Machine Intelligence Infrastructure
The global physical supply chain behind artificial intelligence
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Follow the Chain
Pick a material and watch it travel from origin to your screen.
Chokepoint Atlas
Five lenses on supply-chain fragility — pick one to highlight every node in that category.
Layers
Click to solo · Shift-click to combine
Filter by country
Map Center
Natural Earth 50m
About this map
Every time you ask an AI chatbot a question, you set in motion a chain of dependencies that spans 15 countries, more than 100 companies, and stretches from 380-million-year-old quartz deposits to fiber-optic cables on the ocean floor.
This is a living atlas of the complete physical supply chain behind artificial intelligence — 109 nodes, 213 typed dependencies, 384 cited sources, and 394 real-world sites. Each dot is a critical stage. Each arc is a dependency. Click any node to read its story, sources, and confidence rating.
What you're seeing
Ten layers stack from the ground up: raw materials at the bottom, AI services at the top. Each layer feeds the one above it. The map currently covers 109 nodes across 15 countries, with 32 chokepoints — stages where a single company, country, or site controls most of global supply.
Every claim is sourced. Hover any node in the panel to see its citations, last-verified date (YYYY-MM), and confidence rating (high / medium / low) based on how many independent sources back it up. The full methodology — data schema, source rubric, risk model, edit workflow — is covered in the sections below.
How to use
- Play — animates the supply chain building up layer by layer, from raw materials to AI, with connections drawing in as each stage appears.
- Labels — toggle node names on the map.
- Flow — animates link directions to show the direction of material and data flow.
- Chokepoints — pulsing red rings mark stages where a single company or region controls most of global supply.
- Top 20 Risks — highlights the twenty highest-risk nodes by composite fragility score (see the Risk model section below).
- Cables — submarine fiber-optic cables carrying ~99% of international internet traffic.
- Data Centers — major AI and cloud data-center locations.
- Stories — eight amber pins on places where a single dot tells a big chunk of the chain (Carajás, Spruce Pine, Mariupol+Odesa, Bayan Obo, Dammam, Abilene Stargate, Kolwezi, Veldhoven).
- Sites — ~400 real-world locations the chain actually runs on (fabs, plants, mines, R&D offices, HQs), colored by chain layer. Click any site to open its parent node.
- Export Controls — highlights nodes subject to US, China, Japan, or Dutch export controls.
- Edge Types — recolors links by what flows through them: material, equipment, design-IP, power, data, or service.
- Search — type any node name in the search box at the top of the panel.
- Dotted terms — underlined acronyms in node descriptions have inline definitions; hover to read them.
The ten layers
The chain is stratified vertically. Toggle any layer in the side panel to isolate it; use the × next to Layers to clear them all.
- −3 · Raw Materials — quartz, iron ore, copper, germanium, rare earths, sugarcane, tin.
- −2 · Energy & Water — grids, nuclear, gas turbines, transformers, cooling, ultra-pure water.
- −1 · Chemistry & Refining — polysilicon, neon gas, fluorochemistry, photoresists, industrial gases.
- 0 · Processed Materials — silicon wafers, photoresists, ABF film, copper foil, fiber cable.
- 1 · Parts & Design — substrates, photomasks, EDA software, ARM, laser chips.
- 2 · Manufacturing Machines — ASML EUV scanners, Zeiss mirrors, TRUMPF lasers, etch & deposition tools.
- 3 · Chip Making & Assembly — TSMC, Samsung, Intel foundries; CoWoS advanced packaging; HBM stacking.
- 4 · Chips & Networking — NVIDIA, AMD, Broadcom, custom ASICs, switches, submarine cables.
- 5 · Cloud & Data Centers — Microsoft Azure, AWS, Google Cloud, Oracle, CoreWeave.
- 6 · AI Services — OpenAI, Anthropic, Google, Meta, xAI — and the assistants you use.
A parallel Chinese chain (SMIC, YMTC, CXMT, Naura, AMEC, Huawei HiSilicon …) is included — toggle Chinese Chain under Follow the Chain to isolate it.
Risk model & Top 20
Each node carries a composite risk score from 0 to 100, computed transparently from a fixed rubric. The Top 20 Risks view highlights the twenty most fragile nodes; every detail panel shows the breakdown so any score is fully auditable.
| Signal | Weight |
|---|---|
| Bottleneck flag | +30 |
| Sole supplier (atlas) | +25 |
| Geographic concentration | +15 |
| SME oligopoly | +10 |
| Capacity-constrained | +10 |
| Trade-war flashpoint | +10 |
| Subject to export controls | +5 |
| Downstream fan-out (log-scaled) | 0 to +15 |
| Low confidence | −5 |
Signals are not mutually exclusive — a node like ASML EUV picks up bottleneck + sole + geographic + trade, then gets a downstream-fan-out bonus because almost every advanced chip depends on it.
Chokepoint Atlas
Five lenses on supply-chain fragility — pick one to highlight every node in that category:
- Sole Supplier — if this one company stops, the chain breaks. No alternative exists (ASML EUV, Zeiss EUV optics, Shin-Etsu photoresist…).
- Geographic — one country or one site controls supply (Taiwan for leading-edge logic, China for refined rare earths, Spruce Pine NC for high-purity quartz).
- SME Oligopoly — four companies (Applied Materials, Lam, TEL, KLA) control most non-EUV semiconductor equipment.
