Programme Thesis
Open Philanthropy's Early-Career Funding for Individuals Interested in Improving the Long-Term Future provides flexible funding (primarily for graduate study, but also for other career-capital-building activities) to early-career individuals who want to pursue careers that help improve the long-term future, and who don't qualify for the biosecurity-focused program. It exists to lower financial barriers and enable promising individuals to build skills and pursue paths that could have large positive impacts on the far future.
Selection Criteria
- Alignment with improving the long-term future (e.g., AI safety, biosecurity, governance, global catastrophic risks, and other cause areas prioritized by Open Philanthropy).
- Early-career status (typically students or recent graduates, but open to others).
- Demonstrated potential for a career that contributes to the long-term future (e.g., research, policy, operations).
- Need for funding (e.g., for graduate study, research costs, or other career capital).
- Quality of the proposal: clarity, feasibility, and expected impact.
- Letters of recommendation and academic record (transcripts) as evidence of ability.
- Rolling assessment: applications are reviewed as they come in, so early application may be advantageous.
Past Winners / Cohort Profiles
The page does not list past winners, but based on Open Philanthropy's broader programs, past recipients are typically early-career individuals (e.g., graduate students, postdocs, or professionals) with strong academic records and a clear plan to work on long-term future issues. They often have backgrounds in AI safety, biosecurity, economics, philosophy, or other relevant fields, and have secured admission to top graduate programs or have concrete career plans. Named examples are not available on this page.
Ideal Candidate Fingerprint
The platonic ideal applicant is an early-career individual with a strong academic record, a clear and credible plan to build a career that improves the long-term future, and a demonstrated commitment to cause areas like AI safety, biosecurity, or global catastrophic risk. They have a well-defined project or graduate study plan, strong letters of recommendation, and a compelling narrative connecting their skills to long-term impact.
Recommended Framing
Eniola should frame his work as directly contributing to reducing global catastrophic risks, particularly through the intersection of neuropharmacology, computational modeling, and AI safety. His CCT model for addiction prevention and his protein-language-model work for drug-resistance prediction can be positioned as tools to mitigate risks from biological and chemical threats, while his dynamical-systems and AI infrastructure skills align with AI safety and governance. He should emphasize his independent research track record, his pre-registered and rigorous methods, and his unique multi-domain expertise as evidence of high potential for long-term impact, and highlight how the funding would support his M.Sc. in Digital Health and subsequent research trajectory.
Watch Out
None apparent. However, the program is competitive and rolling, so early application is advised. Also, the program may prioritize applicants with a clearer direct link to long-term future cause areas (e.g., AI safety, biosecurity) than Eniola's current focus on addiction neuroscience and drug resistance, so he must explicitly articulate the long-term future relevance.