Generalist Megastream

The Generalist Megastream is a single project listing backed by a pool of generalist mentors rather than one named mentor. Mentees apply to it like any other SPAR project, indicate which project ideas interest them in the project-specific question, and are then matched with a mentor and project from the pool.

Some megastream projects now have their own listings, each with a named mentor:

• Update the AI Safety Fieldmap (Joseph Kostousov, Harry Waterman)
• Differential Data for Automated AI Safety Research (Alec Harris)
• AI Safety Said Simply (Kaustubh Kislay, Christine Corry)
• Strengthening European AI Safety Unigroups (Manon Kempermann)

If one of those fits what you want to work on, apply to it directly instead of here. Apply to this listing if you are unsure which project you want, or if the generalist work you have in mind is not covered by them. We are still adding mentors and projects, so this is also the right place to apply if your idea is not listed yet.

Possible project ideas include:

Research & analysis

• Analysis of hiring, talent, and fellowship outcomes in AI safety
• Talent pipeline research: where people entering AI safety get stuck, what roles the field is bottlenecked on
• Mid-career pipelines scoping study: interview hiring orgs, pipeline programs, and career transitioners, then write a landscape report
• "Make AI safety managers better": interview AIS managers, survey existing resources, synthesize a curated guide
• Fellowship-hopping study: understand why people cycle through fellowships instead of landing roles, and what upskilling or routing would break the cycle

Field mapping & monitoring

• Visual field maps of the AI safety ecosystem
• AI Lab Watch successor v1: read labs' safety frameworks and model specs, produce a rigorous scorecard and methodology

Learning materials & curricula

• Curate better learning materials and curricula for people entering AI safety
• Draft university group fellowship curricula for different audiences
• University group organizer playbook: map what top groups do differently, turn it into resources and tooling
• A "what's next" guide for AI safety: a centralized, well-maintained resource mapping the best next steps and upskilling paths for people at different skill levels (also usable by mentors and fellowships as a fallback advising resource)
• Better rejection pipelines: help fellowships (including SPAR) turn rejections into upskilling pathways so applicants come back stronger

Knowledge bases & shared infrastructure

• Plug-and-play retreat knowledge base: project plans per retreat type, vendor/venue/caterer lists, common mistakes
• Shared AI-uplift infrastructure across AI safety orgs: prompt libraries, MCP connectors, eval harnesses
• Tabletop exercise (TTX) kit: research a scenario in depth, build character profiles and facilitation guides, and package TTXs so any group can run them

Comms & outreach

• Translate research into clear public content: partner with 3-5 researchers to turn their work into accessible writing
• Outreach experiments: sourcing and contacting potential mentors or applicants at scale
• Idea explainers: write clear, thorough cases for neglected ideas scattered across forums, the kind of post you wish existed when you were learning

Exploratory / AI-powered

• Proof-of-concept automated macrostrategy: identify trends in AI forecasting ability and how they scale to scenario modeling

Further reading and more sources of project ideas

• AI Safety's Biggest Talent Gap Isn't Researchers. It's Generalists.: why the field needs more generalists, and the motivation behind this stream
• A reading list for generalists (LessWrong)
• The case for AI safety capacity-building work by Asya Bergal
• AI safety fieldbuilding career review (80,000 Hours)
• Project Ideas, from Roman

The projects strengthen the AI safety field's capacity and infrastructure directly. Talent pipeline research and analyses of hiring and fellowship outcomes show where the field loses people and which roles are bottlenecked, so programs and funders can respond. Field maps and lab safety scorecards improve coordination, visibility, and accountability. Better curricula and organizer playbooks raise the quality of people entering the field. Shared knowledge bases and AI-uplift infrastructure eliminate duplicated effort across AI safety orgs, and comms projects make safety research legible to policymakers and the public. This kind of generalist and infrastructure work is increasingly the field's binding constraint (see AI Safety's Biggest Talent Gap Isn't Researchers. It's Generalists.), so each completed project relieves a real gap.

Mentees drive a small, scoped generalist project (research, interviews, analysis, or resource-building) with guidance from a mentor in the generalist pool. Project scopes are designed to fit SPAR's part-time, remote form factor.