AI Talent Concentration: Research Report

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Executive Summary

Finding	Key Data	Implication
US dominance	57% of top 2% AI researchers; 60% of top institutions	US remains primary AI hub
Chinese talent pipeline	38% of US AI researchers are Chinese-born; China produces 47% of top researchers	US depends on foreign talent; China building domestic capacity
Declining mobility	Only 42% work abroad (down from 55% in 2019)	More talent staying home
Policy risk	$100K H-1B fee (up from $2-5K)	May accelerate “reverse brain drain”
Key uncertainty	Depends on immigration policy vs. competing hubs	Critical inflection point

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Background

AI talent concentration—where the world’s top AI researchers choose to work and study—is a critical factor in the AI capabilities landscape. The geographic distribution of talent affects which entities lead AI development, the speed of capability advancement, and AI safety outcomes.

Why This Matters for AI Safety

If talent migrates to jurisdictions with weaker safety norms or less transparency, it could accelerate risky development outside the reach of current governance efforts. Understanding talent flows helps predict where frontier capabilities will emerge.

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Key Findings

US Remains the Top Destination

According to the MacroPolo Global AI Talent Tracker 2.0, the United States remains far ahead as the destination for elite AI talent:

Metric	US Share	Change from 2019
Top 2% of AI talent working	57%	Slight decline
Top AI institutions hosted	60%	Stable
Top-tier talent staying after US graduate school	80%	Stable

The Stanford AI Index 2025 reinforces this: US institutions produced 40 notable AI models in 2024, compared to China’s 15 and Europe’s combined 3.

Data Source Limitation

MacroPolo’s tracker used NeurIPS 2022 conference acceptances as a proxy for top-tier talent. After eight years, MacroPolo ceased operations in 2024, so this dataset will not be updated. Future analysis will need alternative methodologies.

China’s Rising Talent Production

China has dramatically increased its share of top AI researcher production:

Year	China’s Share (by undergrad origin)	US Share	Gap
2019	29%	20%	China +9%
2022	47%	~20%	China +27%

Talent composition in the US shows significant reliance on Chinese-born researchers:

Origin	Share of Top-Tier AI Researchers in US
US-born	37%
Chinese-born	38%
Other countries	25%

This creates a complex dynamic: the US relies heavily on Chinese talent for its AI workforce, while China increasingly retains its own graduates domestically.

Declining Global Mobility

Year	Top-tier Researchers Working Abroad	Change
2019	55%	—
2022	42%	-13 percentage points

Trend Implications

This 13-point decline in mobility suggests a structural shift. Contributing factors include: expanded domestic opportunities in China, tighter immigration environments globally, and nationalist policies encouraging domestic retention. If this trend continues, talent distribution could fragment significantly.

Immigration Policy Risks

Recent US immigration policy changes pose risks to talent attraction:

Policy Change	Details	Impact
H-1B Fee Increase	$100,000 fee (Sep 2025), up from $2,000-$5,000	”Essentially a startup tax”—disadvantages startups vs. Big Tech
Visa Uncertainty	Duration of status changes proposed	Creates planning uncertainty for researchers
Competing Programs	China K visa, South Korea K-Tech Pass	Other nations actively recruiting

Key statistic: 60% of top AI startups have immigrant founders, and 70% of those first came to the US on student visas.

Competing National Programs

China: Launched K visa (October 2025) for research stays without employer sponsorship. South Korea: K-Tech Pass (September 2024) for graduates of top 100 universities. UK/Australia/Canada: Straightforward post-graduate work permits allowing immediate employment after graduation.

Global Investment Gap

Country	2024 Private AI Investment	Relative to US
United States	$109.1 billion	1x
China	$9.3 billion	0.09x (12x less)
United Kingdom	$4.5 billion	0.04x (24x less)

Source: Stanford AI Index 2025

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Causal Factors

The following factors influence AI talent concentration. This table is designed to inform future cause-effect diagram creation.

Primary Factors (Strong Influence)

Factor	Direction	Type	Evidence	Confidence
Immigration Policy	↑↓ US Concentration	leaf	80% retention depends on visa pathways; $100K fee threatens startups	High
Research Ecosystem Quality	↑ Concentration	cause	US hosts 60% of top institutions; talent follows opportunities	High
Private Investment	↑ Concentration	leaf	$109B US investment creates positions unavailable elsewhere	High

Secondary Factors (Medium Influence)

Factor	Direction	Type	Evidence	Confidence
Graduate Program Quality	↑ Concentration	cause	80% of US graduate students stay; key pipeline	Medium
Competing National Programs	↓ US Concentration	leaf	China K visa, South Korea K-Tech Pass compete actively	Medium
Cost of Living	Mixed	intermediate	SF Bay Area costs may deter some; not quantified	Low

Minor Factors (Weak Influence)

Factor	Direction	Type	Evidence	Confidence
Nationalist Sentiment	↓ Mobility	leaf	55% → 42% abroad suggests “stay home” effect	Low
Language/Cultural Barriers	↓ Cross-border flow	intermediate	Limited; AI research primarily in English	Low

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Open Questions

Key Uncertainties

These questions represent the highest-value areas for follow-up research:

Question	Why It Matters	Current State
Will H-1B fees persist?	$100K fee dramatically changes startup economics	May face legal challenge or policy reversal
Is China’s talent surge sustainable?	47% share may reflect training expansion vs. durable advantage	Unclear; depends on retention rates
How does concentration affect safety?	Talent in weak-safety jurisdictions could accelerate risks	Under-researched
What are the lag effects?	2022 data won’t show 2024-2025 policy impacts for years	Need better real-time indicators
Industry vs. academic split?	90% of notable models from industry—academic talent may matter less	Frontier work concentrated in labs

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Sources

Research Organizations

• Stanford AI Index 2025 - Comprehensive annual report on AI development and talent
• MacroPolo Global AI Talent Tracker 2.0 - Detailed NeurIPS researcher analysis (2024; now archived)

Policy Analysis

• Brookings: Trump’s immigration policies may threaten American AI leadership - Startup founder immigration patterns
• Bulletin of the Atomic Scientists: H-1B fee impact - Cost analysis of visa fee increase
• Lawfare: Trump’s Immigration Policies Overlook AI Talent - Policy gap analysis

News Coverage

• Fortune: Top AI Talent Faces US Visa Hurdles - Visa uncertainty reporting
• IBM: Stanford 2025 AI Index Summary - Key findings overview

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AI Transition Model Context

Model Integration

This research relates to the AI Capabilities factor in the AI Transition Model. Talent concentration affects algorithmic progress, geographic distribution of development, and which entities have capabilities relevant to AI takeover or misuse scenarios.

The concentration of talent in the US (and dependence on Chinese-born researchers) creates both:

• Opportunities: Potential for safety coordination among concentrated talent
• Vulnerabilities: Policy disruptions could fragment safety-conscious community