Announcing our $160M grant from Coefficient Giving

· Source: AI Alignment Forum · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, short

Summary

Resolution, formerly known as Sequent, announced on July 9th, 2026, a significant \$160M grant from Coefficient Giving to advance rigorous AI alignment research. This funding, comprising a \$108M base and a \$52M conditional component, aims to accelerate progress towards higher-confidence alignment or identify challenges in the field. The organization highlights the urgent need for ambitious alignment funding, citing the rapid development of artificial superintelligence and the expanding AI safety funding ecosystem, including contributions from Coefficient Giving, the OpenAI Foundation, and potential capital following the Anthropic IPO. Resolution plans to establish a fast, tight-feedback research approach using a critical mass of world-class researchers and compute resources, including a small regranting budget for external research and community infrastructure. They are actively hiring for various technical and operational roles to achieve these goals.

Key takeaway

For AI researchers or engineers seeking impactful roles in AI safety, Resolution's \$160M grant signals a significant opportunity to contribute to higher-confidence alignment. You should explore their open research scientist, engineer, and operations positions, as they offer competitive compensation and resources to tackle urgent superintelligence alignment challenges. This funding influx also indicates a maturing AI safety ecosystem, suggesting increased philanthropic capital for future initiatives.

Key insights

Resolution secured a $160M grant to accelerate AI alignment research, emphasizing urgency due to rapid superintelligence development and scaling safety funding.

Principles

Method

Resolution will build semiautomated alignment theory and rigorous empirics via high-communication-bandwidth teams of world-class researchers and dedicated compute resources for reasoning and low/medium-scale empirics, not large training runs.

In practice

Topics

Best for: Research Scientist, Investor, AI Scientist, AI Ethicist, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Alignment Forum.