Skip to content

Geopolitics & Coordination

📋Page Status
Page Type:ContentStyle Guide →Standard knowledge base article
Quality:64 (Good)⚠️
Importance:67.5 (Useful)
Last edited:2026-01-29 (3 days ago)
Words:4.2k
Backlinks:2
Structure:
📊 9📈 1🔗 27📚 4332%Score: 13/15
LLM Summary:Comprehensive quantitative analysis of US-China AI competition finds US maintains 12:1 private investment lead and 74% of global AI supercomputing, but model performance gap narrowed from 20% (2023) to 0.3% (2025). Military AI market growing 19.5% CAGR to $28.7B by 2030, while international governance scores only 4.4/10 effectiveness with 53-point implementation gap despite 87% framework convergence across 47 countries.
Critical Insights (5):
  • Counterint.Despite the US having a 12:1 advantage in private AI investment ($109.1 billion vs $9.3 billion), China produces 47% of the world's top AI researchers compared to the US's 18%, and 38% of top AI researchers at US institutions are of Chinese origin.S:4.5I:4.5A:4.0
  • ClaimInternational AI governance frameworks show 87% content overlap across major initiatives (OECD, UNESCO, G7, UN) but suffer from a 53 percentage point gap between AI adoption and governance maturity, with consistently weak enforcement mechanisms.S:3.5I:4.5A:4.5
  • ClaimChinese surveillance AI technology has proliferated to 80+ countries globally, with Hikvision and Dahua controlling 34% of the global surveillance camera market, while Chinese LLMs (~40% of global models) are being weaponized by Iran, Russia, and Venezuela for disinformation campaigns.S:4.0I:4.5A:4.0
Issues (2):
  • QualityRated 64 but structure suggests 87 (underrated by 23 points)
  • Links21 links could use <R> components
See also:80,000 Hours
DimensionAssessmentEvidence
US-China Competition IntensityHigh and IncreasingUS maintains 12:1 private investment advantage ($109B vs $9.3B in 2024), but China closing gap with 47% of top AI researchers produced domestically (Stanford AI Index 2025)
International Governance EffectivenessWeak (4.4/10)87% framework convergence across 47 OECD countries, but 53-point implementation gap; mostly non-binding with no enforcement (OECD AI Policy Observatory)
Military AI Arms RaceMedium-High, AcceleratingGlobal military AI market growing at 19.5% CAGR, from $11.5B (2025) to projected $28.7B by 2030 (Grand View Research)
Technology Transfer RiskHighChinese surveillance AI deployed in 80+ countries; Huawei/Hikvision control 34% of global surveillance camera market (Atlantic Council)
Talent CompetitionUS Leading but DecliningUS employs 57% of top-tier AI talent, but 68% of Chinese AI PhDs relocate to US due to $185K vs $67K salary premium—dependency risk (MacroPolo Talent Tracker)
Model Performance GapRapidly NarrowingGap decreased from 20 percentage points (2023) to just 0.3% (2025) per benchmark tests; DeepSeek gained 13% global LLM market share in 2 months (MIT Technology Review)
Cooperation-Competition BalanceCompetition DominantBilateral safety agreements exist (US-UK, US-Saudi) but enforcement weak; China excluded from G7 Hiroshima Process

The geopolitical dimension of AI development significantly shapes both capability trajectories and risk profiles. International competition can accelerate development timelines and reduce safety standards, while effective coordination could enable shared safety measures and governance frameworks.

This page tracks key metrics across:

  • Capability gaps between major powers
  • Talent flows and human capital distribution
  • Cooperation mechanisms and international agreements
  • Arms race indicators suggesting competitive pressure
  • Governance effectiveness of international institutions
  • Technology proliferation to authoritarian regimes
Loading diagram...

Current Status: United States maintains overall lead, but gap is narrowing in specific domains.

MetricUnited StatesChinaRatioSource
Private AI Investment (2024)$109.1 billion$9.3 billion12:1 US leadStanford AI Index 2025
Total VC Funding to AI (2025)$159 billion (79% of global)≈$125 billion total AI investment≈1.3:1 US leadCrunchbase AI Trends 2025
Government VC for AILimited direct government VC$184 billion to ≈10,000 AI firms (cumulative)China leads in state fundingStanford FSI Analysis
2025 AI Capital SpendingNot specifiedUp to $98 billion projected-SCMP Analysis
AI Compute Infrastructure74% of global high-end AI supercomputing14% of global capacity5.3:1 US leadRecorded Future 2025

Key Finding: The US private sector invests nearly 12x more than China’s private sector, but China’s government-directed investment significantly narrows the total funding gap. However, the US maintains overwhelming compute infrastructure dominance (74% vs 14% of global AI supercomputing capacity).

