An international AI x-risk strategy think tank that conducts scenario research and governance analysis to mitigate risks from transformative AI technologies.
An international AI x-risk strategy think tank that conducts scenario research and governance analysis to mitigate risks from transformative AI technologies.
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Updated 04/02/26Funding Details
Updated 04/02/26- Annual Budget
- $950,000
- Current Runway
- -
- Funding Goal
- $880,000
- Funding Raised to Date
- -
Org Details
Updated 04/02/26Convergence Analysis is an international AI existential risk strategy think tank with the mission of designing a safe and flourishing future for humanity in a world with transformative AI. The organization was originally founded as a research collaboration in existential risk strategy between David Kristoffersson and Justin Shovelain from 2017 to 2021, engaging a diverse group of collaborators. It was formally incorporated as a 501(c)(3) nonprofit in 2018. Through 2021 to 2023, the founders laid the foundation for a research institution and built a larger team, and in 2024 Convergence relaunched with a cross-functional team of academics and professionals. Convergence operates three core research programs. The AI Clarity program builds a foundational series of sociotechnical reports modeling plausible and highly consequential AI scenarios, including the Threshold 2030 conference (co-organized with Metaculus and the Future of Life Institute) that brought together 30 leading economists, AI policy experts, and professional forecasters to evaluate AI's potential economic impacts. The Governance Recommendations program evaluates the most critical and neglected governance strategies, providing detailed analyses of specific AI governance proposals. The AI Awareness program disseminates research through books, podcasts, and public education. In 2024, Convergence published over 470 pages across 19 articles and reports, provided consultation to the US Bureau of Industry and Security that directly informed proposed rules on dual-use AI reporting requirements, and had specific recommendations incorporated into the EU AI Act GPAI Code of Practice. Key team members include CEO and co-founder David Kristoffersson, co-founder and Chief Strategist Justin Shovelain, Dr. Justin Bullock (editor of the Oxford Handbook of AI Governance), and Dr. Christopher DiCarlo (author of Building a God and host of the All Thinks Considered podcast). Notable 2025 publications include The Manhattan Trap, A Global AGI Agency framework, the 200-page Threshold 2030 Full Report, and Pathways to Short AI Timelines.
Theory of Change
Updated 04/02/26Convergence Analysis believes the most critical task is to steer the evolution of AI technology in a direction that ensures it continues to advance human productivity and well-being while reducing the likelihood of existentially risky outcomes. Their theory of change operates through three channels: (1) building rigorous sociotechnical scenario research that maps plausible AI development pathways and their consequences, providing the analytical foundation for informed governance decisions; (2) translating scenario research into specific, actionable governance recommendations that directly inform policymakers and regulatory bodies (as demonstrated by their influence on the EU AI Act and US Bureau of Industry and Security rules); and (3) raising public awareness of AI risks through accessible media including books, podcasts, and public education, thereby building broader societal support for responsible AI governance.
Grants Received
Updated 04/02/26Projects
Updated 04/02/26
AI Clarity is the scenario planning research program of Convergence Analysis, exploring possible futures with transformative AI and evaluating strategies to mitigate existential risks through systematic scenario analysis.
Discussion
Key risk: Counterfactual impact is uncertain: as a small early-stage think tank distributing effort across scenario reports and public awareness, their speculative modeling may have limited uptake among key U.S./EU decision-makers relative to more established governance orgs, risking high-quality output with low policy penetration.
Case for funding: Convergence Analysis is uniquely focused on rigorous sociotechnical scenario planning (AI Clarity) that feeds directly into actionable governance proposals, with early but tangible policy traction (BIS proposed rules, EU AI Act GPAI Code, Threshold 2030 convening), making marginal funding a high-leverage way to convert short-timeline foresight into regulation.