Scrutica
Methodology overview, data source inventory with coverage notes, limitations disclosure, and CC-BY-SA 4.0 data license.
What Scrutica covers, what it does not cover, and how to cite it. RAND flagged that monitoring infrastructure for AI compute governance does not yet exist; this platform is an attempt to build it, with full transparency about methods and limitations.
The core dataset connects layers that are typically accessed separately, priced separately, and structured incompatibly:
Data centers and fabrication plants with location, operator, power capacity, and (where disclosed or estimable) GPU deployment across 30+ countries.
Thousands of bilateral supplier-customer and parent-subsidiary edges from FactSet Revere, WRDS, and SEC Exhibit 21 filings. Named-entity relationships, not market-share proxies; the graph resolves specific interdependencies between specific companies.
Government compute commitments tracked separately from private investment, with both announced and deployed figures where the two diverge (they usually do).
BIS Entity List designations mapped onto the supply chain graph, connecting regulatory actions to the infrastructure they actually affect.
Facility-level FLOP estimates via three independent paths (hardware inventory, power draw, capital cost), each with estimation bounds. Every parameter documented on the methodology page and adjustable in-browser.
Much of the underlying data derives from institutional sources (FactSet Revere, WRDS, SEC EDGAR, CSET) that are individually expensive, access-restricted, or both. The platform cross-references across them and maintains provenance (source, URL, confidence tier, estimation flag) on every value.
Coverage is structurally biased toward entities with public disclosure obligations. State-owned enterprises, privately held companies, and facilities in jurisdictions with limited reporting are underrepresented, and often precisely the entities most relevant to compute governance.
Compute capacity estimates carry real uncertainty, particularly via the power-based and cost-based paths where assumptions compound. The methodology page makes this visible rather than hiding it.
Cascade analysis is stronger on topology than edge weights. Graph structure from regulatory filings (Tier 1–2); substitutability decay rates are expert-assessed (Tier 3). Useful for identifying structurally critical nodes, less reliable for precise severity prediction at multiple hops.
David Gringras, physician (Edinburgh) and law graduate (University of Law), Frank Knox Fellow at Harvard (MPH Health Policy; cross-registered at MIT, Harvard Law School, and the Kennedy School). Evaluations and Collaborations Lead on the FATF-to-AI governance translation project at Arcadia Impact / The Future Society; project supervisor at Orion AI Governance on evaluation-independent governance mechanisms. Recent research: Safety Under Scaffolding (arXiv:2603.10044), IatroBench (arXiv:2604.07709), Defensive AI (SSRN).
Built at Harvard as part of ongoing research on AI governance infrastructure, informed by work on evaluation-independent governance mechanisms (Orion AI Governance) and FATF-to-AI regulatory translation (Arcadia Impact / The Future Society).
Corrections, data contributions, and methodological critiques welcome.