Distilling the technical AI safety literature and co-authoring the AI Safety Atlas

The AI Safety Atlas is a living distillation project. It has eight chapters, thousands of readers, and is used by ML4Good, ENS Ulm, Sciences Po, and dozens of other universities and programs. But the AI field moves fast, and the technical material hasn't had a serious update in about a year, which is a long time in this field. This project aims to upgrade the technical half of the textbook.

An individual unit of work can be thought of as a literature review of one subfield, written to the standard of a good survey paper. You read research, blog posts, watch technical podcasts, and become the expert in that area, deciding what's load-bearing and what's noise. Then you write it clearly enough that a technically literate non-specialist comes away actually understanding it. Imagine someone who has a Physics, or a CS graduate degree, but is unfamiliar with AI safety. You can work in different domains depending on your specific area of expertise:

• Significantly improving the existing explanations (reward misspecification, goal misgeneralisation, scalable oversight, possibly evaluations): bringing them to 2025-26, raising the technical level where it's too shallow, and fixing the places that list arguments instead of building one coherent end-to-end picture.

• Writing new content: interpretability; AI security and cybersecurity; multi-agent systems, complex systems, and systemic safety (coordination, emergent and multi-polar risk) and potentially agent foundations.

It is important to have both AI safety expertise, and be a good writer. Someone who knows the area has to make judgement calls in which concepts connect together, how to explain them, which examples and analogies to use, and what to leave out. You will also be responsible for communicating with the original research authors when something is unclear, or when you have questions and clarifications about how to communicate something.

Coherence is intended as a property of the whole book and is not limited to individual chapters. If you write something on goal misgeneralization, then this should not live in isolation. The way that you explain things will have an influence on the writeup for interpretability, or for the writeup on evaluations. We have to use (or come up with) consistent definitions, consistent conventions and standards that allow the reader to be able to understand the breadth of the field end to end. A change in one area might ripple through the consistency of definitions, the assumed technical level, and the narrative across the entire text.

The number of people who deeply understand the technical problems in AI safety is small, and that's a real bottleneck. Most learning material is either a shallow list of links or a frozen snapshot that goes stale as the field moves. The Atlas fills a specific gap: an integrated, current distillation that other courses can trust and use. Its alumni have gone on to found evaluation orgs, lead recruiting governance organizations, and publish papers at ICML.

Keeping the work current and adding the explanations the field needs most raises both the quality and the throughput of people entering technical safety. Publishing each as a paper forces the writing to a citable standard and makes the work discoverable as scholarship. A mentee finishes having authored a published survey in a core safety area, which is one of the higher-leverage things an early career researcher can do to show their depth of understanding of the field.

This is a research- and writing-heavy project, for someone who already has, or can quickly build, real technical expertise in their area. You run your own literature review sweeps, make the calls about what to include and how to frame it. I will read and review for technical correctness, for coherence with the rest of the book. I expect you to be self-driven and autonomous.

I am open to a single mentee who owns the whole technical stream, or a team of experts that take one area each.

Must Haves:

• Real technical experience in at least one target area (interpretability, AI security, multi-agent and systemic safety, or the alignment problems the existing reviews cover).
• You should be able to read current papers, understand what they say and judge what matters.
• Strong technical writing and distillation. You can build a single coherent account, come up with good analogies, compelling narratives instead of just listing arguments.

Nice to have:

• a published survey, blog post, distillation, or well-received technical explainer (link it);
• having facilitated or taken a course that used the Atlas.

Remote. EU-overlapping hours mildly preferred, not required.