MetricUnited StatesChinaSource
Top 2% AI talent currently working57% of global total28% of global totalMacroPolo Talent Tracker
Top AI researchers produced (2022)18% of global47% of globalMacroPolo 2024 Update
Top-tier talent at US institutions (origin)37% domestic, 38% Chinese-origin90% retention of domestic graduatesUS Council of Economic Advisers
Graduate retention rate80% of those who studied in US90% of those who studied in ChinaMacroPolo Talent Tracker
PhD salary premium$185K average$67K averageSecond Talent Analysis
AI papers annually28,40041,200Stanford AI Index
AI patents filed (2023)≈30% of global69.7% of globalStanford AI Index

Key Finding: The US employs the majority of elite AI talent globally (57%), but China produces more than twice as many top researchers (47% vs 18%) and is increasingly retaining them domestically. The $118K salary premium (US vs China) drives 68% of Chinese AI PhDs to relocate to the US, creating a US talent dependency risk.

US Advantages:

  • Dominance in advanced chip design (NVIDIA, AMD)
  • Leading-edge semiconductor manufacturing access
  • Export controls limiting China’s access to cutting-edge GPUs

China Challenges & Strategies:

  • Significant lag in domestic chip fabrication (estimated 2-5 years behind)
  • Huawei developing AI chips to circumvent US restrictions
  • Building massive chip clusters with domestic hardware
  • Government subsidies for electricity costs at data centers using domestic chips
  • Focus on efficiency and open-source models (e.g., DeepSeek)

CSET Assessment: “China still lags significantly due to constraints imposed by U.S. export restrictions” in semiconductor fabrication, though working to close the gap.

MetricUnited StatesChinaTrendSource
Benchmark performance gapLeading0.3% behind (2025)Rapidly closing (was 20% in 2023)Stanford AI Index 2025
Global LLM market share93% of site visits13% (up from 3% in 2 months)China gaining rapidlyRecorded Future
Model lag estimateLeading3-6 months behindCould shift with algorithmic breakthroughsCSET Georgetown
Manufacturing AI adoption45%67%China leads deploymentMIT Technology Review
Enterprise software AI71% Fortune 500 adoption45%US leads enterpriseMorgan Stanley

US Leadership: Dominates frontier model development (GPT-4, Claude, Gemini) and controls 93% of global LLM market share.

China Progress: DeepSeek R1 (January 2025) demonstrated near-parity capabilities; Chinese LLM market share surged from 3% to 13% in just two months. China leads in AI deployment scale (67% manufacturing adoption vs 45% in US).

Competitive Dynamic: While the US focuses on AGI development, China is “outpacing the United States in diffusing AI across its society” according to CSET Georgetown, though “China has by no means de-emphasized its state-sponsored pursuit of AGI.”


Data primarily from MacroPolo Global AI Talent Tracker 2.0 (March 2024 update).

2019: 55% of top-tier AI researchers worked abroad (foreign nationals)

2022: 42% of top-tier AI researchers worked abroad

Change: -13 percentage points, indicating declining mobility

Interpretation: More top-tier talent staying in their home countries rather than migrating.

  • 60% of top AI institutions globally are in the United States
  • 57% of the top 2% of global AI talent works in the United States
  • 75% of top-tier AI talent at US institutions are of American or Chinese origin (up from 58% in 2019)
OriginPercentage
United States37%
China38%
Other25%

Critical Dependency: The US AI sector is heavily dependent on foreign talent, with Chinese-origin researchers comprising the single largest group.

