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Scrutica
Updated 2026-09-26

Related Work

Seven organizations researching AI and compute infrastructure

Sources and related research

Data sources

Facility estimates, hardware specifications and model-training observations come from Epoch AI.

CSET supplies semiconductor supply-chain records, used alongside the licensed corporate databases.

Research references

RAND’s firm-network research is a methodological reference for the cascade analysis. Other entries provide context on AI capability, its economic effects, electricity demand and compute governance.

Epoch AI

Data-center capacity, AI hardware and model training compute.

Epoch estimates data-center capacity from satellite imagery, permits and company disclosures.

CSET (Georgetown)

Semiconductor supply chains and export controls.

CSET's Emerging Technology Observatory maps semiconductor production inputs and the firms that supply them. Its mid-2025 dataset draws on TechInsights CMRS, WSTS and SIA; country market shares are assigned by the headquarters of each firm's ultimate parent.

RAND Corporation

Firm-network systemic-risk research for semiconductor supply chains.

Welburn and colleagues use customer–supplier relationships to estimate firm-level input–output linkages and study how a shock at one company affects aggregate production. Their 2023 working paper addresses missing data through inference and examines how network structure concentrates systemic risk.

Stanford HAI

Annual statistics on AI research, performance and investment.

Stanford HAI's AI Index collects annual evidence on AI development and its economic and social effects. The 2026 report includes research infrastructure, technical performance, private-sector investment and national AI policy, with separate chapters on science and medicine.

METR (Model Evaluation & Threat Research)

Evaluations of AI agents on extended tasks.

METR measures the length of tasks an AI agent can complete at a specified success rate, using the time those tasks take human professionals. Its March 2025 study fits success rates against human task duration on software and reasoning tasks. The resulting time horizon depends on the task suite and human baselines.

GovAI (Centre for the Governance of AI)

Compute governance and AI regulation.

GovAI studies how compute infrastructure can support AI oversight and enforcement. Its research on computing power examines the practical advantages of concentrated hardware supply chains alongside the risks of intrusive monitoring and greater concentration of power.

Scrutica’s datasets and analyses

Methodology gives the assumptions and source definitions used in these analyses.

Supply-chain relationships

Supplier and customer relationships come from licensed corporate databases and CSET ETO, with product or service descriptions where available. One licensed layer is limited to relationships between entities already in the register. The cascade model uses the recorded criticality and supply share; missing values default to 5 out of 10 and 30%, respectively. Sole-source relationships have a minimum criticality of 9. These assumptions affect the simulated disruption.

Sovereign AI programs

36 programs, with separate amounts for announced funding, commitments and disbursements. Country records identify the disclosures behind these amounts and distinguish government funding from private investment included in a headline pledge. Facility and chip-allocation records provide evidence of implementation where available.

Export-control designations and company matches

3,435 BIS Entity List designations, linked to their published sources. Company matching combines names with recorded ownership relationships. An ownership connection can identify a company for further review; the applicable restrictions depend on the rule and its effective date.

Compute Cost Index

GPU rental prices divided by dense BF16 throughput, expressed in dollars per petaFLOP-day. Daily refreshes cover AWS, Azure and Oracle pricing; GCP, CoreWeave and Lambda prices are checked manually against published price pages. Each observation has its retrieval date. Hardware throughput comes from Epoch AI's ML Hardware database; prices on this basis exclude the extra throughput available from structured sparsity.

Cascade Simulation

Simulates disruption through supplier and customer relationships, with adjustable severity and propagation assumptions. Downstream effects depend on supply share and criticality; upstream effects use a customer's share of the supplier's weighted relationships. In historical checks, the ASML blanket-halt scenario did not reproduce the selective 2020 restrictions, and the CoWoS comparison used parameters calibrated after the event. A comparison labelled ‘Russia helium 2022’ used a Qatar–Hormuz helium scenario unrelated to the 2022 disruption of Ukraine’s neon supply.

Capacity Thresholds

Compares model training budgets and estimated facility throughput with published compute thresholds. Facility calculations use recorded hardware where available and convert power capacity to throughput otherwise. Training duration and utilization are assumptions; the threshold descriptions state the associated legal conditions and whether the instrument remains in force.

Compute Visibility Index

Attributes data-center capacity to its host country and to the jurisdiction identified through corporate ownership. A separate ownership-transparency assessment classifies facilities by the available sources for their corporate ownership. The two assessments use their own stated facility populations and capacity totals.

Allied Coordination Gap Analyzer

Examines facilities whose host country and corporate ownership connect different export-control jurisdictions. Ownership chains are assessed against a versioned classification of national regimes. A documented connection to a jurisdiction outside the coordinated group remains visible when a later part of the ownership chain is unknown.

Methodology

Documents the formulas and source definitions behind the analysis, including default values and uncertainty. The capacity and cascade calculators allow researchers to change assumptions and inspect the resulting estimates.

Outside Scrutica's scope

Model-training data

Epoch maintains the model catalog and its estimates of cumulative training compute.

AI company revenue, funding and staffing

Epoch's AI Companies dataset tracks revenue, funding, staffing, usage and compute spending. It labels observations Confident or Likely according to their source and specificity. Scrutica's company records focus on infrastructure and corporate relationships; the financial disclosures they include are not a comprehensive financial history.

Classified procurement

Scrutica uses public procurement disclosures and licensed corporate databases. Classified contracts are outside its coverage; public award records may also omit information needed to identify the facilities or equipment involved.

Live electricity draw metered at individual data centers

Facility records describe reported or planned power capacity and public grid-connection requests. These figures do not measure a site's electricity consumption in real time.

Citing this page

Scrutica. “Related Work.” 2026-09-26. https://scrutica.com/related-work