# Talent

The researchers, engineers, and operators who design, build, and run every layer of the AI stack. Talent is arguably the scarcest resource in the entire supply chain—the global pool of people capable of pushing the frontier numbers in the low thousands, concentrated at a handful of labs.

**Measure:** AI Research and Engineering Talent Pool

**US & Allies Score:** 7.0/10  
**China Score:** 5.0/10  
**Leader:** US & Allies

## Key Metrics

- **Frontier researcher pool:** Pool = (PhD-level ML researchers) × (Publications at top venues) × (Lab affiliation concentration)
- **Engineering capacity:** Capacity = (Infrastructure engineers + Data scientists + Safety researchers) × (Retention rate)

## What matters in this layer

Talent is arguably the scarcest resource in the entire supply chain. The global pool of people capable of pushing the frontier numbers in the low thousands, concentrated at a handful of labs. Researchers, engineers, and operators design, build, and run every layer of the AI stack.

### Concentration at frontier labs

A small pool of elite ML researchers is highly concentrated at Anthropic, OpenAI, Google DeepMind, Meta, xAI, and a few other labs. They design architectures, run training, and push the capability frontier.

### Infrastructure engineering

Platform engineering requires specialized talent to build and maintain distributed training infrastructure, optimize GPU utilization, and develop ML toolchains. Operating AI-scale data centers requires expertise spanning electrical engineering, cooling, networking, and cluster management.

### Safety and alignment expertise

Safety science requires specialized expertise in alignment research, interpretability, red-teaming, and evaluation methodology—a nascent discipline with very few experienced practitioners worldwide.

### Global competition for talent

Both the US and China are actively competing to attract and retain top AI talent. Immigration policy, research funding, compensation, and lab culture all factor into where the best researchers choose to work.

## Recent Developments

### Frontier Lab Researcher Concentration
*1 week ago | Research*

A small pool of elite ML researchers is highly concentrated at Anthropic, OpenAI, Google DeepMind, Meta, and xAI. These individuals design architectures, run training, and push the capability frontier.

### China Expands AI Talent Pipeline
*2 weeks ago | Education*

China is producing more STEM PhDs than any other country and has made significant investments in AI education and research. However, many top Chinese researchers continue to work at US labs.

### Immigration Policy Shapes AI Talent Flows
*3 weeks ago | Policy*

US immigration policy remains a critical variable in AI talent competition. Visa backlogs and policy uncertainty have pushed some researchers to consider labs in the UK, Canada, and other countries.