- Capacity — demand outpaces production; backlogs measured in years (HBM, CoWoS, large transformers).
- Trade Wars — subject to export controls between the US, China, Japan, and the Netherlands.
Follow the chain
Pick a material and watch it travel from origin to your screen. The map dims everything else and highlights only the nodes and links on that single chain.
- Full AI Chain — the entire path from raw materials to AI services.
- Quartz → Chips — Spruce Pine, NC quartz becomes silicon ingots and then wafers.
- Sugar → GPUs — Brazilian sugarcane becomes ABF film inside NVIDIA GPUs.
- Neon → Chips — Ukrainian steel mills supply the neon gas inside EUV lasers.
- Tin → Light — molten tin droplets, vaporized 50,000 times per second, create the EUV photons that print chip circuits.
- Water → Chips — 10 million gallons a day, purified to parts-per-quadrillion.
- Germanium → Internet, MSG → AI, Sand → Internet, Copper → Cloud, Steel → Power, Chinese Chain.
Stories — eight places
Eight curated geographic pins surface the cartography in narrative form. Each pin is a place where a single dot tells a big chunk of the chain. Toggle Stories in the View controls to drop the pins, then click any to read.
- Carajás, Brazil — the world's largest iron mine, in the eastern Amazon.
- Spruce Pine, North Carolina — the quartz monopoly almost no one knows about.
- Mariupol & Odesa, Ukraine — the steel mills that were the world's neon supply.
- Bayan Obo, Inner Mongolia — the mine behind China's rare-earth dominance.
- Dammam, Saudi Arabia — the oil that fuels the grid that fuels the chips.
- Abilene, Texas (Stargate) — the new gigawatt-class AI data-center build-out.
- Kolwezi, DR Congo — the cobalt belt powering every laptop and EV battery.
- Veldhoven, Netherlands — the small Dutch town that builds every EUV scanner on Earth.
Sites overlay — 394 real locations
Each chain node is an aggregate. Toggle Sites to see the 394 individual fabs, plants, mines, R&D offices, and HQs that aggregate up. Sources: SemiAnalysis Global Fabs Database (161 fabs), GLOBAÏA-curated manual sites (214), and HQ fallback for the rest. Dots are colored by their parent layer.
This is the view to switch on if a node like NVIDIA or ASML feels too “Silicon Valley.” You'll see the actual geography — Israel, India, Singapore, Taiwan, Germany — that the headline name hides.
Data & methodology
The map's data is in JSON, not code — three canonical files in public/data/ai-supply-chain/: nodes.json, edges.json, sources.json. Each node carries layer, country, coordinates, a sourced description, a list of source IDs, a last-verified date, and a confidence rating.
Confidence rubric
Auto-assigned by source count: high (4+ independent sources), medium (2–3), low (0–1). The detail panel surfaces this as a colored dot next to the verified date.
Source quality
Sources are prioritized in order: primary company filings → government statistical (USGS, EIA, IEA) → peer-reviewed papers → think tanks (CSET, RAND, CSIS, OECD) → specialist trade press (SemiAnalysis, TeleGeography, TrendForce) → secondary news. Wikipedia is avoided except for stable definitional content.
Edge types
Every connection has a type: material (default), equipment, design-IP, power, data, service. Toggle Edge Types in the View controls to recolor links by what flows through them.
Sites & cartographic exports
The site overlay is built from a 1,213-fab SemiAnalysis dataset filtered to the chipmakers covered, plus 214 GLOBAÏA-curated manual sites and HQ fallbacks. A GIS bundle (GeoJSON + Esri shapefile) is produced for downstream projects but kept private to the staging folder.
References
- Chris Miller, Chip War: The Fight for the World's Most Critical Technology, Scribner, 2022
- International Energy Agency, Energy and AI, IEA, 2025
- OECD, Competition in the AI Infrastructure Stack, OECD, 2025
- OECD, Mapping the Semiconductor Value Chain, OECD Science, Technology and Industry Policy Papers, 2024
- International Energy Agency, Global Critical Minerals Outlook, IEA, 2024
- Semiconductor Industry Association (SIA), various reports and data, semiconductors.org
- Epoch AI, Nvidia B200 Cost Breakdown, 2025
- TrendForce, HBM market share and semiconductor industry reports, 2025–2026
- SemiAnalysis, Global Fabs Database, 2025 (1,213 fabs worldwide)
- ASML, annual reports and investor presentations, 2024–2025
- TSMC, corporate social responsibility and earnings reports, 2023–2025
- Wood Mackenzie, Power Transformer Supply Outlook, 2025
- Vertiv, CoreWeave, NVIDIA, AMD, Broadcom — quarterly earnings reports and investor filings, 2025–2026
- TeleGeography, Submarine Cable Map, 2025
- U.S. Geological Survey, Mineral Commodity Summaries: Germanium, 2025
- John VerWey, Tracing the Emergence of Extreme Ultraviolet Lithography, CSET, 2024
- Pilz, Mahmood, Heim, AI Power Requirements, RAND, 2025
- Lennart Heim, US AI Diffusion Framework, RAND, 2025
- Scaling Limits to AI Chip Manufacturing, ICML, 2025
- Andre Barbe and Will Hunt, Preserving the Chokepoints, CSET, 2022