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AI Safety Training Programs

training-programs (E468)
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---
title: AI Safety Training Programs
description: Fellowships, PhD programs, research mentorship, and career transition pathways for growing the AI safety research workforce, including MATS, Anthropic Fellows, SPAR, and academic programs.
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llmSummary: Comprehensive guide to AI safety training programs including MATS (78% alumni in alignment work, 100+ scholars annually), Anthropic Fellows ($2,100/week stipend, 40%+ hired full-time), LASR Labs (5 NeurIPS papers in 2024), and academic pathways. BlueDot Impact has trained 7,000+ people since 2022, with hundreds now working in AI safety. Provides concrete application criteria, timing recommendations, and structured self-study pathways with 1-5 year timeline to research contribution.
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import {Mermaid, EntityLink, DataExternalLinks} from '@components/wiki';

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## Quick Assessment

| Dimension | Rating | Notes |
|-----------|--------|-------|
| Tractability | High | Known how to train researchers; programs have proven track records |
| Scalability | Medium | Bottlenecked by mentor availability and quality maintenance |
| Current Maturity | Medium-High | Ecosystem established since 2021; 298+ MATS scholars trained |
| Time Horizon | 1-5 years | Trained researchers take 1-3 years to contribute meaningfully |
| Key Proponents | [MATS](https://www.matsprogram.org/), [BlueDot Impact](https://bluedot.org/), <EntityLink id="E22">Anthropic</EntityLink>, <EntityLink id="E521">Coefficient Giving</EntityLink> |
| Estimated Impact | Medium-High | Produces 100-200 new safety researchers annually |

## Overview

The AI safety field faces a critical talent bottleneck. While funding has increased substantially—with <EntityLink id="E521">Coefficient Giving</EntityLink> committing roughly \$50 million to <EntityLink id="E297">technical AI safety research</EntityLink> in 2024—the supply of researchers capable of doing high-quality technical safety work remains constrained. Training programs represent the primary pipeline for addressing this gap, offering structured pathways from general ML expertise to safety-specific research skills.

The landscape has evolved rapidly since 2020. [MATS (ML Alignment Theory Scholars)](https://www.matsprogram.org/) has become the premier research mentorship program, with [78% of surveyed alumni now working in AI alignment](https://www.lesswrong.com/posts/jeBkx6agMuBCQW94C/mats-alumni-impact-analysis). Anthropic launched a [Fellows Program](https://alignment.anthropic.com/2024/anthropic-fellows-program/) specifically for mid-career transitions. [BlueDot Impact has trained over 7,000 people since 2022](https://bluedot.org/), with hundreds now working at organizations like Anthropic, <EntityLink id="E218">OpenAI</EntityLink>, and the <EntityLink id="E364">UK AI Safety Institute</EntityLink>. Academic programs are emerging at York (SAINTS CDT), Berkeley (<EntityLink id="E57">CHAI</EntityLink>), and Cambridge (CHIA). Independent research programs like SPAR and LASR Labs provide part-time pathways. Together, these programs produce perhaps 100-200 new safety researchers annually—a number that may be insufficient given the pace of AI capabilities advancement.

The strategic importance of training extends beyond individual researcher production. Programs shape research culture, determine which problems receive attention, and create networks that influence the field's direction. How training programs select participants, what methodologies they emphasize, and which mentors they feature all have downstream effects on AI safety's trajectory.

## Program Comparison

| Program | Duration | Format | Stipend | Selectivity | Key Outcomes |
|---------|----------|--------|---------|-------------|--------------|
| **MATS** | 12 weeks + 6mo extension | In-person (Berkeley, London) | Living stipend | ≈5-10% | 78% in alignment work; 75% publish |
| **Anthropic Fellows** | 6 months | In-person (SF) | \$2,100/week | Selective | 40%+ hired full-time at Anthropic |
| **LASR Labs** | 13 weeks | In-person (London) | £11,000 | Moderate | All 5 Summer 2024 papers at NeurIPS |
| **SPAR** | 3 months | Remote, part-time | Varies | Moderate | Papers at ICML, NeurIPS; career fair |
| **ARENA** | 5 weeks | In-person (London) | Housing/travel | Moderate | Alumni at Apollo, <EntityLink id="E201">METR</EntityLink>, UK AISI |
| **BlueDot <EntityLink id="E631">Technical AI Safety</EntityLink>** | 8 weeks | Online cohorts | None | Low-moderate | 7,000+ trained; hundreds in field |

