The AI safety field has a talent bottleneck, and fellowships are a primary pathway for people to build relevant career capital. However, the fellowship landscape is fragmented: MATS, AISC, BlueDot, SERI, Astra, and dozens of others each target different career stages, require different backgrounds, and lead to different outcomes. Currently, information about these programmes travels largely by word of mouth or referral lists shared by organisations. With a limited field of view of their options, applicants either apply - potentially to a poor fit - or don't apply at all, unaware that better-matched options exist.

Phase 1: Research & Database. Mapping of AI safety fellowships, including requirements (technical background, career stage, time commitment, funding), focus areas (alignment, governance, field-building), application timelines, and post-fellowship outcomes. If possible, also acceptance rates. This should involve verifying the info with the Fellowship organisations.

Phase 2: Tool Development. Building an interactive advisor that asks users about their current situation (background, available time, financial constraints, interests, timeline, etc.) and returns tailored recommendations. The MVP version is a filterable database; a more sophisticated version could use decision-tree logic or AI to provide personalised guidance, similar to how Google Flights suggests "fly a day earlier for lower prices."

Phase 3: Validation & Launch. Testing with target users, verifying with the organisations, refining recommendations based on feedback, and publishing on a shareable, visually appealing platform. Potentially, we could host it on FIG's website.

Contributions: A publicly available tool that reduces friction in the AI safety talent pipeline, helps candidates self-select into appropriate programmes, and potentially surfaces lesser-known fellowships that might be better fits. Secondary output: a comprehensive fellowship database useful for career advisors, 80,000 Hours, and the broader community.