONLY announced figure (falling back to the all-capital headline only where no government-only figure is published). A reality ratio of 0.1 means 10% of the government's own commitment has reached operational deployment. It is deliberately not disbursed-over-headline: headline figures roll private capital in beside the government programme, so dividing by them measures announcement composition rather than execution.
Governance relevance
The reality ratio measures execution: what fraction of a government's own AI commitment has reached operational deployment. Multiply it by the government-only figure, never by the headline — the UAE announces $518.74B of which $3.54B is government, so the headline answers a different question. The ratio is also unavailable for most programs, because most states do not publish disbursement at all, and that absence concentrates in the least transparent ones; ranking countries by reality ratio therefore ranks disclosure, not execution. The complementary metric is allied governance reach, which measures supply chain exposure to allied export control jurisdiction.
The sovereign AI dashboard tracks reality ratios for every country with a national AI program, alongside the government-only figure each ratio is computed against.See on page
A range of values within which the true value is estimated to fall with a stated probability (typically 90%). Scrutica reports estimation bounds rather than point estimates where multiple estimation paths are available.
Governance relevance
Confidence intervals communicate honest uncertainty. A wide interval (e.g., 50-200 PFLOP/s) tells a policymaker the estimate is uncertain; a narrow one (e.g., 80-100 PFLOP/s) supports stronger conclusions.
The documented origin and transformation history of a data point. Every Scrutica value has data_source, source_url, and is_estimated fields.
Governance relevance
Provenance enables citation and verification. A policy researcher can trace any number on the platform back to its original source and assess whether to trust it.
A boolean flag on every Scrutica data point. True if the value is derived or inferred; false only if it comes from a primary source with direct measurement.
Governance relevance
The is_estimated flag distinguishes verified facts from modeled estimates. Policymakers should cite estimated values with appropriate caveats.
The range produced by cross-validating multiple estimation paths. When hardware, power, and cost paths agree within 15%, confidence is high; when they diverge, the bounds are wide.
Governance relevance
Estimation bounds are Scrutica's mechanism for honest uncertainty. They prevent the false precision that undermines trust in data-driven governance tools.
The FLOP engine shows cross-validated estimation bounds for each facility.See on page
Breadth-first search: a graph traversal algorithm used in the cascade simulator to model disruption spreading hop-by-hop through the supply chain. Each hop applies a decay factor based on edge substitutability.
Governance relevance
BFS propagation models how a disruption at one point in the supply chain ripples outward. The number of hops before the impact becomes negligible indicates how resilient the supply chain is to that disruption.
The per-hop reduction in disruption impact during cascade propagation. Calculated as 1 minus substitutability score, clamped at 0.8. Higher decay = faster attenuation = more resilient supply chain.
Governance relevance
Decay rates encode how easily a disrupted supplier can be replaced. A low decay rate (hard to substitute) means the disruption cascades further before dissipating.
Decomposing a company's total capital expenditure into components: land, buildings, power infrastructure, networking, GPU/accelerator hardware, and other IT equipment.
Governance relevance
Capex disaggregation enables the cost-based estimation path for facility-level compute. When a company reports $5B in data center capex, disaggregation estimates how many GPUs that purchases.
One petaFLOP (10¹⁵ FLOP/s) sustained for one day (86,400 seconds) = 8.64 × 10¹⁹ FLOP. Used as the unit for the Compute Cost Index.
Governance relevance
Petaflop-days normalize compute across different hardware and providers so that cost comparisons are meaningful. The $/petaFLOP-day metric shows how much it costs to train AI in different jurisdictions.
The cost index reports compute pricing in $/petaFLOP-day across cloud providers and regions.See on page
Scrutica's coinage for 1 petaFLOP-day of BF16 dense training compute (= 8.64 × 10¹⁹ FLOP). $/SCU is the platform's normalized cost metric: hourly instance price x 24 / (num_gpus x BF16 dense TFLOP/s / 1000). BF16 dense throughput per GPU (989.5 for H100 SXM, 312 for A100, 2,250 for B200) comes from Epoch AI ML Hardware; the SCU normalization itself is not an Epoch metric.
Governance relevance
SCU makes provider, region, and hardware-generation pricing directly comparable. A reader can answer "is AWS on-demand H100 cheaper than CoreWeave A100 per unit of training output?" without doing the unit conversion by hand. Distinct from raw $/hour because $/SCU bakes in the GPU's effective throughput.
A 3-year reserved CoreWeave H100 8-GPU instance prices at ~$10/SCU; AWS p5 on-demand prices at ~$50/SCU.See on page