China’s Growing Talent Production & Retention

Section titled “China’s Growing Talent Production & Retention”
  • 2019: China produced 29% of world’s top AI researchers
  • 2022: China produced 47% of world’s top AI researchers
  • Current: ~2,000 AI university majors across China

Retention Improvement:

  • 90% of researchers who attended graduate school in China stayed in China
  • 28% of top AI researchers globally now work in China (2022)

Driving Factor: “The growth of the domestic AI sector in China and the job opportunities it has created”

Gaining Ground: United Kingdom, South Korea, continental Europe “slightly raised their game as destinations”

Relative Declines: India and Canada as sources of AI researcher talent

  1. US Vulnerability: Heavy reliance on foreign talent, particularly from strategic competitor China
  2. China’s Trajectory: Rapidly improving domestic talent pipeline and retention
  3. Decentralization: Global AI talent becoming less concentrated, more distributed
  4. Brain Drain Reversal: Traditional “brain drain” to US showing signs of reversal for Chinese talent

3. Multinational AI Cooperation Agreements

Section titled “3. Multinational AI Cooperation Agreements”

Major Multilateral Initiatives (2024-2025)

Section titled “Major Multilateral Initiatives (2024-2025)”
InitiativeMembersBinding?EnforcementKey LimitationSource
OECD AI Principles47 countries + EUNoNoneImplementation guidance lackingOECD
UNESCO AI Ethics194 member statesNoNoneNo enforcement mechanismUNESCO
Council of Europe Convention46 + 11 non-membersYes (treaty)Variable by countryLimited ratifications so farCouncil of Europe
G7 Hiroshima Process7 major democraciesNoNoneExcludes ChinaG7
EU AI Act27 EU membersYesFines up to €35M or 7% revenueOnly applies to EU marketEUR-Lex
UN Global AI Resolution122 co-sponsorsNoNoneNormative onlyUN

Status: Launched 2024

Members: 44 countries + European Union

Significance: Most comprehensive international AI governance partnership

December 2024: GPAI Belgrade Ministerial Declaration issued

2024 OECD AI Principles Update:

  • Now endorsed by 47 jurisdictions including EU
  • Updated to address general-purpose and generative AI
  • Enhanced focus on safety, privacy, intellectual property, and information integrity
  • 87% content overlap with UNESCO, G7, and UN frameworks (indicating global convergence)

Adopted: May 17, 2024

Signatories: 46 Council of Europe members + 11 non-member states

Non-member signatories include: Argentina, Australia, Canada, Costa Rica, Holy See, Israel, Japan, Mexico, Peru, United States, Uruguay + European Union

Nature: First international AI treaty with legal framework (though implementation mechanisms vary)

Adopted: March 2024

Co-sponsors: 122 countries

Significance: First global consensus resolution on AI

December 2024: UN General Assembly adopted first resolution specifically on AI in military domain, stressing importance of humanitarian and international human rights law

Status: Ongoing

Key Outputs:

  • International Guiding Principles for AI
  • Voluntary Code of Conduct for AI developers

Bilateral AI Safety & Cooperation Agreements

Section titled “Bilateral AI Safety & Cooperation Agreements”

Signed: April 2024

Signatories: US Commerce Secretary Gina Raimondo, UK Technology Secretary Michelle Donelan

Scope:

  • Joint development of tests for advanced AI models
  • Align scientific approaches to AI safety evaluation
  • Accelerate development of robust evaluation suites

Follow-up: July 2025 OpenAI-UK Government MOU for AI-powered growth and innovation

Signed: November 2025

Focus: Strategic cooperation on AI development and deployment

Scale: Tracking over 900 AI policy initiatives across 69 countries

Types: National strategies, action plans, regulatory frameworks, sectoral guidelines

58 governments conducting Readiness Assessments for AI implementation

Focus on trustworthy and inclusive AI development

  1. High Convergence: 87% overlap in principles across major frameworks suggests genuine consensus
  2. Broad Participation: 122 UN co-sponsors shows wide buy-in
  3. Interoperability: OECD definitions being adopted by EU, Council of Europe, Japan, US, creating common language
  1. Non-Binding Nature: Most agreements lack enforcement mechanisms
  2. Implementation Gap: OECD notes “concrete guidance for implementation is often lacking”
  3. Monitoring Deficit: “Weak” evaluation mechanisms limit ability to measure outcomes
  4. Capability-Governance Gap: 53 percentage points between AI adoption and governance maturity
  5. Resource Constraints: “Skills shortages, outdated legacy systems, difficulties in data sharing, and financial constraints all hinder scaling”

OECD Assessment (2025): “Although AI use is increasing, AI use in government has not yet made a transformative impact.”


