Global AI Analysis
Semiconductor Supply Chain by Subcategory & Country
The global semiconductor supply chain reveals stark geographic concentration across all four resource types. Design resources command the largest share at $648B, led by US-headquartered fabless firms and IP licensing giants. South Korea and Taiwan anchor Process capabilities through Samsung and TSMC's combined foundry scale, while Japan's Materials strength forms an indispensable but often underappreciated layer of the stack.
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Source: CSET · SIA · McKinsey Global Semiconductor Report
Country Rankings in Global Semiconductor Supply Over Years
Between 2019 and 2024, rankings within the semiconductor supply chain have remained broadly stable at the top, masking important shifts in mid-tier positions. China has moved up steadily despite export restrictions, reflecting sustained domestic capacity investment. The Netherlands holds an outsized position — disproportionate to its economy size — through ASML's monopoly on EUV lithography tooling, making it a pivotal single point in the global supply network.
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Source: CSET · SIA · Rank by share of total supply chain value · 2019 → 2022 → 2024
Global Semiconductor Market Breakdown by Country and Resource Type
The US dominates Design resources by a wide margin, reflecting the concentration of leading fabless chip companies (Nvidia, Qualcomm, AMD) and EDA software vendors that underpin global chip development. Taiwan and South Korea lead in Process through world-class foundry infrastructure, while Japan's Materials and Tool strengths complete a triangulated dependency that no single nation can replicate alone. This interdependence is simultaneously a stabilizing force and a geopolitical vulnerability.
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Source: CSET · SIA · Sorted by total market size
Global AI Innovation & Workforce by Country
The US and China present two contrasting profiles in AI capacity. The US dominates deployed workforce with 170,061 AI workers versus China's 5,342, reflecting deep integration of AI talent into industry. China counters with sheer publication and patent volume — 22,665 papers and 48,660 patents — suggesting a strategic emphasis on output and IP claims over workforce deployment. These profiles reveal fundamentally different approaches: US depth versus Chinese breadth.
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Source: PARAT · OECD AI Policy Observatory · Area proportional to capacity across papers, patents, and workers
Global AI Patents By Sub-Domain
Computer Vision dominates the AI patent landscape with 27,304 filings — more than double any other sub-domain — reflecting its commercial maturity in surveillance, autonomous vehicles, and consumer devices. Control systems (11,662) and Analytics (10,733) follow, signaling that applied industrial AI is drawing serious IP investment alongside consumer applications. Emerging domains like NLP and AI Safety remain comparatively thin, suggesting the patent race has yet to fully reach generative and safety-critical AI.
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Source: PARAT · USPTO · WIPO · Patent volume across AI technology sub-domains
AI Research Capacity: Publications vs. Citations
Research output and citation impact are highly unequal: a small cluster of elite institutions — primarily US and Chinese universities and research labs — account for a disproportionate share of high-citation work. The top 20 organizations sit in an exponential outlier cluster well above the trend line, meaning their research is cited far more than their publication count alone would predict. Volume and impact diverge sharply; the citation leaders define the field's direction.
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Source: PARAT · Semantic Scholar · Top 20 organizations highlighted by total citation impact
Global Research Collaboration Network
The US-China research collaboration link is the single thickest arc in the global network, representing 162,646 co-authored articles — far larger than any other bilateral pair. This paradox of deep scientific interdependence amid rising geopolitical tension is the defining dynamic of the field. Decoupling AI research would require severing one of the world's most productive scientific partnerships, with cascading effects on citation networks, talent exchange, and benchmark development.
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Source: PARAT · Semantic Scholar · Arc thickness proportional to co-authorship volume
Global Cross-Border AI Research Collaboration
Cross-border AI research collaboration grew substantially across all tracked fields from 2015 to 2023. Computer Vision and core Artificial Intelligence saw the sharpest absolute increases, driven by shared benchmark datasets and open-source frameworks that lower collaboration barriers. Emerging fields like AI Safety and Large Language Models show accelerating growth from a small base — suggesting the collaborative frontier is shifting toward the most consequential and contested areas of the field.
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Source: PARAT · Semantic Scholar · Cross-border co-authorship volume by research field · 2015–2023
AI Research Collaboration by Field — US & China
Despite escalating geopolitical friction, US-China AI research collaboration remained robust through most of the 2015-2023 period before showing signs of deceleration at the tail end. The collaboration is deepest in Computer Vision and core AI, where shared conference culture (NeurIPS, CVPR) and open-source norms keep researchers connected. Any structural decoupling would need to overcome deeply entrenched researcher-level networks that have produced some of the field's most-cited and foundational work.
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Source: PARAT · Semantic Scholar · Collaboration trends across 8 AI research fields · 2015–2023
Partner Diversity — Global AI Research Hubs
The upper-right quadrant — high partner diversity and high collaboration volume — identifies the world's true AI research hubs. The US occupies this position most definitively, collaborating with the widest range of countries at the greatest aggregate volume. Countries in the middle cluster show that raw collaboration volume often concentrates with just a few major partners, limiting the diversity of ideas and talent networks. Hub status confers compounding advantages in attracting talent and setting research agendas.
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Source: PARAT · Semantic Scholar · Top 15 countries highlighted by unique collaboration partners
US AI Policy Stance Over Time (2019–2025)
US AI policy has shifted decisively toward restriction since 2022, with Restrictive measures consistently outnumbering Enabling ones from 2023 onward. The 2024 peak of 65 Restrictive policies reflects a wave of export controls on advanced chips (H100, A100), investment screening via CFIUS expansions, and computing access restrictions targeting Chinese entities. Balanced and Neutral policies remain present but are now secondary to a national security frame that has fundamentally reoriented US AI governance.
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Source: OECD AI Policy Observatory · CSET · Policy count by stance category · 2019–2025
Global AI Patent Landscape — Sector Categories
AI patent activity clusters in industrial and consumer applications, with Healthcare, Mobility, and Finance representing the dominant commercial sectors globally. Asia-Pacific leads by volume across most sectors, driven by Chinese strategic filing at scale, while US and European patents tend to concentrate in higher-value, more specialized categories. This divergence reflects fundamentally different IP strategies: volume-based portfolio building in Asia versus targeted protection of core innovations in the West.
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Source: PARAT · WIPO · USPTO · Click any slice to drill down by region, country, and sector
Global AI Investment Heatmap — Top 15 Countries & Fields
US AI investment is concentrated overwhelmingly in Mobile/Internet services at $168B, reflecting Big Tech's platform-scale bets on AI-native products. Healthcare and Data & Analytics represent the next priority clusters, both areas where AI offers clear productivity leverage. China's profile is more evenly distributed at lower absolute values, while European nations show targeted investment in industrial automation and specialized verticals — revealing a fundamental difference between platform-building and sector-specific application strategies.
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Source: CSET · PitchBook · AI investment across countries and technology categories
Global AI Publications Racing Bar (2015–2023)
China's rise in AI publication volume is the defining story of the 2015-2023 period. China surpassed the US around 2017 and reached 103,672 publications by 2023 — more than double the US total of 50,359. India's emergence as a significant third contributor (43,816) is the decade's secondary story, while European nations have grown at slower but steady rates. The raw volume race, however, obscures quality differences better captured in citation analysis — China's publication lead does not directly translate to equivalent research influence.
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Source: PARAT · Semantic Scholar · Top 15 countries by annual AI publication count · 2015–2023