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80,000 Hours technical AI safety upskilling resources
webCredibility Rating
3/5
Good(3)Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: 80,000 Hours
Published by 80,000 Hours in June 2025, this resource is aimed at career-changers and students seeking structured pathways into technical AI safety research; useful as a starting point for newcomers to the field.
Metadata
Importance: 55/100blog posteducational
Summary
A curated guide from 80,000 Hours providing resources for individuals looking to develop technical skills relevant to AI safety research. It aggregates learning materials, courses, and pathways to help people transition into or advance within the technical AI safety field. The resource supports field-building by lowering barriers to entry for aspiring AI safety researchers.
Key Points
- •Curates technical learning resources specifically aimed at building AI safety research skills
- •Targets individuals seeking to transition into or upskill within technical AI safety roles
- •Likely includes programming, ML, and alignment-specific learning pathways
- •Part of 80,000 Hours' broader career guidance mission for high-impact work
- •Supports the AI safety talent pipeline by making skill development more accessible
Cited by 2 pages
| Page | Type | Quality |
|---|---|---|
| 80,000 Hours | Organization | 45.0 |
| AI Safety Field Building and Community | Crux | 0.0 |
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Are you enthusiastic about [technical AI safety](https://80000hours.org/career-reviews/ai-safety-researcher/) but need concrete ideas for how to enter the field?
Below are our top picks for upskilling in technical AI safety research, the field focused on ensuring powerful AI systems behave safely and as intended. In practice, upskilling involves developing the machine learning and research skills needed to work on challenges such as alignment and interpretability.
We developed this list in consultation with our advisors to highlight the resources they most commonly recommend, including articles, courses, organisations, and fellowships. While we recommend applying to [speak to an advisor](https://80000hours.org/speak-with-us/) for tailored, one-on-one guidance, this page gives a practical, noncomprehensive snapshot of how you might move from being interested in technical AI safety to starting to work on it.
### Overviews
These resources outline the technical AI safety landscape, highlighting current research efforts and some practical ways to begin contributing to the field.
- [AISafety.com](http://aisafety.com/)
- [Shallow review of technical AI safety](https://www.lesswrong.com/posts/Wti4Wr7Cf5ma3FGWa/shallow-review-of-technical-ai-safety-2025-2) by technicalities et al.
- [AI safety technical research career guide - how to enter](https://80000hours.org/career-reviews/ai-safety-researcher/#how-to-enter) by 80,000 Hours
- [Levelling up in AI safety research engineering](https://docs.google.com/document/d/1b83_-eo9NEaKDKc9R3P5h5xkLImqMw8ADLmi__rkLo4/edit?tab=t.0#heading=h.fke682cxqkxr) by Gabriel Mukobi
- [Recommendations for technical AI safety research agendas](https://alignment.anthropic.com/2025/recommended-directions/) by Anthropic
- [Technical AI safety research areas](https://www.openphilanthropy.org/tais-rfp-research-areas/) by Coefficient Giving
- [An overview of areas of control work](https://redwoodresearch.substack.com/p/an-overview-of-areas-of-control-work) by Ryan Greenblatt, Redwood Research
- [AI safety needs great engineers](https://forum.effectivealtruism.org/posts/DDDyTvuZxoKStm92M/ai-safety-needs-great-engineers) by Andy Jones
### AI safety courses
These courses can help you gain technical knowledge and practical research experience in AI safety.
- [ARENA’s curriculum](https://github.com/callummcdougall/ARENA_3.0)
- BlueDot Impact’s [_AI Alignment_ course](https://bluedot.org/courses/alignment?from_site=aisf)
- Andrej Karpathy’s [_Zero to Hero_ course](https://karpathy.ai/zero-to-hero.html)
- His [YouTube videos](https://www.youtube.com/andrejkarpathy) can also be great intro-friendly resources, as can [3Blue1Brown’s deep learning videos](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi).
- [_Deep Learning Curriculum_](https://github.com/jacobhilton/deep_learning_curriculum) by Jacob Hilton
- [Google ML Course](https://developers.google.com/machine-learning/crash-course/prereqs-and-prework)
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