How to apply for Funding, Work That Builds Capacity to Address Risks From Transformative AI

About Coefficient Giving

Coefficient Giving is a grantmaking and research nonprofit that funds high-impact work across multiple cause areas, including mitigating risks from transformative AI. It is notable for being 80,000 Hours' largest funder, giving it significant influence in the effective altruism and AI safety talent ecosystems. Working here means shaping how philanthropic capital flows to some of the most pressing long-term problems.

About the role

This role centers on funding projects that build the talent pipeline and capacity for addressing transformative AI risks, from helping new people enter the field to supporting existing researchers and practitioners. You will evaluate grant proposals, make funding decisions within tight 8-week cycles, and contribute to discourse and research on AI risk mitigation. It is a high-leverage position because your funding choices directly influence who works on one of the most consequential problems of our time.

A typical day

A typical day might involve reviewing a batch of grant applications from AI safety training programs, conducting a call with a promising researcher seeking support, and drafting a funding recommendation memo for internal review. You might also spend time researching emerging talent pathways, coordinating with colleagues on discourse contributions, and making final decisions on time-sensitive grants to meet the 8-week deadline.

Who Coefficient Giving is looking for

• Has hands-on experience in grantmaking or funding allocation, ideally in a fast-paced or high-stakes context where decisions must be made quickly and defensibly.
• Possesses a strong working understanding of transformative AI risks, AI safety technical agendas, and the surrounding governance and strategy landscape.
• Can rigorously evaluate project proposals, including assessing tractability, neglectedness, and potential impact, while remaining open to unconventional ideas.
• Understands how talent enters and develops in technical fields, including fellowship programs, upskilling pathways, and community-building efforts in AI safety.

Tips for this application

• Demonstrate concrete grantmaking or funding allocation experience in your application—quantify budgets managed, number of grants evaluated, or decision turnaround times you've achieved.
• Show fluency in transformative AI risk discourse by referencing specific research agendas, organizations, or debates in the field rather than speaking generically about 'AI safety'.
• Explain how you would build talent capacity for AI risk work, including which entry points (e.g., fellowships, reskilling, community building) you think are most promising and why.
• Acknowledge Coefficient Giving's relationship as 80,000 Hours' largest funder and show you understand the ecosystem of funders, talent pipelines, and nonprofits in this space.
• Address the 8-week funding decision requirement directly—describe how you prioritize, gather evidence, and make timely decisions without sacrificing rigor.

What to cover in your cover letter

Your cover letter should emphasize: (1) specific grantmaking or funding allocation experience with measurable outcomes, (2) your nuanced understanding of transformative AI risks and the current talent landscape, (3) a concrete vision for how you would identify and fund capacity-building projects, and (4) your ability to make fast, high-quality funding decisions under deadline pressure.

Research before applying

• Read Coefficient Giving's published grant announcements and research to understand their funding priorities, decision criteria, and tone.
• Study 80,000 Hours' content on transformative AI risks and talent pathways, since Coefficient Giving is their largest funder and likely aligns with their worldview.
• Map the landscape of AI safety talent programs (e.g., fellowships, courses, community groups) and identify gaps that funding could address.
• Review recent discourse on AI risk mitigation, including key papers, blog posts, and debates, to speak fluently about current challenges and opportunities.

Likely interview topics

Based on the job description, expect questions about:

• How would you evaluate a grant proposal from a new AI safety fellowship program versus an established researcher seeking support?
• Walk us through a funding decision you made quickly—what was your process, and what did you learn?
• What are the most promising pathways for new talent to enter transformative AI risk mitigation work, and how would you fund them?
• How do you assess the tractability and potential impact of a project aimed at contributing to AI risk discourse or research?
• Describe how you would handle a situation where a promising applicant lacks traditional credentials but shows high potential in AI safety.

Common mistakes to avoid

• Speaking about AI risk in vague or alarmist terms without demonstrating specific knowledge of technical safety, governance, or strategy debates.
• Failing to show any prior grantmaking or funding allocation experience—this is a core requirement, not a nice-to-have.
• Ignoring the 8-week decision timeline or suggesting a slow, consensus-heavy process that conflicts with the role's fast-paced funding mandate.