Fiscal YearAI R&D Budget
FY 2022$874 million
FY 2024$1.8 billion
FY 2025$1.8 billion (requested)

Additional commitments:

  • $90 billion: AI data center expansion (Trump administration)
  • $200 billion: Micron Technology semiconductor manufacturing investment
  • 685+ AI-related projects currently overseen by Pentagon

Official estimates: Significantly understated

Actual estimates: 40-90% higher than publicly announced

  • Public 2024 defense budget: ≈$230 billion
  • Estimated actual defense spending: $330-450 billion
  • AI investment portion: Potentially matching or exceeding US DoD spending

Key capabilities: Autonomous military vehicles, drone swarm technology, AI-enhanced command and control

  • €2 billion redirected from 2024-2030 defense budget to AI (March 2024)
  • €300 million budget for Ministerial Agency for Artificial Intelligence in Defence (MAAID, established May 2024)
  • $20 billion National AI action from National Development Fund (2025)
  • Established Indian Army AI Incubation Center
  • Focus on autonomous platforms, surveillance, predictive maintenance, intelligent decision support
Metric2024-20252030 ProjectionCAGRSource
Military AI Market$11.5 billion (2025)$28.7 billion19.5%Grand View Research
Autonomous Military Weapons$15 billion (2025)$27 billion (2029)10.7%Business Research Company
Automated Weapon Systems$41.6 billion (2024)$73.6 billion (2034)5.9%Precedence Research
Defense AI (incl. security)$13.8 billion (2026)$22.8 billion (2029)18.5%GlobeNewswire

Regional Distribution: North America dominated with 32.8% market share (2024); Asia-Pacific is fastest-growing region driven by China and India defense budgets.

Context: Global military spending reached $2.44 trillion in 2023 (6.8% increase, steepest since 2009) per SIPRI. The EU’s ReArm Europe Plan aims to mobilize up to €800 billion for defense, prioritizing AI and quantum technologies.

Global automated weapon system market: ≈$15 billion (2025)

Key developments:

  • Multiple nations deploying Autonomous Weapon Systems (AWS) with lethal decision-making capability
  • Ukraine producing ~2 million drones in 2024 (96.2% domestically manufactured)
  • AI-powered GPS-denied navigation advancing
  • Drone swarming experiments accelerating

Ukraine as testing ground: Real-world validation of AI military technologies creating rapid capability improvements

  1. OpenAI: Removed blanket ban on military use from policies (January 2024)
  2. UN Response: First resolution on AI in military domain (December 2024)
  3. Export Controls: US tightening restrictions on advanced chip exports to China
  4. National Security Framing: Increased rhetoric linking AI leadership to national security

Competition Drivers:

  • “Both the United States’ and China’s military strategists fear falling behind their rivals in harnessing AI”
  • “This is the kind of dynamic that stokes costly arms races, increases the probability of international crises”
  • “Makes crises that do occur more likely to escalate to large-scale war”

Command & Control Evolution:

  • China recognizing need for less-centralized command in AI-era warfare
  • Could erode traditional US agility advantage
  • May make Chinese units “more aggressive and unpredictable”

Current Level: Medium-High and Accelerating

Evidence:

  • Military AI spending growing 15-20% annually
  • Rapid expansion of autonomous weapons programs
  • Real-world testing in Ukraine driving iteration
  • Increasing national security rhetoric
  • Policy barriers to military AI use being removed
  • Spending levels still modest relative to total defense budgets, but trajectory is steep

5. International AI Governance Body Effectiveness

Section titled “5. International AI Governance Body Effectiveness”

Scope of International Governance (2024-2025)

Section titled “Scope of International Governance (2024-2025)”

89 national AI strategies documented worldwide by end of 2023 (UNCTAD Technology and Innovation Report 2025)

Geographic distribution: Concentrated in developed nations; developing countries face “significant infrastructure and capacity gaps”

Reach: 47 jurisdictions (including EU)

Policy tracking: 900+ initiatives across 69 countries

Influence: OECD AI system definitions adopted by EU, Council of Europe, Japan, US

Strengths:

  • High interoperability across jurisdictions
  • Comprehensive policy observatory
  • Regular updates to principles

Limitations:

  • Non-binding nature
  • “Flexible and adaptable” principles can mean weak enforcement
  • Implementation guidance often lacking

Recommendation on Ethics of AI: Adopted by 193 member states

Readiness Assessments: 58 governments conducting evaluations

Focus: Ethical frameworks, inclusive development

March 2024: First global AI resolution (122 co-sponsors)

September 2024: Partnership with OECD Office of the Tech Envoy

December 2024: First resolution on AI in military domain

Scope: Broad normative frameworks, human rights focus

Limitations: No enforcement mechanisms, slow-moving relative to AI development pace