## Major Training Programs

### MATS (ML Alignment Theory Scholars)

MATS is the most established and influential AI safety research program, operating as an intensive mentorship connecting promising researchers with leading safety researchers. Since its inception in late 2021, [MATS has supported 298 scholars and 75 mentors](https://www.matsprogram.org/).

| Attribute | Details |
|-----------|---------|
| **Duration** | 12 weeks intensive + 6 months extension |
| **Format** | In-person (Berkeley, London) |
| **Focus** | Technical alignment research |
| **Mentors** | Researchers from Anthropic, DeepMind, Redwood, FAR.AI, ARC |
| **Compensation** | Living stipend provided |
| **Selectivity** | ≈5-10% acceptance rate |
| **Alumni outcomes** | [78% now working in AI alignment](https://www.lesswrong.com/posts/jeBkx6agMuBCQW94C/mats-alumni-impact-analysis) |

**Research Areas:**
- Interpretability and mechanistic understanding
- <EntityLink id="E6">AI control</EntityLink> and containment
- <EntityLink id="E271">Scalable oversight</EntityLink>
- Evaluations and red-teaming
- Robustness and security

**Notable Alumni Contributions:**
MATS fellows have contributed to sparse autoencoders for interpretability, activation engineering research, developmental interpretability, and externalized reasoning oversight. Alumni have published at ICML and NeurIPS on safety-relevant topics. [Nina Rimsky received an Outstanding Paper Award at ACL 2024](https://www.lesswrong.com/posts/jeBkx6agMuBCQW94C/mats-alumni-impact-analysis) for "Steering Llama 2 via Contrastive Activation Addition." Alumni have founded organizations including <EntityLink id="E24">Apollo Research</EntityLink>, Timaeus, Leap Labs, and the Center for AI Policy.

### Anthropic Fellows Program

Launched in 2024, the [Anthropic Fellows Program](https://alignment.anthropic.com/2024/anthropic-fellows-program/) targets mid-career technical professionals transitioning into AI safety research.

| Attribute | Details |
|-----------|---------|
| **Duration** | 6 months full-time |
| **Format** | In-person (San Francisco) |
| **Focus** | Transition to safety research |
| **Compensation** | \$2,100/week stipend + \$15,000/month compute budget |
| **Target** | Mid-career technical professionals |
| **First cohort** | March 2025 |
| **First cohort outcomes** | [Over 80% published papers; 40%+ joined Anthropic full-time](https://alignment.anthropic.com/2025/anthropic-fellows-program-2026/) |

The program addresses a specific gap: talented ML engineers and researchers who want to transition to safety work but lack the mentorship and runway to do so. By providing substantial compensation and direct collaboration with Anthropic researchers, it removes financial barriers to career change. First cohort fellows produced notable research including work on [agentic misalignment, attribution graphs for mechanistic interpretability, and autonomous blockchain vulnerability exploitation](https://x.com/AnthropicAI/status/1985505322522296542).

### SPAR (Supervised Program for Alignment Research)

[SPAR](https://sparai.org/) offers a part-time, remote research fellowship enabling broader participation in safety research without requiring full-time commitment.

| Attribute | Details |
|-----------|---------|
| **Duration** | 3 months |
| **Format** | Remote, part-time |
| **Focus** | AI safety and governance research |
| **Target** | Students and professionals |
| **Output** | Research projects culminating in Demo Day with career fair |
| **Scale** | [130+ projects offered in Spring 2026](https://forum.effectivealtruism.org/posts/AHzW8H3k575Scpm5D/spar-spring-2026-130-research-projects-now-accepting)—largest AI safety fellowship round |

