Resolution secures $160M grant to speed up AI alignment research
Massive $160M grant from Coefficient Giving aims to close the gap with frontier labs
Resolution, the nonprofit formerly known as Sequent, has announced a $160M grant from Coefficient Giving to accelerate high-confidence AI alignment research. The grant is structured as a $108M base plus $52M conditional on hiring success and compute requirements. The entire process from initial conversation to grant confirmation took just six weeks, signaling the funder's ability to move at scale. Resolution plans to use the funds to build a critical mass of world-class researchers working in high-bandwidth teams and to acquire compute for semiautomated reasoning and rigorous empirical tests (excluding large training runs). Compensation will be above nonprofit and academic norms, though below frontier-lab equity levels.
The grant arrives at a pivotal moment in AI safety funding. Coefficient Giving is leading a broader shift toward larger philanthropic capital, with potential follow-on from the OpenAI Foundation and after the anticipated Anthropic IPO. Resolution argues that superintelligence may arrive within a few years, making it urgent to close the speed gap between rigorous alignment research and fast-paced AI development. The organization also reserves a small regranting budget for high-quality external alignment work and community infrastructure. By creating a tight feedback loop between theory and empirics, Resolution hopes to either make alignment much more likely or uncover clear obstacles—essentially forcing the issue at scale.
- Grant is $160M total: $108M base plus $52M conditional on hiring and compute goals
- Process from first conversation to grant confirmation took only six weeks
- Funds will support high-bandwidth research teams and compute for semiautomated alignment theory and small-scale empirics
Why It Matters
Largest known alignment grant aims to match nonprofit rigor with frontier-lab speed, potentially shaping safety outcomes before AGI arrives.