Participants: Major democracies (US, UK, France, Germany, Italy, Canada, Japan)

Output: Voluntary code of conduct, guiding principles

Strength: Alignment among major AI developers

Limitation: Excludes China and other major players

Merged with OECD in 2024 Members: 44 countries

Focus: Multistakeholder approach, working groups on key challenges

Strong: Geographic reach (122-193 countries in various initiatives) ✅ Strong: Policy tracking and documentation (900+ initiatives) ⚠️ Moderate: Inclusion of key players (China participates in some but not all)

Strong: 87% content overlap across major frameworks ✅ Strong: OECD definitions being adopted across jurisdictions

Weak: “Concrete guidance for implementation is often lacking” (OECD) ❌ Weak: 53 percentage point gap between AI adoption and governance maturity ❌ Weak: “Monitoring and evaluation mechanisms are also weak, limiting the ability to measure outcomes or detect risks effectively”

Very Weak: Most frameworks are non-binding ❌ Very Weak: No meaningful sanctions for non-compliance ⚠️ EU Exception: AI Act has enforcement mechanisms (fines up to €35 million or 7% of global turnover)

⚠️ Moderate: 2024 OECD principles updated 5 years after initial adoption ❌ Weak: Traditional intergovernmental processes lag AI development by years ✅ Strong: Some initiatives (G7 Hiroshima Process) moving on accelerated timelines

OECD Finding (2025): “Although AI use is increasing, AI use in government has not yet made a transformative impact.”

Key Challenges Identified:

  1. Skills shortages in government
  2. Outdated legacy systems
  3. Difficulties in data sharing
  4. Financial constraints
  5. Gap between strategy and implementation

Normative Frameworks: 7/10 (Strong convergence, broad participation)

Technical Standards: 6/10 (Good interoperability, but implementation lags)

Enforcement & Compliance: 2/10 (Mostly voluntary, weak monitoring)

Speed & Adaptability: 4/10 (Improving, but still lags technology)

Practical Impact: 3/10 (Limited transformation of actual practices)

Composite Score: 4.4/10 (Moderate-Low Effectiveness)

Trajectory: Improving infrastructure and convergence, but implementation gap remains critical weakness


6. Technology Transfer to Authoritarian Regimes

Section titled “6. Technology Transfer to Authoritarian Regimes”

Established: 2017 (annual since then)

Participants: China’s Ministry of Science and Technology + Russia’s Ministry of Economic Development

2019-2024 Work Plan: Combines “China’s industry, capital and market with the resources, technology and talents of Russia”

LLM Sources: ~40% of all LLMs globally originate from China

Russia’s AI Funding: 5.2 billion rubles ($57 million) in 2024

US Comparison: US government allocated 50x more than Russia in 2022

Implication: Russia increasingly dependent on Chinese AI models and technology given funding constraints and Western sanctions

MetricValueTrendSource
Countries with PRC surveillance80+ countriesExpanding via Digital Silk RoadCarnegie Endowment
Global surveillance camera market share34% (Hikvision + Dahua)DominantAtlantic Council
AI export country reach2x US reachGrowingPerth USAsia Centre
Smart city projectsHundreds in developing countriesAcceleratingProject Syndicate

80+ countries have received PRC-sourced AI-for-surveillance solutions per the Atlantic Council. Recipients include: Both authoritarian regimes and democracies—Malaysia, Vietnam, Thailand, Serbia, Kenya, Pakistan, Laos, and throughout the Middle East.

Hikvision + Dahua combined: ~34% of global surveillance camera market (2024, first 3 quarters). These firms supply integrated systems combining facial recognition, predictive policing, and real-time geographic monitoring.

Capabilities: Advanced facial recognition, behavioral analysis, mass monitoring. Per ASPI, “AI has become the backbone of a far more pervasive and predictive form of authoritarian control.”

Export Advantage: China exports AI technology to nearly twice as many countries as the United States (Perth USAsia Centre).

Target Markets: Focus on “autocracies and weak democracies”—researchers find China exports disproportionately to these regimes, and such governments are likelier to import during periods of domestic unrest.

Strategic Intent: “Spread its ideology and facilitate the adoption of techno-authoritarian practices.” Per the National Endowment for Democracy, China’s advances including DeepSeek “will boost the government’s surveillance capabilities, including overseas.”

