Methodology & Limitations
Every number on this dashboard is either filed data sourced from SEC EDGAR, or a modeled estimate built from explicitly stated assumptions. This page documents the math, the sources, and the things this analysis does not do.
Quarterly capex is pulled directly from each hyperscaler's 10-Q and 10-K filings via the SEC EDGAR XBRL API (data.sec.gov/api/xbrl/companyconcept).
The us-gaap concepts used:
- MSFT, META, GOOGL, ORCL →
PaymentsToAcquirePropertyPlantAndEquipment - AMZN →
PaymentsToAcquireProductiveAssets(Amazon stopped tagging the first concept after FY2017)
Cash-flow items in XBRL are filed period-to-date within a fiscal year. We diff consecutive YTD entries (Q1 → Q2 = H1 − Q1, etc.) to recover discrete quarterly values. Each row in the dataset carries the filing accession number so the source 10-Q is one click away.
Forward guidance is loaded from a curated JSON file (data/guidance.json) seeded with the most recently disclosed full-year capex commentary per company. Each row carries a sourceUrl to the press release or 10-Q where the guidance was given.
CaveatGuidance shifts quarterly. The asOf field on each row indicates the earnings cycle from which the range was extracted; re-verify against the most recent earnings press release before relying on a specific number.
Current asOf: 2026-Q1
Capex covers more than AI. We discount each hyperscaler's guided capex by:
- MSFT — 75% (Azure AI infra excl. office/Xbox capex)
- META — 70% (DC build + GPU; excludes Reality Labs)
- GOOGL — 70% (TPU, GPU, DC; excludes office, fiber, Other Bets)
- AMZN — 65% (AWS GenAI; carve-out vs. fulfillment build)
- ORCL — 90% (Stargate / OCI Gen2 is overwhelming the FY26 mix)
These shares are anchored to management commentary (e.g. Microsoft's "cloud and AI" segment disclosure) but remain a judgment call.
AI-attributable capex is split into seven infrastructure layers using a single industry-baseline mix applied to all hyperscalers:
| Layer | Mix % | Rationale |
|---|---|---|
| Compute Silicon | 45.0% | GPUs + custom AI ASICs (~45% of AI capex per SemiAnalysis composition) |
| HBM & Memory | 10.0% | HBM3E/HBM4 stacks (typically bundled in GPU BoM but allocated here) |
| Networking & Optical | 8.0% | Switches + optical (back-end fabric ~7–9% of cluster cost) |
| Power & Electrical | 15.0% | Switchgear, UPS, busway, transformers — rising with density |
| Datacenter Build | 12.0% | DC shell + electrical EPC + land prep |
| Foundry & Equipment | 7.0% | Imputed via foundry/wafer cost back to AMAT/LRCX/ASML pass-through |
| Neoclouds | 3.0% | Hyperscaler take-or-pay to GPU clouds (MSFT↔CRWV style deals) |
Within each layer, dollars allocate to suppliers by approximate market share or disclosed customer concentration. Hyperscaler-specific overrides apply where disclosure or strategic preference differs materially (e.g. GOOGL skews to AVGO via TPU; ORCL is almost pure NVDA).
- GOOGL: compute_silicon → NVDA 45%/AVGO 50%/AMD 5%
- META: compute_silicon → NVDA 78%/AVGO 17%/AMD 5%
- AMZN: compute_silicon → NVDA 65%/AVGO 5%/AMD 5%
- ORCL: compute_silicon → NVDA 90%/AMD 10%; neocloud →
- MSFT: compute_silicon → NVDA 70%/AVGO 15%/AMD 15%; neocloud → CRWV 100%
In-house silicon is partially excluded. Amazon Trainium/Inferentia and Microsoft MAIA are not in the supplier roster (they are internal). Per-hyperscaler compute_silicon shares are intentionally below 100% for these names; the residual stays internal.
HBM coverage is partial. Micron is the only HBM name in scope. SK Hynix and Samsung Electronics dominate global HBM share but are not US-listed, so they are out of scope for a US-listing demo. Total memory dollar flow understates industry HBM TAM by approximately 3×.
Capex ≠ supplier revenue. Hyperscaler capex is the address-able dollar base; supplier revenue recognition lags and depends on mix, take-or-pay terms, and ASP. Treat modeled flows as TAM share, not booked revenue.
No FX effect. All figures are USD. TSM and ASML reported figures are reported in TWD/EUR respectively and are not currency- adjusted within this model.
No double-counting adjustment. AVGO supplies both compute silicon (TPU, MTIA) and networking (Tomahawk/Jericho); both lines flow to AVGO and aggregate at the supplier level — by design.
npm run fetch:capex re-pulls all SEC EDGAR data into data/capex_actuals.json. The fetch script is in scripts/fetch-capex.mjs.
npm run extractruns the Claude CLI extraction pipeline that pulls latest 10-Q MD&A commentary on capex and surfaces it as structured JSON.
All assumptions live in src/lib/flow-model.ts as named constants — edit there to A/B alternative scenarios.