[SPAR research has been accepted at ICML and NeurIPS, covered by TIME, and led to full-time job offers](https://sparai.org/). Mentors come from <EntityLink id="E98">Google DeepMind</EntityLink>, RAND, Apollo Research, UK AISI, <EntityLink id="E202">MIRI</EntityLink>, and universities including Cambridge, Harvard, Oxford, and MIT. The program works well for:
- Graduate students exploring safety research
- Professionals testing interest before career change
- Researchers in adjacent fields wanting to contribute

### LASR Labs

[LASR Labs](https://www.lasrlabs.org/) provides cohort-based technical AI safety research, preparing participants for roles at safety organizations.

| Attribute | Details |
|-----------|---------|
| **Duration** | 13 weeks |
| **Format** | In-person (London) |
| **Focus** | Technical safety research |
| **Stipend** | £11,000 + office space, food, travel |
| **2024 Outcomes** | [All 5 Summer 2024 papers accepted to NeurIPS workshops](https://www.lasrlabs.org/) |
| **Career Outcomes** | Alumni at UK AISI, Apollo Research, OpenAI dangerous capabilities team, Coefficient Giving |
| **Satisfaction** | 9.25/10 likelihood to recommend; NPS +75 |

Research topics include interpretability (sparse autoencoders, residual streams), AI control, and steganographic collusion in LLMs. Supervisors include researchers from Google DeepMind, Anthropic, and UK AISI.

### Global AI Safety Fellowship

Impact Academy's Global AI Safety Fellowship is a fully funded program (up to 6 months) connecting exceptional STEM talent with leading safety organizations.

| Attribute | Details |
|-----------|---------|
| **Duration** | Up to 6 months |
| **Format** | In-person collaboration |
| **Partners** | CHAI (Berkeley), <EntityLink id="E70">Conjecture</EntityLink>, FAR.AI, UK AISI |
| **Funding** | Fully funded |

## Academic Pathways

### PhD Programs

| Program | Institution | Focus | Status |
|---------|-------------|-------|--------|
| **SAINTS CDT** | University of York (UK) | Safe Autonomy | Accepting applications |
| **CHAI** | UC Berkeley | Human-Compatible AI | Established |
| **CHIA** | Cambridge | Human-Inspired AI | Active |
| **Steinhardt Lab** | UC Berkeley | ML Safety | Active |
| **Other ML programs** | Various | General ML with safety focus | Many options |

**University of York - SAINTS CDT:**
The UK's first Centre for Doctoral Training specifically focused on AI safety, funded by UKRI. Brings together computer science, philosophy, law, sociology, and economics to train the next generation of safe AI experts. Based at the Institute for Safe Autonomy.

**Key Academic Researchers:**
Prospective PhD students should consider advisors who work on safety-relevant topics:
- <EntityLink id="E290">Stuart Russell</EntityLink> (Berkeley/CHAI) - Human-compatible AI
- Jacob Steinhardt (Berkeley) - ML safety and robustness
- Vincent Conitzer (CMU) - <EntityLink id="E439">AI alignment</EntityLink> theory
- David Duvenaud (Toronto) - Interpretability
- Roger Grosse (Toronto) - Training dynamics
- Victor Veitch (Chicago) - Causal ML, safety

### Academic vs. Industry Research

| Dimension | Academic Path | Industry Path |
|-----------|---------------|---------------|
| **Timeline** | 4-6 years | 0-2 years to entry |
| **Research freedom** | High | Varies |
| **Resources** | Limited | Often substantial |
| **Publication** | Expected | Sometimes restricted |
| **Salary during training** | PhD stipend (≈\$10-50K) | Full salary or fellowship |
| **Ultimate outcome** | Research career | Research career |
| **Best for** | Deep expertise, theory | Immediate impact, applied |

## Upskilling Resources

For those not yet ready for formal programs or preferring self-directed learning:

