Funding for AI Alignment Projects Working With Deep Learning Systems
This is a pooled grant program — not a standalone organization — administered by Open Philanthropy as part of its technical AI safety grantmaking. Open Philanthropy published a request for proposals in August 2021, authored by Nick Beckstead and Asya Bergal, inviting researchers in academia, industry, or working independently to submit proposals for up to $1 million covering up to two years. The program targeted four research directions: measuring and forecasting risks from advanced AI systems, techniques for enhancing human feedback (such as iterated amplification, debate, and recursive reward modeling), mechanistic interpretability of neural networks, and truthful AI. In total, $16,604,737 was awarded across the program. Open Philanthropy rebranded as Coefficient Giving in November 2025, and the program now falls under their Navigating Transformative AI fund.
Funding Details
- Annual Budget
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- Monthly Burn Rate
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- Current Runway
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- Funding Goal
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- Funding Raised to Date
- $16,604,737
- Fiscal Sponsor
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Theory of Change
By funding empirical, deep-learning-focused alignment research at an early and relatively underfunded stage, the program aimed to grow the field's capacity to identify and address risks from advanced AI systems before those systems become dangerously capable. The causal chain runs from grants to researchers producing better tools for measuring risks and improving human oversight, which in turn makes it more feasible to ensure that highly capable AI systems pursue intended goals. Mechanistic interpretability and truthful AI research were seen as near-term levers for reducing the probability of misaligned or deceptive AI behavior in future systems.
Grants Received
from Open Philanthropy
Projects
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Details
- Last Updated
- Apr 2, 2026, 9:52 PM UTC
- Created
- Mar 20, 2026, 2:34 AM UTC