{"id":58544,"date":"2026-05-18T02:05:03","date_gmt":"2026-05-18T09:05:03","guid":{"rendered":"https:\/\/svch.io\/llm-firm-network-reconstruction-supply-chain-open-data-corporate-intelligence-nvidia-centrality-surge\/"},"modified":"2026-05-18T02:05:03","modified_gmt":"2026-05-18T09:05:03","slug":"llm-firm-network-reconstruction-supply-chain-open-data-corporate-intelligence-nvidia-centrality-surge","status":"publish","type":"post","link":"https:\/\/svch.io\/es\/llm-firm-network-reconstruction-supply-chain-open-data-corporate-intelligence-nvidia-centrality-surge\/","title":{"rendered":"The 44,000-Company Supply Chain Map That Cost $0 to Build"},"content":{"rendered":"
\nCorporate Strategy — Supply Chain Intelligence • Complexity Science Hub Vienna<\/span><\/p>\n

The 44,000-Company Supply Chain Map That Cost $0 to Build<\/h1>\n

Here is the most expensive piece of information your company buys that should now be free: your supply chain map.<\/strong><\/p>\n

Most large corporations spend between $500,000 and $5 million annually<\/strong> on proprietary databases — Bloomberg SPLC, IHS Markit, S&P Global, Capital IQ — to understand who supplies whom, which firms are central in their value chain, and where concentration risk lives. These datasets are expensive. They are incomplete. And they are slowly updated — often quarterly at best, with weeks of lag between a real-world event and its reflection in the database.<\/p>\n

A research team from the Complexity Science Hub Vienna, the Medical University of Vienna, and the Austrian Institute of Economic Research just proved that this entire industry is now obsolete.<\/p>\n

They used large language models to crawl archived corporate websites, press releases, and investor relations pages — publicly available web data, no paywalls, no subscriptions — and reconstructed the global production network of the semiconductor industry. The result: a map of 44,000 firms connected by 2.1 million unique relationships<\/strong>, spanning 1,145 cities and 64 countries, covering 25 years of structural evolution.<\/p>\n

The cost of the data: zero<\/strong>.<\/p>\n

The cost of the proprietary equivalent: millions<\/strong>.<\/p>\n

For anyone responsible for corporate strategy, investor relations, supply chain resilience, or M&A, this paper is not a technology curiosity. It is a demonstration that the information monopoly on supply chain intelligence has been broken.<\/p>\n

\n$0<\/span>
\nCost of the data — 44,000 firms, 2.1M relationships, 25 years<\/span>
\nvs. $500K–$5M annually for proprietary database licenses<\/span>\n<\/div>\n

Executive Summary<\/h2>\n

The study:<\/strong> Researchers used LLMs (GPT-4o-mini) to extract firm-to-firm relationships from open web data — no proprietary databases, no paywalls, no surveys. Validated on the semiconductor industry, seeded with 124 firms, expanded to 44,000+ firms through iterative crawling.<\/p>\n

\n
\nFinding 1 — NVIDIA Centrality<\/span>
\nSurge from 2022<\/span><\/p>\n

Among 44,000 firms, one centrality line dominates. NVIDIA’s betweenness centrality exploded in 2022 with ChatGPT’s launch. No other firm shows comparable shift.<\/p>\n<\/div>\n

\nFinding 2 — Structural Shifts<\/span>
\nFabless + Foundry Rise<\/span><\/p>\n

Network captures the rise of fabless chip designers (AMD, Qualcomm, Apple) and the growing centrality of contract manufacturing (TSMC). All from public data.<\/p>\n<\/div>\n

\nFinding 3 — Database Replacement<\/span>
\nValidated vs S&P Global<\/span><\/p>\n

67% agreement with proprietary S&P data. 89% human annotation precision. Year-by-year temporal granularity that no proprietary database provides.<\/p>\n<\/div>\n

\nFinding 4 — Strategic Analysis<\/span>
\nMulti-level capability<\/span><\/p>\n

Centrality measures, community detection, geographic concentration, temporal evolution. A dynamic model of your competitive environment from free data.<\/p>\n<\/div>\n<\/div>\n

\n\ud83d\udd11 The Strategic Conclusion<\/span>
\nThe era of closed, expensive, proprietary supply chain databases is ending. Any company can now map its supply chain dependencies, identify bottleneck firms, and track structural shifts — all from public data. For corporate strategy and investor relations, this is the equivalent of GPS replacing paper maps.<\/span>\n<\/div>\n

Paper at a Glance<\/h2>\n\n\n\n\n\n\n\n\n\n
Metric<\/th>\nValue<\/th>\n<\/tr>\n
Title<\/strong><\/td>\nReconstructing Temporal Multi-Relational Firm Networks at Scale Using Large Language Models: The Case of the Semiconductor Industry<\/td>\n<\/tr>\n
Authors<\/strong><\/td>\nK\u00f6se, Diem, Dervic, Friesenbichler, Heiler, Koller, Klimek<\/td>\n<\/tr>\n
Institutions<\/strong><\/td>\nComplexity Science Hub Vienna, Medical University of Vienna, Austrian Institute of Economic Research<\/td>\n<\/tr>\n
Published<\/strong><\/td>\nMay 15, 2026<\/td>\n<\/tr>\n
Categories<\/strong><\/td>\nphysics.soc-ph, cs.SI, q-fin.GN<\/td>\n<\/tr>\n
Relevance Score<\/strong><\/td>\n96\/100 — Validates open-data supply chain reconstruction across industries<\/strong><\/td>\n<\/tr>\n
Paper URL<\/strong><\/td>\narxiv.org\/abs\/2605.15842<\/a><\/td>\n<\/tr>\n<\/table>\n

What the Paper Found<\/h2>\n
\n

Finding 1: NVIDIA’s Centrality Surge Is Visible in Open Data<\/h3>\n

Among 44,000 firms tracked between 2015 and 2025, one line on the chart tells the story of the last three years in the global economy. NVIDIA’s betweenness centrality — a network measure that captures how much of a bottleneck a firm is in the flow of goods, services, and relationships between other firms — was relatively stable until 2022. Then it exploded.<\/p>\n

The surge coincides with ChatGPT’s commercial launch in November 2022 and the AI compute boom. NVIDIA’s centrality trajectory is so dramatic that it visually dominates the chart of all 44,000 firms. No other firm — not TSMC, not Samsung, not Intel — shows a comparable shift.<\/p>\n

So what:<\/strong> The market has been pricing in NVIDIA’s dominance. This paper provides independent, open-data confirmation that NVIDIA has become the structural bottleneck of the AI economy. Every AI company, every cloud provider, every automotive firm building AI features routes through NVIDIA’s supply chain.<\/p>\n<\/div>\n

\n

Finding 2: Semiconductor Industry Structural Shifts Captured in Open Data<\/h3>\n

The network reconstruction reveals two major structural shifts consistent with known industry narratives:<\/p>\n