### Structured Curricula

| Resource | Provider | Coverage | Time Investment |
|----------|----------|----------|-----------------|
| **AI Safety Syllabus** | <EntityLink id="E510">80,000 Hours</EntityLink> | Comprehensive reading list | 40-100+ hours |
| **[Technical AI Safety Course](https://bluedot.org/courses/technical-ai-safety)** | BlueDot Impact | Structured curriculum | 8 weeks |
| **[AI Safety Operations Bootcamp](https://bluedot.org/courses/ops)** | BlueDot Impact | Operations roles in AI safety | Intensive |
| **ML Safety Course** | <EntityLink id="E89">Dan Hendrycks</EntityLink> | Technical foundations | Semester |
| **[ARENA](https://www.arena.education/)** | ARENA | Technical implementations (mech interp, transformers) | 5 weeks |

[BlueDot Impact](https://bluedot.org/) has become the primary entry point into the AI safety field, training over 7,000 people since 2022 and raising \$35M including \$25M in 2025. [ARENA alumni have gone on to become MATS scholars, LASR participants, and AI safety engineers at Apollo Research, METR, and UK AISI](https://forum.effectivealtruism.org/posts/MDw6QDyrLP3kpehd2/arena-7-0-call-for-applicants).

### Self-Study Path

<Mermaid chart={`
flowchart TD
    subgraph FOUNDATIONS["Foundations (2-4 months)"]
        A[ML Fundamentals] --> B[Deep Learning]
        B --> C[Transformer Architecture]
    end

    subgraph SAFETY_BASICS["Safety Basics (2-3 months)"]
        C --> D[80K Hours Syllabus]
        D --> E[Key Papers]
        E --> F[Alignment Forum]
    end

    subgraph SPECIALIZATION["Specialization (3-6 months)"]
        F --> G[Choose Focus Area]
        G --> G1[Interpretability]
        G --> G2[Scalable Oversight]
        G --> G3[Evaluations]
        G --> G4[Governance]
    end

    subgraph APPLICATION["Application (ongoing)"]
        G1 --> H[Apply to Programs]
        G2 --> H
        G3 --> H
        G4 --> H
        H --> I[Independent Research]
        I --> J[Contribute to Field]
    end

    style FOUNDATIONS fill:#e1f5ff
    style SAFETY_BASICS fill:#fff3cd
    style SPECIALIZATION fill:#d4edda
    style APPLICATION fill:#f0f0f0
`} />

## Career Transition Considerations

### When to Apply to Programs

| Your Situation | Recommended Path |
|----------------|------------------|
| Strong ML background, want safety focus | MATS or Anthropic Fellows |
| Exploring interest, employed | SPAR (part-time) |
| Student, want research experience | LASR Labs, SPAR |
| Early career, want PhD | Academic programs |
| Mid-career, want full transition | Anthropic Fellows |
| Strong background, want independence | Self-study + independent research |

### Success Factors

Based on program outcomes, successful applicants typically have:

| Factor | Importance | How to Develop |
|--------|------------|----------------|
| **ML technical skills** | Critical | Courses, projects, publications |
| **Research experience** | High | Academic or industry research |
| **Safety knowledge** | Medium-High | Reading, courses, writing |
| **Communication** | Medium | Writing, presentations |
| **Clear research interests** | Medium | Reading, reflection, pilot projects |

### Common Failure Modes

| Failure Mode | Description | Mitigation |
|--------------|-------------|------------|
| **Premature application** | Applying without sufficient ML skills | Build fundamentals first |
| **No research output** | Nothing demonstrating research capability | Complete pilot project |
| **Vague interests** | Unable to articulate what you want to work on | Read extensively, form views |
| **Poor fit** | Mismatch between interests and program | Research programs carefully |
| **Giving up early** | Rejection discouragement | Multiple applications, iterate |

## Talent Pipeline Analysis

### Current Capacity

| Stage | Annual Output | Bottleneck |
|-------|--------------|------------|
| **Interested individuals** | Thousands | Conversion |
| **Program applicants** | 500-1000 | Selectivity |
| **Program participants** | 150-300 | Capacity |
| **Research-productive alumni** | 100-200 | Mentorship |
| **Long-term field contributors** | 50-100 | Retention |