Iran, Russia, Venezuela: “Purposefully experimenting with and weaponizing generative AI to manipulate the information space and undermine democracy”

2024 US Election: AI-generated fake images and deepfakes “flooding social media platforms”

Scale: Enabled by ~40% of LLMs originating from China that “can be made to adhere to the standards of authoritarian regimes”

  • Surveillance cameras and software
  • Facial recognition systems
  • Smart city infrastructure packages
  • Belt and Road Initiative technology components
  • Bilateral cooperation agreements
  • Training programs for foreign officials
  • Open-source AI models usable by any regime
  • Academic collaborations
  • Commercial AI services available globally

US & Australia leading coordination on:

  • Export controls for sensitive AI technologies
  • Investment screening mechanisms
  • Restrictions on collaboration with military-linked Chinese institutions

Challenge: Difficult to control dual-use technologies with legitimate commercial applications

Approved: December 2023, passed March 2024

Scope: “Protect fundamental rights, democracy, the rule of law and environmental sustainability”

Approach: Risk-based framework with stricter rules for high-risk applications

Impact on exports: Sets baseline for AI systems sold in EU market

April 2024 Workshop: Hoover Institution, Stanford Global Digital Policy Incubator, National Endowment for Democracy

Focus: “Map the expanding frontiers of digital authoritarianism” and “discuss the diffusion of authoritarian technologies”

Conservative Estimate: 80+ countries with PRC AI surveillance

Market Value: Hikvision + Dahua represent billions in annual surveillance equipment sales

Accessibility: Chinese LLMs and AI services available to most countries globally

Control: Minimal restrictions on export of general-purpose AI models

Hundreds of projects in developing countries incorporating Chinese AI surveillance and management systems

Current Risk Level: High

Evidence:

  • Dominant market share in surveillance technology (34%)
  • Deployment in 80+ countries
  • Explicit weaponization by Iran, Russia, Venezuela for disinformation
  • Rapid proliferation of generative AI capabilities
  • Limited effective controls on dual-use technology exports

Trend: Accelerating

Proliferation outpacing development of effective control mechanisms. Open-source AI development further complicates restriction efforts.


Competition Indicators: Dominant

  • 12:1 US private investment advantage, but China government investment competitive
  • Military AI spending growing 15-20%+ annually
  • Autonomous weapons proliferation accelerating
  • Technology transfer to rivals ongoing

Cooperation Indicators: Present but Weak

  • 87% convergence across governance frameworks
  • 47 countries signed onto OECD principles
  • Multiple bilateral safety agreements
  • BUT: mostly non-binding, weak enforcement, significant implementation gap
  1. US Talent Dependence: 38% of top US AI researchers from China
  2. Declining Mobility: Global talent staying home more (13 percentage point drop)
  3. Governance-Capability Gap: 53 percentage point gap between AI adoption and governance maturity
  4. Surveillance Proliferation: Chinese AI surveillance in 80+ countries
  5. Military AI Testing: Ukraine conflict accelerating autonomous weapons development

Near-term (1-3 years):

  • US maintains overall capability lead but gap narrows
  • Military AI spending continues rapid growth
  • Governance frameworks proliferate but remain weak
  • Technology transfer to authoritarian regimes continues

Medium-term (3-7 years):

  • China could achieve parity in specific AI domains
  • Autonomous weapons become standard military capability
  • International governance either strengthens significantly or fragments
  • Competition vs. cooperation balance likely determines AI risk landscape


  • Investment data from multiple authoritative sources (KPMG, EY, Stanford)
  • Talent data from rigorous academic tracking (MacroPolo)
  • Government spending from official budgets where available
  • Think tank analysis from CSET, RAND, Carnegie
  1. China data opacity: Actual military spending and government AI investment likely understated by 40-90%
  2. Talent attribution: Based on undergraduate institution, may not perfectly reflect current national affiliation
  3. Technology transfer: Difficult to quantify; much occurs through commercial channels
  4. Governance effectiveness: Largely qualitative assessment, hard to measure concrete impact
  5. Rapid change: AI landscape evolving faster than data collection cycles
  • Investment data: Quarterly to annual
  • Talent flows: Major update every 3 years (MacroPolo)
  • Military spending: Annual budget cycles
  • Governance: Ongoing policy tracking, major reports annual
  • Technology proliferation: Ad hoc reporting, no systematic tracking

Last Updated: December 2025 (using latest available 2024-2025 data)