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The AI Risk Mitigation Fund (ARM Fund), spun out from the Long-Term Future Fund and operated by Effective Ventures Foundation, has distributed over $20 million in grants over five years to support AI safety research and governance. Individual grants range from approximately $12,000 to $232,000.
The fund supports three focus areas: technical research to uncover dangerous AI capabilities and design safer AI systems that are easier to understand, monitor, and control; AI policy work to ensure governments and corporations appropriately guard against catastrophic risks; and programs to bring new talent into AI safety research.
The team includes AI safety researchers, forecasters, policy researchers, and experienced grantmakers who evaluate proposals on a rolling basis.
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Search similar grants →Extracted from the official opportunity page/RFP to help you evaluate fit faster.
R e d u c e c a t a s t r o p h i c r i s k s f r o m a d v a n c e d A I The AI Risk Mitigation Fund (ARM Fund) is a non-profit aiming to reduce catastrophic risks from advanced AI through grants towards technical research, policy, and training programs for new researchers. → Our views on risks from advanced AI → The case for independent AI Safety funding → Our grant making process The ARM Fund launched in Dec 2023.
The ARM Fund is now actively supporting projects across our focus areas. We are currently working on preparing detailed grant announcements, which will be published here soon. Below are some grants made by the ARM Fund team as part of their past grantmaking for the Long-Term Future Fund .
We are proud to have funded many of these grantees early on in their careers, many of whom subsequently went on to make significant contributions in technical AI safety and AI governance.
Building research capacity Start-up funds for computing resources for a deep learning and AI alignment research group at the University of Cambridge Centre for the Governance of AI Two-year funding to conduct public and expert surveys on AI governance and forecasting.
Building research capacity 4-month stipend for a research visit to collaborate with academics in Cambridge on evaluating non-myopia in language models and RLHF systems Year-long stipend for research into shard theory and mechanistic interpretability in reinforcement learning Technical AI safety research Seed funding for a new AI interpretability research organization University College London Compute for empirical work on AI Safety Via Debate Technical AI alignment research Technical research can uncover dangerous capabilities before it’s too late, or enable us to design future AI systems that are easier to understand, monitor and control.
Good policy can ensure that governments and corporations appropriately guard against catastrophic risks. Building AI safety research capacity Investment has poured into AI capabilities development, yet strikingly few researchers are working on key problems in AI safety, particularly outside of major industry labs. Grants in this area aim to bring new talent into the AI safety field.
This fund was spun out of the Long-Term Future Fund (LTFF), which makes grants aiming to reduce existential risk. Over the last five years, the LTFF has made hundreds of grants, specifically in AI risk mitigation, totalling over $20 million. Our team includes AI safety researchers, expert forecasters, policy researchers, and experienced grantmakers.
We are advised by staff from frontier labs, AI safety nonprofits, leading think tanks, and others. Help reduce catastrophic risks from advanced AI
Based on current listing details, eligibility includes: Researchers and organizations working on technical AI alignment and safety research, AI policy and governance, and building research capacity in AI safety. Both individuals and institutions are eligible. Past grantees include academics, independent researchers, and policy organizations making significant contributions in technical AI safety and AI governance. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates Varies Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
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The Long-Term Future Fund (LTFF), managed by Effective Ventures Foundation, makes grants aimed at reducing existential risk with a strong emphasis on AI safety. The fund supports individuals, small teams, and organizations working to positively influence the long-term trajectory of civilization. Over the past five years, LTFF has distributed over $20 million in grants specifically targeting AI risk mitigation. The fund operates on a rolling basis, reviewing applications continuously. Grants support technical AI alignment research, AI safety field-building, upskilling programs for new researchers entering the AI safety field, and policy work related to advanced AI governance. The fund is part of the broader Effective Altruism Funds ecosystem and accepts applications from researchers, organizations, and projects worldwide.
The Effective Altruism Infrastructure Fund (EAIF) provides grants for projects that build the effective altruism community and support high-impact cause areas, with AI safety being a primary focus. The fund supports AI safety field-building, community organizing, research infrastructure, educational programs, career development initiatives, and organizational capacity building in the AI safety ecosystem. EAIF complements the Long-Term Future Fund (which focuses on direct AI safety research) by funding the infrastructure and community that enables safety research to happen. Applications are reviewed on a rolling basis with decisions typically made within 4-8 weeks.
CIFAR and the Canadian AI Safety Institute fund Catalyst Project proposals addressing sociotechnical considerations in AI safety. The program supports interdisciplinary research in machine learning applications to science and society, with recent funded projects spanning misinformation combat, trustworthy language models, democratic alignment of AI systems, Indigenous AI governance, and real-world safety in autonomous systems. Designed to catalyze new research areas and collaborations at the intersection of social sciences, humanities, and AI safety.
The IAPS AI Policy Fellowship is a fully funded three-month program for professionals seeking to strengthen practical policy skills and contribute to impactful projects in AI governance and policy. The Summer 2026 cohort runs from June to August 2026 with options to participate in Washington DC or remotely. The program begins with a two-week in-person residency in Washington DC followed by remote or in-person work with weekly mentorship and career development support. Fellows work full-time on independent AI policy projects covering areas such as AI regulation compute governance international AI agreements AI safety policy AI workforce impacts and responsible AI deployment. The fellowship received 240 applications for the 2026 cohort representing a 35 percent increase over 2025. IAPS is a remote-first organization and legally supports fellows in many countries. This fellowship is distinct from the Vista Institute for AI Policy Fellowship which focuses specifically on AI law and from the Cooperative AI Foundation fellowships which focus on multi-agent cooperation problems.
The Pivotal Research Fellowship is a nine-week AI safety research program (June 29 to August 28, 2026) based at the London Initiative for Safe AI (LISA), with optional extensions of up to six months for strong projects. Fellows receive a GBP 6,000-8,000 stipend, GBP 2,000 housing allowance for non-London residents, London travel coverage, compute resources, and weekday meals. The program offers weekly one-on-one mentorship with established AI safety researchers, dedicated in-person workspace at LISA, research management support, workshops, and speaker sessions. The selection process involves a written application, video interview, mentor-specific work task, and personal interview. Pivotal Research reports that 70 to 90 percent of fellows who applied received extensions in recent cohorts, indicating strong support for continued research development. The fellowship accepts researchers from diverse backgrounds including ML, philosophy, policy, physics, and biology.