### Scaling Challenges

| Challenge | Description | Potential Solutions |
|-----------|-------------|-------------------|
| **Mentor bandwidth** | Limited senior researchers available | Peer mentorship, async formats |
| **Quality maintenance** | Scaling may dilute intensity | Tiered programs |
| **Funding** | Programs need sustainable funding | Philanthropic, industry, government |
| **Coordination** | Many programs with unclear differentiation | Better information, specialization |
| **Retention** | Many trained researchers leave safety | Better career paths, culture |

## Strategic Assessment

| Dimension | Assessment | Notes |
|-----------|------------|-------|
| **Tractability** | High | Known how to train researchers |
| **If AI risk high** | High | Need many more researchers |
| **If AI risk low** | Medium | Still valuable for responsible development |
| **Neglectedness** | Medium | \$50M+ annually from <EntityLink id="E521">Coefficient Giving</EntityLink> but scaling gaps |
| **Timeline to impact** | 1-5 years | Trained researchers take time to contribute |
| **Grade** | B+ | Important but faces scaling limits |

## Risks Addressed

| Risk | Mechanism | Effectiveness |
|------|-----------|---------------|
| Inadequate safety research | More researchers doing safety work | High |
| <EntityLink id="E239">Racing dynamics</EntityLink> | Safety talent at labs can advocate | Medium |
| Field capture | Diverse training reduces groupthink | Medium |

## Complementary Interventions

- <EntityLink id="E476">Field Building</EntityLink> - Broader ecosystem development
- <EntityLink id="E78">Corporate Influence</EntityLink> - Placing trained researchers at labs
- <EntityLink id="E13">AI Safety Institutes</EntityLink> - Employers for trained researchers

## Sources

### Program Information

- **MATS:** [matsprogram.org](https://www.matsprogram.org/) - Official program information; [Alumni Impact Analysis (2024)](https://www.lesswrong.com/posts/jeBkx6agMuBCQW94C/mats-alumni-impact-analysis)
- **Anthropic Fellows:** [alignment.anthropic.com](https://alignment.anthropic.com/2024/anthropic-fellows-program/) - Program details; [2026 cohort applications](https://alignment.anthropic.com/2025/anthropic-fellows-program-2026/)
- **SPAR:** [sparai.org](https://sparai.org/) - Supervised Program for Alignment Research
- **LASR Labs:** [lasrlabs.org](https://www.lasrlabs.org/) - London AI Safety Research Labs
- **BlueDot Impact:** [bluedot.org](https://bluedot.org/) - AI safety courses and career support
- **ARENA:** [arena.education](https://www.arena.education/) - Alignment Research Engineer Accelerator
- **Global AI Safety Fellowship:** globalaisafetyfellowship.com

### Funding and Ecosystem

- **<EntityLink id="E521">Coefficient Giving</EntityLink>:** 2024 Progress and 2025 Plans - \$50M committed to technical AI safety in 2024
- **<EntityLink id="E521">Coefficient Giving</EntityLink>:** Technical AI Safety RFP - \$40M+ available

### Career Guidance

- **80,000 Hours:** "AI Safety Syllabus" and career guide
- **Alignment Forum:** Career advice threads
- **EA Forum:** "Rank Best Universities for AI Safety"

### Academic Programs

- **University of York SAINTS CDT:** york.ac.uk/study/postgraduate-research/centres-doctoral-training/safe-ai-training
- **Stanford Center for AI Safety:** aisafety.stanford.edu
- **CHAI (Berkeley):** humancompatible.ai

---

## AI Transition Model Context

AI safety training programs improve the <EntityLink id="ai-transition-model" /> through multiple factors:

| Factor | Parameter | Impact |
|--------|-----------|--------|
| <EntityLink id="E205" /> | <EntityLink id="E261" /> | Produces 100-200 new safety researchers annually to address research talent bottleneck |
| <EntityLink id="E205" /> | <EntityLink id="E20" /> | Mentored researchers produce higher-quality alignment work |
| <EntityLink id="E60" /> | <EntityLink id="E167" /> | Trained researchers staff AI Safety Institutes and governance organizations |

Training programs are critical infrastructure for the field; their effectiveness is bottlenecked by limited mentor bandwidth and retention challenges.